Update Pinot Site from dev branch 37567226ae5f6b9baade9b37ac94293ac9b1f2b9
commit 37567226ae5f6b9baade9b37ac94293ac9b1f2b9
Author: Xiang Fu <xiangfu.1024@gmail.com>
Date: Tue May 24 09:20:10 2022 -0700
Adding what is apache pinot video (#74)
commit d6bcec1830514f701019c28e26389ad83950708a
Author: Kenny Bastani <kennybastani@gmail.com>
Date: Mon Apr 4 19:25:07 2022 -0400
0.10.0 announcement blog post edit (#73)
diff --git a/404.html b/404.html
index f9b3cd7..c2d82cd 100644
--- a/404.html
+++ b/404.html
@@ -12,13 +12,13 @@
<link rel="search" type="application/opensearchdescription+xml" title="Apache Pinot™" href="/opensearch.xml">
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Ubuntu|Roboto|Source+Code+Pro">
<link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">Page Not Found | Apache Pinot™</title><meta data-react-helmet="true" property="og:title" content="Page Not Found | Apache Pinot™"><meta data-react-helmet="true" property="og:url" content="https://pinot.apache.org/404.html"><meta data-react-helmet="true" name="docsearch:language" content="en"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="default"><link data-react-helmet="true" rel="shortcut icon" href="/img/favicon.ico"><link data-react-helmet="true" rel="canonical" href="https://pinot.apache.org/404.html"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/404.html" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/404.html" hreflang="x-default"><link data-react-helmet="true" rel="preconnect" href="https://BH4D9OD16A-dsn.algolia.net" crossorigin="anonymous"><link rel="stylesheet" href="/assets/css/styles.f0793de6.css">
-<link rel="preload" href="/assets/js/runtime~main.dfc58ce0.js" as="script">
-<link rel="preload" href="/assets/js/main.16915bd7.js" as="script">
+<link rel="preload" href="/assets/js/runtime~main.82dd65bd.js" as="script">
+<link rel="preload" href="/assets/js/main.9b2ca7ec.js" as="script">
</head>
<body>
<script>!function(){function e(e){document.documentElement.setAttribute("data-theme",e)}var t=function(){var e=null;try{e=localStorage.getItem("theme")}catch(e){}return e}();null!==t?e(t):window.matchMedia("(prefers-color-scheme: dark)").matches?e("dark"):window.matchMedia("(prefers-color-scheme: light)").matches?e("light"):e("dark")}()</script><div id="__docusaurus">
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class="themedImage_TMUO themedImage--light_4Vu1 footer__logo"><img src="/img/logo.svg" alt="Apache Pinot™" class="themedImage_TMUO themedImage--dark_uzRr footer__logo"></a></div><div class="footerCopyright_-piB">Copyright © 2022 The Apache Software Foundation.<br>Apache Pinot, Pinot, Apache, the Apache feather logo, and the Apache Pinot project logo are registered trademarks of The Apache Software Foundation.<br><br>This page has references to third party software - Presto, PrestoDB, ThirdEye, Trino, TrinoDB, that are not part of the Apache Software Foundation and are not covered under the Apache License.</div></div></div></footer></div>
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similarity index 84%
rename from assets/js/3e46ac02.f8487d65.js
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index 851788a..474c2d1 100644
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similarity index 99%
rename from assets/js/c4f5d8e4.568261cd.js
rename to assets/js/c4f5d8e4.f47938b0.js
index 801e4d5..e01dff0 100644
--- a/assets/js/c4f5d8e4.568261cd.js
+++ b/assets/js/c4f5d8e4.f47938b0.js
@@ -1 +1 @@
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"})),I.createElement("image",{x:370,y:82,width:51,height:83,xlinkHref:"data:image/png;base64,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NN+YOCCCAAAIIpF7gwCHjTb4y2ZbPmY1542zXgdQdvgnv1zca79XR0ubLAFTFmCcccf7GzTvPBb4c9dzSieH24enPf1p8Rlsvo8e3EUAAgRQKkKimMKh0CQEEEEAAgUYLGM1Ef+0LUjw1MdHv1uQqTVAHQuNsC0PzPsdx71zN/fXaT4hr/j4nzot+aMadwDyzqbd24sFPD5ccpgivhpZzEUAAgcQIkKgmJlQ0FAEEEEAAgfUVsFN6D/efa5mdczZqMnpd3nG2+MbsMUY+rAnksLau7u8rHGPmQle+kRfncd84p0PHPN4eyFHZ0T3HFOH1fT1wdwQQQKCRAnX/hdLIxnJtBBBAAAEEEFhbgZGHTe7wM+f65ua9a1zjXO+40hsas0Nz0vdoS4bWsjWOI3M6Rfif9J7P6zrXscDIc+1N7qNDTveZz/2OVBltXctocC8EEECgsQIkqo315eoIIIAAAggkTuDBB03+q1Nj/TVx3uW5zt26drTHD8N9Ooq6TztTiEmHdDDXvOJ67s8cMWf9IPyJmzM/2NTGFOGYxIdmIIAAAqsSIFFdFR8nI4AAAgggkHwBu970Xz90rvX4SWdPreJ82HXd3dqrbn3s0seVCenhER3lfVGXsI6bMDjlmPCvdw8Wnuk62jUzMuKECekDzUQAAQQQOC9AospLAQEEEEAAgQwKHNIqvX8/M9M5OlHZF4r3UZ02e41Oq+1Rip2a8HXpxwS/RzAlbf7zruOMhUaOiIR/3dnt//SKXQPjI3c7fgbDTZcRQACBxAkk+JdQ4qxpMAIIIIAAAusqYNebHnluatPotP9Bx3UPuJqQGjEdmtTZtabt69q4xt28rJc+pW94pkIJZ3Vo9Zs9Lc7fDQ30vPLvD0iZda2Ng+fKCCCAwGoESFRXo8e5CCCAAAIIxFxgsVLv/MDsbPn9OqX33+gv/nYjosmp2FFTN+bNr3fz7BTgWX1Ma2GmahCEf9ranP/L/dd0vMpIa72puR4CCCCwOgES1dX5cTYCCCCAAALxENB1pnd9VrxC65lia5u0luYKQ+KHt2uxoX+lDdymD1sEqVkf/O5XhPOHHW2t6GM2DMP/5DrmW61t5kRlzpk/2dVXfuzT4gv7tl6w4iMCCCCwpgL8slpTbm6GAAIIIIBA/QTsaOl35EihpaW1q+jlB7Uy7xV+4B4U13xYf8HbpJRjmQI62lzV7Xd+mHfdLxQ956mFWjAauLXJzq2DZfZtXSYmT0cAAQRWIUCiugo8TkUAAQQQQGAtBWx13s98XnLnxmc7FtzSppai11sumZ3iOVoMyf3IWrYlK/fSpPX7JpS/KRblcFgzE6ETHmvd1j9x6IDUWN+alVcB/UQAgfUQIFFdD3XuiQACCCCAwBIF7KjpER01PdPc0Zdz3J2OazbVTLjfCZ2DOi11q14ma+tMlyhX/6eZ0Mw4rvxFLuf+wPjh6YrjvShSPnvb/GCJLXDq780VEUAg2wIkqtmOP71HAAEEEIihwAHdOkZOzHTOVmo7vZx7hYjpC0O5SkfwPqjN3RLDJmexSVPGhF91xP2Z44VnNXF9Nddknh9qHpj4/GecWhZB6DMCCCBQTwES1Xpqci0EEEAAAQRWKGC3jnn+sdmuab98levm3u24zoAfBDc54tyiI6dNK7wsp62NQFWT1sOe6z6itztVC+RJrVz11LZrus987j6pMkV4bYLAXRBAIF0CJKrpiie9QQABBBBIiMDietPTzadnC1t839zh5b3tuhZSR07Nfk1s3p2QbtDMdxZ4xpHwp67rjfqBfzo04beu7Xdf+YNf75sjaX1nML6KAAIIvFWARPWtIvwbAQQQQACBBgnY9aanhifbT08HO0Nx79HVpftM6AyJMdfqqOmg3pb1pg2yX7fLLq5r/YkmqCcDY45ovP+huW3h2Wv3bZxm79Z1iwo3RgCBBAiQqCYgSDQRAQQQQCC5Aod0velXSqWh0VOl2z3P+bDjSLcx0qe/gHdrr/qS2zNavgKBWT3nBcfIqHHC6cC43+tt8r797t0dx373w47dz5UDAQQQQOC8AIkqLwUEEEAAAQTqLDByyBQOn54cml0I3pNzvU+KJqeByJD+0t2kt8rV+XZcLpkCge7ZesZ1zCkRZ06nfH+lxTNf3d87e+SBT22rMEU4mUGl1QggUD8BEtX6WXIlBBBAAIGMCtj1pv/2S2NtL77iXl2qmE+4rhZAEqdNjHRqksqU3oy+LpbTbU1axx0xE/q6mddtcJ5zPfPf9mzNP975TMcUW98sR5LnIoBAWgRIVNMSSfqBAAIIILA2ApqU3vVZ8TbvOFNsNR1dp8fmrq0G7id0eeld+ktVi71Kmz6K+uB3rCJwrEjAbm8zq3/oqBjXPOlK+IWervyP8m5tLOf3lTecloDkdUWunIQAAgkS4JdogoJFUxFAAAEE1l7AjpZ+VhPTw3KuqdThdna35IbHpn3dz9T5LZ2e2a0tsgWQ7HReb+1bxx0zIBBqH319hFp0a8aIOdTZJH8buLlXJufDiR3d3QsPflp8pgpn4JVAFxHImACJasYCTncRQAABBC4vcEALIMm5c83zpXxva97p0z0yhxdq5r1inF/TsymAdHlCntF4AX1Zmv/eWjRfdbz8icp8ZaK91TtbHeqZf+igo0uiORBAAIFkC5CoJjt+tB4BBBBAoE4CIw+b3AvPTrdPzYSbinnZqiNXuyqB+ZhWaL1VHNdO5eVAIK4Cvo62HvY8c6jgec+UA+d4wZSPX7t1cOqBA1JjtDWuYaNdCCBwKQES1Uvp8D0EEEAAgdQKRFN6vyDFJ8enBsuh2V/wnL4wDHfUQnm/vrG/NbUdp2NZEHjGE/P1nCfPV407Hkr43PYtwbH/dKB/nqQ1C+GnjwikQ4BENR1xpBcIIIAAAksQsMnp//6QNL184txG38/dXMjL5lrNXBka+Zi+gbfrTTkQSJWAVhMuORJ+u5jzflIJw1OBLz/Yv6F25N99cnCBpDVVoaYzCKROgEQ1dSGlQwgggAACbxQYGTHu4f5zLeWSuykI5GbPc/eGgewyjtytz+t/43P5HIGUC5Q1cf2mjrQ+a3w5Ejr+D1s7c69Kd/cc61pTHnm6h0ACBUhUExg0mowAAgggcGkBm5we3TLXe/ps9QbXNXc5jtutlWc2aoHe6/TMTZc+m+8ikAkB3bPVPOaKezR0wkmdVfCT9qLzo33D3aMjB51qJgToJAIIxFqARDXW4aFxCCCAAAJLFfj0gyY/tjDWN1+RG3KOp1N5pT90ZLsxcqX+srP7m3IggMA7C/g6K/4V/aPOi7p361Qg4bfyjv/dbftmT/3xh3dX3vkUvooAAgg0VoBEtbG+XB0BBBBAoEECdr3p//XFMy0vjbu7KxXvo47r3KDTGjv1F9uQ3nKXPuzephwIILB8gWP6h55junPrdGiCF918+NDGtvzhDae7Z0dGHLuvKwcCCCDQcAES1YYTcwMEEEAAgXoJjBjjTn1hquPVM9Wrqk7u4zql9706fbFVrz+sj059uPW6F9dBAAHdOdiReZ2VcNIRM6seT+p/YH/Z11T96db9Q5Mjdzs+RggggECjBEhUGyXLdRFAAAEE6iJw6JDxHvHn+54/Vr5Xf2n9tuO6fXrhJn106cMmqRwIILA2AmWdGjwpjpT0drquNfjLgW75ux1dfUdY17o2AeAuCGRJgEQ1S9GmrwgggECcBXQq712fFa+//1xTZ6GldaFaG56eC+/SIZ3PaLPtdF67zrRZH/zuUgQOBNZZQGfai12/ah9VY4L/2tbsfKW72Hx8dGphbqirr/z5T4svjmOfx4EAAggsW4Bf9ssm4wQEEEAAgXoJ2Oq8jw2fbqou5DvbvNyQ75srq7Xwlx1x3qdvcG1SyoEAAskSqGmF7cc81/mT1pbcE+WyP1poCialv7906ICE7N2arGDSWgTWU4BEdT31uTcCCCCQMQFbAOmzD0n+sWOzHRJWtjQV8r3VwN9T9R2t0uvcmzEOuotA6gV0OPWxnCt/VcjLk1XfTPi+nLhloGfsgU9JhaQ19eGngwisSoBEdVV8nIwAAgggcDkBWwBp+qETxZdeae0Xz93VVHA2LlTCa0MTflLfqA5c7ny+jwAC6RDQP1TNG3H+trXofrfqhyf8sPZC35AZ3fnJwdKIQzXhdESZXiBQPwES1fpZciUEEEAAgfMCIw+b3PMvzHbNztR2a2XeXTkn7K+EcrXWEL1Pn2LXm3IggEC2BRa0YveXC67zeOA4Z0PfP9pdkOd2bewbf+CA1BhtzfaLg94jYAVIVHkdIIAAAgjUReDBR03+Bz8613t2zttv9zT1XDNU8+UW3dbiBnHcYl1uwkUQQCCNAjUx5nn9mfE9/VlxIjDh4dam3JObBjtH//0BKZO0pjHk9AmBywuQqF7eiGcggAACCLyDgF1v+kffPNPyw8P5beWaudPNuVv0zeaAH5jrdBT1+nc4hS8hgAAClxcwzguuGz7uinvKD4MxXd/67esGnOfkpZ65kRGmCF8ekGcgkA4BEtV0xJFeIIAAAmsiYJPTz3z+dPPoTNOumpEP5VxzpX5pQxiG1+tIiN3flN8raxIJboJARgSMWXBceTTnOEf9UI67xnx520Z55nOf6JllpDUjrwG6mVkB3lBkNvR0HAEEEFiagC2GdOxPxlpHx81OE+Y+oG8ab9VfHv2hyF792Lu0q/AsBBBAYNUC8/q3sMO6nGDUOHLYmNpX23qanpWOjumHDjrBqq/OBRBAIFYCJKqxCgeNQQABBOIhYPc3PTU823N8snKrvjH8F67rDugbww4xslFbaEdOORBAAIH1FJjRN7HHdQ7HpO7bOhmI+dJwe+5bBbfr5Oc/49TWs2HcGwEE6iNAolofR66CAAIIJF5g5JApvDg6MTC1EN5uXO/X9BfERt0DsVM7Zqv05hLfQTqAAAJpFdDRVDPuiDOhP7PmJQj+n+am8Jt3XjFz8nfu21VlinBaw06/0i5Aopr2CNM/BBBA4C0Cdp3pZz+r8+c0+ewenu2YLPt7ZxfCX/Zc9wM6atqmT2/SR7s+vLecyj8RQACBuAtoriqzOhOkpMlrKQyDHxcK7n/f1F18YkFap+ZbpfbQAQnFcezzOBBAIMYCJKoxDg5NQwABBOolcOCQ8crPnC4GLbmOzs6mrvnZ6lW1wDmoIw0f1Xu4+rAjpiSm9QLnOgggEBcBu3bV10cooXkklzP/b2uL+9OKbyb8Ujh9w7v6SiN3O/b7HAggEDMBEtWYBYTmIIAAAvUQsKOmBx+S/MyJmbYWpzZYzOc2zMyH7wrF/JbrOHadKT//6wHNNRBAIJEC+jPSjrr+aWez941aJTi+UKuc3tU2N/O532GqcCIDSqNTKcAblVSGlU4hgEAWBeyoafvsWMupMdmQd/MbvVw4WKuZG4w4B9VjSxZN6DMCCCCwBIEZMeH/yOfkB67Jna744WhXh3tiz5Wds4y2LkGPpyDQIAES1QbBclkEEEBgLQQOaXL6t2PTHdPTtR1e3tvrOLKpWjP36Mc7dA1W81q0gXsggAACKRKoijFPeJ58w3Xcl6uBebmt1X95X3//xAMHpEZhphRFmq7EXoBENfYhooEIIIDA6wJRIaQvSPGxsTPDgcnfkMs5w7plzAbfN7drYnrH68/kMwQQQACB1Qroz9wncp7zPc91jvp+cM4Y+cmV1/nH/t0HBhdIWlery/kIXFqARPXSPnwXAQQQWHeBEWPchT8Za3122t3i+3JLIefsDIJwWxg4d4qriepiMaR1bycNQAABBFItEJoZ/Zn7j/m8PO/XnJPi+t/f3Zt7+bbWrtmDBx1btIkDAQTqKECiWkdMLoUAAgjUS2DkYZObOjHW//JpuTFwvNs8R3rD0GzS9aY36D3svqYcCCCAAALrJzDjinnU8dxXTRhOhCb86UB77gc3bO06/bsfdirr1yzujEB6BEhU0xNLeoIAAgkXOHTYFP7x0fH+I6Nyi04z+6hOK+vW5HSHVqbco10rJLx7NB8BBBBIq4BubxMe1b2oX9CpwZOBke/2tgXfvaGv79jvH5AyU4TTGnb61WgBEtVGC3N9BBBA4CICdr3pH35J2p4+NnbFxLzz8+K6V2sRpM7QmA2anO7S0/gZfRE7vowAAgjEWOCE68gxbd+0MeGR1kL4/121Nf+k/KxrZmTECWPcbpqGQKwEeBMUq3DQGAQQSLuArdL7jMx0Hh4N9y+UzC8Y17nFEdOuf4W3a0079MHP5bS/COgfAghkSaDkOOaEq0lrYJwXip78xZXDhZ/cO9w6fvfdjo7EciCAwMUEeEN0MRm+jgACCNRJYDE5LW986mTp/lLVfMpxnV6t1NtsRLr0Fi11ug2XQQABBBCIt4Bduzqpb74XdKR1Wgvj/c/tvYW/uWZr28u/drdTjnfTaR0Cay9Aorr25twRAQRSLDAyYtwf9byUHwz6mqXFGzo3699jQvk/9IdtjyamRe16kz742Zvi1wBdQwABBJYgoL8SpKK/DCr6SRg68l+HWpxDofGOHxsbn79LtlUfeEAM61uXIMlTUivAm6XUhpaOIYDAmgjoOtP7/lgKQeVMZ3tTrsc13uB8ObhVc9Ff18fuNWkDN0EAAQQQSIuArSD8YHPReTjnFk7OBOXJDlOe2jezqcL61rSEmH4sVYBEdalSPA8BBBA4L2D3NX35j840n5GmgVbPbDFOsL1Slfsdce4Tx2kFCgEEEEAAgVULmLCilZe+n8+7f+V57vPlcu1Ee5c3um9P9/wI61tXzcsF4i9Aohr/GNFCBBBYZwFbnfezD0n+x0fO6fTd3M7mnDMQBMHmaij3Oo77kXVuHrdHAAEEEMiAgFaE/34hb77uiftSNQjHdL7wc7v295z73H1SZYpwBl4AGewiiWoGg06XEUDg8gJ21PTU5083nZ5xB0MpXJXLybbAN3uC0Nyvyek2vYJ3+avwDAQQQAABBOovoH9AndDtzP6m6LlPVYLwZM6ET29qMycHfqt/YcRhC5z6i3PF9RAgUV0Pde6JAAKxFDigW8cMlGa7Xj5dvjLnuNe5Oa83DM3O0Mh7tcFbY9loGoUAAgggkGkBLcY07jrmYc+R50Jxx4IwfLa3M3yqRfrGPv8Zp5ZpHDqfaAES1USHj8YjgMBqBR581OR/+ORo19lzuat1pPQex3P6ddR0v4Ryg643tRV6ORBAAAEEEEiKQFWMedbz5KdGnLP6x9Yfd7W4j757R9fo737YsdvjcCCQGAES1cSEioYigEA9BKL1pl853fz80eL26XnzPs919+pmMT2anO7QKr036D2Y0lsPaK6BAAIIIBAHgeddkWf199tYYMJjbU3h127aJi/8m4/2zbGuNQ7hoQ2XEiBRvZQO30MAgVQI2PWmpYcm24+cCvfMVJwPeY4mpI70amGKPfrLu187qb/HORBAAAEEEEitgM4QNjOuOM/qb7yzJnRezDv+V3ZuLD79keH2qbupIpzawCe5YySqSY4ebUcAgYsKjIwYV66aH3jiWOnuauAddFzp1d/SnWJkk57Uc9ET+QYCCCCAAAIpF9BCTHP6+/CY/tF20oThpOvIN7YM5/9+oNhxbOSgU0159+leQgRIVBMSKJqJAAKXFzh02BR+9MTU8AsngvcGjvsb+ou4X9eadhv7USR3+SvwDAQQQAABBDInEGiPJzRZndDigRUTBl/ctrH45Xfd3n70U9ukwhThzL0eYtNhEtXYhIKGIIDAUgWidaafFedHPZLfNTTXMT3pXz0xHf5qKOZOLYjUaow067Xa9cGU3qWi8jwEEEAAAQRUYHG01Zk3YsqOCX/W1er+3/2dxcem/JbJ+VapPXRA/wTsODpJiQOBxgqQqDbWl6sjgECdBA7p1jFfnDxddKbyXYPdTd1TlerVsyXnlzUT/YDewiakdsSUQkh18uYyCCCAAAIIqECoD7vFjZZ1MI815eTPerSKcLkWntOvzeze37PwwF0SMOqqGhx1FyBRrTspF0QAgXoIRKOmD0n+Zy/NtvtNtaHuNm94YT64faEqv6l/yR2oxz24BgIIIIAAAggsX8CEZqaQl79sb3a/VirJ0dmwcvKK4uB07+9IbcRxbHLLgcCqBUhUV03IBRBAoF4CdtT0W5OTbecWwg0mzG0o5sOh+bK5WecY/aLeY6he9+E6CCCAAAIIIFA3Abt36xc7mt3/5fvOKd83Z3t73GObd3bMjFBNuG7IWbwQiWoWo06fEYiRgK3Oe2TrVMf4OX+H53lX5V3ZWvbl/bqv6bt15LQpRk2lKQgggAACCCBwaYGaK+Zwseh9VUddXyrVzIttTfkX9u1vmyRpvTQc3327AInq2034CgIINFhg5GGTO/PsdPuR6eq2XC73Ljcnm6tVc4eucXlvg2/N5RFAAAEEEEBgrQSM/CznOt/WhONoxfef7G4On73r9oGJz9zk2HWvHAhcUoBE9ZI8fBMBBOohEK03/YIUn52d3jhVCW/JO84OXcLSGwSyT+sL3q33YOuYekBzDQQQQAABBGIqoGWCH8s55lHjuqeDwJxuyoffu62399j/+UlZoBhTTIO2zs0iUV3nAHB7BNIqMGKMKw+da3nqhLe1FDi3e457jZhgyDfOTfqDZ6P2mwq9aQ0+/UIAAQQQQODiArqLXLRv6w/1vcHxIAhe8vLhd3d05166rbVr9uBBx+7ryoGAkKjyIkAAgboJ2PWmsme257FT5ZuDwP2I67m9WmChPzDOXv1hs6FuN+JCCCCAAAIIIJAWgQndu/UZR5xRY8JxV8LvDnd6j9x4Y/dppginJcQr6weJ6srcOAsBBM4LPPioyT/1s7H+Vyacm8W4Bz3XDIahM6x/Ld2lTykAhQACCCCAAAIILFHA1+cd8xw5oXvczOnmrX+9td39x6u2dp3+nfukyhThJSqm5GkkqikJJN1AYK0E7HrTP/qmtPzsxekrJhf8/00T0ve4jtsRGunWNtgpvfm1agv3QQABBBBAAIH0Cuh7jFOuI+fEyJyOtj7V2uT++Y3D3U+Vn5HZkRH2a01v5Bd7RqKa9gjTPwTqIKDJqfsHD0n7U+OTV09Myy+6ntynvzyK+oujSy/fpg9+ltTBmUsggAACCCCAwDsK6NsOKeubjQl9x1EKTfjjtqLzxV3Dhceul7YJ1rW+o1niv8iby8SHkA4gUF8Bu870Rz2Sd3PTLVva3YGx8drt8xXze+K4drTUVuct6oMpvfVl52oIIIAAAgggsHQBX9e1lrUqU01rYUwVcvJnw73el6cq7tHTp8fn+q/aVjt0QEKmCi8dNI7PJFGNY1RoEwJrKGAT0yNbpTAxfbaz2Xg9XsEbmi2FdwSh8xvajM364OfEGsaDWyGAAAIIIIDAigTsqGvJEfNfetpz/+AH/onZqpna0Fqd3HB6uMxU4RWZrutJvAFdV35ujsD6CBw6ZLxvzI61nDxrBltb8pvznrNzoex/LAic94njNK9Pq7grAggggAACCCBQN4GqK+ax5mb3L/W9zTML8/6Jzvbc6J1e59ynPy06IuvYxJYjxgIkqjEODk1DoF4CtgDSH39dCk8fH+s5MWt2N+cK/Vr+fcd82XxAXOf99boP10EAAQQQQAABBOIooO+FHm3Ky9dynvP8QsmM97R6L1w/2Dn6+wd07StJaxxDxpS+WEaFRiFQB4ERLYDU+s0zzY883zRUq5l9TZ7ZIoHsLfnmo/qXxU16C/5QVQdnLoEAAggggAACyRLQpHW2Ked8ycs7j5bL4em8MU9eu9E5edUneuYPOk6QrN6kt7W8UU1vbOlZBgUeftjkvnp8rufEePmq2Zqzv5Dz+vzQ7PZDuVP/Y9+QQRK6jAACCCCAAAIIXEpgVr/57aaC+4z+Yf9cwTUvbBg0T95R6Dl74IDUGG29FF1jv0ei2lhfro5AwwUefNTkX3rlXO/Tr+Suz3lyZ851en0j1wRBcK1W6rUVejkQQAABBBBAAAEELi9Q8xzzouc5j4WhjAa+PL6x2/3J3u7OU0wRvjxevZ9BolpvUa6HQIMF7HrT//wdaX362OSOk+fkQ4GRHZ4j3TpquldvfU2Db8/lEUAAAQQQQACBrAi87LnyjBHnXGjMmU293t/t2NzxXPkRmaWKcONfAiSqjTfmDgisWsAmp5/9ijS/8NLUnula+FHXc27Q/3j7Ak1OdQ+xHr0B/y2vWpkLIIAAAggggAACFxVY0JHWp3Xf1jNhYF4uFpy/uqbfPPXAJ3pmmR58UbNVfYM3t6vi42QEGiigyekffEnaHn9lbPdM1b3fcZ336X+wnZqYbtW7djfwzlwaAQQQQAABBBBA4OIC8/qe7FXHkUlNWh9tygV/u3VL8ekT0jH90EGKMV2cbXnfIVFdnhfPRqChAiMjxpUbZ3uefKlyS6nq/pbrOJv1hi260Vevfuxq6M25OAIIIIAAAggggMCyBDRZnRMjYzq3bS4MzUQuF/6PHf3e17s3d58audvxl3UxnvwmARLVN3HwDwTWTsAmpV8eFu/a3FhTkykMHp+o3R6I+y91++kdxhFbBKlDH7m1axF3QgABBBBAAAEEEFiFQKjnzmmCtaCDDNPihP95c6/zLdPmnJx8pru0T8R/4AExTBVemjCJ6tKceBYCqxaw60wPPiT5hZenW7s7nF7HBHumSuHBIHTu131NW/UGnj7y+uC/y1VrcwEEEEAAAQQQQGBdBTRXFTuiqvuymrIORDzSVpQ/bW7OPzU+K2O9veNz245uq1KU6eIx4g3xxW34DgKrFhjRfU0PvzDZ6s8Fg4V8occx4e7psvm4ThP5uVVfnAsggAACCCCAAAIIJE5ABy/+qaXgHCoUnacXymaiJcydvqu9ffrTnxaf0dbXw0mi+roFnyGwagE7avq7X5fC6NPnuucdd2t70dvoh+G1pYr5hO5pul1v4K76JlwAAQQQQAABBBBAIBUC+t5xuphz/qxYcL83VwpHdRucV/du7xnvPCCVEcexU4kze5CoZjb0dLxeAocOGe/781PtRyeqW/0gtytf8PqqQXh1GMj9eg+bnHIggAACCCCAAAIIIHA5gQnXMX9XzDuP1wJzxgnNscGOwssb9rRPZbEwE4nq5V4ufB+BdxCwU3qnX57peG7c35N3nZtyeWcw8MNba4HcputNm9/hFL6EAAIIIIAAAggggMBSBaqeY35WyDnfrQXOiYoJDm9sMs9sGewbe+CA1LIwRZhEdakvFZ6XaQE7pfc/PCRNz0/MbDoxV7vNc7wtOhujr+bL9Vr76I5M49B5BBBAAAEEEEAAgUYLPJnznJ/oTU75QXBmuN397ube7iOatM6nNWklUW30S4rrJ1ZgxOiepn8+0fbshLt9phTckXdzV7luuEFHTW/WMm5D2jHWmyY2ujQcAQQQQAABBBBIrMBMLiePaCXhY4EvRws58/CeIe/5G/KdMwcPOlplOB0HiWo64kgv6iRg15s+I/P9z50t3zq7IPe6rttrjAyExrlSS4sP1uk2XAYBBBBAAAEEEEAAgXoITOtuEoc9xzkVBOFkS8F8f2tv7rttTZ0nRw461XrcYL2uQaK6XvLcNzYChw6bwjPPTQz89FV5dxC6v+S4pscRR6v1mu3aSLuvKQcCCCCAAAIIIIAAAnEX8F1HTmrielwHWmYlDL+2Z6jwzf1b2o996i6pJG2KMIlq3F9utK/uAna96R9+SdpOTM1cefycf1AXqN/suNKu/0H36pTejXpDr+435YIIIIAAAggggAACCKypgHPGE3M2dDRpNeELWwe9P7u6t+uJs4/K9MhI/Le+IVFd0xcLN1svAU1O3f/w9zNdT74aXjO2YH7FhHK3vvibNDnt1uS0RdvFfwvrFRzuiwACCCCAAAIIINBogbKOtk7o+96SK+Znna3un165Mf+j2zpax+++2/EbffOVXJ835ytR45xYC4yMGPewSK6jY6atvc8dnJrzP3hmNvjXjuP26gs+p/+BNmkHmNIb6yjSOAQQQAABBBBAAIEGCfg6PbisAzY1MWa6vckcGujI/8+S773q585Nbzu6rRqHEVcS1QZFn8uunYCdyvvZ74j3+I/HmnNerqetKdww6zvvrVbl9zQpvVAAidf62oWEOyGAAAIIIIAAAggkS0DfNkuo+7b+x84W+dL0jHNS8mZ8oKVr/sFPiya2WmN4jQ/evK8xOLerj4AdNT01fLppbLa5v+aEw02eO1CphTf4xvlFvcPe+tyFqyCAAAIIIIAAAgggkEUBc1YTxT9vLcgj+v561Df+mb5O58xse9/CQ2u0BQ6JahZfdwnts9065huzYy2nxmRDPp/bW/DMpmrN3BME8kFxnNaEdotmI4AAAggggAACCCAQXwFjyp7n/KCQk6/7xrxaDd2X+4vesR372qdHGri+lUQ1vi+JzLfMTun9469L4UcnJ/onZ+XqnHE3uBIMVn25TRz3fgXi9Zv5VwkACCCAAAIIIIAAAmspoO/Rf1DMOd8x4h6thP7Z/qbcE9cPdo7+/gEp13OKMG/01zKq3OuyAiNanXfui9L86uS5DeWKd13Oc/ZoPrqtFph79OSt+mDrmMsq8gQEEEAAAQQQQAABBBouoPWYZDyfk2944rxY8eVY0TGPbho2x+4o9MwfXOUUYRLVhsePG1xOwK43rWyf7X55qnLNXMV5T87zenX7mM2BMTfpuTY55UAAAQQQQAABBBBAAIF4C4y5rvzQc+TVWmjGmz3n8S29TY/v3dd89jM3ObXlNp1EdbliPL8uAg8+avLPvDDf88qJyrWByIc9z+uX0OwOwuAandZbrMtNuAgCCCCAAAIIIIAAAgish0BVk9YXPeM854uZCIw8vKVTHnn/7d2jB/c51aU0iER1KUo8Z9UC0RYyX5HmU6cmd45Oy32anF6nL96uIJSNevEr9cG+pqtW5gIIIIAAAggggAACCMRS4GXPdV4JRaYcE7w02Jb/281bO5574D6Zvdi6VhLVWMYxHY2yyekffknaDp8av3J8xv2oceV2V6RDJ7Nv1vnsPdpL/ScHAggggAACCCCAAAIIZERA0wCZdR05oonoVCjh0y1N8tf7BvNPyDMdUyMjjuayiweJ6gUJPtZV4OOfG99UKsnvuY7zAa3N26LJab/eoKOuN+FiCCCAAAIIIIAAAgggkGSBBceRc9qB2SA0P8w74X+ZXeh78jsjjk+imuSwxrTt946c25Bv8f5Rm7dFH80xbSbNQgABBBBAAAEEEEAAgfgIVETMdFjzfqV9d+d3mHoZn8CkpiVOwf1N7cw2fZCkpiaqdAQBBBBAAAEEEEAAgYYKaEFVZ0C84LcWXp7uIFFtqHUGL65bzeQ896D2nMq9GQw/XUYAAQQQQAABBBBAYDUCrut8vOKE7SSqq1Hk3LcJHDisK1I5EEAAAQQQQAABBBBAAIEVCuSMoxuEcCBQR4Fz+0hU68jJpRBAAAEEEEAAAQQQyJxAmHMcEtXMhZ0OI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFiBRjXd8aB0CCCCAAAIIIIAAAgggkDkBEtXMhZwOI4AAAggggAACCCCAAALxFsjFu3m0DgEEEEAAAQTiIGDCQIwJRc5/jD7Xf5tQvyZGv2cf+rl+tP9e/Hjhg/3a4uE4zoXPROynjqsf9BP9evQ9+299RF+3n7vn/+16i18/fzYfEEAAAQTSLUCimu740jsEEEAAAQQuLmCTy9DX3LOmDz/63IT2c01K9evmta/ZJPWNCehiYrqYkNrL67+j5HTx8+iGr+Wmr30SfXkxO33jp+cTV5uoRl9eTFrtp4tJ7VuSWDenyasnrmc/nn/o56793Mu/9vXzd+ADAggggEBCBUhUExo4mo0AAggggMBSBOwop/Ft8lmRoFaR0K9Gn4f6NaMJqjFBNOz5xkT0TZ+/YXR0Kfdr+HOihPZ8MvvGz+24bDQSqx9tIpsraNKqD/sxehQXP7r5aPS24e3kBggggAACqxIgUV0VHycjgAACCCCwngI6Wnl+wNImpKFvk9GyPkoSRh8XE1PNSM+PeL725Df/ez27sNx721Fg2+kLXbnY+W9LYu147YUkVpPWQlG8XLN4+SbxCs2a1GoCe35Md/GDfT4HAggggMB6CZCorpc890UAAQQQQGDJApqa2am35x92vWjga0JaXTj/KEX/Fl0vupi/XUjkzmdzS75Pip54sYRWB5BtIi/lxUT0tQnHOn3YK7Ro4qrJqyau9qObK0ZrZKMEVxNf/UeKgOgKAgggEG8BEtV4x4fWIYAAAghkUCAqSmSTTjsSqh+jabuVBfHPJ6Z2tDR6TgZt6tZlm/TrEY3O2o92SrQ+ajIVfd3+n10DGyWtmsDmbPJaaI3WwS4WeDpf3MkmsBwIIIAAAnUXIFGtOykXRAABBBBAYHkC0TrSqHjRYhEjO+IX2MS0ZkdMS9Fa0uVdkWfXQ8AWlPLLs9Gjcv6Crk4VztmR1+hxYdT1fIEnTWx1oWw9bs01EEAAgcwLkKhm/iUAAAIIIIDAWgtEialW1LXVdm1Bo8VpvJqcRiOmmphqgsQRTwE7ml3Vh8xPREmpLdi0OOpqpwtrAmunC0fVh/P6kbdZ8YwirUIAgSQI8BM0CVGijQgggAACiReItoGJppcuVt+NRk11tNR+tNvAvFYdKPE9zVAHdPqwLWBlH7UFnTKso6merTR8fo2rHXW161yj5DWniStrXDP04qCrCCCwWgES1dUKcj4CCCCAAAIXEbBrHu1oaVSB93xSaqvy2vWQrDG9CFqSv6yJa2ArL9vEVde62sT0taT1QnXhnFYZztsiTbwFS3KoaTsCCDRegJ+SjTfmDggggAACWRGwI2xB9XzRo8WpvFGSqsnq4qhpViDopxWwf4wIKvPRw462RtOENWG161ztdOGcnSqso65MEeb1ggACCLxdgET17SZ8BQEEEEAAgaULnJ/+6WtC4lfmFvcwjaaD6qipbiPDgUAk8IZpwlKeEVdHVN2cThO2U4M1Wc0V2yTX1MZIKy8XBBBA4LwAiSovBQQQQAABBFYgYKf1+ppwRFVhtQhSVBiJKb0rkMzgKdHIey16zYj+gcMpaeKqa1jtiGuuqUNyze1aWVi3wnG9DOLQZQQQQGBRgESVVwICCCCAAAJLFLB7mtrk1BbOqem2JSa0VXt91psu0Y+nvbOALbQVVPUhJR2Vnxd37lw00ppv7pB8c2c04qpZ6zufzFcRQACBlAqQqKY0sHQLAQQQQKB+AnadYXVhIkpQ7UiqndJLMaT6+XKl1wWipNUmrrVKNJW8PHMmSlQLLd2S14edLsyBAAIIZEGARDULUaaPCCCAAALLEDCahNptR6pRYlrV/TKD2oKIjqaSnC6DkaeuUkBfh/YPIvoI/Zr4Oj3YmTyh04J1lLWlR0daO6KpwWx5s0pmTkcAgdgKkKjGNjQ0DAEEEEBgLQUWkwI/GsWyyalNDOy6Uw4E1l9g8Y8nJgilOjcePVzd4qbQ2quPnmifVl3gyj6t6x8oWoAAAnUUIFGtIyaXQgABBBBImIAdOdVploujp5M6gjoZrRFMWC9obgYFQp0aXJ46JZXpUR1h7YqmBduqwa6XP1+EycmgCl1GAIE0CZCopima9AUBBBBAYEkC0XRK3e800Gq9tdK01Oanov1Pl3QyT0IgRgJ2OrqdAVDVAl9evlkKmrTa6cGe3a9Vqwjb/Vs5EEAAgSQKkKgmMWq0GQEEEEBgRQKL1VVtZdW5KEG1W8vYpJUDgcQLaMIaVOelpA9bNdhWC87Zh+7RahNYEtbER5gOIJA5ARLVzIWcDiOAAAJZE7CFkWrRm/haeU7Xnk5HI6kURsra6yA7/bVT2Suz56JiYJ7ux5pv0YS12BZVD2Zv1uy8DugpAkkXIFFNegRpPwIIIIDARQQ0QdV1fHa/U1sYyY6iBn5ZtHTvRZ7PlxFIl4AtBhaWpqK9f71iqyar7eeT1lZdx8pbwHRFm94gkD4BfkqlL6b0CAEEEMi8QFArRyOnNV235+s61NCvZN4EgOwK2NkDdpq7/WONX54WT0dX8826lvV88aXsytBzBBCIswCJapyjQ9sQQAABBJYlYBNUm5za6r1BrRRV813WBXgyAmkW0NkEfmVep77rOm2dZWBHWQst3Yt7smq1YA4EEEAgTgIkqnGKBm1BAAEEEFiRwGKCulj5NNQ34ex/uiJGTsqIgB1hXfxDTiUaac1pwprX/VhtxWCmBGfkRUA3EUiAAIlqAoJEExFAAAEE3lnATumtzo3r1hyLI6gmsBV8WYP6zlp8FYE3C9iE1dj/hnSrJjs12P63VGzrjZJWx3Hf/GT+hQACCKyxAInqGoNzOwQQQACB1QosVvGtzo9Fb6ztaKoJQ70oCepqZTk/owI6Jfj1wkuzktOKwcWOoWiElW1tMvqaoNsIxECARDUGQaAJCCCAAAKXFzD6ZtpoFdPq/Hi09YadukgF38u78QwElixg/xsz/mt7DFd0H9amjgEtutSh+aqOsDrOki/FExFAAIHVCpCorlaQ8xFAAAEEGitwfrTHFkkqz5yJ9kNt7A25OgII2GnBtiiZX57RLW26pNjeL7lCm65h9UhYeXkggMCaCJCorgkzN0EAAQQQWL7A4hRfvzIrlbmxqJovI6jLV+QMBFYjYMIgmmJvqwRHBZe06FKuoPuweryFXI0r5yKAwOUF+ClzeSOegQACCCCwxgJ2vVygez5W5yf1MaFrUP01bgG3QwCBNwrY/yYrOqPBL01LoVULLukoq93ehqJLb1TicwQQqKcAiWo9NbkWAggggMCqBOzoTVBdiKr42gqktqovBwIIxEfAFi8rTZ2K1rEW2vqiPVi9fDPTgeMTIlqCQGoESFRTE0o6ggACCCRXwK6HC/UNsF0TZ0dRbbJqv8aBAAJxFDDi64wHW9CsVmqXgt3SRgsvuV4hjo2lTQggkFABEtWEBo5mI4AAAmkRiCr52gRVR1D9yjzTfNMSWPqRegE7A8IWOQuqJfGbZzRh1RFWrRBMdeDUh54OIrAmAiSqa8LMTRBAAAEE3i5g9E3udFQoyS/PMs337UB8BYFECNgp+rbgWZSw6tpVu4bVK+h0YA4EEEBgFQIkqqvA41QEEEAAgZUJRG9sZ8eiPVHZD3VlhpyFQKwEdBupxenA5eijHV0tNHdRHThWQaIxCCRLgEQ1WfGitQgggEDiBao6zbc8fVqr+uo6VKr5Jj6edACBNwrY/6ZrWhnYrjP3m6elqXNYR1db3vgUPkcAAQSWJECiuiQmnoQAAgggsFoBOy3Qbm9hE9XQr+rlzGovyfkIIBBHAR1dtf+NL647X5Bie3+0ftVl79U4Ros2IRBbARLV2IaGhiGAAALpEIhGWLSSb2lmNBpl0XK+6egYvUAAgUsK2MrdQXVeShO2OvC0NHdv0NFV9l69JBrfRACB1wRIVF+j4BMEEEAAgXoKRG9StYpvWRPUmiaqbDdTT12uhUByBOx/+3brKVs0rdgxKE36cHN57YCTnE7QUgQQWHMBEtU1J+eGCCCAQNoFzk/7020r7FRfu1aNAwEEELCzK8pTJ6NR1qauDZKzo6uuBwwCCCDwjgIkqu/IwhcRQAABBFYi8Pooqq5FnZ/QZajhSi7DOQggkGKBxb1Xde1qx5AUW3vEzRe1t4yupjjkdA2BFQmQqK6IjZMQQAABBN4qYIun1Eo6ijp9Rnxdl8aBAAIIXEzA/rxYHF21xZb6JFds09FV3pZezIuvI5BFAX4iZDHq9BkBBBCos0Cga1Ers+ekMj8mJvDrfHUuhwACaRQwYbC4l7IuDyjafVfbenXtqh1d5UAAAQRESFR5FSCAAAIIrFhgcc/EGU1Sz4pfmqFg0oolORGBjApoFXC7jj3aW9mvRAmrV6QycEZfDXQbgTcJkKi+iYN/IIAAAggsVSColaMEtaZrUQN9g8m2M0uV43kIIPBWgTCoSVVnZdjtbIptdt/VXqYCvxWJfyOQMQES1YwFnO4igAACqxbQERC/MqfbzpzRLSemtF4SU31XbcoFEEAgmpHhl+ck9GvRH78Wt7FhKjAvDQSyKkCimtXI028EEEBgBQI2KbXVfCszZ6PpeuyNugJETkEAgUsKhDpD48LPGLuNTb6pXZ9PVeBLovFNBFIoQKKawqDSJQQQQKARAlGVzunTUp0bkzAqmGQacRuuiQACCEQzNWqlabFLDFq6NkqhvR8VBBDImACJasYCTncRQACBZQvYYie1kpQmjuv2M9MUTFo2ICcggMCKBPRnT6iJ6sLEscWpwJ3Dum7VW9GlOAkBBJInQKKavJjRYgQQQGDNBOz2EbWFSSlNntBkVQsmCaOoa4bPjRBAIBKwhZbKU6fEboPV1L1RcoVWnQnMVGBeHgikXYBENe0Rpn8IIIDACgWMfXOoBZNsksqBAAIIrKeAXQ9f1T+a2anAzd2bJN/SxejqegaEeyOwBgIkqmuAzC0QQACBRAnYqb5azMTua2i3i+BAAAEE4iJglyHYqcBNQVW3sOkT18vHpWm0AwEE6ixAolpnUC6HAAIIJFnATvWNtp7RJNUvzbAeNcnBpO0IpFTAVgW2U4FtgbeiFlny8s1MBU5prOlWtgVIVLMdf3qPAAIIvCZg14HZ9ah2um9QXdDlqKxHfQ2HTxBAIFYC9udVRX9W2WJLxc4hyekWNo7jxqqNNAYBBFYnQKK6Oj/ORgABBFIhYEcmKrrtTPTGT0crOBBAAIG4C1xYtxrq/s5NHUOSb+0mWY170GgfAssQIFFdBhZPRQABBNIoYEckyrNnF/dH1YSVAwEEEEiSgF+elZIuWzCasNr9VhlZTVL0aCsCFxcgUb24Dd9BAAEEUi6gRZOqJS2aNKrVNCfEBH7K+0v3EEAgrQJ2uUJJ163adfbFjkEqAqc10PQrUwIkqpkKN51FAAEEzgvo+lO/oqMQ+sbOjkbYN3ccCCCAQJIFoiJLWgjOrl9t6hrWisCFJHeHtiOQeQES1cy/BABAAIHMCWiSavcjtPuj2q0eKJqUuVcAHUYgtQJRkSVdyhDq9jV2v9WoInBqe0vHEEi3AIlquuNL7xBAAIG3CVTmx6U0fjQadXjbN/kCAgggkHABO0OkNj8ZLWdo7tkiuWJrwntE8xHIpgB1vLMZd3qNAAJZFNCRVFvVd2HsCElqFuNPnxHIkICtCFzTvaAXxo/ox2lmjmQo9nQ1PQIkqumJJT1BAAEELipgRxjKuh51YeJYVBnzok/kGwgggEBqBHQtvq7BX9AZJFHBOE1eORBAIDkCTP1NTqxoKQIIILAigWjNllb2LetoKkWTVkTIScsQ8FxH7MPVh+OIPhY/RpcwOrClnxgd3Q81ZwhC+1Ef+m8OBBolEFUEnjgh+kJb3GvV9Rp1K66LAAJ1FCBRrSMml0IAAQTiJhDqvqhlrYJZmT3HSGrcgpPA9tjEs5BzpbmYk6aCFz2KBVe/5kkhbz+60tLk6fc9KeY9yeu/c577WtJq89EgCMUPdBp6TUf5q4GUKvpRH1X9eq0WRl+v6McL3ytV/Oj5CeSiyTESsIXjSlMn9A8loRRae9m+JkaxoSkIXEyARPViMnwdAQQQSLhAUCtLRUdSK3NjJKkJj+V6Nd8mnF1tBelsLUhHa14/5qWnoyh9nUXp1Y/d7fq96Pt5aW/JS2tTLkpKl9NeO5Za80OZL/kyu1CT6fmqTM1VZWKmKuMzFRmbrujnFZmZX/ze9FxNZvR59hwOBJYjYH8mliZPRutVC202WeVt8HL8eC4Cay3Af6FrLc79EEAAgTUQCNgPnGcAAEAASURBVKolHUkdlapW+DWhvwZ35BZpEGjRRLNfk9CB7qbosaGvRYZ7m2Wwuzn6t01ObfJaz0MHaaOR2IImvTbxFXl7hVabpJ6dKsuZiZKMTpRldHxBRifLck4fZ/TrE5rMVklc6xmW1F7L7rVamjqp089DKbb3k6ymNtJ0LA0CJKppiCJ9QAABBF4TMLKYpJ7WJHWCNamvufDJOwnYdaQ2Odw62CqbB1pli37ceD45HepplnYdQbWJ5HofdjTXPnZtbI+a4us04cnZapS0ntak9fhZ+5iXY/YxOi/zZf44s94xi/P9oyURWlzOJqtNHUNMA45zsGhbpgVIVDMdfjqPAALpEjifpOobsOqC7iGolX45EHirgKsLTe303Su2dMgVmztkx4Y2HTVtkaGepmgary2EFPfDrnvt72qKHvt3dElV17TaacKjOuJ6erwkzx6ZlmePTcsrp+Z0DSxJa9zjuR7ts0Xm7KwTR+eeF7uGtegXG2GsRxy4JwKXEiBRvZQO30MAAQQSJBBUdf2VTmmrLUyRpCYobmvVVFvYaP/OLrlxT69ctbVTBnXE1CZ7bc3JfytgCznZKcr2cf1ukVuu7IuS1hPnFuTJlyfliZcm5KiOtNoqwxwIXBAwNlmdGRWjf5thZPWCCh8RiI9A8n87xceSliCAAALrJrBYJOT4YpLKXoHrFoc43tiuM715b6/cdnV/NL23r6so7c35ODa1bm3qtcWe9LF3S6fceEWvnNN1rC+emJHvP31Ok9ZJmdZiTRwIWIFo+y7dusseJKsRA/+HQGwESFRjEwoaggACCKxMwOgWNCXd0L5Wmo7WXK3sKpyVJgG79nT3pg55341DcpMmaoN2Wq9W7k3CtN56xsHznKhCsa1SbKc4v/uqfjl6Zl4eefqsfPvR0zKuRZgYY62neDKvZdesVjRZ1R1/pRitWWUacDIjSavTJkCimraI0h8EEMiUgF2HOj9+RKqlqWjLhUx1ns6+ScDRtactWpF33/Yu+citm+TaXd1RASK7n6nd/zTrh3UY6PaikdartnXKL9y5Rb77xFn58vePy0mdImynBdt9XjmyKRAVWNJpwPYodmqBJdasZvOFQK9jJcCvrliFI/mNuWvE5Fqapw5rdYI9ye8NPUAg3gJh4MuCTVK1uq8uSo13Y2ldwwRyOmrYoaOl1+3slo/evlnXaPaI/ZodVeW4tEAQGKn4gXztByflKz84Icd0tLVSDSUkY700XIq/63oFTVSHdRrwANWAUxxnuhZ/Ad9xdzCiGv840UIEEEDgbQK2CEhp4pjUSFLfZpOVL9hEtEe3lrlGE9Sfu2NLtA41K32vVz/t1OAWLye/cNdW+dAtG+XrPzop/6BTgo+cnpO5kq8jrAyx1ss6KdcJA50GPH1aR1R1GnC0z2p99w1OigPtRCAOAiSqcYgCbUAAAQSWIWDfSJUnTy7uk8pI6jLk0vNUu/epXYN6/60b5f03DjN6WofQ2urHBzRhvfPaQfnaD0/K9548K0dG52SBPVnroJusS0Q/Y88nq4W2PkZWkxU+WpsiARLVFAWTriCAQPoFonVU+gaqMjfOFjTpD/fbetjekpctg61yzw1Dcp+OAHZpwsoE37cxreoLA91N8s8/uEPes79fvqpTgn/87JicHFuI9mpd1YU5OVECoV/RfVZPi2apUmjtIVlNVPRobFoESFTTEkn6gQACqReIpqTNnpXq3JgmqX7q+0sHXxewe6Bu6m/R5GkgquS7c0O72K9xNEbgQtXkf/XPWuRduifrPzx6Sh59blwmZqqsX20MeSyvarf9Kk+figor5Vu7KbAUyyjRqDQLkKimObr0DQEEUiNg9/qrzo5JRRNV+zlHdgS62gpyiyZLd+soqi2U1NGa7j1Q4xTZ1qZctP/s9uE2XQs8Jt9+7LQ88eJEVCE4Tu2kLY0TCKolKU+dEp1fL4XmLh1hZQ5D47S5MgJvFiBRfbMH/0IAAQRiJ2BHT6s61bes+/zZqb8c2RDIea5sG26Ve2/aIHdfP6gjqq28R16n0A/3NsvHbt8keza1R9WBH/7pGZme47/FdQrHmt/Wr85HdQEcx5N8c8ea358bIpBVARLVrEaefiOAQCIE7D6p1flJTVJHNUmtJKLNNHL1Ak0FT+68blD+2W2aHG3uELs2lWN9BVwdSbN71PZ3Ncm+bV3y1/90TJ47Or2+jeLuaybgV+akNHlcXG+7eIWWNbsvN0IgywIkqlmOPn1HAIFYC9itMWrlmWjaWahrpTiyIWD3RP2le7bKB27eIBt1XSpHvARssaUPvGuD2FHWv9Fk1VYHrvnsYxyvKDWmNX7ZJqsnpKV3m7i5QmNuwlURQOA1ARLV1yj4BAEEEIiXQFBd0CT1pAS1UrwaRmsaJrBrY7v82od3RXuiMoraMOZVX7ighayu29UTja7awlZfeuS4nJnkj0mrho39BfSPhwtTUvZOSnPPFioBxz5eNDDpAiSqSY8g7UcAgVQK2HWppYnj4lfmU9k/OvV2AVvR9zMf3SPbhlqp6Pt2nth9xfMc2TzQKr/4vm2yVWP2hW+8LC+dmI1dO2lQfQWM7l1dmZ8QxytIc/fG+l6cqyGAwJsESFTfxME/EEAAgfUXsOtS58eO6rRfXf+m03850itgC4jabWZ+7vYt8svv3yaD3c0UTEpQuG38bGXgO68bikZX/9tXX5LHXxgXP+C/2wSFcdlNNVp5vTJ7RpNVT5raB6kEvGxBTkBgaQJswrY0J56FAAIIrImA/Wt9efKkFlAaI0ldE/H1u4nnOrKhr0V+5+N75V9+ZJcM9ZCkrl80VnfnnI6uXrOzW/7tr1ytBbA2i91SiF1MVmca97NtBfbK9GhUkV03to57c2kfAokUYEQ1kWGj0QggkEaBaErZzFkp61/qGUlNY4Rf75Nd47hnS4d84v3b5dar+6WY917/Jp8lVmBDX7P85s/tkcGeJvnS/zouoxNlCZkVkdh4Xq7hgRa5K8+cjkZW8+yxejkuvo/AsgVIVJdNxgkIIIBAAwRshd8F3YZGN5a3U3850itgt565ble3/OoHd8j1u3vEbnvCkR4BWwTrVz+wIxpV/YtvvSrHzs7zd6f0hPdtPbF1BMo6sup4eckVW/X7/Pf8NiS+gMAKBZj6u0I4TkMAAQTqKWD36CtPn5ZQ1z5xpFeguejJu67sk1+/f5fcuKeXJDWlobbTuj92++aogvPWwbaU9pJuXRDwy7O6ZvWs7nVdvfAlPiKAQB0ESFTrgMglEEAAgdUI2O1nyjOjElQW9DIUYVmNZZzPtSOpt+7rl0/dt1P27+iOc1NpW50E7r15OEpWtw+TrNaJNJaXscs2aloJuDI3JibwY9lGGoVAEgWY+pvEqNFmBBBIjUBUkGPmjE77ndbpgRTkSE1g39KRQt6V268ZkE/qlNA9mzve8l3+mVYBO6377usHo8mgf/b3un3NSbavSWusQ01QK1pjwNUpwIXWXvZYTWug6deaCjCiuqbc3AwBBBB4XcDulWr/Al+dG9d1qfwV/nWZdH1mp4HefvVANJJKkpqu2C6lN3b7oTuuHZB//qGdsnsTf6RYillSnxP6lWgJh50KzMLkpEaRdsdJgEQ1TtGgLQggkB0BWzypNC3V2XOsS0151O2a1N/Q7Wd2bmhPeU/p3sUE7LTv2/cPaJGl7bJjA9OAL+aUhq8H1ZKUbL0BTVo5EEBgdQIkqqvz42wEEEBgRQJBdSGaJhb45RWdz0nJELB7a37mY3tkO0lqMgLWwFbaQlp2+vevvH+HbBmw1WE50irgl2eikVWWc6Q1wvRrrQRIVNdKmvsggAAC5wWi6WG6LpXpYel+Sdhpvr/98b2ye2M7G1akO9RL7l1LMSf33DAkB+/ZJoPdzUs+jycmTEBnzETLOnTGDAcCCKxcgER15XaciQACCCxbwK5FrUbrUrU6JMWTlu2XlBM264jZb//8FXLV1k5xdY0qBwIXBOzI6n3v3iA//97N0tVeuPBlPqZMwO6HXZo8oX+QnElZz+gOAmsnQKK6dtbcCQEEsi4QrUtlSljaXwZ9XcVon9RrdnWL55Gkpj3eK+mfHVm1+6zee9OwtDSxAcNKDJNwjt0Xe2H8OPurJiFYtDGWAiSqsQwLjUIAgTQKRPulTp7U4klU+E1jfG2fWptzcuDObfKeq/ulmPfS2k36VQeBrraC/NL7tsm79vaKrQzMkU6BoDovpakTWtk9SGcH6RUCDRTgJ2MDcbk0AgggcEEgDKqL08D0TQtHOgXsyNj9794oH7xlg7S35NPZSXpVV4ENvS3RtjX7d3RJjtH3utrG5WJ2iUd1flIqs2dZ7hGXoNCOxAiQqCYmVDQUAQSSLFCd1f1S5yeS3AXafgmBYt6Vd1/VJx/V6ZyD3U2XeCbfQuDNAlfqOuZP3KuVgAfbxHGYKv5mnXT8y+gUYJuosl41HfGkF2snQKK6dtbcCQEEMirgl+ekPHsmo71Pf7dtcrFDt5+xaw7ZKzX98W5ED2/TqeI/f8cWaWW9aiN4Y3HNsFbRZFX3za6xJVksAkIjEiFAopqIMNFIBBBIqoDdisauTwpr1aR2gXZfRqCvsxhN97V7pnIgsFKBD75rg9yh+6wyqLpSwXifZ6cA10rTum3NObEjrBwIIHB5ARLVyxvxDAQQQGBFAiYMpXxhv1QxK7oGJ8VbwI6AvffaQXn/DcPSVKB4UryjFe/Wtbfk5Ffu3S57t3TGu6G0bsUCRgvpVefGo4RVF6yu+DqciEBWBEhUsxJp+okAAmsuUCtNRetSqfa45vRrckO79YxdX/gLd22RXh1V5UBgtQI7htvkX3xop3S2UoxrtZZxPT/QWTYV3UvbVoHnQACBSwuQqF7ah+8igAACKxIIqgtS0dFUO/WXI50Cna2FaF3q1qG2dHaQXq25gOs6csOeHrHTgDlSKqAjqX55NkpW7T6rHAggcHEBEtWL2/AdBBBAYEUCdgS1MqMVHitzwvSuFRHG/qSCVvl9/43DYovgUKc19uFKVAPtXrz337pJrt3FmudEBW4ZjbW/I6paWMnXNav8jlgGHE/NnACJauZCTocRQKDRArWFKV2DNMUG742GXsfrb+pv0SmaO8TuncqBQD0FXK2mtF2nAB+4axv78dYTNmbXsqOptgpw4FMFOGahoTkxEiBRjVEwaAoCCCRfwI6i2v3yArYgSH4wL9KDQs6VX/3ATunpYF3qRYj48ioF8voau2Znl3zg5mGx04E50ilQK89Es2+oApzO+NKr1QuQqK7ekCsggAACkYB9s1GdHdPR1BlEUixw1/WDcs8NQynuIV2Lg0B/V5N86JaNUcGuOLSHNjRAQNer2loGNV2zyhTgBvhyycQLkKgmPoR0AAEEYiGgbzjsHnlVnfar7zhi0SQaUX+BrraCVvndKoUco1z11+WKbxXYsaFN7rpuUFqKTDF/q01a/m33Vy1Pn9IpwBTeS0tM6Uf9BEhU62fJlRBAIMMCdqsBu+VAyHqj1L4KPJ2C+fPv3SI7N7aLo+sIORBotIBNUG3Brpv29ohdu8qRTgG/rEtGdGTVJq0cCCDwugCJ6usWfIYAAgisSMCEuon7wqRWcGTK74oAE3KSLXDzvhuHpLnA6FZCQpaKZtrtj95z9YD0siY6FfG8WCcqc1pYSRNWDgQQeF2ARPV1Cz5DAAEEli8Q7Yk3F61N5a/hy+dLyhlNBS/aMmS4p1lHU5PSatqZBgE7kn/zlX1yve6vmvN48aUhpu/UBxP4UtIpwOyt+k46fC2rAiSqWY08/UYAgboIRFsM2L+E69RfjvQK7N3aIe+6spftaNIb4lj3bLi3WW7d1ycD3U2xbieNW52Ar0WVqnPjq7sIZyOQIgES1RQFk64ggMDaCtgRVLtfam1BN23nSK2AXSd4z/XDMtzbkto+0rH4C9y0t0/2beuSvMdbt/hHa2UtNGGgieo58SvzK7sAZyGQMgF+2qUsoHQHAQTWTiDUKo3l6VGxa1Q50iuwd2un7mnZLc1FL72dpGexF+jrLOqoar/06keO9AoEVS3MZwsradLKgUDWBUhUs/4KoP8IILAyAV2bWp3VKb/VhZWdz1mJELBrU9+zv1+2DrUmor00Mt0Ct+0fkF2b2sWuW+VIp4CdqeOXZ6LtztLZQ3qFwNIFSFSXbsUzEUAAgdcE/IpuJ6CJKke6BWxSsG97l9iElQOB9RbobM1H29V0tuXXuyncv4ECdk9V+/vFztrhQCDLAiSqWY4+fUcAgRULlGdGqc64Yr1knGj3rbxWp/zu1n1TORCIi8Cd1w5KfydFleISj4a0Q2fsBPrH0Or8ZEMuz0URSIoAiWpSIkU7EUAgNgLVuTEtoDQVm/bQkMYI7NQE9cYrqPTbGF2uulKBbt1P9d6bh6W1mf18V2qYhPNsRfnq/LhOA55NQnNpIwINESBRbQgrF0UAgbQK2DcPiwWUKHSR1hjbftk1gFds6ZC9WzrT3E36lkABuzr1ruuGpKedokoJDN+ymhxo9d/q/ASFlZalxpPTJECimqZo0hcEEGiogNHpWLYaI3umNpQ5Fhfv72qS63b1SHd7IRbtoREIvFFgY3+L3Kz7+uY8iiq90SVtn7+2BVrJboFm0tY9+oPAZQVIVC9LxBMQQACBRYGgyl+3s/BasG/9Nw+0ytVaRIkDgbgKvO+GYSnkKPIV1/jUq112u5rawqQWVqrV65JcB4HECJCoJiZUNBQBBNZTYHEj9jGqMK5nENbo3na/1D2b2zVZbVmjO3IbBJYvYP+QsnmQ1+jy5ZJ3Rq00E21Zo3OAk9d4WozAKgRIVFeBx6kIIJAdAb8yq/vazbBWKAMh79Npv9fv7hGXvSozEO3kdjGfc8VWAOZIv4DdpsZWAA78avo7Sw8ReIMAieobMPgUAQQQeCeBxeqLOvWqVn6nb/O1FAnY5HRDX4tctY1pvykKayq7orsnyW37B9jjN5XRfXunauXpaFTVrlvlQCArAiSqWYk0/UQAgRUL2O0BfDuayhuEFRsm5cQWnfZ75dZOiiglJWAZb+dG/aPKFZs7Mq6Qje6bwI+2qwkZVc1GwOllJECiygsBAQQQuISAfVNg90wNfEZTL8GUmm91tBbkup3dqekPHUm3QEGn/95yVV+6O0nvXhPwy3P6R9Np/mj6mgifpF2ARDXtEaZ/CCCwKgG/Mid2ypW+M1jVdTg5/gJ2KmVvZ1Gu3sm03/hHixZaAbtO1Saq9iNH+gVsUb/K7Dkxup83BwJZEOAnWxaiTB8RQGBFAraARbQtQI0CFisCTNhJxbwnV+m035ZiLmEtp7lZFVj840qT7NnE9N+svAb8aJu0Sf3jKWtVsxLzLPeTRDXL0afvCCBwSYFoNFWn/bLR+iWZUvPNpoIn1+/pSU1/6Eg2BOy66ut2M109G9HWXursnsrMGQl1dJUDgbQLkKimPcL0DwEEViQQaIXf6ty42Iq/HNkQaGvOyf4dvOHPRrTT08tmnQFwtb5uPbZTSk9QL9OToFaS6szZyzyLbyOQfAES1eTHkB4ggEC9BfQv1oFOr7L7pnJkQ8C+yd+5sV162gvZ6DC9TI1AznNkY1+zDPc2p6ZPdOTyApW5c/wh9fJMPCPhAiSqCQ8gzUcAgfoLhEE1Gk01oV//i3PFWArYRPXq7RRRimVwaNRlBdqa87JrU/tln8cT0iMQaA2FqhZW4kAgzQIkqmmOLn1DAIHlC+hoql9hNHX5cMk+w9NRqau2kagmO4rZbb2dtr5jmEQ1U68A/V1VndflKeyrmqmwZ62zJKpZizj9RQCBSwqEOopq16YymnpJptR901b63bmhLXX9okPZELDrVLcOtbJNTTbC/Vovo1oKmqxyIJBWARLVtEaWfiGAwAoEdG2qHU21+6ZyZEpgU3+LtLfmM9VnOpseAbtOtb+rSXo7iunpFD25rIAJw8Ut1HQaMAcCaRQgUU1jVOkTAgisSMD+0q/Mjelm6qxNXRFggk+y6/sc/R8HAkkVaNd1qhu0qBJHlgRs4b+SJqt2GzUOBNInQKKavpjSIwQQWKFAUF3gF/4K7ZJ+2o4NrO9Legyz3v62lpxW/m3JOkPm+h8tV1mYZK1q5iKfjQ6TqGYjzvQSAQQuK6CFKVibelmltD5h+1CbOAyopjW8mehXW1NeBrubMtFXOvkGgWg7NR1VZcnKG1D4NC0CJKppiST9QACBVQmENS31Pz+xqmtwcjIFbMXfzYOtyWw8rUbgvEBLsyeDPU2iOy1xZEzAbqlmp/+aoJaxntPdtAuQqKY9wvQPAQSWJFDR0dQw5Jf8krBS9qShnmYp5vl1mLKwZq47rk4J6NCCYJ1thcz1PfMdtqOqFV26Up7NPAUA6RLgN3O64klvEEBgBQL2r9CV2bMi+sueI3sCm/paxGMYKnuBT2GP27SgUnc7lX9TGNrLdsluVRONqobBZZ/LExBIigCJalIiRTsRQKBhAlGlX90/lSObAkO9zeKSqGYz+CnrdWtTTrra2GYpZWFdYneM+NV58StzS3w+T0Mg/gIkqvGPES1EAIEGChj96/PiX6HDBt6FS8dZYEAL0NhpkxwIJF2gpZiTTvYDTnoYV9z+ULeq8XX6rzH8PlsxIifGSoBENVbhoDEIILDWAjZJDWolvS3TftfaPi736+0o6ohqXFpDOxBYuUBz0RM7/ZcjmwI2QbWJqt1blQOBNAjwqzkNUaQPCCCwIoFoNLVkKyUy7XdFgCk5qaejoFvTMKKaknBmuhtNJKqZjr/tvN0PPLDTfxlVzfxrIQ0AJKppiCJ9QACBFQkEFbueZ55pUivSS89JXVollam/6YlnlntSzHvS0uRlmSDzfQ+1OKAdVbUfORBIugCJatIjSPsRQGCFAibaID30Kys8n9PSIGCr/bY25XVENQ29oQ9ZF8jnHGnWdarMEMj2K6FWYfpvtl8B6ek9iWp6YklPEEBgGQJBraJ/dZ7T2VGU8l8GW+qe2lTwxL6550AgDQJ2ZkBB9wTmNZ2GaK68D2GtGlX/NVSzXzkiZ8ZCgEQ1FmGgEQggsNYCUcEJ3XeOI9sCdvSJab/Zfg2krff5nE1UeXuXtrgurz86Y6g0LaFfXd5pPBuBmAnwkyxmAaE5CCDQeIFQiyctruHhl3jjteN9h6KOPjFNMt4xonXLE8h7juQ93t4tTy19z6YGQ/pimsUe8ZMsi1GnzwhkXCCqiqiVEbWKUsYl6L4deWJ9Kq+DNAl4uteSXXvNkW0Bu1WNHVWlqn22XwdJ7z2JatIjSPsRQGDZArZ0f1DTRJUj8wI5HX0iUc38yyBVAHZPYJdENVUxXWlnoum/VP9dKR/nxUCARDUGQaAJCCCwdgJ2I3Q77deE4drdlDvFVoD1qbENDQ1boYCdys4fX1aIl7LTjCaptYVJigamLK5Z6g6JapaiTV8RQCAaSfXttF8OBFSAyd+8DBBAIM0CJKppjm76+0aimv4Y00MEEDgvYEv1+5V5rYTI3qm8KBYFwpBUlddCugSMvqZZfp+umK6mN/Z33uIfZ/lZtxpHzl0fARLV9XHnrgggsA4C0d6puj6VA4ELAr5OAedN/QUNPqZBINAXdMAfYNIQyrr1oTY/oT/nSFTrBsqF1kyARHXNqLkRAgisq4D+kg5rJbEVfzkQuCBQrdlElTdwFzz4mHyBINBEVR8cCFwQoPrvBQk+Jk2ARDVpEaO9CCCwIoHQTvstz1Gqf0V66T2pUg2Fwaf0xjeLPfMD3ZZEHxwIXBAI/WpURPDCv/mIQFIESFSTEinaiQACqxKIflFXZld1DU5On8BC1WdENX1hzXSPqr4RO1OAA4HXBfQ1odV/ORBImgCJatIiRnsRQGDZAnbj8yCa9lta9rmckG6Bqo6oVnhTn+4gZ6h3tjhYpRro1F8S1QyFfUldtduy2T/YciCQJAES1SRFi7YigMCKBEwYLO6dqgkrBwJvFAh1fersQk2n/7Km740ufJ5MgYofykJFZwkks/m0uoECUdX70kwD78ClEai/AInq/8/ee0fZcV93nrfqpc4JHZDRIMAIZpGiKJEWKUuUZFGSZZv0eMaz47FmJW/w7O5Z+ZzZ/Yfw7jm7lsdjH8+cmVnJUfKYCjRFiUHMJBiRSIDIoZEa6G50Ti+Hqtp7q9FgA+jwwq/eq/D9SY+vX72q+7u/z6/wqm79blDPFBJBAARcRsC+QGdwgXbZtLhGnZlEDpl/XTMbUKQSArKamsoUKhGBY31KQDyLculpn44Ow/IrARiqfp1ZjAsEQGCOAK+UFbIpdv3NgAgILEpgYjZHqKe6KBps9BiBDBuqiTQMVY9NW3XU5WuhweXZTFwLq8MbvSghAENVCUYIAQEQcCsBy2K33/QMYcnMrTNUe71Gp9Nw/a39NEADBQRSGYNmk3kFkiDCjwQk+30e3kV+nFrfjgmGqm+nFgMDARAQAibHp+YzbKiigcASBIYnM3yeLPElNoOAhwiI2+8MDFUPzViVVZV8DRKnipj8KoNHd+USgKFaLjkcBwIg4AEC4uqUZFenrAd0hYq1IjA8wSuqKKZaK/zoVyGBJBuq0xxzjQYCixGwJBQmx6EwBVwTF+ODbe4jAEPVfXMCjUAABBQRkItyXtx+0UBgGQJDE3zjhhWGZQjhK68QSKTzNBWHoeqV+aqFnnPJBVFTvBbs0WfpBGCols4MR4AACHiFAGc5tONTvaIv9KwJgUlOppTgEjVoIOBlAgXDoqlEnpJsrKKBwFIELKNgJ1Vi/9+ldsF2EHANARiqrpkKKAICIKCagJFLc7ZfuDip5uo3eeL22z+S9NuwMJ6AEUhm8jQ6lYb5EbB5L3W4UqZGro1mASvvpbLD/tUnAEO1+szRIwiAQJUI5O3i5nhqXCXcnu7m7MUE8ot4egahvJSlGZ5MAwQIrEjANPJsrKZW3A87gECtCcBQrfUMoH8QAAHHCBSQ7dcxtn4TfGYIMVt+m9OgjSeRKtBFTgyGBgIrETCNHNcXhxfJSpzwfe0JwFCt/RxAAxAAAQcImJzVUNyb0ECgGAJ9A3F2mcTqezGssI87CcQ5znpoDL957pwdd2llcZkaI8/uv7yyigYCbiYAQ9XNswPdQAAEyiaQz7DhwbE4aCBQDAFxmZyYQTxzMaywj/sI5AomXeRzeDqJuEP3zY47NZKHuWY+407loBUIXCIAQxWnAgiAgC8JFGCo+nJenRpULm/SiQuzTomHXBBwlECK66ee4zhr1AN2FLOvhEsyJVlVRQMBNxOAoerm2YFuIAACZREQt6ZCho0O1MYsi18QDzI48+/hM1NBHDrG7AMCEp/aN4g4ax9MZdWGYBuqHB4j9cbRQMCtBGCounVmoBcIgEDZBAxOEiHGKhoIFEvA4BqUB09PkxisaCDgJQJyxorL76kBeAR4ad7coKu4/poFuP+6YS6gw+IEYKguzgVbQQAEPEygkOVSIybiUz08hVVX3eRVhcHxFA2MoWRD1eGjw4oIiNu6ZK2eiiM+tSKQATzYsONUEZsfwKn3zJBhqHpmqqAoCIBAUQTY4CjkeEXVwopqUbyw02UCmZxBh+D+e5kH/vAGgXS2QIfPznhDWWjpKgLi/itJldBAwK0EYKi6dWagFwiAQFkEJN2+nckQcTdl8QvyQZmsQQdOTaFITZBPAg+OPZXhByynEV/twamrucqWWeCEShn2QCrUXBcoAAKLEYChuhgVbAMBEPAsASOX4syXWE317ATWUPE8l/jo4zg/lKmp4SSg65IISJbfC6NJOs8vNBAoh4AYqmYB9VTLYYdjnCcAQ9V5xugBBECgigTEUMXT4SoC91FXdlIajvM7iNUpH82qv4ci9VN3Hx1HWRp/T7Ojo7PrqRqIb3YUMoSXTQCGatnocCAIgIDrCLC775yhihVV182NRxSaTeXZ/XfSI9pCzaATEC+AXUfHgo4B46+AgG2ocqwqGgi4kQAMVTfOCnQCARAoi4DJT4UNueAiPrUsfjiISBIq9Q3EaWgiDRwg4HoCJ9lVXVx/0UCgXAJSyk1K1KCkW7kEcZyTBGCoOkkXskEABKpKAEkhqorbl53JM47hyTQdRvZfX86vnwZl8cn6zoFRKnANYDQQqISAHafKiQjRQMBtBGCoum1GoA8IgEDZBGxD1UD2wrIB4kCbwNhMlvadnCRxq0QDAbcSSHOW6h0fDbtVPejlIQJ4yOuhyQqYqjBUAzbhGC4I+JmAlKUxkWbfz1NclbEV2EA9MxQncatEAwG3Eth5ZJym41gFc+v8eEkvuXZaeMjrpSkLjK4wVAMz1RgoCPibgF0/VQqXW1gF8/dMV2d0F0ZTdk1VKf+BBgJuI2Cwu+8rHwxR3sDvndvmxov6SHyqvaqK66cXp8/XOsNQ9fX0Vn9w8TWkkWZq1e8ZPQadwFyKfawuBP08UDX+6USOPuqbpMHxlCqRkAMCyggcOjtNR85MoyyNMqIQZObTSKiE08B1BGCoum5KvK1Qd/YUzilvT6FntRdD1UIyCM/OnxsVPzUYpyNsEJjIIu3G6QmsTpI86Y19w5TMIB4/sCeBAwOfi1NFaTcH0EJkBQRgVFQAD4deSyA92RrRuF37DbaAgLMETC5LYyLGxlnIAZN+kUvU7O+boqk4agwGbOpdPVwpR7P32LhdSsnVikI5TxGAoeqp6QqMsjBUAzPV1RmoHtZbefEhVJ3e0AsIzBGwOK7GXlFFIiWcEooJSJmavgtIqqQYK8SVSUBK0kim34lZjsdHAwGFBKQO+ZxXEuLyFWKFqAoJwFCtECAOv5JARDev45+42JVb8QkEnCUgF1ezgPhUZykHU/p5Tqokq6ozSZxfwTwD3DXqwbEUr6ZOUDINt193zYwPtOGHIIYdpwpD1Qez6ZshwFD1zVS6YyBaSH+A/X4b3aENtAgKATvjrwlDIijzXc1xFjir6s4jY3TuYqKa3aIvEFiUwLuH+FwcTiBuelE62FgpAXH/Reb8SinieJUEYKiqpBlwWeySpOmkf4YxNAUcBYZfZQJiqCKRUpWhB6g7MQz2Hh+nWayqBmjW3TfU/pEk7eHYVKzuu29u/KKRXU8VJWr8Mp2+GAcMVV9Mo2sGoVkadbA2iFF1zZQEQxHb9RcZf4Mx2TUYZb5g2llWUaqmBvDRpU1AEk/v4pX94+dnUJIG54RjBFBL1TG0EFwmARiqZYLDYdcSeOqppzS2UJHx91o02OIkAb6Dk/hUy0BafScxB132eV7NenP/MFazgn4i1Gj8Uipp11FeTU0gxKFGUxCIbucSKiH+ORCT7ZFBwlD1yER5Qc2uxx6DkeqFifKZjhZn+rU4WyEH1vhsZBiOmwgYpkUv7Byg8yOID3TTvARBl1ye46QPj9HB01OITQ3ChNdyjPaDX84oLUv4aCDgAgIwVF0wCVABBECgfAImG6oSo4oGAk4TmJrN0c/eukBxxKo6jRryLxEQe+HouWl699AIpTJY6cKJ4TwBI5/ix74wVJ0njR6KIQBDtRhK2AcEQMC1BCxDDFVZUUUDAWcJyK3by3sHaTcntJEVVjQQcJrAZDxLr+8bpsNnpp3uCvJBwCYwl/kXv284HdxBAIaqO+YBWoAACJRJQFx/UUO1THg4rGQCssL10zf7aXKW3ePQQMBBAlIa6RC7+77Pbr8wGxwEDdFXEDDz8tuGM+4KKPhQMwIwVGuG3n8dP+S/IWFEHiBg8oqqGKtoIFAtAsf6Z+gX7w5gVbVawAPYjzwQGRhL0S93DdLQeCqABDDkWhGYy/wLQ7VW/NHvlQRgqF7JA59AAAQ8RMDiem92MiUTGX89NG2eV9W8lFjpxPlZz48FA3AngXS2QDu5HM3e4xPuVBBa+ZbAXIJC5H3w7QR7bGAwVD02YVAXBEBgAQHTRCKlBTjwZ/UIjM9k6MlXz9DYdKZ6naKnQBAwDIuOnJuh594boEwOD+ECMekuG6RZQGiDy6YksOrAUA3s1GPgIOB9ApZlcGkaPPn1/kx6bwQFNib28GrXK3uHSAwLNBBQRWAqkaOn3+qnsxcTqkRCDgiURMCwDVX8rpUEDTs7QgCGqiNYIRQEQKAaBCx2+UVpmmqQRh+LEUimC2yoXqT9fZOLfY1tIFAyAXkAIg8/dh8dL/lYHAACqgjMJVRSJQ1yQKB8AjBUy2eHI68lgMdv1zLBFgcJSIwqDFUHAUP0sgRMznhzbjhBP3v7PPWPJJfdF1+CQDEE9vdN0D/t6IfLbzGwsI9jBGzXX9zROcYXgosjEOPdYKgWxwp7FUHgjzmfOUfT4KetCFbYRQ0BWVGVOqpoIFArArm8yQlvxunZdy/QbApu6LWaBz/0O8rxzn/9/Ckankj7YTgYg4cJoDa5hyfPZ6rDUPXZhNZyONs1jZe3qI91QBR+LSciSH1L1l/EqAZpxl051gS7AL91YIR2cZZWNBAol8CPXz9HJ87P4GlvuQBxnDICcP1VhhKCKiQAQ7VCgDj8SgIFMl/lLfErt+ITCDhAgN0u7RqqbKyigUCtCVzkVbCn3zpP+05OktTARAOBYgnI+fLMO+fpVY5NzfIKPRoI1JqA7a2E+uS1ngb0zwRgqOI0UEqA11Q/YIEI1lJKFcIWI4D41MWoYFutCEht1cNnpulHr52lkxdmSeJX0UBgJQIGnzc79g/b583EDJyRVuKF76tHwCjkqtcZegKBJQjAUF0CDDaXRyCXCw3zkQgaLA8fjiqBgBiqUpgcDQTcQkCM03cPjdJP3jxHg2MprKy6ZWJcqocYqR9xxui/e/E0XRjl88WlekKtYBKwDBiqwZx5d40ahqq75sMH2nA2CDYffDAQDMHtBNgoQHyq2ycpmPq9/uFFemHnIE3FszA+gnkKrDhqeahxZihBT/IK/JkhRMusCAw7VJUAX13JLCA5XFWho7NFCcBQXRQLNpZLoItSMFLLhYfjSiOAFdXSeGHvqhGQTMDPvneB3tg3TJks50JHA4GrCAxPZDimud+uwSsrq2gg4DYCyPzrthkJpj4wVIM5746NeoxuYX9MHVddxwhD8DwBi1ckJJkSGgi4kcBUPGevlr3D2YDzBTy/c+Mc1UqncY5F/QWXM9qxf4TSeJBRq2lAvysQwPV1BUD42nECdXVIpuQ45KB18FDQBozx1pAAYlRrCB9dF0FAMgH/7S9P0fuHxxCvWgSvIOwyOZu1a+6+sGuAZpKIAQzCnHtyjLzcgNAaT86c75TGiqrvphQDAoFgEJAVVUmhjwYCbibQP5K0jdXdR1Fj1c3zVA3dZhI5ev79AXs1FRl+q0EcfZRPQK6viFEtnx+OVEUAhqoqkpADAiBQXQJ2MiUYqtWFjt7KIXBqIE5/88Ip2n10vJzDcYwPCCTSBY5bHqCn3z5Po5JzEA0EXE4Arr8un6CAqBcOyDgxTBAAAZ8RsMvTWDBUfTatvhyOZHg9dn7GXlmVxDmfvrXLl+PEoBYnkM0Z9PSOfvonTp4k8aloIOAFAuKxJJ5LmqZ5QV3o6EMCHKJKWFH14cRiSCDgewJw+/X9FPttgIZh0dFz0/R3HLP67sFRvw0P41mCgMkPJn70+ln68RvnYKQuwQib3UqAA1URXuPWyQmGXkimFIx5ruooP1vV3tBZQAlIjTfLRMbfgE6/Z4ddYGNVVlb/+oU+2vHRiGfHAcWLJ/B3L56mH79+jqY5PhUNBDxFwH4gjOusp+bMh8rC9deHk4ohgYDvCUhGQjzp9f00+3GAsrLaxzGr//XnJ0iS63zhnjVUHwuze50fRxvMMfH9vZ3R9285LvnF3YMk8aloIOBFAiZfZ+F66cWZ84/OMFT9M5cYCQgEiAAy/gZosn03VHEHPc/ZgL/37EkamczQ1x5YT91tdaTrsFa9Ptl5w6SzQwk7HnnXkXHK5hFH7/U5Dar+8FwK6sy7Z9zJrGXCUHXPfPhCE/H83eOLkWAQ7iYghqrpbhWhHQisQGAqnqN/fPWMnQX28Yc30XVrmykcgrG6AjbXfp3OGrTv5AT94KUzdjyyJM5CAwHPErA9l3Cd9ez8eV/xVJqX9GGoen8iMQIQCB4BuYAi42/w5t2HI84VTHph5wBNxbP0zz+/mW7f2k6REJztvDbVM8k8vX1ghONRz9IZXlFFAwE/EMB11g+z6M0xmKb2fj4RTsNQ9eb8QWsQCDQB2yXJwpPeQJ8EPhv8+4fH2FjN0T//wmb65E2d1NIY8dkI/TkcSZA1PJGm1z68yHVSL9BF/hsNBPxBwF5S9cdQMArvEdCsNxpyiTQe23pv6lyt8UMPcTpWNBCoBgEYqtWgjD6qSOBY/wz9p6eP2+VMLowmq9gzuiqHgNRH/ZBdfb/33En60WtnYaSWAxHHuJoAQmxcPT2+Vk4j7Vic1uSwourraa7J4GCo1gR7wDqVtJqIUQ3YpAdjuKNTGXry1bP2Kt1XP7OBbt/SjrhVF079LLv6vrFvmJ5/f4CO9k9zKIILlYRKIFApAYTYVEoQx5dJQNesiYf4Tg+GapkAcdiSBMQr8zxptJX3wIr9kpjwRaUELNwZVooQx7uUgMStvs5G0OB4mj5/z2r67B091NNR71Jtg6fWifOz9PzOC/TuwTEanoSrb/DOgICMmK+xuM4GZK5dOExLM3PbnyALhqoLJ8fLKmmaZn3xu5OvhYge4HHUeXks0N3NBOQCihhVN88QdKuMQJ6N1SNnp9mdNEViGH31M+vp1s1tvLqK53+VkS3/6Ay7+r5zcJR+/vZ5OnJumkvP4DeofJo40gsEcJ31wiz5VMdQ2K7tBUPVp/Nby2FZ+fw7FI3kWQcYqrWcCN/3jZtE309xwAdo8orG+EzWdjGVTLIP372avvTJtdTdjp/Wap8ax/lhwS/evUB7j4/TRV7plrlBAwHfE8B57vspdvsAYai6fYY8qF84qp1htVHl3INz5ymVcQH11HRB2fIJyEreyQuzNMSrq4fOTNHXOHb1nhtXUX2MfVfQHCUwcelBwUt7hrjsTJxkLtBAIDAE4LkUmKl220DnDdT5d7fpB308TKCgmfGwRKqigYBTBPjsgp3qFFzIdSMBWcGTBD67joyxsTpND97eTf/sc720aXUTki0pnjD5bUnnCrTv5CT99M1zdPTcDKWzBudvw2VNMWqIczkB3Mq5fIJ8rJ5uzt3mwVD18STXamihWLxAmVW4otdqAgLRr5xeOMUCMdUY5BUEpG7nTCJHL+4apB0fDdPXH9hAv/1wL9ddjVIsgvjVK2CV+EEeBohB2jcwSz9+/RztOTZO2ZwJN98SOWJ3fxCwr7B4IuyPyfTiKC6VEoeh6sXJc7nOG2JbzYHMlMu1hHreJwBD1ftziBGUS8Dg1b1EqkBPvnKWXvtgmH7jVzbSr35iNbU1RakhFiZNK1dy8I6ThVIx/gfGUvT0W/12wqRUphA8EBgxCFxBANfYK3DgQ00IwFCtCXZ/d7r2IlkDDf4eI0bnAgK4hrpgEqBCrQnIP4MRLpHy/WdP2quskmzpvls6afWqemppiJCuw2Jdao5kdXpyNksX2EB9aTevUO8fpkQaBupSvLA9gARwnQ3gpLtryDBU3TUf0AYEQAAEQAAESiYgK6znhhP0V8/30Yt7BumB23roEzd2UC/HsHa2xSiCsjaXmYp778hUmvouxGk3u/e+c3DEjv+9vAP+AAEQAAEQcAUBGKqumAafKfFZHs9en40JwwEBEAABDxAQg7V/OMmvM1zW5iLddX0H3XVDB21d10wbe5rYLTiYmYIlEdI0u/f2jyTpWP+MnSjpo1OTlMQKqgfOaqgIAiAQNAJGlixN0ywYqkGbeYwXBEAABEAgEAQuTqTp4sQgvXd4jK5nQ/XmTa106+Y22rq+mdZ2BiM+I8vlZM7ySvOpgTgdZQP1GGfw7efPaZSZCcS/AQyyQgKIHKgQIA4vhwDn8BrLWWyqcoOhWg5BHLMsgVseImsPVlSXZYQvFRDABVQBRIgIAgFJFPTBiQn7tamnkbaw0XrDhhZeZW3hv5uou63OV7Gssqp89mLCrj3bxwbqKc7iK58nOB4VDQRAAARAwN0ELI3ej+StadEShqq758qT2h39Y64bEoyH9Z6cH/8oDUvVP3OJkVSLgLi+nufXTq7HunZVA63prKdedgm+YWMzbVnbQuu66rnMjffcg+OpvO3We3owTsfPz9CF0RQNcpKksekMieGKBgIgUCoBucbiOlsqNeyvgIBl7jYKWlwkwVBVwBMiriTwxBNkfeVPtVOkWZ+88ht8AgFFBLj2BscuKBIGMSAQLAJitklCodNDcfv1QXSCVrXEqKMlytmCG+i6NU1stDbTdWubbBdhN/5Ty7Dr7oXRJJ0ZSthjOMdxuWNTGZqMZ2liJgvjNFinNEbrAAH7CuvGf/wOjBUi3UVAt/T+RmrPiFYwVN01N77QRoKfv/gnEy+FNA2Gqi9m1K2DgKHq1pmBXt4iIEbf4DivQPJLEg3trgtTU32EX2Hqao3RZjZaN61upA3djbS+q4HauVZrNcveGFxG5iKX4JGV4P6RBPVfTHJJmSQnR8pzMqS8XVJGYk4lYRIaCICAOgL8SFidMEgCgSIJ6CGK0y1kyO4wVIuEht1KI8D3C++x85jcNeBXrjR02LtYAnjSWywp7AcCRROQ2qKzyfzlci2nBzXa3zdF0YjOLsE6RfjV2hhlt+F6Wt1RTz0dddTZWsersXMrsm38XQMbuqX885Q+46mcbXhOx3M0zrGk4rIr9WGH+TU0nrZXgLN5g3J5k+z3gkmccAMNBEDASQKl/EN2Ug/IDhQBM2Tltx0l6ykeNQzVQE199QYbscxzRN6Lc6oeIfRUKQG4/lZKEMeDwMoEJL4zlS3w6+N9B7UUneA4UFlV1flG9uN3sj+HeLsYq/XREMWiOoW5hmsoNLc2I8ZlgWXm2eCUlVyRLW7Ist3i/5j2i+zVUelbVkkRY/oxe/wFAlUlAEO1qrjR2RyBUCFkbucwQtoOQxXnhEMEwqGGRIEW3Nk41A/EBpUAL9RrelAHj3GDQE0J2MYmr4KSvJZoCzPsLuZWs/SRSwjEZhAAgeoSkMssrrPVZY7eLhOQa4RcO3CndxkJ/lBKoJCwfcuVyoQwEJgnIL9euIDO08A7CLiagNxwXP1ytcJQDgRAgAlI0kKYCTgVqk+AT7vLzzJxBlaffyB6bO1ouHySBWLAGGSVCeACWmXg6A4EQAAEQCBoBHSYCUGbcreNF2eg22bEJ/pk8h8/DfHJkDAMlxHAk16XTQjUAQEQAAEQ8BUBXGd9NZ2eGQxHlVxe7IKh6plpg6IgAALzBCQti4YnvfM48A4CIAACIAACignIdRZJMRVDhbiVCUwZhpmRUpeyKwzVlYFhjzIIhGPxy09Dyjgch4DA8gTsJA+4gC4PCd+CAAiAAAiAQJkEkEypTHA4rBIClqV9QJoxNi8Dhuo8CbwrJZDJN8FQVUoUwq4kwFdQrKheiQSfQAAEQAAEQEAZAVlRRRVLZTghqCgCJhn7MqHYxPzOMFTnSeBdKYF4olDgEgZnlQqFMBCYJ8C13eCSNA8D7yAAAiAAAiCgloAk1ye4/qqFCmkrEuCS2xe69Jnk/I4wVOdJ4F0pgfZoMmdq5stKhUIYCCwgoGlw/V2AA3+CAAiAAAiAgFICOgxVpTwhbGUCvAiRGJtdn5/fE4bqPAm8KyUgJ5muW+8pFQphIHCJAAfZkxaCoYoTAgRAAARAAAQcIQDPJUewQujyBCzLyj9EZM7vBUN1ngTelRJ4iE+yiBk6p1QohIHAZQJsqGoSO2M7J13eij9AAARAAARAAAQUELANVcSoKiAJESUQYNffK3LcwFAtAR52LZ7A9ifIikQpXvwR2BMESiNgr6rCLak0aNgbBEAABEAABIogoCORUhGUsItqAlyUxnqCbYh5uTBU50ngXTmBnGkZyoVCIAjME9B0JFSaZ4F3EAABEAABEFBIQAtFFEqDKBAojoDJvr8L94ShupAG/lZHgAv16uHoFSebOuGQBALs9MtuSXoIbkk4F0AABEAABEBALQG+vmJFVS1SSCuGQJpXIHJ8f3fZfsBdXjHYsE9ZBHTj46X7sgTgIBBYjgAbqoQL6XKE8B0IgAAIgAAIlE6AL69YUS0dG46ojAAvph4xLOviQilYUV1IA38rJWBo1uWsXUoFQxgIMAGNXX+xoopTAQRAAARAAATUE9DDcP1VTxUSlyPA6/gHjDANLtwHhupCGvhbKQEjbeb56cioUqEQBgLzBJCRcJ4E3kEABEAABEBAKQGsqCrFCWFFELA083xdwZxeuCsM1YU08LdSAqYVirPz7ztKhUIYCFwiYK+owvUX5wMIgAAIgAAIKCXABeDYYymqVCaEgcBKBNhRLh5Lj2YX7gdDdSEN/K2UgBFJprQQva9UKISBwDwByfqLrITzNPAOAiAAAiAAAsoIwFBVhhKCiiSgmVaBbrnlioohMFSLhIfdSifQ29KT08k6W/qROAIEViaArL8rM8IeIAACIAACIFAOAa7cUM5hOAYEyibA6/jmtqNXJmKFoVo2Thy4EoG1F8nQKDyz0n74HgTKI8DOSez6q+mh8g7HUSAAAiAAAiAAAtcQEG8lCa9BA4FqEuBT7nJZmvl+cRbOk8C7cgLbnyCLSyEVlAuGQBCYJyDuvzoyE87jwDsIgAAIgAAIVEogFMFqaqUMcXzpBKRUyBNsOyw8EobqQhr4Wz0B07rC11x9B5AYZAKymooU+kE+AzB2EAABEAAB1QT0cEy1SMgDgZUImJZF19gMMFRXwobvyycgy6maKSuqufKF4EgQWJqApqOW6tJ08A0IgAAIgAAIlE5AD9dxsfLSj8MRIFAuATZSzxgGDXD+EayolgsRx5VOwDStacuk/aUfiSNAYGUCGqeV1pBCf2VQ2AMEQAAEQAAEiiQwt6IKS7VIXNhNBQHNOmSFzdNXi8KK6tVE8FkpgUwoNmFp1ntKhUIYCFwiYLv+okQNzgcQAAEQAAEQUEYgFIHrrzKYEFQUgZBGF1r1yOjVO8NQvZoIPisl0KXPJDndTZ9SoRAGApcISFZCXQxVDU9+cVKAAAiAAAiAQKUE7NJviFGtFCOOL5GARVq8sb0ldfVhMFSvJoLPSglsm12f18PWlFKhEAYC8wTYQEWJmnkYeAcBEAABEACByghIOA1K01TGEEeXToAN1XxvL11TKQSGaukscURpBEwydSRTKo0Z9i6BgBYKz62qlnAMdgUBEAABEAABELiWgO32Cy+la8Fgi6ME2C/OpLf4dVWDoXoVEHxUS0BqqVr6temm1fYCaUEmICuqtvtvkCFg7CAAAiAAAiCggIAe4Yy/SPmrgCRElEKAs/5aV9dQleNhqJZCEfuWTEByTFsG5/1FAwGHCOhcS1VDQiWH6EIsCIAACIBAkAiE7NI0yPsQpDl3wVineFlr/OrSNKIXDFUXzI6fVbBPOlOb5SclA34eJ8ZWOwJzrr/R2imAnkEABEAABEDAJwRCvKLK2R98MhoMwxsEtD7T1I4vpisM1cWoYJtSAnk9f9GyrL1KhUIYCFwicLlEDWJqcE6AAAiAAAiAQNkE7Ez6UpoG19OyGeLAsgj05yJ0drEjYaguRgXblBKIGlFezqfDSoVCGAhcJsDPftn1V+dYVTQQAAEQAAEQAIHyCOi2kRoq72AcBQJlEtDImmoyCpOLHQ5DdTEq2KaUgNk4lgrp1jVFfJV2AmGBJqCHOZ0+p9RHAwEQAAEQAAEQKI+AHqnnkm8wDcqjh6MqIJCNbe7MLnY8zsbFqGCbUgJNq7cWNC2cUSoUwkBgAQHJ+quHIwu24E8QAAEQAAEQAIFSCIQlPlWDaVAKM+xbOQFO+Gtu67q2NI1IxtlYOV9IWIHAtqOcy8syjBV2w9cgUDYB2/UXmX/+N/FiAABAAElEQVTL5ocDQQAEQAAEQEBWVHlJFSBAoKoEODzQoodgqFYVOjr7mIDURbI0HSVqPkaCvxQT0EPhOddfJIBQTBbiQAAEQAAEgkBgPpESVlSDMNsuG6OlW/THi+uExyaLc8FWhQTmStSYKRYZVygWokBgAQGNQhynioRKC5DgTxAAARAAARAokoDObr+4hhYJC7upJJC2LHNWFrUWEwpDdTEq2KacgFGgAa6lekS5YAgEgUsE9HDMzv4LICAAAiAAAiAAAqURsOun6sj4Wxo17F0pAY5PHTAsOmovai0iDIbqIlCwST2BWIwG2AUdhqp6tJB4iYBk/pWkSmggAAIgAAIgAAKlEZAVVU2DoVoaNexdKQGN6EI4pB1dSg4M1aXIYLtSAtE6YyocootKhUIYCCwgIPXfJKkSGgiAAAiAAAiAQGkEQnZpGhiqpVHD3pUS4NQi06vqw0vaBzBUKyWM44si0B7uylqkJYvaGTuBQBkE5ElwSNx/4bpUBj0cAgIgAAIgEFQCtkcSeyXxkmpQEWDcNSKgkZa5rnFiSfsAhmqNJiZo3a69SIZJVj5o48Z4q0vAdl3Sw9XtFL2BAAiAAAiAgIcJ2DkecO308Ax6WnWDensLS40AhupSZLBdKQG7RI25eI0kpR1BWKAJ2MkguFQNGgiAAAiAAAiAQHEE5NopZd7QQKDaBEzOpvTEQ2Qs1S8M1aXIYLtSApLNi5dUBznz7wWlgiEMBBYQkBgbpNdfAAR/ggAIgAAIgMAKBOz4VOR4WIESvnaAwCTbBSeXyvgr/cFQdYA6RC5OQAtpx0mzDi3+LbaCQOUENH4iHIrWc5gNftoqpwkJIAACIAACficg2fLnMv7iuun3uXbh+AZ5dWHncnrhrFyODr5TSqBdCw+FSRtQKhTCQOAqAqFoAydUggvTVVjwEQRAAARAAASuISBGKkq7XYMFG6pBQKPJVt3oW64rGKrL0cF3SgnEVjUnSNfiSoVCGAhcRcBeUUXm36uo4CMIgAAIgAAIXEtAEilJ1l80EKg2Ac4xneng8pXL9QtDdTk6+E4pgd5eKnDMNDL/KqUKYVcTQKzN1UTwGQRAAARAAAQWIcDlaOaSEKIG+SJ0sMlpAhYZ3V1dueW6gaG6HB18p5bAW2Ry0PSSmb3UdgZpQSUgbr+IUw3q7GPcIAACIAACxRJAfGqxpLCfEwTYJrDoKC1Zmkb6hKHqBHnIXJTA9u2aaRrWND/ASyy6AzaCgCIC4VgT/7rh500RTogBARAAARDwIQFx+w3xCw0EakAgy3bqqJSvXK5v3MktRwffKSdgknaMayadVS4YAkFgAYFwrJEz/4YWbMGfIAACIAACIAACCwnMxafCUF3IBH9XiYBFk4ap7VquNI1oAkO1SvOBbuYIRPTCSQ6e7gcPEHCSgJ3F0H5KzGcbGgiAAAiAAAiAwBUEpIybxKci4+8VWPChWgQ0mo5EjA9X6g6G6kqE8L1SAp1dkTFdo2UzfCntEMICSUAuwOG6Jl5VhaEayBMAgwYBEAABEFiWgGT6leSDfKFcdj98CQJOEOCzLtUdrTu/kmwYqisRwvdKCTTHx9NkaVmlQiEMBBYhEKlrxgV4ES7YBAIgAAIgAAJ6iA3VKBuqaCBQAwIcmFrY1Dy+YsnKcA10Q5cBJvDg6q2FM/1TxvKh0wEGhKErIxCua2FDVZ7FIdG0MqgQFAgC8wssGl1aaeG3+TWX+e8ERCQcooYYv+rC/B6m+rq5z3XREMUiIYpGdH7Xeb+5l87uNJxQj3KGSfm8SVl+ZXIGvxv2eyZrUMp+FSiVmXsZnNRgvnGGyI8vHZf/nvtevkMDARAonoAe4URK7PqLBgI1IaCRcUtj74olK2Go1mR2gtvpY4+R+YM/IzkxTX5hRT+4p4LjI9f0EEXqWyiXmHC8L3QAAl4hIIZmiA1GMRovv/NG+SxGZUtjlNqbo9TWFKHWJv6bX7KtpTFCXW0x2tDdSPVsiM63eff6eQN27v2SibvAwLX3F2t3gUFp/8n/mdvE+R8vfffx+6UNfFg8VaDTQ3GaSeZoNpmnmQS/+O9p+Tueo6kEb0/lqVAwSYxbk1/yPv+3fP5Y2rz2eAeBYBKQuNRQtPHSw9xgMsCoa0ogbZr00eOPayuuJMBQrek8Ba9zye71pe9OHuP7lRG+oVkTPAIYcTUJROpbYahWEzj6cg0BMULnVzKjvKIZi/KqZkinxvowrVlVT6s7+HXpvae93jZCmxv45pWPc2OTVduejuVXfwq8WjvNBuvYdIaGJ9I0OJGy3+Xv8ZkspXn1NseruDlewc2xQZvnV64gBjJMWDfOOXRyjoDGbr+SHR8NBGpEYIafkrxcTN8wVIuhhH2UEtDC1j7N0C/wc3QYqkrJQtjVBMT9V1ZWLXPFh3ZXH4rPIOAJAjo/8atj91tZ5ZT3RnHD5VcTG6RijK65ZJBuWt1of5bv3GmKVo47HNKoszVmv27e1HqFQFlRjacLNDiWogujSRqeTNtG7EU2YsUFWVZn0+x2LK7I8+7IsF+vQIgPviHAHhSSSAnxqb6ZUQ8OJBMyc4eL0RuGajGUsI9SAutaCmeHpyMTfN+ABgKOEpi7GDdSITPraD8QDgLVICAro7Ii2lQfsd/FLVcMU1lpnF8l3djTxO65DdVQx1N9iGtzK7svtza20i29VxqxsqJ6tH+WLvIK7IgYsJMZmpzNUpLjZGfYtVhcjZPpPCXY0F0YM+spAFAWBC4RkIe3dq1xHSYAToraEODnq4Webn2smN5xlhZDCfsoJbA+nJq5SC3pj9NzKBUPYSBwmYBEyon7LwzVy0jwh0cISEKiNjZEWzlW1H7nONGOlhj1tNfZry5+37y6iRrYcPXrCmm1pkribLex8Sqv+SarqXGOeZXV18FxNmCnMjTKBqzEwk7Fs7aL8TTHyco+4kKMBgJeIaCFwmyoNnlFXejpQwKWqZnhQmemmKHBUC2GEvZRS6C/N2fVT0+RZklSpYha4ZAGAgsI8A2oJFTKTOsch4abyQVk8KfLCEiyou62OhIDVIxRcdvtks+cwMjezn+L2y5adQjwT4edQGrb5jaS13yTFdbRqTSvvGbsWNhRjocdFSOW38cuvYsLMRoIuJWAlKVBfKpbZycQepl8/z/yvW9R4fvfXnm8uOqtzAh7KCawfbtmfvlPJj/kZYAv873AWsXiIQ4EriCgc/r9ULSBCtnEFdvxAQRqRWDeDXVtZwOt72ogeRfjtHv+xUapJDZCcx+BOffhCF2/nstfcTM4gZPEty40VMV1eIBjYYf4JQmdkuwyjAYCbiCg6fqc2y9n/UUDgRoRyJmW9YYkVy2mfxiqxVDCPsoJmIaxO6SHRlkwDFXldCFwIQGNa6mGeVUVhupCKvi7mgTENVdKvUhCo81rmmnL2iZaxUl/VrErryT/kb+l7iia9wiEOIGTuGTL66aNc67DEts6Pp2lCY5zlWzDsgJ7/Pws9Q8naIiTN0mNWDQQqAUBTeP4VKkxjgYCtSOQ1zTzzWK7h6FaLCnsp5RANBY7b1qFBLIqKsUKYYsQkCfIEqeamRnmRNNw/10EETY5QECSHG1kw3Tr2ma675ZOe4VUYk2lRqm8wlwqBs2fBGTuG1eH7QcTMkLJIiwGq5TOibPrsBirH52apNODcRri+FfJOowGAtUgoHG230h9czW6Qh8gsBSBQms99S315dXbYaheTQSfq0KgN9ocP5OZkhhVNBBwmICk4o9RmIubF7Jxh/uC+KASkIy869iNV1bVxDCVv8VgkTIxsmIqZWTQgklAEmOJi7e8pGW5jutnbuuyswinswU6cnaaDpyeomP9M7z6mkVd12CeJs6PWnI2xJq5ZBtu/Z2HjR6WIWCt6emcWub7K77C2XoFDnyoFoEHV1PhzDlZ3sLNW7WYB7kfnbMcylNkGKpBPgvUjl3szlbOxHv7lnb6xI0ddOfWDrtsTD3XMhXjNMKGKxoILEZA3LwlWdZ827quhb5w71q7jut0PEc7Phqmg2y49g3E7dXY+f3wDgKVEJAwmEjDx4nBKpGFY0GgXAIcmGrdT5T7iyIFwEooEhR2U0/gS9+d+EteZfgmS25ULx0SQeBKAvn0NCVG+tj7Fxk5rySDT8sREINUVkMlFjHEtTi3sCvvndd30P3buug6jjUVg1RWU8WVV/ZFA4FKCEg4jKy4Sskbqdt6lFdZ9x4bpwOnprhETpoKnLxJarmaKEReCeZAHhuK1lPrutt5fQA/VIE8AVwyaNPUXnvp37V/oVh1sKJaLCnsp5wAl1F6VafQN1gwDFXldCHwagK2+29dM+VT01d/hc8gcAUBMUxjUZ3EZVMSHt20qZXuvqHDfslnydpr/w/3e1dww4fKCYgNIeedvCTzs6y8PnxnDxXYMD0/kqR9Jydpf98knRqYtWu4pjn+NYcY18rBB0BCpKEdRmoA5tnlQ7RIM54pRUcYqqXQwr5qCWQjRyhmJtUKhTQQWJyAXTvONlRneIeisqIvLghbfUlAbM5GdtltYXdeqVt6x1Zx6V3FRmoLNdejlIMvJ90DgxLDlcs4UJQfjmxd12y/Hn94E41zRmFZZd19dIxjXGdoNsWJmlIFyrLhigYCVxMQt9+oGKpoIFBjAh0t9EEpKsBQLYUW9lVKYP3a/PjwRCinVCiEgcASBDSd0/LHGjmxUpTMQnaJvbA5aATEOF3VHKOejjo73lRiTW/mFVTZjgYCbiXQySv7v3r3avrcXavtGq6yynrgUibhMc4wPDWbs12I3ao/9KouAZ3dfkN8/UMDgVoTiJnaxVJ0wJW4FFrYVymB++7ozPzijSnk5VdKFcKWI6CH69hYbaIcDNXlMPn+uwZOeNTdXk/ruxvsFaptvW10F8edwjj1/dT7boCy4trTXkdf+uRaeuSeNRzHmrFXWiWD8JmhOA1y+ZsxrulaMHCp9d3klzCgaEOHHa5QwiHYFQSUE2BftngimyrJkxKGqvJpgMBiCXzrE1T4+RvamEaW+Cqh2n2x4LBf2QRkNVUMVYlTtezTrmxRONBjBKIRnVazcbrlkvvklvXNdAdn7JXapmgg4AcCEju9ZlW9/foiG64Do0k6zKVvzlxM0IkLs3SW3yemMwh88MNklzAGKUcTaWhFfGoJzLCrMwQ4Wdy7M0akJJc2GKrOzAWkFkGA426sL/3p+Nu6pt/PJ29TEYdgFxCoiIDE6YRiDaRH6sjIlfRQr6J+cXBtCEhSpM42TobEtU1vu67Nrm0qbr0LS4PURjP0CgLOEpCV1g09jfZLeurj5EtirA6MpuiDExPUP5ywswo7qwWku4FAhHMzhNibCA0Eak1A18xfjrX0lBTyB0O11rMW8P5N03xb1/V/yxhgqAb8XKjW8CVFf5iNVRiq1SJe/X4kY+oNG1roVjZOxa1Xysj0rsZPTPVnAj26hcD161tIXlL25lPbOtktOEFHzk3TsXMzNDCWsre7RVfooZZApJGTKOmo66yWKqSVQyAcCh396kUyPizhYBiqJcDCruoJ1GnaqTxRXjJuooFANQjoochl91/T4LMPzTcENnQ30p2crfcuLiWzqaeJ1nVy3UC49vpmfjGQyglI3V9JGHbr5jb69K1dNDSetldb55IxTdF0oqTFjsoVggRHCYQi8mC2iTNHw1B1FDSEF0WAb78mtz/B0Qfbi9rd3gmGavGssKcDBLZs7pw6cQ4JlRxAC5FLEtDY/beJ3X/rCYbqkpA880VLY4Ru45vuB27voes3NFNXa51d+zQUwuMvz0wiFK06gXCIY7a5Rqu8bu5tpc/c2k3jMxn6kOu07jw8RifZVVhWX9G8TSDMsakaWwdoIOAGAnoulOWnJiXVB4Sh6oaZC7AO9xPlTqCoZYDPgNoMfc79t5GMbIKTKuFmrDazUH6vIU4aI6unn72zhz55cyet5ZXT9uYoxSLIyVY+VRwZVAL17CovGbDtLNjsHvzFe9fSueE4vfbhML13aBSxrB49MSSJUrS+lfQQbvU9OoW+Uptz0ZzKFQqpUgeFs7dUYthfKYHHH9eML3138hDfd65jwfBNUUoXwpYiIG5Q4TqO1+Lsv0Y+vdRu2O4iApIYKRLW2F2xm371E6vpDnZflDIzEo8q2U7RQAAEKifQxPWD5SXZg2/f0kG/9+UttGP/CO34aIRO8SqryXeb/H80DxAIcxIl8RzidL8e0BYq+p2AReYrphWOlzpOGKqlEsP+yglwmZCfs2/K51kw6kQopwuBSxGI1HE9VU6sBEN1KUK13y4GaIxj6jrb6ujhu1bTI1xyY31XA4XZrVdWVdFAAAScISCu8+JW38yv332kgb7x4AY6yomXXt47RPvYPXgmmbNdg2G0OsO/YqnyYE9WU7kkGxoIuIFAfYx2Z3TO3FZig6FaIjDsrp5AZ2No11SapJYqGghUjYDE7ciFvMDuv2YBCUSqBr6IjiR+rrkhTJvXNNFDbKCKK6LcNKOBAAhUl4A8DpJ/j5KU7H5OviSv04Nx+uXuQdpzdJyGJ9OUyhj2Smt1NUNvyxEIxxrZawhJlJZjhO+qSyCkhQfvm9yaf7HEbvFIukRg2F09gW99b7ZzYKZwjiU3qpcOiSCwNAEjn6Hk2GkqZEr2RllaKL4pm4BkJO1qjXFSpBY7/vT+bV3Uhqy9ZfPEgSDgJIGzFxP01oERev/QGJe4SdJ0PMfpPNFqToBXU+ta11A9v5BIqeazAQXmCGQimvnQz7+zao+GZEo4J7xGYCo5k+QyqgnOqQRD1WuT53F9Q5E6kmLoRi5FlolF/VpNpxio4tJ7C9c8feD2bvrEjR3UXI8V1FrNB/oFgWIIiMeDvD5/92p6l43Vdw6OUP9wkiZms8Ucjn0cIhAKxygSa4aR6hBfiC2dAOesPJA3ralSjVTpCa6/pfPGEYoJnGlcX+iemn6X89v8pmLREAcCKxKINLRTLjlJBgzVFVmp3oEvWrR1XbNd9/RTnL33EzetoigbrWggAALeIbCeM3D/9uca6TO3ddH7XNpmN7sEn2L34LHpjHcG4SNNQ+z2G4o2+GhEGIrnCYRoR9aMTZQzDhiq5VDDMUoJfPUiGR820/OGAUNVKVgIK4qAxPJIXVWjwKsAyAxSFDMVO123tonuu6XLLi/zqVs6VYiEDBAAgRoR4GdOdsmo33qogWuydtG+vilOujRhJ16CwVq9SRFX37lsv0iiVD3q6GklAmGy+nrWTLD3ZOkNhmrpzHCEYgLbnyDrt//SOBI3sJKiGC3EFUOAl/KjTason54myygUcwT2qYCAuAp+5rZuuvP6dvrkTZ1ccgb/7ivAiUNBwFUEJBu3rLDK6/5tnbahuufYOO06Mk6TcbgEOz1ZYV5JFbdflKRxmjTkl0JAJ2uyt7+3rKyVMFRLIY19nSHAgdXmv58eJzKdkQ+pILACgQjXVA1HG9lYneU9kQ5kBVxlfb2Bb1w/x7Fsd9/QQds2t1FjHS4/ZYHEQSDgEQJdXFbqi1xS6o6t7Xbs+TsHR+kdTr6USOOBoBNTqOnhudVULruGBgJuIcB3VJOGSbPbt2tl3eTjTsEtMxlwPbK5bMoKh4c4Zm1twFFg+DUgoOkhe1VVsv9acP9VOgPd7XM3q5++tZvE3belAUmSlAKGMBBwOYHVHfUkr5s2ttIj96yhF3YN0o79I1Tgu1c0dQRCEU6iVN9CGnsJoYGAWwiwobqHo9WHytUHhmq55HCcWgJhPWWR/qJG1jfVCoY0ECiOQLShgzKRYTsDcHFHYK/lCEjd0y/ft46+dN9aWrOqnlobETO1HC98BwJ+JyC/A/LqZff/R+9fTz94+TTtPznp92FXZXxinEpsquRbQAMBVxHQaL9ViI6WqxMM1XLJ4TilBGLpzqzWPPF23tBhqColC2HFEtBCYXtVNT2ZKvYQ7LcIAYk5FRff33poE21kd18xWNFAAARAYJ6ArK52s1vwxp5G2t83ST954xydvCBhF2jlEtClJE19G1ZTywWI4xwjwLcEg1pTM5egLK/BUC2PG45STGAbUeGwpp/NK5YLcSBQCoFYUxdlZ0fILJQV819KV77aV8rMRMIa3X5dO/3uI5vpNn6vj4X4polTgaKBAAiAwFUEdE66JKurXW1r6K7rO9gVeJiee3+ALoymuFSYiQTsV/Fa9iOvpspKaqS+ddnd8CUI1IAAR1OZifsmKf9imZ3jLqJMcDhMPYFv/IeRO7JG5AOWjAco6vFCYhEE+AeVMtNDlJ4aKGJv7CJ2aH0sTFvWNtPXH9hAD9/dQ/XRMBuoYAMCIAACxRMwOTfA0Fiant85QG+y0To8kaZcATGsxRCU1dTGzs0UaWgrZnfsAwJVI8D/rPs5P+X/8OK/6yjXToVBULXZQkcrEkiasdmQZR7km9y7V9wZO4CAAwQkzkdK1WTjY7yqilIKyyGui4a4BEUDff7uNfToZ9bTqpbYcrvjOxAAARBYkoDOF375PfmDr99An72zh/7prX7ad2KSpAarYSIT+5LgmJvUAsdq6pKE8EVtCewq6IVTlaiAlatK6OFYpQQawtZUrkAv89MXGKpKyUJYKQR0LpgeY2M1zSuraNcSCIc02yj95M2d9OsPbqQbN7RQiLehgQAIgIAKAjdvaqU/+u1t9M6hUXruvQE61j+NkjZLgNW5JE2suYvLpuI3eAlE2FxbAucaU5GxSlSAoVoJPRyrlMCG0Hh6kNqPZfJIra4ULISVREBq0UUa2imbmMCq6lXkJO5U4lC/cO8aknIz7c3I5HsVInwEARBQQKCOf2u+wKVs5EHYy3uG6I19w3RuuOx8LAo0cqOIudXUMGJT3Tg50IkJcOqK6cgtbRVlqIShilPJNQRWTW7NX2iaGneNQlAksAT0SB27AHO5mumLgWWwcOCS+GR9VwM9eHsPPcJG6g1884gGAiAAAk4TkMzA/+bR62nr+mZ6afeQnSU4nkLaReGu8e9yrKUHmX6dPgkhv2wClmblth2lwlNlS+DzvIJjcSgIKCfw1T+ZfKCg03N8YiIrgHK6EFg8AYvy6VlKjp8lM8+lqgPcQnwz9KlbuujRT68jcfdtqMPzzQCfDhg6CNSEAKcOpaHxNL2yd4he/eAinb2I1VVZSW3uuYEN1lBN5gSdgsAKBLjmlPW/v/BHHX/DFQDKDjTHHccKlPF1dQmkTGM0QvoeflL4SHV7Rm8gsJCARqFoA0XZBTgzE9xV1S6udfjFT66lR9gF73qsoi48QfA3CIBAFQlIqat17NXx+Od6afOaJnqZDda9xycomS5UUQt3dVUnq6kwUt01KdDmYwKWdtzStL5KjFQRBkP1Y6T4ywUEmuvDo5yS/n1OqARD1QXzEWQV9BDHqvIT63x6moxcOnAobt/aTr/12U32KmprYyRw48eAQQAE3EegkT06Hri9mzZzSawbNwzT81x7dXC8ohA49w2yCI3k2hSuay5iT+wCArUhoOvWiahpna60dxiqlRLE8UoJdDe0JS8mpvsNo2wvAaX6QFiQCXCiijopot7Ghqq4/wbjnGxuiNDn7l5NX7l/Pd20sYUiYSQ3C/K/AowdBNxGIBzSaRPHrv7Gr2ykDVzS5mdvn6fDZ6YDU3dVVpclNlUy/qKBgFsJaJY2umlzcqJS/XAHUilBHK+UwPe+RQXLNGbYJsgpFQxhIFAGATsDMD+5FjfgIDS5+fsff/1GO3nJLb2tMFKDMOkYIwh4lEALe3o8wAnevsOlbL7KtZyb6oNhuMnD03CsCSVpPHreBkJtiwqGZcb//LH1FSf5gKEaiDPGO4MUX3Yrp/eZZL3hHa2hqZ8JhOt5VbGhVVIs+naYUoLvNi478788djN9+VNrqbM1RpJECQ0EQAAE3EwgFtGpl2NW/+DrN9A3v7LV9yWzJCbVXk0NIxzDzedl0HVj/7ODHD3+fqXxqcLRv3deQT9LPDz+SF4b0Ej70MNDgOo+IqCxgSpJlcKxRh+Nam4oYqDKKsSX71tH/+e/vJXu46y+sQgySPpuojEgEPAxgbnfsQj91kO99H99807qXd3k2wdt0aZVFIqJhw8eJPr4lPb80Pge/rRF5hEVA4GhqoIiZCgl0L2mLRkKaWNKhUIYCFRAQJJWiLEqrsB+aVIbdc2qBvpXX9pCf/Q72+ybO9mGBgIgAAJeJBAOaXTPjavoL//tvXT/rV1UH/PXQ7cQ1/eONXWSHop6cXqgc4AI8K3EzLruxKSKIcNQVUERMpQSsONULVOKpCFOVSlZCKuEQKRB4oJkVdX7xpwYpFs4a+a/eXQr/e4j11Fd1F83dJXMM44FARDwNoHu9jp64vdutz1F2pr8YdSJZ0+ksYP0SL23JwfaB4FAwSQz2dvfq+QeHoZqEE4Zj41RfNpzFl3gAt8Vp7X22NChrosJSEKlSGM7P8329qqquMlt40RJEtMlLr9oIAACIOA3Ak31EfrW125gd+BN1NNR5/nhzdf11kOITfX8ZPp8AByfOmKa2rHt2zVTxVBhqKqgCBnKCdRpoT5d0/YpFwyBIFABAXH/DXk426IkSBLXuP/pGzfSp9k1Dg0EQAAE/EpA6j8/xobqv/jCdXZog1d9YSSBknj0hKJYTfXrueqncfG/s3O6XtitakwwVFWRhBylBDY05MZ0nS4oFQphIFAhAT0co1gzxwh5MFZV4rUeums1/c/fuIlu39JRIQkcDgIgAALuJyAlbH6NPUckFv+mTa0kcaxea1KKJuKzHAlemwPoWzwBfh4+uakp2l/8EcvvCUN1eT74tkYEuse6UqZB0xbXYqqRCugWBBYlEOG6qmF+kfjQeqS1NEToK59ab8ekbl3f7CXVPUIYaoIACLiVQCNnNv/snT30+7+2le66voOiYe/c+oqrL1ZT3XpmQa+rCfA9u2kUzNmem1vjV39X7mfv/Gstd4Q4zpMExLc9b9FRsuikJwcApX1LQDL/1rWu5lhVb8QKtXIyka8/uIF+5/O9tLGnkVeDvWNg+/YkwsBAAASqSkA8Sj7J5bd+78tb6RMc/hDl+quub/wwdC7jfAc/XPSAvq4HCgWrQGCEA1N3bX9YU7bIhDO/CrOGLsojoFvaIf6dPlze0TgKBJwjEI42ktSzc3sGYDFSf/NXNtpxWlKKhuO+nYMCySAAAiDgYgJinN6+pZ3+9a9tsVdWIy5fWQ1xqEnUzvTrj8zFLj41oJoiAnyLMRQO6zsUibPFwFBVSROylBK4OZYY4RwCqKeqlCqEKSHAv8Z1Latdndyimd19H71/Hf06r6Z2tdXB3VfJxEMICICAlwlIjOotm9rov//q9XTrdZygyKUxqxon6ZAQE4lNdfsDUS+fD9BdOYFET4ehLD5VtIOhqnyOIFAVgT//39ZnOE51lt1/lbkQqNINckBAiq7HWrpdCaKhLkyP3LuGHnt4E3W2er80gyshQykQAAFPEhDj9KaNrZxY7ka7nrQbHU30ECfua+okyfiLBgIeIWCSZSU2/n5nUqW+MFRV0oQspQSknqplWjtY6CGlgiEMBFQQ4LubaCPHOtlPvFUIVCMjFg3RZ+/otksydLfVYyVVDVZIAQEQ8BEBKdUlxuof/c42Wt3hrrIvkgch1txFku0XDQQ8Q8CiEcuyfrpdU1M/dX7cMFTnSeDdlQQKunmYNFLqRuDKgUIpTxLQQ3xD0bbGNYmVwiHdrpP6za9cT2tWwUj15EkFpUEABKpCQBLLbetts41VCZVwR+MESrHGOW8dNy71ugMStHAjAY2mKR9+T7VqMFRVE4U8pQTWdScm+bc6oVQohIGAMgJ8UxFt4JuKnppnZZQVgts45urbX7ue1nU1KBshBIEACICAXwmILXjvTavoD3/jJmrj5HO1bno4QnVta9nlN1xrVdA/CJRGQKNc56bMcGkHrbw3DNWVGWGPGhLo7e/Nsf+vGKqIU63hPKDrpQnIDYVkAJYyArVq7CZPW9Y10ze/spWuX99SKzXQLwiAAAh4joB4onzuE6vpn/1qL7U01nJldS6cRGp1o4GAxwhwfColvlr3dka13jBUVROFPKUEpJ6qYdJhLiKM7L9KyUKYSgK6lBHgxBeSYKkWrbM1Rv/dF6+z6wPWon/0CQIgAAJeJtDICege/fR6+uK9a6me4/xr0ULROts7pxZ9o08QqJBAxrCsXY8//rhRoZxrDoeheg0SbHAbgahu7eQVowtu0wv6gMA8ASnGLk/BZWW12oXZpTbqV+5fzysCa+bVwTsIgAAIgECJBFa1xOxyXndslZIw1W2S3be+bR2FIrHqdozeQEANgWSYzBfUiLpSCgzVK3ngkwsJrG+KnCXNmnahalAJBC4T0MNRuzh7iBNhVLNJfNXXH1jPOcfQQAAEQAAEKiGwvquRvvaZDdS7uroZdyMNHVwztY1Vxy95JfOHY2tGILehxTjiRO8wVJ2gCplKCfTc3Bq3DCvO7r+mUsEQBgKKCYQ4sZKsqorRWo22eU0T/Ut2+e1uQ63UavBGHyAAAv4mEI1w5nR++CduwLLCWo0m1406rsmNmqnVoI0+nCBgmlbyrnt7ppyQDUPVCaqQqZTA9oe1gmFab7NPJeJUlZKFMNUE5EYjKk/G69scdwFuiIXptx7aRNs2t5GUWUADARAAARConICUqnn4rh765M2dJImWnGxyzahrWU1znjj4HXeSNWQ7RqDAq0i//PY9Wt6JHpz9F+iExpAZSALhOv1NjazRQA4eg/YUAVlNjfGqaijqbBH5B27vpvu3dVGsRok/PDUpUBYEQAAESiCwuqOevnDPGrpurbMuwNGGdnb5bXX8wWYJQ8euIFAqgXw4rD9T6kHF7g9DtVhS2K+mBFa3Guf4ZE3WVAl0DgJFEghxqZq5LMDOlDoQl9+v3L+OutvrENFU5JxgNxAAARAoloB4qdx1QwevrK52rL5qmPMZRJu7apYtvlgW2A8EViBgrmrMHl9hn7K/hqFaNjocWE0CG3+/M5k3rGmOU1We+rqa40BfwSAgmX9jzd12cgzVWYClQP3n7l5NN/e2Ugguv8E4oTBKEACBqhOoY2+Vz/OqqtSoVt20UNguRROp47rX8qOOBgIeJWAY1myoLj3rlPowVJ0iC7lKCWzXNFMj7YeaZk0oFQxhIOAQAYk9inHskR5R5wIs9zOfuqWLPntHDzXXO7Na6xAOiAUBEAABzxFY19lgZ1Vf26nud1wM0/lcBjBSPXdKQOGrCGgh678kG3sdiU+VrmCoXgUcH91LoLmB3uNHj3H3agjNQOBKAuLaVd+2VlkW4LamKH2e66VuXc9P4dFAAARAAAQcJSAPB8X99/YtHRQNq7hl1khWUWOc5bda2eEdBQThgSfQFNPeeOox56pyqPhXF/hJAoDqEHjw+okRzaJMdXpDLyCghoCUq5FXpU/OJWbqbo6ZunWLZBRWoxukgAAIgAAILE9AMv+qygmgR2IcFtJF4ZizSZqWHxG+BQE1BCQcL2zqQ3xTYqmReK0UGKrXMsEWlxL4wy9vzXGc6mFWzzEXA5cOHWp5nICUHwhHGysahdT0u+/mLtrYXZmcipTAwSAAAiAQQAL33LiK7tjaQZzdtOzRz5Uva7NzF5QtBAeCgIsIcOnIo9l0yFFPx/L/xbkIFFQJBgGNn9joYe2HPNp0MEaMUfqFgB6OUX37enb1Kq+APJ/7dOOGFr5RavcLEowDBEAABDxFQFZVm+vDZeksSfXC9a12AiVNL09GWR3jIBBwkEA4rP3DVG7C0YocMFQdnECIVk+gs9XYz/4FWFFVjxYSHSYQrm/hwu49pOml/+y2NkboTn6av767wWEtIR4EQAAEQGAxArdd187Z1ttILzn2QrOT6snvf0hhcr3FdMQ2EKgmgdaI9VHXLc4lUpKxlH7HVE0C6AsEriLwIHWOWyZWVK/Cgo8eICBP1KW2aoQLvJfa1nU10N03dpRxg1RqT9gfBEAABEBgMQLhkEZfuo+T45VYFszOAM9xqXYpmsUEYxsIeJGAZaUyhj76UwcTKQkWGKpePDkCrPO3v63lLct6iRE4FrgdYLwYusME9FCEV1U5XrWu+EQaUsvvpo0ttHlN8cc4PAyIBwEQAIFAEpCEdhtK8Wzh1ddoE2cMVpBQL5DAMWjXErDIeiOvGeMSluekkjBUnaQL2Y4QiMXoaRZsOiIcQkHASQJ80xLikjV1LWvYBayuqJ7am6N01/UdJAYrGgiAAAiAQO0ItDRE6f5bu4pWIBJr5hJl60geUqKBgJ8I1EX0F+opPen0mGCoOk0Y8pUTaIrqx5QLhUAQqBIBcQGONHBSjaYujldd2fiUbL+SbRINBEAABECgtgRC7Pb74O09RSmhsXFa31F+Er2iOsFOIFAjAqYeOrttdn3W6e5hqDpNGPKVE0jlacYwrIRywRAIAlUiIFkfI+wKFmloW7ZHWUW9eVMrdbREl90PX4IACIAACDhPQPIoievvDetbVuysrnUte9AgZGNFUNjBewRM67VsNndu+3bNce9GGKreOz0Cr3E21Jbmi8VfBR4EAHiaQIgLv0u8aoRLFizVmrgUwp3s9lt6lsmlJGI7CIAACIBAJQTqYyG656ZVy4jQuAzNaoo1d5J40KCBgN8IaCHtNX58PlSNceFfUDUoow+lBO6bpHx9E72iVCiEgUDVCWicVKmZ6lrXUJjjVon4Uf1VrbkhQts2L23IXrU7PoIACIAACDhMIBYJzT1AXCz7ryRPauzguNQ1iEt1eB4gvmYELD71z8c2d6aqoQEM1WpQRh9KCTzxBFnRvDVgWWQoFQxhIFADAuL+K0/f9fCV7r0SCyVlaXra62ugFboEARAAARBYjID8Nm/sbqSuttg1X4fZ1beunUvYhK/97pqdsQEEPEjANK3hrGGNP/W4VpV7cBiqHjxJgq6ypMJOWjTL/1j6gs4C4/cHAXkCH21cxcmVwpcHFAnrdMOGleOgLh+AP0AABEAABKpCQNx/t6y98vdZD9fZ4RyhCB4uVmUS0ElNCOg6vRwO6Ser1TkM1WqRRj9KCSRi5owe1p9UKhTCQKBGBCT7b11rj51caT6mSQzV61A7tUYzgm5BAARAYGkCMU5017taQjbm2lyN7G47o/v8b/j8d3gHAT8RCGvayVVWdrxaY4KhWi3S6EcpgYfGujINIesjpUIhDARqSEBcxSSuKVzHT+k5zikc0mhTDzJG1nBK0DUIgAAILEqAa0jSeg7NkCaeMNGmTvu10Ctm0QOxEQQ8TIBD7gqGRRNbkj3pag0Dhmq1SKMfpQQ4TtXIWdYY/6PJKRUMYSBQQwKhaCPVc3xTONpAsqK6phMuZDWcDnQNAiAAAosSiLCh2tNRT+EwG6mN7ZxnoOeaPAOLHoiNIOBhAlyLpi9nWFUpSzOPCYbqPAm8e4qAxKkWjNCYaVm7PKU4lAWBFQjIimp92zpqaaqnxrqPY1ZXOAxfgwAIgAAIVImAlAxr4qzsrW1zRmooUlelntENCNSOQDhk7a5r0I5XUwMYqtWkjb6UEuioM8aiYe1lpUIhDARcQEAyAUebeyhXcLyWtgtGCxVAAARAwFsE2JuLClaEIk3dl8qLeUt/aAsC5RDQLW1w65rkWDnHlnsMDNVyyeG4mhO4/raOVESzTtdcESgAAqoJcJH4WaOFXvwwoVoy5IEACIAACFRIYCph0KuHspTXF6+BXaF4HA4CriPAz2ayBlmTrUfXZ6upHAzVatJGX0oJbH+IjKxpTWiWhbt5pWQhzA0E0gWdnjtQoNcPzrpBHegAAiAAAiDABFJZk37y3jTtPktkEW6jcVIEhYC1z7CMvdu3a1V19cK/sKCcX34cJ8ep6lQ4zf9invfj8DCmYBMQ17KRWY2e/qBA7x+Pk3xGAwEQAAEQqB0BSXn69M4pevWYRtmCVjtF0DMIVJlASNeP9TTUHa1yt3gUVG3g6E8tga2b8sMNER0JldRihTSXEDDZOD0/qdGPd+Xpg1MJMmQDGgiAAAiAQNUJ5NlIfW7vFD2zX4eRWnX66LDGBArsPzB63XjzVLX1wIpqtYmjP6UE/vyx9ZmsaQ6y0LhSwRAGAi4hICupp8c0+tHOHB08l7KLmLlENagBAiAAAoEgkM2b9PqBGXpyt6ykBmLIGCQILCBgfWQZxivVdvsVBWCoLpgG/Ok9AlKmxjAL+/lJD9x/vTd90LgEAidGNPqnPTk6OZSBsVoCN+wKAiAAApUQECN154kk/eMui1Ko3F4JShzrUQK6pvd3tBhHaqE+DNVaUEefSgnc1Ns9FAppNfkHpHQgEAYCKxA4NGDRM3szdG40CzfgFVjhaxAAARColICUCNt3Jm2HX0wlK5WG40HAkwQk6Gj6gXt7qu72K7RgqHrynIHSCwn8+WOUMUw6S6SNLNyOv0HAbwQMS6MPznGc1L4MDU7myUSGJb9NMcYDAiDgEgJ5vrE41J9hT5YsDU1Lhl80EAgeAYusccMwj377Hi1fi9HDUK0FdfSplIC4/2qmKe6/O5UKhjAQcCGBnKHRe30GPf9hisZmCsgG7MI5gkogAALeJiCJ644PZOmZDzJ0epRIHhKigUAQCeikna6v116p1dhhqNaKPPpVSqChybygkcarqmgg4H8C6bxOO44b9OwHSZpJIbOH/2ccIwQBEKgWAfFU6buYs8MsjnCqxrwJI7Va7NGP+whoGk3dcV37mVppBkO1VuTRr1IC28a6UqQZEywUqQ6UkoUwtxJI5nR67ahBP9udoAwn+0ADARAAARCojIBEU5wbzfPvaoo+umCReLCggUCACUxZlrX7iUcpXSsGMFRrRR79KiUgKbMtS/uQNOuYUsEQBgIuJpDI6vTyYYML0M+6WEuoBgIgAALeIDAwkaef7EzQh/0WaqV6Y8qgpYME+DHNUChs/lJC7BzsZlnRMFSXxYMvvUSgu9H4SNc0GKpemjToWjEBMVZfOGjQU+9ztg80EAABEACBsgiMTBfov70Tp70cRJQpYCW1LIg4yFcE+F9BfFO3frKWg4KhWkv66Fspgd5busbZbacm6bOVDgTCQKBEArNpnZ7db8JYLZEbdgcBEAABITCVNOivX5+hPWykZmGk4qQAASFgGJY1tepfdCRqiQOGai3po2+lBLY/rBWMfOF5FnpYqWAIAwGXExCfnKmURr/YZ9KP352mglEzLx2Xk4J6IAACIHAlgZmUQX/x/CTtOacjJvVKNPgUaALauKbT32/XtJomwYChGuiT0H+D715Xt5tP6pplJ/MfUYzIKwRsYzXNxiqvrP7o3VlKZmp6bfEKNugJAiAQUAKSOGkyXqB//4tJ2nc+TAX8ZAb0TMCwFyPA9unsnetiby32XTW3wVCtJm305TiB6842T/G1Zog7yjreGToAARcSmM1o9MKBAj21M06TCdRZdeEUQSUQAIEaE5ASNIOTOfqLF6bpwGCY4INS4wlB924jYFqmdjF6snGs1orBUK31DKB/pQQk+y+R9QsWelapYAgDAQ8RiHOCpZcOFegZLl0zMp0nuSlDAwEQAAEQIDs04hTXSf3+a3E6OKjz7yOogAAILCTAtVNTROZfz91TL/ym+n/DUK0+c/ToMIHWaGE/Zyqr+VMgh4cJ8SCwLAExVl/h0jXP7E3SwHieDNyNLcsLX4IACPifQI5rTh85n6EfvJ2kgwMau/siu6//Zx0jLJUA3y5k17fpNXf7Fb1hqJY6e9jf9QSa67snLTJnWFFEnLh+tqCgkwQSOZ1eP8p1Vvek6NxIFsaqk7AhGwRAwNUE0jmD9p3J0JPvp+nwIFEeRqqr5wvK1ZRA6vMPtA/XVINLncNQdcMsQAelBL7/bS3PASfvs1AUllRKFsK8SCCd1+ntE5wNeFeaTg5mYKx6cRKhMwiAQEUEkhmDdp1M0092Z+kY335jJbUinDjY3wQs0qw3Ht+m5dwwTBiqbpgF6KCcQGOD/jw79IwqFwyBIOBBAjlDo91nLPrBu1k63J9CTJYH5xAqgwAIlEcgni7QW0dT9NPdeTrFdwUGVlLLA4mjgkLAbK23/sEtg4Wh6paZgB5KCWzrzZzWLEorFQphIOBhAnJzdmiAjdV3cvTR6ZrW7/YwRagOAiDgJQKzqQK9eiBJT+8t0PlJjgdC4iQvTR90rQ0BqyVSf7Q2XV/bKwzVa5lgiw8IPPHomrRhWT/nzGW4I/fBfGII6gicHCH6+3dztOt4XJ1QSAIBEAABlxGYTRn03Adx+vk+g4ZnXaYc1AEBlxLghzk/m0pOuiZ0DoaqS08UqFUZAU3TrJZm6yn+B4fLU2UocbTPCMiCwtlxjX74Xp7eOIh/Hj6bXgwHBECACSQzJj35zjQ9d8CiiSQy++KkAIFiCTTEtH+k9etdEZ8qOsNQLXbmsJ/nCNzcteo0X54ynlMcCoOAwwTE/e3ClEb/uLNAP98zS7kCEmQ7jBziQQAEqkRAYlL/80uT9MpRjeIZGKlVwo5u/EHALFDoxFOPa4ZbhgND1S0zAT2UE9j+uJazLPMvWHBWuXAIBAGPExBjdWRW4yyYBfr7N2dpZCrH8VsI4PL4tEJ9EAgsAakVfWY4Tf/PMzP07ukQZQswUgN7MmDg5RHQ6P/TLYMDhNzTYKi6Zy6giQMEuur0ZzlONe+AaIgEAc8TELN0llccfnnQpL96I2GXr8kbMFY9P7EYAAgEjEA6Z9KuEwn67nMpOjSkc2bfgAHAcEFAAYGYTi/eOdPmqtwuMFQVTCxEuJdAek37sGVqQ+7VEJqBQO0JSOH7nWc0+tu30rT/TIqyedzl1X5WoAEIgEAxBGY4adJrB+L0vR15GpjWCY4hxVDDPiBwDYGxrKENbN+uFa75poYbYKjWED66dp7AtqNUsKjwfed7Qg8g4H0CR4Y0+od30vTe8ZSdjMT7I8IIQAAE/ErAYot0ZDpPz+2N00/3GDSRgKuvX+ca46oCAc18qqBlXbewA0O1CnOPLmpHYPsTZDXV0Y7aaYCeQcBbBM5MhOi/vZehF/cn7bhVrE54a/6gLQgEgYAkgOsbytJPdybp+QMGTaZgpAZh3jFG5whE9dDeG2M9M871UJ5kGKrlccNRXiHAZWrqo43D7AuU9orK0BMEak1gJB6ip/bk6Mn3UnT6YpoQtVrrGUH/IAAC8wRSWYP29KXph+z98eZxk+JZ3MrOs8E7CJRDgK/xOdKtkf/4h/zusoZ/3S6bEKijnoCRT8dDuvWaesmQCAL+JZDI6fT6MYv+4d00HTiT9O9AMTIQAAHPEIinOR71YJJ+tDNLH13glP7I7OuZuYOi7iUQ0ugDrkszqPHijtu0DLtNIegDAqoJNMY60hSe/elUwviqatmQBwJ+JiBXrH3nNUpkMjSZtOj+G+qpPhby85AxNhAAARcSkBCEkekchySk6K0TBo0lsM7iwmmCSh4lUBfWXu3q1vjRj/sa/qW7b06gkWIC3/sWFQzDOq5YLMSBQCAISL3Vk6M6PckrGD/ZmaCLk5lAjBuDBAEQcAcBMVKP9Cfpe68n6aVDMFLdMSvQwk8EDKLzD0daZ904JhiqbpwV6KSUwJwrQ34ootNzSgVDGAgEhIAYq8MzOtdbNejvuITN8QupgIwcwwQBEKg1gbcOz9B/fSNHH/ZbJCEJaCAAAuoIhEPa+7qmH3n8cY3tVfc1/It335xAIwcI3NSUmKyvs37ugGiIBIFAEGBblZJZjfacJfqrHWl6+0gc9VYDMfMYJAjUhkA8XaAn35miH7xnUP8EkcH1ntFAAATUEgjr1rvXd1kn1UpVJw2GqjqWkORiAk/8Xm82k9fPsIp5F6sJ1UDA9QTy/Mz15IhO33szTz/cMcOuwDlOqu16taEgCICARwgY7MJx9HyS/uz5WfrZhxaNxTVkHvfI3EFNzxEw8oY2UNfX6rqyNPMkYajOk8C7rwmI+2/EMvqiIe0ffD1QDA4EqkBAXIGn0xo9e4DoP700S0cuZOzVVdirVYCPLkDApwRM/mFJZkx67UCc/uKlDLv6apTOw0j16XRjWC4gEA7Ra3X19M727ZrpAnUWVQGG6qJYsNGPBB68adV4NGK+58exYUwgUAsCYrAeGAzRf3ghQc99EKfR6TxWV2sxEegTBDxOIFewqO9ilv7mjRn623fyNMQx8fDU8PikQn3XE9B17ci2rvwpNysKQ9XNswPdlBL4wy9TLpehAY20EaWCIQwEAk5gNBGiv3vXpL/fMUunhrOUK7j24WzAZwrDBwF3EbDYGp1JFmjXyRR9/40kvXyEKJHFram7Zgna+JEAR3xPGwXqf+KxLlcXSsevgR/PPoxpUQLi/ttYR0fDIevZRXfARhAAgYoIvN2n0395ZZbePpqm8VmEg1cEEweDgM8JFAyLTvEq6s/2JOeyiQ/jltTnU47huYiAHqI9LXXWm3OVMVyk2FWq4FfhKiD46G8C923tGON/lPzMFg0EQMAJAidHw/Q3b2Xpn3Yl6MRghvJYXXUCM2SCgKcJJDMG7TyRoh+8naRnPjRIvDLQQAAEqkjA0s7feUNOkoy6usFQdfX0QDnVBMT9t2BZfZpGrvbJVz1uyAOBahKYzWj0yhGLb0JT9NqhFI3PYHW1mvzRFwi4mUD/SIae3s1G6jtp2n9BJ8NC2Rk3zxd08yWBYdOkg995pMf1RdFhqPry/MOgliIgLg4dDeYBDiDfsdQ+2A4CIFA5gWxBo4ODGv14V45+sjNJRy+kKZdH7GrlZCEBBLxJIJkp0HvHEvTDd1L08315ujiLVVRvziS09jqBkE7HOhqN19zu9iucw16HDf1BoFQC161aNbYvNX2+1OOwPwiAQGkEJGvneEKjN4+bdHosQw/ckKeHtzVQexMuPaWRxN4g4G0CZ4Yz7F2Row/PFdhA1cjAMytvTyi09zgBbXzLLavOemEQ8LfwwixBR+UEvvpnk1/mC+V3WfBtyoVDIAiAwDUE5GLT1mDRHRs0+sKtUdq2sZ4iYTj1XAMKG0DARwQko++eU2nacSxPJ4bJrovqo+FhKCDgRQKDpNH//cJ32r+PFVUvTh90DgSBDasK+wcmQvsKpgZDNRAzjkHWmgAvrtJUSqP3T1l0fDhHD92Qpd+8v40aYjBWaz036B8EVBMQb4qzHIv6zN40fXSBaCaNVVTVjCEPBMohoGt0fsMq/VUvGKkyPvhflTPLOMbzBLp7u8bPj0+N8kDk/hmeBZ6fUQzAKwRyhkbDM0S/+Ijo0MAU/ea9Ybr3hlbiiycaCICAxwmIgSqrqK8ciLPLv0UXZzTKGx4fFNQHAR8R4H+is3fe3sqPj7zRcGvgjXmClg4QePT/HXvIDIX/lG3Vex0QD5EgAAIrEODs29QYtegzW0z6yt2NtKGrjqJhXJZWwIavQcB1BOSJr5Sc+ehMip7dl6WToyEqsIEq29FAAARcQ2DAMqzvvPh/rPqJazRaQRGsqK4ACF/7l8C2LZ37j16Y6TMMGKr+nWWMzM0EZPUlkdXo5aMh2nsuTV+7K0u/sq2J2ht1NljhEuzmuYNuIDBPIJU1aGzWpJ++N007+uS2Etl859ngHQTcRIAfDk98clPk9RfdpNQKusBQXQEQvvYvgT95jGYf/TPzLP/DTfANc5N/R4qRgYD7CUxy/Oo/7jJpz5kZ+tLtUbp9Uz21NYUoEsIKq/tnDxoGkUAmZ9B43LRLzrx40KTxJG4pg3geYMyeIZDXSDtFJ5snPaMxK4o7AC/NFnRVTuCx/xi/JZXNf48N1QeUC4dAEACBsgi0xEy6c6NGD95cRzetjVBbY5i49nFZsnAQCICAWgL5gkmjswU6cC5Lbx3L0okRneNQ8e9TLWVIAwHFBDhkvD4W+sbT/2vrbsWSHRWHx1+O4oVwtxPYtrrp1N5zU0dYz0/wq97t+kI/EAgCgdmsTm/3EZezSNE9m8N0KtQyMgAAQABJREFU39Yo3bQuRo11cCkMwvxjjO4kYJoWr6AW6PD5HO3qy9KBAXHdx79Jd84WtAKBKwiYvDR58rbWyQNPX7HZ/R/wCMz9cwQNHSYwV1PV+kt2MLje4a4gHgRAoAwCvR0G3bslRvdeF6KbNzQgQ3AZDHEICJRLQBIixVMFOtifpQ/OFGh/f4HdfBFDXi5PHAcCNSCQCevaHzz7nfYf1KDvirrEimpF+HCwHwisaab3B2e1sf+fvfcAj+S67nxPVXUGGhkYABM4gZxhphhFUhQlahVJW8kSJdvPXvvZltZhtU6r9e7bfea+71tbK/nZWsv2WwVbsmwFixJlkZIokpKYRFFMQw7TkJwATAImIIfOVfX+53aqbjSAbqBDdfe5ZE9V3bp1w+9WN+pf595zMfxXhGordKi0oeUIjM8YdHw2RS+dTND1F9gYFqzTnhEZANFyHS0Nch2BeNKi58aitP+YSc+OJ2liXifbFpHquo6SCgmBtQkkh7oTP1g7iTvPilB1Z79IrepI4LMf6V249ZOzD8Kp0qUotquORUtRQkAIlEkAow7p5Umdjk3H6dkxja7elaKrd3tpB5a0kSAEhED1CTx3dImeOGrRAVhQT80RpSwRqNWnLDkKgdoTsDXtvm2nhqZrX1L1S5Chv9VnKjk2IYEP/fXsFUsx+9t4Ft7VhNWXKguBtiLAfpXCfpt2DUGwnmfQDXv9NNLnbysG0lghUCsCr5xYpp8exhDfcZNOzhIlxFFSrVBLvkKgLgTgROm2b32s617SNB7J31RBLKpN1V1S2VoRGB3uee3V8dlorfKXfIWAEKgeAbauzsc0ev4E0di5FD01hjmsu6L0pos7qL/LW72CJCch0EYEDp2K0MOvJOjAcbgHnbcpisUsJAgBIdD8BDrIfLEZRSqTl1+h5r//pAVVIGDbtnbrJ2Y/rhn035BdRxWylCyEgBCoEwG2sIZ8Nm3tIbrxfKJ3XNlN4aB4I60TfimmyQmMnY7R/Qei9Bxe/Jxdsime0jAPtckbJdUXAkJAETB07a8oHP2v93x0NNKMSESoNmOvSZ1rQuD3vhzdNXY6+gT+Pg/WpADJVAgIgZoSwDxz8kGf9nda9Oa9RO+8qou6Qx7CH2q8TK5p0ZK5EGgaAhZUaCJp09iZGP3gOQjUkxrNRzVKmUSiT5umG6WiQqAcAqnOIL354unex++4Q7PKucBtaeRPt9t6ROrTMALf+IZtfGl89gVU4EJ85LvRsJ6QgoXA5gmoeawBm956oU03X9JJI71eCvg0JVo3n7vkIASaj0AyZdNSzKRXT8XpvucjWAfVg/mnEKeiTpuvM6XGQqAMArZlf1vTzd/5/seHTpeR3JVJ5GHcld0ilWoUgfd/evbdsYT9TZQvE90a1QlSrhCoMgF2vHTDHotu3BeCAyYfdYV08nnEg2mVMUt2LiUQiZs0H7HpxWNRevClKB2YkD9vLu0qqZYQqCqBgM/+sDHa9+07b9cSVc24jpmJM6U6wpai3E9gIGj89GQidQY13eb+2koNhYAQKIfAYlyj+1826PEjUbpsa4Su3xuk84e9NNhlUBBjhWVYcDkUJU0zEbDgcWwhYtK5RYueH4/Szw4n6NWzHjItEanN1I9SVyGwYQLwiWbF7bFvfZCa2i2aCNUN3wFyYSsSeIbC88M0+2WMhPovrdg+aZMQaGcCi3GdfnqU6NkTcdozGMVarH7aN+qhbX0e6gt7RbC2883RIm1PpCw6N5+i49MmvXAcaw6Pp2hiXscaqPK41yJdLM0QAmUR0DTt6/GAfwzbph7cL0N/y+puSdQ2BOD990P/a/qaxYT+ZNu0WRoqBNqYwI4eky7e5qHLz/PR7kGDRvt9Mo+1je+HZmw6P4Xy8N5jZxN05KxJLx5P0MsTNs1EZHh7M/an1FkIVINAwKv96kCo5+uf+6iWrEZ+jcpDXrE1iryU604CePNkffLsCZuMp+Cg/1p3VlJqJQSEQLUIHJ8z6PicjbVYI3TBFoMu2prClmgn5rJ2d8gwyWpxlnxqQ2ByOk6vnUnSa3CVwk6Sjs8QLSdYoIodojbEJVch4H4CmM6y30zprzW7SGXSIlTdf79JDetMoGNweS4+2/3lVIpEqNaZvRQnBBpFYHrZoGkeFozhkjv6bNo9lKJ9Izo+XmVlFedLjeoZKbeYAFtPee3TV09b9PIp7J9L0dlFgyxbLKjFrORYCLQjAUO37/F1mq+0QttFqLZCL0obqkpg57GdiYWeuedTsqJcVblKZkKgGQjEUxodOqvR2JRJz4ylaBhDg/cMxeniUZ0uPS+AdVllLmsz9GOr1ZGdI03Aevoi5le/PKnR8akUnZ7HcjOYd40RQK3WXGmPEBACGyWAJZJN0vdfMtmzeOdG83DRdTI2xEWdIVVxD4F3/vnsTk2zPqvp2tvdUyupiRAQAo0gEPDY1NtBykvwhcMWXbEDc1p3dpLOi7VKEAI1JDC/nKID4xF6/oQFyynR1KJJc1ENzpHk3qshdslaCDQtAV2zv2jo9Kff+aP+E03bCEfFxaLqgCG7QiBLYEdf9OzMcuA7kRSJUM1Cka0QaFMCMVhZJ+cJFiyLDp+x6ZHXUjTSM09XbrPoyl0B2j0SalMy0uxaEGDPvc8eWaRnj5mYf6rT9JJFizEitvYTyfDeWjCXPIVAqxCAf5UjWzrHMWu9NYK8kmuNfpRWVJmADe+/7/v07DsTSfoespbvSZX5SnZCoNkJsDE14LWpw6/R1h6brtxBdO2eEG0b9JMOTxayNmuz93B96q/WjcA/Job2vnx8mZ44HKPnTho0u0wUS9qUMPHY2dSLS9SHo5QiBIQA8YS1J/DI+p/u/Xjvw63CQyyqrdKT0o6qEuB1pz78meUDlpX83ynT+u2qZi6ZCQEh0PQEoCsoktDwIVi8NHppguhfnorStt4lunK7RlftDtK2AT/ErEZejyZL3jR9j1enASw6U7h5Uibml8YsOnQqSvvHMuI0giG9pgHRqh44UaC8I60OdclFCLQHAfypeW7PAB24t4WaK7+CLdSZ0pTqErjjQdtz4JnZD8Ut+jxyDlY3d8lNCAiBViewtTtFl4wSvQ7Dgy8YDVIQq90EfTr5IV7xMqzVmy/tyxBga2k0YVEsYcNSatHBE5hzirVOD542MN9UhvLKjSIEhEBVCEQ0nf7Hd/+o98/Z2FKVHF2QifyldEEnSBXcS+DWT8xchilBn0QN3+neWkrNhIAQcDMB/kPb32HSvi02XbjVR3tGAnDOpFOnn6gzaJAsfePm3qu8bvyEGMMSMmwxXYoTnZlL0UvHI/TqpEnjM0ZmndPK85UrhIAQEAKrEcC7zx8EAtYffOvfD7TEsjTZdsrQ3ywJ2QqBEgS29fYeObM0+5OkKUK1BB6JEgJCoAwCLFymsE7rFNZpfeyoBcvqMu3oNbFWq057hgM03OuhbozZ6A7q1N3hIY8h75DLwOqaJDycN44/EvPL+ERtfIiOn4vTkdMJOnrOppPzHswzZcupWE9d02lSESHQWgSiZGsvXbal/+i3WqtdMgGixfpTmlNlAuxU6ef/Yu69lm3/JbLeWeXsJTshIATanABL0p6gibmtNm3v99COwQANdZFaDqe3w8C6rR7yeUXguOk24RcPkViKZpdMNZR3NgKP0HMmnYA4PTFj0ak5nSJJ6TM39ZnURQi0OIHnPbr+x3f/cc8DrdZOsai2Wo9Ke6pKgMf53/rJs49rtucBW6PfqmrmkpkQEAJtT4BFz2wUXl5hhXsBDpkwaJQGMEx4pJtouMdDW3q9NBjWqA8r4PR16tQf9lBHwJA1XOt457DjowWsZzq9lKIZLBUzvawpYTo5m6QzWLLo7KJGi/GsxVQEah27RooSAkIABDDs98R5wyY8/rZekPFFrden0qIqE/jGN2zjy8dnf8uy7P+On4OhKmcv2QkBISAEViVg6DaF/TaEKj5hFqpeGsB2oMOm/k7CvkED3V6IV3nvvCrECk6w46P55SRNLbAwhShdSnt1nlrEMX+WbJqBUBWLaQVQJakQEAI1JGCfxcyCT37/j/r+spWcKGWByV+2LAnZCoFVCNx+u2b+wl9OPxq3tWfwDPOuVZJJtBAQAkKg6gRMS4NnWP4QHZ3m7FPkMyBeAzZ1BTCvNaRTT0cK1lYNDpssfOC4CeKVLa+9ELXiqGn1LonA4dHMAkQpBOjUokVTWGaIraVzPNc0YtIC5psugPsyliFKoR9kjunqLOWMEBACjSEAa+rxQED/ZiuKVCYqQrUx95WU2mQEwr19R2JTs+NNVm2prhAQAi1IIGGmBdX0MhrnEK8hn00hH+FjUiiQos5AAvNf2RoLAQvrax88Dfd3efDxUlcIa+W0QeBhu7OLCWUNnVFDd2ERxZzSmWVdOT1axvpjEXjnXY7bEKQYeI25pbgEgYWpDDprg1tEmigEmpnAsm3SYx8e6jl5VzO3Yo26y6/wGnDklBBwErjtU/O32bb5CcRd6oyXfSEgBISAGwnwUq1eDB32eWzy47W0H6vBs2OmANZxDfo0iFgr82HLLI4hZLtCaQdO3R1eCvgMnvvk2sDDdJdjsHxiqC5bQNMfi+YiEKcRtkKzGNXgkdfGx6JEClt8EinEpYjYWq00qWtbKBUTAkJACKxOAL/PY30B/fZ/+vc9T6+eqrnPiEW1uftPal9HAoMD1mPTU/Zrpq2JUK0jdylKCAiBjRHgZVPY+sofXs8zHVia2UqAeiBiDfj+8eDDc2E9ukWGYWGbwnGc/DC6dvot6oSllj8hzJXtwNqvwYzQDfrSotcP0etHJj7Ee7H1IlNeYofzNnQNZWmEjfL4AU/qWKqFCHP+IRTTH7Z6JlIWJZWITItKFpfRhEWxBLbYVxZPtIGH4S7H0R7ewvqZZMGJ61O5vFiAYoA0jlOWqcRopuFcgcwnHyN7QkAICIGmJWBTZE9n94tNW/8yKu7id6Vl1F6SCIE6EuClam79n7P/Dkvh/Vd8cUbrWLQUJQSEgBCoO4GMtlSiVs+IW7awqg9EX34fVeN43nBk+jAdkd1Xsel/clbMzI7a4B9IWPyHrfqHj9P7StxiH9pTnYPMzu2nc5R/hYAQEAJtR2A+6Nc/8q3/0PONVm65WFRbuXelbVUlwBPVf+eLS98+PpX4EB6YRKhWla5kJgSEgNsIZIUiK0ZTydByashXVTukxW+1c5X8hIAQEAJNTGDmup3d936riRtQTtVlwa9yKEkaIZAhMHSs46yt2WM4zA2kEzhCQAgIASEgBISAEBACQqBOBEzdtr/8n96jLdapvIYVI0K1Yeil4GYkcMcdmtXtNf4Go9tYrEoQAkJACAgBISAEhIAQEAL1JGDu3ub5Sj0LbFRZIlQbRV7KbVoC11+Aies2vYoGJJu2EVJxISAEhIAQEAJCQAgIgaYjAEd1+2fN6ETTVXwDFRahugFockl7E/jYrVocni2/Cgpz7U1CWi8EhIAQEAJCQAgIASFQTwLhgPaJPUe3ROtZZqPKEqHaKPJSblMTuHALPQD3HktN3QipvBAQAkJACAgBISAEhEDTEIC7umc0y3qGp6I1TaU3UVERqpuAJ5e2L4E//6XuOZ9B7Gwt0b4UpOVCQAgIASEgBISAEBAC9SKAZ8/Pe2cWpupVXqPLEaHa6B6Q8puSAC9VM9JtfBaVjzVlA6TSQkAICAEhIASEgBAQAk1DANbUl3UPPfalP93ZNitPiFBtmttTKuo2AkN7usZJs1t9CSu3YZf6CAEhIASEgBAQAkKg7Qh4de1rniidIBhL2qXxIlTbpaelnVUncMctWqrL62GrqgQhIASEgBAQAkJACAgBIVAzArZmP3dJrLfl1051AhSh6qQh+0KgQgJmzHzFsuwfVniZJBcCQkAICAEhIASEgBAQAmUR0DTrn01be6FdnChloYhQzZKQrRDYCIHdvUuhgPWZjVwq1wgBISAEhIAQEAJCQAgIgXUIJL268aNt4Z62WDvVyUKEqpOG7AuBCgncebtmJjTfQVzWNh7YKkQkyYWAEBACQkAICAEhIAQ2SEAj+5GESc997qNacoNZNO1lIlSbtuuk4m4hkEhEJzRd+4Rb6iP1EAJCQAgIASEgBISAEGgNAl6P/tDFw/FDrdGaylohQrUyXpJaCKwgcOPylqjfsH+GE2JVXUFHIoSAEBACQkAICAEhIAQ2QkDTaMI0tdc+9StbIhu5vtmvEaHa7D0o9W84AZ7YnkqYY7qu3d3wykgFhIAQEAJCQAgIASEgBFqCgKFr93Z3eR/V2mhJGmfHiVB10pB9IbBBAsM9A+c8Bj20wcvlMiEgBISAEBACQkAICAEhkCOAxVInbNv62bt7Q2dzkW22I0K1zTpcmlsbAmqCu6m9bGj0RG1KkFyFgBAQAkJACAgBISAE2oWATvYThpV68HY47myXNhe3U4RqMRE5FgIbJNDXm3zNo9M9G7xcLhMCQkAICAEhIASEgBAQAkxgyta0x1937dCxdsYhQrWde1/aXlUCf/9/DiwlLe0xDNXYX9WMJTMhIASEgBAQAkJACAiB9iGg0UtBL913xy1aqn0avbKlIlRXMpEYIbAhAjzRfcBDz3l1+tGGMpCLhIAQEAJCQAgIASEgBNqegGbTxKje+2q7gxCh2u53gLS/qgS++Ps985ap30dkP13VjCUzISAEhIAQEAJCQAgIgZYnoGv0StDn+epnPqbFW76x6zRQhOo6gOS0EKiEAFtVd3QmntBIfxTXWZVcK2mFgBAQAkJACAgBISAE2poAZpDR2O5d8YfbmkKm8SJU5S4QAlUm8He/O7Rk6/SvyPapKmct2QkBISAEhIAQEAJCQAi0LoHjMHp87pPvGVxs3SaW3zIRquWzkpRCoGwC+3b0PKXrxMN/E2VfJAmFgBAQAkJACAgBISAE2pVASiM6+HP7ove3K4DidotQLSYix0KgCgT+6nYtapsm/9AcqUJ2koUQEAJCQAgIASEgBIRASxPQpjXL/LuP/vxopKWbWUHjRKhWAEuSCoFKCAwOeR6BVVWEaiXQJK0QEAJCQAgIASEgBNqPgAlHnM++ZffAD9uv6au3WITq6mzkjBDYFIEv/lrPvEfTntQ0mtlURnKxEBACQkAICAEhIASEQCsTiBk6/fUfYkReKzey0raJUK2UmKQXAmUSYA/AQ53aV3Wyj5Z5iSQTAkJACAgBISAEhIAQaDsC2kvn7+h9qO2avU6DRaiuA0hOC4HNEBjd233M8BgPYHL8wmbykWuFgBAQAkJACAgBISAEWpOAR7O+wP5NWrN1G2+VCNWNs5MrhcC6BO64RUuFfcY/kWafWTexJBACQkAICAEhIASEgBBoOwLdAUPmppbodRGqJaBIlBCoJoE9F3ceItu4E3mKF7dqgpW8hIAQEAJCQAgIASHQ5AT8hvYFisXONnkzalJ9Eao1wSqZCoE8AbaqjvTSF4g0Wbw5j0X2hIAQEAJCQAgIASHQ9gT8PvrKnuUtMuy3xJ0gQrUEFIkSAtUmsO1U9zHNps8h31S185b8hIAQEAJCQAgIASEgBJqPgG7oX43HPC/fcYdmNV/ta19j+HiRIASEQD0I/Obnp7dNzGrPo6zeepQnZQgBISAEsgRW/LFfEZFOuUp0NhvKnc/t5E6pnXS0XRhZdKTSrHK9M+m6STIJ1k3nyHTNtCVOlohy5Lb6Ll+XpcBb27bxyccV56vSZs7btkYWPipu9SLkjBAQAs1PIN7ht99LI30P3Hm7hnVUJRQT8BRHyLEQEAK1IXD4VN/pUHD6U6Tpf1abEiRXISAEmoUA1lcmD8Y0eQ0bHyIDxzqOOb5YxHCb0nGQLpmT+TT5PZUOhxyTzUedzcTxCZY/uXOZeHWMIR+cVuc49bGJh1zxvorLXsfnVDzO5/aRJlsm8nHG58vK55fOP59POr9MWUXlINtMndL1y+WXK8+Rj6M+uTyL0qUZZK9Jl6nSckHMoDgPFZ9Prw6L0uTiOCOEbH6QppQybYqnbIrELVqKmhRJWBCs+Fi85TaxHLUhTHVKmDpFUgYtxH00G/PRYsJLSYvjDUpZmcxVCfKPEBACrUAAvwH/uLBkHbhPROqq3Sm/fKuikRNCoPoEPvTXc3sWY9ZPkPNw9XOXHIWAEHAzARZBfrweDvtt6g7atCVs0Ui3TQOdNvXguMNnq/NOIcftwZLMDvGXFmxZwcWyiNNLaA4CpmlSLB6naCwG3ykxSqVSxHEsWp2BLaqLCQ+dXgrRycUOOh0J0hKOYxCyfE6CEBACTU9gxu/z/NtfHA3fe7sI1VU7Uyyqq6KRE0Kg+gQiM5FJrSP4d3go+X+qn7vkKASEgBsJsKwIByBMu2za3W/R+UMW7ei1qBOCtfIgIqVyZu65wjAM6giF1IdFaiQapeVIhOIQr3ycDTpeTnT7k/jM096+eZqDlXVsPkzH5jvp7HKAoimPssVm08tWCAiB5iKgk3aXZSYPiEhdu9/kL97afOSsEKguAUw+eu//O39V3LLuxpdvtLqZS25CQAi4jUAAr4N39Fl0+VaTLh4xYUXdiDh1W6ukPtUkkBWsS0tLytJabF11lsVDgsfmwnR4totOLwfJFOuqE4/sC4GmIIC/AtMejT529TW93+CVIZqi0g2qJE8rkSAEhEC9CGAMn0n6MXzxvlWvIqUcISAEGkNgGBbUW/am6P2vS9KbsRWR2ph+cHupHo+HusJh6u/vV1sPrK6rhd5Agq4anqabtp+hff3z5DfEUehqrCReCLiVAEZMPEKatV9E6vo9JEJ1fUaSQghUlcDVS+E5w6s9VNVMJTMhIARcQ8DAX9a9GN77rkuS9PaLkrQdw3xl+JJruse1FfH7fNTX20u9+Pi83jXrOdoZoRu2noVonaJOnxhk1oQlJ4WAywgYuv5sf//iuMuq5crqiFB1ZbdIpVqZANbKgjsMz0FD137cyu2UtgmBdiQQ8Np01XaTfu7SJF2B4b4+8QTRjrfBhtvMc1jZusqC1e/3r5lPly9JV26ZoRtGz1KPP7FmWjkpBISAOwjA2/tzPk174ou/tjPujhq5uxYiVN3dP1K7FiUwGFo8htFe32nR5kmzhEBbEgjBa+/rd5r0zotTtBNOk3jZGQlCoFICGtw4d3R0UF9PDwXWEat+w6QLMQSYras9GBYsQQgIAXcTwOianw1uoafwPReHBWV0lQjVMiBJEiFQbQKf/chIVCP9cfxQ/aTaeUt+QkAI1J8AW1Kv2WHSW/bxXFSLePivBCGwUQIsVkPwDtwDscpDgtcKHt2iPb2L9PqRcxSGlVWCEBAC7iSANaYP6ob2wJZf6p53Zw3dVyv5U+q+PpEatQEBfpM27I0fxCiv+9qgudJEIdDSBDCUiy4etpTjpP4Q1jmVCakt3d/1ahyLVV7Kpru7m9jh0lrBC7G6u2eRLh+cJZ84WFoLlZwTAg0joOn07Gif8fAdmiZe0MrsBRGqZYKSZEKg2gT+9ncGl62kfj/GfjxU7bwlPyEgBOpDgEUpLz/ztgtTNNgpIrU+1NunFBarnRgGHO7sJF1f+5Et4DGVJ+ALehfkZUn73CLS0uYhcNC0tG/8za+GZ5qnyo2v6dq/eo2vn9RACLQsAbaqvv2yrgNej/YjNFIm1bdsT0vDWpUAG055yZl3XJSi7RCrYklt1Z5ubLtYoLJQDQaD63qP7oZTpctgVd0WXl43bWNbJaULgbYikMKw32dvuTz+Q372a6uWb7KxIlQ3CVAuFwKbIfCxW7W4aaXu1ciWuaqbASnXCoEGEOB5qddiXuqlo6aIggbwb6cifZinymLVs86yNcxkBEvX7Oubp5BXlq1pp3tE2upeAhBbRwzN/vJ/fMfwsntr6c6arT3pwZ11lloJgZYicO2OgRf2n5h7NGXa16Fh4ZZqnDRGCLQoAbaejnTbdN3O1hIDpmlSKmWSZfNL/1Z88Q83djBteDHnc72htG67ddmiGggE0D8pslX/rF7D7V3LdGw+QkfmwuhLmTS9OqnWPGPYJhkWPnaKNNsinTDiI3PP2PjxsjkGEyb5Y2oeMnUDcXKf1OhuSADz/jft6HtElnqonLAI1cqZyRVCoKoE7rhdS7z/r87dY1rGW/B35OaqZi6ZCQEhUBMCPiw9c8Muk/o6WkfMJZMpWlhcpOVIlFIQrK0avB4jPe8zDAslPNo1SzAwBJjnq8ZiMUom1/buy+uqnte9RJPLQVpKeJuliVLPCgh4rCSFUsvqE0hFKWBGyWfGyY8P73McH3utBHmsFOkQr6xFWaSmNINSupcShp9iRoBinpDaj+M4bgQRF6SIt4Ming4cByqolSQtJoD3ApOdfusLf3i7Fi0+J8frExChuj4jSSEEak7g8q0DL+4/NvMw3n1ejsJ6al6gFCAEhMCmCAxhCZrLtraOmLMsi+YhUucXFon3Wzmw1TgBUa7mfkKs6k00uZjXVeVhwGVZVTFP9dVANy1DqLbO65RWvjPXbhuL0p74DHXHZ6k7MUu9sWnqjU8hbpa6EnMUTsxTR3JRCVcWppUEtqyyOF32dtKSt4sW/D005++jWX8/PgM47qZ5X6+KW/R1KStsJfm3cdoE3os99vZL+3/6tTaGsJmmi1DdDD25VghUiQBbVX/x0zPfXEjS22FVfX2VspVshIAQqAEBjBylKyBSw/7WefxPwEIXi8VbXqRmbwcW40uRCJZ/CZK+ztIv2WvcsDVgAQ5i+C9bVVlwrxW6AwnaEorSGVhVE6a4JFmLlRvPeWEx7Yudo6HIJA1F8cF2IHqW+hHHArUzsQgDaXV+g3QMD85aZwejZxw4NFhU/TQb6KcZCNbp4CCdDY7Q2RA+2J4JjcIaG3Skl10nAbwDW+gJ0t/9+i1azBkv++UTEKFaPitJKQRqSuDNN/Ue/N7DM/ebprYPf3rEqlpT2pK5ENg4AV439YqtrWV1tC173XmPGyfmzitZrFbnMb++7WOhOo9hwOsJVZ5xONIZpUOzKQhVX30rKaVtiAALxuHIKdqxeJS2L47RyPJJJU5ZsAZTkaoJ0/IrZ2MocYyGl0+pD1+X0P2wrPZCtA7RVHALHQ/vovGuC+gEtgld7jMHW9Pvsb97W3/fU//oiJTdygiIUK2Ml6QWAjUj8NFrtOSvfXbqq+fmtfeiEBGqNSMtGQuBzREY6bZooLO1hKoHVkW21rVT8Ht9TTXsN9s3PPSXnUGtN0+V0w/Bosrrq87LAmhZfK7c8rDd8+dfoX2zLyqR2h89hyG+M+TD/FK3BZ8Vh4X3tPpw3eZ9PUqwsnX11d5L6VDvxXQ6tNVt1W5EfZLdIeMzt2PEXCMKb5UyRai2Sk9KO1qCwM69/YcX9s/cFTe18/Cmv6slGiWNEAItRmD3ALxotthISg8mUoU7OzB3M1mWAGr2Lg0E/BQOdzSd51/mjnUYyYtlajQM/13P+2+nL0VhX5LORQLi/deFN+3I8gm6bGo/XTLznBra2xOfVs6QXFjVVavUjfmx/Nm1cIgunH2BpgJDNNa9l54fuAbbC9Tc11UvbuETAY/2F53bu19q4SbWpWk8MkSCEBACLiLw+1+c3Xloyv4x5qruclG1pCpCQAhkCPzqdQm6dqcJa1xrIeGhsPFEkqIx9voLizF+hFouoM/YGhkMBMnvg9hrIkdKzr6YX1ig6ZmZsuYUP3pymJ4/2yvzVJ0AG7jPS8fw0N7Xn35EWVD7YD3tSC3BK29rjNLgZW7YU/CirxtDgvfQU1tuooN9VyhHTQ3EXtei8QLpaKfX8447/6DrCH5kWvCHtH44xaJaP9ZSkhAoi0DPr/Uc7/hfs19YStB/wwXiF74sapJICNSPwJYuPIq1mEhleuwFNwhLIwu4lg7ovGby9FuqL3iodrkimy2qhjwrl8JY1zheTmYH5p2++eS9dNHM8xROLpDXTDRg3mltm80OntTyONGocvp00cwLdAyC9acjb6bnBl+vnC+1+JqttsfQ/istdB0Xkbr5e02E6uYZSg5CoKoE7tA068/uWvjsY0dSvwuDxmhVM5fMhIAQ2BQBXj+1K9Daj1ksWCW4mwCv/1ruu5KQB0vxiFBtWIeyMyJ2ivS24/fQ6849ibVNY8RW1XYIvExOp7UAYX6A9s6+RGNd99FD29+lhgXzcji8LE7LBdv+umZaD995h8xNrUbfilCtBkXJQwhUmcB/eX/X9Ic+M/3Jxaj2V8i63OeRKtdCshMCQqCYQMhnt9z81OI2yrH7CaiXCWWa9b2GJYadBnSpz4xTH5aReSsE6o0TDyorYwOq4YoieVizbido79zLdAGcRh3A/NUfb78NwvUCing7YINtmcecBc1P/+gfHXCu8eOKPmjWSohQbdaek3q3PIHzBqw7Xz5p/EesGiHu81q+t6WBzULA52mhR6pmgS713BQBtqa2jAzYFIn6XMzW0h547L3i3FP0lhPfo1E4TJKQJ6BBtLJlmS2sj259Gz0+cgvWZR1uEadL9tcSceuF792utYfJPN+tNdsToVoztJKxENgcgTFjYKorMP/puaj1qc3lJFcLASFQLQKt5kCpWlwkH/cS4DmDEupDoBPzTnfPH4IF9cf0uike5ivrAq1GPpRaVtbmCzGHlQUrewmeCfRjOHCzLpOlnTE07d6OnX1iTV2t0zcQL0J1A9DkEiFQDwLf+CAlP/J5/e7FuPWbcMC5rx5lShlCQAisTaAVHeGu3WI560YC5TpS4rqXOULYjc1smjqxFXUwMknXnnmM3jDxIxqMnSFNfizW7T/mdt7iEeo7OkU7Fw7TE8M305Hufcrh0roXuywBLMV3p2x65k6xpla1Z1pwFnNV+UhmQqBhBPAgYscCkVMhr/a5hlVCChYCQqCAgNimCnDIgRBoewLBVIQund5P7z36NXr78e/QUPS0iNQK74pwYp5unHyQfuHwl7H9MXVhXdYmC/DwSw907uqbbLJ6u766YlF1fRdJBduZwJd/ZUvk//ib2Qd8hvZUwrSvbWcW0nYh4AYCYiRxQy9IHSohIEN/K6FVWVoWVLwe6hsw1Hfr0nF4801VloGkzhHQYV1lq2oXROtQ9Aw9hrmrE507yGyGocCa/SO/RY+LNTXXnVXbEYtq1VBKRkKg+gTYqjrSnTjq9dBXqp+75CgEhEClBMSiWikxSe8GAuJMqbq9wMN6t2Co77vG76J3HPtXrI96VERqlRD3xc7Rzafuow+/9vd06dQzzbCUzxGNtPsNsaZW6Q4ozEaEaiEPORICriPwKVhVPbp5n9egf3Vd5aRCQqDdCNjyyN9uXe7K9lYw8VTu2Or2IFv+9mCJlQ8c+hK9ceKH1Bebqm4BkhsFUlGsvfo8fQBDgV9/+mEQce8rQny/fub3mA+LNbU2N64M/a0NV8lVCFSNAFtVP/gN+6h1bPZO29ZuwOGWqmUuGQkBIVARgXo9LulwL+z1erFmq0a6psMhjsiNijqqAYltPEzbWE/Mwidlpsg0TarVUPFK7oZK0jYAW1MVyUur8HzUXzj8TzSyfIo8VrKp6t9sleXh1O858nUKQrj+COuuui1g5afDtmZ+7/L5gTN3ua1yLVIfEaot0pHSjNYmgDd1ifd9avGHtp74F8vWPtbarZXWCQF3E6iVWGUxGgz6KRgIUMDvS4tTqAwMK3M3EKmdgwDEKm4QFq2pZIpi8TgtR2KUSjVwWUW+feQWcvTRxnevOvcz+tBr/0D90XNAWqtfgo3XrxWvHMB81VvHvkkdyUX6wXnvpYQRcE0zdd1+vrPL/8AdH9cs11SqxSoiQrXFOlSa07oEvv3Hnefe+cnpu/Ew+wY8c1zdui2VlgkB9xKohYVM13UKBQMU7uyAFdVQAlUsqO69B8qtmdfjoUDAT13hTlpajtLSUkRZWmtxD61VJ9Goa9Ep7xwvo3L95MP0flhSe+PT5V0kqapCgF8I9MRn1Jqr4cQCfXfXB2nB34PYRt/Z9iG8i/r6Vz8Snv7aR6vSVMmkBAGZo1oCikQJAVcSwJjfwZHUzzAS8EHUT97eubKTpFKtTqCaNhQWoyxkhgb6qL+vm/x+Huorw3xb6R7iPjYMg7q7OmnLln68jOhUw7lbqY2t3ha/Gac3n7wXc1L/UURqgzqbxWonLKo3YU4w98NghJcAauhjUApTMp6/fo95L77j1fyz0CDC7i1WhKp7+0ZqJgRWEODlagxNexEnTq04KRFCQAjUnEC1rGEsYNiK2tfTBbGaGeZb89pLAY0k4IFg7enuhGgNK/G6qbrg/ik7qOdoeZYum5cjIa+R+qaTP6DbMPS0OzHrOCO7jSDgN2N07dnH6N1Hv07DkQnSGydWj1m29YU7bh9aagSHdipThGo79ba0tekJ8Ju7a3bpL3cGtKNN3xhpgBBoQgLVeNxXIjUUgGgJk8/nbUIKUuWNEmCLOQ/xZgsrC9eNhgpkasMHSG60jY2+LpRcUuujvu343WroaaPrI+WnCfhg4b767OP0diwLNBhly2o1fpUropvQdG3/W3f2PVzRVZJ4QwREqG4Im1wkBBpDwLZt45ff0LHl4q2eHp/MMG9MJ0ipbU1gs49ELFLZUVJXmOejype4HW8m9uTc2RFUHxautQ4saisRtrWujxvzZz5+j029IZu29li0L7xIb5n7Cb31xD3Uj3U9JbiLAFtWrzvzGN2CIdkNmDN81vDQ3/zh7VrUXVRaszbyV7I1+7VpWwUhJn9TC3sv+3yhz87OhmZmZi4e6vD/8pv32ntjMZOmljb72FxYmBwJASGQIYCvFr+oT2GpkSQctqZ4OhSOQ/iraWHZEdOEKw+IDL2SIZjIgq1obFHz+3yCuo0JsEDt7AxREt6AI9EY7rUa/pZn/4q0MW9n0/GegIJezHn0Ez68tSkcsKkrCKHKHyNKQ689SUOn7yFfZNJ5qey7iEAwtUw3TD5Ey95OenDbu2jJ21WP2iU8On33tpt6Hr+7HqVJGSRCVW6ChhHAH2Z9fHycn9Z6Upp3l2HZl4wfn9iKY/mzmu8VxQJWGExNpU48FF8ejSev3Ram4Dsusmgp3lBnAvlayp4QaDUC0A3QqEqgxrFUYgJKldfFJDtF8WicpvGiiEUqCw71wb4BL68+rH3qwZYtp8WBLWlBDPnlJWgkCAG+TzpgWU0kk5SE+9BahXZeRgWiQolQJURZjKoPqW03RCkfZ7dwuI0vfJKMgy+R58nvk376RK26RPKtEoFwYh6Orn5AUaODHht9C0U9oSrlvGo2RwcD2ic+eo0mC+iuiqi6J0SoVpen5LYGAbaWnjx5MpBIaNs1D11/7PjJS0n3hzHBoMswre0Y878Plw+tkUXbnuK37fzC3WKTDp6VDTwD7+xnHCsfhtsWkjRcCNSQAN6rKStqKqXDCualOF4SxWIxiicSqlQWq+zd1QuhymLVB4tpdpsVrR6MFwtDmGSPa1hdybpJCAT8fgwF96t1Viu1qsqvf2En+/FE2xW0YBUl6u1IW0dZiLJITVtOIVphPQ35bDJKjbjGH1nt9CQZD95P+rGxwszlyLUEemPTaumaJV+Ynhm6kZJ6zeb9W7jF/voLv9tz/O9/z7U4Wq5iIlRbrkvd1yC2nJ44cWJ4/MSpd5Ot3wKR2o8/sHts0mE9tb2svXgInQQhIASEgFsJsLhkCxh/eLn5UDAIK1iSYvE4LS8vK9HKFtcEhGs0I1p5mG8oFILVLD3Uly2pLGQlCIEsAbayd8DKHovF8QKkNlbVVhO0PFghhLFY/SGIUswp7YMoHcCHBWkHhGgHznVAjHIanndaYnBDFn/BVpufI89DD5Dxykt4KyyjlQrguPxgKDpJPzd2J834B+lQz0Vkl9vpFbQLd8TDHbrnTvwtwFOr+wMbh1BL7c4779Q++MEPqgpjn7DP9Qei5mhHq/1+uf/OaaMa8pdkbOzskGYkPkSkfwBN340PD+2VIASEgBBoCQIWrDBJiNNIJEILS0tKvDoblrWy9vZ0045to2p+qvO87AsBtqSePTdDUYjVcoMFIXXi5MmyxO1UJEB3H95O83GeadN8gcXoYCfEaKeltv3YZ8toEO98AhCiAd5izqkHQ3c3/FAbi5Lnx/eR5767ScOLJwnNSeDF/qvo85f9IS3WYL5qwKO939jWc/edt2sY1+aewM/a0J/6DTec9OFlaY9pans0w3M9tPq1MASlJ+5qma8GBudlaj6DYXqP6rrxyLlz9vjVV49E3SpcN/yddk8XSU3cSABfHO/RY6fejrfF/wNfixHUUYb0urGjpE5CQAhUhQALBx4KPLewQNFodIVzHPbwmxarW7EsTV2cflSlXZJJfQjMzi3SIl50WDwxuoxQmVD1Q6jucL1Q9UFo9nVYNBS2aQs+g2prKUHKllH2dM9pvNjn6S9VCxgJYRx4hrxf/jxpi/NQu9XMvGq1lIzKIJDUfXTfzvfRXXt+uYzU5SfBt/LuoNf8d3f9wWBDvWvdcccd+gc/+KeeUGguZJqL23SP8XrcrW/FFLo9PDYRz9te3L48UbeHP6j3KiNneY6tPYM0/Ing2n82k/pX9+zZcs5tgnWVBqDaEoTABgkcOXKk++jxyU/A0cj78aURgbpBjnKZEBACzUOA56gGMRyYh/bOQ6wuLi6S6Rg+yM5ypqZhNYMjpp07ttGWoYHmaZzUtOYEfFBheEBEOeUJ1XTaMqvlEt2lqpGpSwBPn8PdFo10WTTcZatPP4bvKssoZgKxEyRD36SVtFw8S4vkueebaZHK13AXuIRZmU2QZBkCXitBbzx1Px0L78Z81RuqxcXuCmj/2O0fmKpWhmvlwxZSPv/QQw8ZQ0NDAUwfGbA07RJN874DPxE3k30qzKLU0A2eR4IZ2dSJ4/RwCVxZ3i8Ipt0Rbcl8sKHzDa/5+8eOTf5fTz/99LevueYaiFd3BPkquqMfWqIWuLm9vb1DV+ANz5fxpTkfXxf+IkgQAkJACLQVAR7KyWJ1bn4eTnJWzjv0Yx3V7RgGvH3rKB7G8UQuoe0JxOMJOjc1Syn2LF1G4Hvs+IkTZQ39nY7ConpoB83VYegvL/2iPritdUyBY8snOzQa7k6L0eEwxCn2eTgvC9FsetbovF/3AC+/3q9+kTyP/AiPLM5HfFSmEfWpO4BWLFCjyY6t9HeX/wmd6tyx6Qb6vfq9uJ1/71t/0HN005llMsiIUf3w4cN4ZdPn17TZgB3o9ZG5BOeinj22Zr8FIxLfhZtwGDcm/5Hgge14prbZwFjLOxNrw2oPwnPnH42Pjxy+5RZt5R+wakEoMx9usAQhsGkCY2NjAU3zvMvW9L9EZjvxZdp0npKBEBACQqAZCbC1q6e7W1nIZufmVohVFiVHjh6D46Uk7T5vBxw04RlEQlsT4LnMzTTklJ+U2XOu10hbPb3Y56G5/Ri6u4UtpDxsF9ZS3u+EYyNlLHZbD0OYGk/+lDw/fYRsjH4otFKLWdVt3VV+fWzqj52ltx//Dn1t329SzGCj48YC3+PDXXT3LRd1T35rA1lkBKkxOTnpi0T0Tr8/iXkf3uDJk6eDmE866PGFLiUt/k6ygzeQGcO6ZfgS8U9BTos6n6Wd+xuoTHmXAJZ9K94avWHXrtO/ODEx8fDo6GhDrasiVMvrOEm1BoFDhw75Lc1zq67pf4ZkO9dIKqeEgBAQAm1DoLurS81VZbGq1mB1tJwtYhOTp8mPocJbt47AEYyIVQeetttl77+1EnObzZctnT7MC/XjFmUxyvNFgxho2MdOjmAhHYJzIxak7PCIzzVL0KbPkeeB7xO8oZWusmjV0lyaINZnJuiyqWdorOsCenTrW8nEchMbCdv6DLpwxDv3wRtoXcsii9KHHiJj375JXzweZ0E6PD5+ssc29C7LtnYaHvP6lKW/CWsMjqq6ZL+YBZb8jdSyFtfY3bDqfimaoP/8yivn7rzwwsHFWpRSTp4b67lycpY0bUEAX0zj2LFTb8Bf2P+M33ReB1WCEBACQkAIZAiwWOVlRxYwFJjFqTOkUiadmjyDNVe9NDQ4oNZhdZ6XfSGwKgF+yM0+6K6aKH2CrZ/lBp4byvNE2aNukLcQpLwGKc8f5c8ALKbsdbcnCK8tzfxuhb38QqRqk6fy30t8P3lZkzwvUarl3jduTNcTn6EbJ39MR7ovpBPhnRVXMeDV6Ma9Xrppnx/DbymM32942soNF9TZShqLGWHyJrZoKXvk6LGTHbt2GYFY0sQSjN6LNF17G8YS7MEIeM0gfLHyN1bFdWnQBUN4SXWH15+wscTk17dv345hwfUPIlTrz7ylShybmDgfw2X+Axp1TUs1TBojBISAEKgCgewwYLxhV16Bi7OMwEPwsZMTytsrO1jidVolCIH1CFT2zAvBVXQBa1wfhu2qNUchRHnNUV57lOeT9vDapPiwMOU1SnkpmDI18XrVds15XivV2P8kEb6XWTbqNRK/THI2tvjYNS2QipRDYGT5JF135lGaDg5SxNNRziXKkVcQ34WLRjW6+jwMZw9E3zt5JtKJKRvnIFZNDNPXsPValt6hGclRsowrSLdfBynay6u/aCxK8b+6n8oq0dWJtsONwsdTKX0Gbb4Hf8/qvsCw/EV09f3h7sphOEBYSyXei1re5O6aSu2EgBAQAo0j4IX47OvpobNTUyvmq3KtlpaW6ej4cVhUdWVZZQ/CEoRAtQiw7grhwTuATxhCtAvCU22x3w1BytbR7gBRdzC9FIxTp1WrDm7KR5udIeNnjxLNz6JaLCeKVHxxbOkkbmpSri6a3096dw9pHR2kBUP4BNVWz2zxI0M2L58VjaithS1ckRNvrdlZFV/oVCqXdVPudCSX6PKpp+m1nkvohYGrVrSBh7Lzixj+PqS3aedf3fhO7BnEOi9YxWV+3noTpm68ie8TfvGYXUIqrdn43mkRSbqCTi7iQgjv3zh6dPJZxBzPxdZpR4RqnUC3WjF4s6KPj0++Ad/PX8J3t6/V2iftEQJCQAhUk0AgEKDOzk6aw3zVUiEGy87hsXE1/Hegv089EJVKp+JsLIGXwsOmiYdMe92pU6tm0zInsHwg6Xgg9/KqDfJYU9yvfjxs37hjkfq6w7CW4sEcD+U8nJfXJG3HoD+/n7TXXsHcVHyPOBRZTfF8U2hVXUXMpi9u/L8sTo1t28mzaw95du4mY3AIQrWTtFBGpIZYsKY/aaHKIhWfSFaw8n6EzJlpMk8co+TYUUodPUw21vVthbAlMkGvm3qSpvp34m1ML17I4MVM5sMjCHg//wInPeQ9+7LGhhNu+YVVd8HVute6Dd+Nz0Os1xWJ/KK3wrewAW0YHx8fsjXfrfgyX96A4qVIISAEhEBTEWAraSesHBE8ECYSK5238Jv6paUIvXTwVbrs4gupvzeMB8VDeKDEHLrUAp6WlrDFg2MS+yYeMC0MWTRjYMAjsVr+jf46fc1C1YeJYFBhBkyDBqxJHohWLx7WeesfxIP7Lqw02L9OPq152qunaMA6Rju6thHPmW7noJ2ZJOPAM7CmFr0w4q+Q07BaLFaLzzcYogYnbCxKvfsuIs8Fe8kY2YoPlrvqx/rM60wf0MK4B/hTHNBmC1zMM6fJPImljyBWU6+9SqmxI2Qt8PTM5gw+M06vm38OL2ouJfPK69RLGh7m3oGXNX5RQeV2KhxAaW87evTkD3HBoXIvqkY66aJqUGyzPNiaCgdKV+O56n3t/njUZl0vzRUCQmATBHx4uGSxOlNCqHK2vESJtnCQEs9+nWI9cL9hnU0LUxal+KTFKfb5Nb+E0gTgtoR0L2k6Vnrgj8EfCNfAEOkd55PWfQU+WBGCRStbYps4OHXVWs1g69Di4hKNHztGu3ftojAs++0ajJcOkHYUz9kmG4UAJgcRTzO287iYEJ/nS3IXFCeozzF+Q3yXv44Cb/o35DlvJxlDW0jv6eUfj82Xj7ZxXvzx7r2QfNe+nqxz58iEw6n4009S/KePkjXHw6WbL/Qun6HwuWco5dtJdm97vrDabK/BKdQb4Z38LdAAR/FitW5/hESobrbn2vD6kydP9lhkXK+Rva0azeeHt0DAR154vjRgdWDLggQhIASEQMMI4IHUwrqK7K2X1zqNQ1ia5uZ9SLBVNYi5Yl4MqUtmhx06Gsm/fYYvRMbUYxSbjZMvxMfye+hAtP4ui3jThJZna3M+2EuvYQ7eM6SduV+JVK3zAtJ6ryK991qI2EEkbG7Rmm/p6nssVsfGx2n3zp1qGPrqKVvzjDZxivQXDxA5rYNKfDra6zjmIcCueR7BbweLx+A7byPvpVfAejqCoe7wgFWrwKK1q1t9PDt3wYftJeS/7nqK/eh+ij/zpBoqXKuia5IvfhOMl54n64qryewToboRxvhqDMAr9mWHD5/m6X7nNpLHRq4RoboRam1+DVakuUTT7N/eLAY/hGlnZ4gCPL8CTkT4D4Jr/ihstnFyvRAQAk1NgB9Ss59k0qRlDNldjsSUgN1Mw9ixUhC/eaWEKucb9wxTzINhfInDlIho5IPVz8AyCRI2ScDGi4bUMoZP44Ph1LT4CmnnHiQTQ4ONwTeTvvPftvTQYL6D8CeWFuG4awyW1V07IVZh3W+noB9Gnx9+jd9CZZqd9tEKS1HGsso7CE7LKqKch2lHQwCJ/+sS0GnG1u0UuvXd5Hv9DcqCynNS6xpgrdUxpNh33Q1qqLH/xecpes+3KXnwJbJLvHCra90qKEybnSb9hWfJ2rELVlXWWhIqJYA/R1f7fNZeXFc3oVqFsQKVNlPSNzOBp59+2hsIBF+PIVO/stF2sBgNd3ZQb08XLKksUjHcTUTqRnHKdUJACNSAQPY3ia2gHo+hfqt8Xg8srKlNiVXOl621LHxLBjwJeM05CidexVBfPCTDQIjhVqQb9XoyLlmr1otUllcsC5icI2vxIFlnf6SUnB7aAeB4h1/nYcE8JXJpGZ5XcyJqfeTzWJvXLCM9v3CJRGO4n9JCjF+SRHH/BeFkx+fD3N71i2r6FNrpCTIe/iGG/R7OtSXbbrVyavZAncVB5ji94ZfoucvSOysiis5v9pBfUG3ZQqH33U7h3/go+a66hozeXsy5rsC+xDcVj1fmLd8n2Y+Kz1SwgnZoPOIN94wHjpv8N9xEnu3nYWjwWbLZQl3GfbhZJJu+Hu3WFhfJOn8f2QMYRVFB2zdddstkoG3BLfWzT3/6L9kDcF1CBXd8XeojhbicwPDwcFcyRddttJr80Ncd7qBOfHiYrwQhIASEQDMQ0PFQE4IXTV7ndGpmTg0J3ki9Wah6Md2BP6Wtqhot+i+kkcXvInuLzKRN8WWLsOhE2rJa/MC8kUrINYUEeP5vZByOY/6CrInvkL7jV2BBusH9XoQreNDm24Y/SqrigZ0tq+PHjtN527dROBxWL4sLobTQEUSUPn6E9NcOAgBbU9NfIraUZgNrtzxOFnc4if8VL/WvIzFfxCeKorJ5bXrrwe/DxZdQ+Nc/oobc4k1VeVminTamKdhxvJTgJWcwxcBanIeQXFBi0prHPg+BxdBXtpDq/djyUjbwSM7OmTD/Kr3NgyhdLn4D+brAv3m7svIu/u/PUPwnj5Adw4sflwdt+hwZ8PpsQ2zb4W6X19aN1bO9GP7rwcsv/ClTYxFqXkkRqjVH3FoFYAWFfviqeOdGfp/5AY2H+nbCmioitbXuC2mNEGgXAj5MWejv7aZzU7OUwkPfRgKPIuEhwKWFKmHo7ygljB7ymTMqeytlUwJi1dsB6y4PA97ID/BGKtpu11gJeDd9iayD/530vuuw5MftcCxzJbyoutP5UCW3AeuqrOiCXUkdLS0v0YmTp2jbtq0Y5RRWlvtW7HJtboa0gy8QYf3U0qGU6iyKKzqslVLlYb2+q66lzo/8Lnm2luEGBOLUQj/aS4tknj1LyZdfhDO2Zyh56BWyl5dLN0dT0f8AAEAASURBVNcRy8vWeLbvIM/5e8l7wT4scbMb9z4L2G61pI0j6cpdCGiex9r1Bx+npVAHRTF/1UZd3B70F58j7TqsrtgJz8friXK3N6Yh9bP4ByT9I1KH8kWo1gFyKxVhGVqHR6OLNtKmIIb5hjuCaj7qRq6Xa4SAEBACbiDg9/uwzEcnzczBUuEcRldm5dRwYrZgwOpRMmDYXwxzVbNCldOYEKsUgTUoJGK1JLNqRmL5H+vcw2p5IGPbh0kfuY00P5b9aNI3BPySmJ/H+cMiNf2Uyf/ynNUlOnVqgraOjsKy2gmxWqb1Tl3dBP/wcM/JCdJfPahGwKZ1SUZ1OjZKeGZNrGk0RVqU56wXDQEuNMNuGgZbNv033kTh3/pdZfFcL0MWp8kjmMsO50aJ55+lFPZtWBMqCbyeahJryvKHf424Dt4L046T2IGSDq/CasmbNe4LdurU+Zu/rYRt9Ic/IGt6qpIq1D2tDi/GPF/ZGsWLgHrP9617a2tRYH1/I0So1qIPWzhPn6bpGFxScQvZghAM4geQH84kCAEhIASanEAHXrotLUeVR+BKm8JiYK1RJSwmTB3rfxYFHgYsYrUISg0P7egEpcb/AY6tpmBd/QAexLfjSb45XXuwplLvVLIizMGNxerE6Uka1UbU0jWtJFY1DIPlYb8axIl6cuF/FIPMTu7YASS3i5N1GgKswSIZeNNbqPPXfyu93EyuDit37FiMUsfHKPHkExS59x41T3Rlqo3FcN6J556hxIH9SqD6rrmO/DfdTF7M62RL62qBBW7HL2HIfF8fRb5zF5kTJ1dL6op4Hv5rXnM92SJUXdEfa1VChOpadORcAQEek370xKSm8w97hcELJyQBWCEkCAEhIARagQBbqVis8tI1lQa+dm0xoFHSwLC0EkHEagkotYyCsyXz5DeIErNkbP8waV0YUKTV4NGphIBct1lp8+C6yTjBetnz0jWT2mmi4eHWGgaMdT+1V+CdVqn0DImcOM3tZBgWewHORecBIh/MznPwzIvZTOqKNxocWgVveSt1/OpvrClS7VSKzBPHMLT3aYo9+jAlX3lZLcVUcYHlXIB2mlPnKHr/vWoosf8NN5P/+jeo4cHaKt6ieRhx8B0YfYA5tsv/8s8YinymnJIakkYfO4ylqibJ7u5lb3UNqYMUWh6BGvzallewpGpOAmwPrXRWFj+U8Xws9pwpQQgIASHQCgT4d42HALNHXivjTbXcdq0nVG04TkqVsKhm8y8Qq7LOahZL7bZYk9U8/X04qpmGWP1F0gfeUBuxWrsWlDUOisUq0WnShuGZH8OA+T5t6mCZSow4Pf3m2pPTqHmhyVG58b7O84WyNJckl5eiu0FWEEksAEMf+BBEak8+y6I9O7JM8Scfp9iPH8Aw3+fqt44p5sCaZ06r5WgSLxxQgtp/8y1qmZyiKqpD9grsf+ObyMS84Oi/fgvOnBZKJWt8HCzHBuaqWrsvIPLVebmfxre+qWogQrWpuqs5K8sPcl5vegma5myB1FoICAEhsJIAD99lL8CJRHLlyTJiWAjkLT3OCzQI1bXXuGSxasPBEmyzZIhYdcKrzT6Ws7GmfwqxOkMec5n04VurWs4GZU5V68CZLcEb8MRkfhhwM4tVDWJEP3oIS5LwXHI0DpAzNtOV3HLClE8Vp8LJzBDg9IUFiVeNSp9Y+1/f5a+j0PtvJ2N4FPUrfRdYsArHHvwhRb/7HUqdOtGQpWB4vdTUoVdpGfNPTQyjDt72HvLs3F3SGqn39FLwre8ka2oKwhoOliqcN7s2seqd1SH4tXe9l2wRqtWDWoOcxN5dA6iSZSEB/kNn6GJNLaQiR0JACDQ7AX6u5Pn3tQg2rf8eOesNWFlYa1EJyXMFAXvxFUqNfYGsuf0rzrk5orQEKq4xCzAbc6/TYnXVtX6LL3PrMdqhv/py/mUQNw+BZajazRxzjDOkj/BvYXTB8coXTMWJnTmW3mcPu6H3/ILyuIsfkpKJ2JoZuetOinzz65Q6ebwhItVZMWtmWnn3XfqHz6p5rCxgSwVjeIRCP/9etf7ram0rdV094/QzE6Sx8M8NC69n6VJWuQREqJZLStJtjkB5fyU3V4ZcLQSEgBCoM4FVjCB1qwV7A+ala0Ss1g05LNlHyTzy/2H4JYRDA0O5f1adMypZbbGkyki1FbVPyy2bIpGo8gYciURWpGmKCIgPbWaKtLEjqrUr65wRljl9iZ3c/srUxSeZ/Qqxuub1hXnyOqbBW99NviuvIZ6jWiqYJ0/Q8le+RNHv303mubNcYKlkdY+zcU8k9j9Ni5/727RYxdzZFQE/jGxxDb3vdvLu29BCESuyrHoErynLyxa5hGvV29ciGYpQbZGOlGYIASEgBIRA/QmUKxZqWTMWqSJWa0m4KG8ba1fO8XIgf4uhwLNFJ+t4WO5bEr5J8UknL1yeprC2fC6TGCciWLrk5MQEtqsso1R4sbuOkgnS4DCHl18pEKDrab2C8zhY5TgXXSByOH3uzOo8DI+ax8lefnlOZ6nAjoyW7/oGxR7+Mdb2nS+VpKFx7NgpdfQILUGs8rZkuzEtwnvJpRR8y9vIGBhsaH1XK1w/+CJOldFnq2Ug8TUnIEK15oilACEgBISAEBACtSUgYrW2fFfkbiUwB+8RMsc+h+GYla1duSKvOkSwfirUUCsfztNDYjk+fY4thstYgulUE4lVnmrEzhuD8I3hO/oa+ibbzuzWCTsTlztVeFwcnb4SsbkTmZhCsM4CSu5795xPgRtuIr27tPMkG8sFxX7wXYphTVLbzS8J0O7UsXFa/PT/XNXDL6+x6ocg9+y7sOR81pKA6hipTZwgzeXrvtYRhyuLEqHqym6RSgkBISAEhIAQqIyAEqtLGAacKHqSriwbSV0ugdQyWafvJfPYlyGIKl+mqNxiqpEubyfN5sYxhSGfxnmOxSosq6cmsF2G2K3/vcXikz/smDG7BjEveRfC2uxd4Q7q6+2iocE+Gh0ZpO1bt9DI8CD1h+DJ9dWDqoGqxvl/ckKTo7JDoPPNyrQvt8mmcLLKnFTXO+Mz+/nMVpzkJVx8111P3kuvyJq4C9Kw46HoA/fS8te/QrymqesD2po8cpgW/vovVnWapHd3w7nSu8gYgcMolwUNc2wNOImS4F4C63trcG/dpWZCQAgIASEgBISAgwDPWY1jzioWziGPF4LDqTkc6WS3OgTYC7B56i4i/yAexH8OvN35WJUWZdxmFl58U/C/hTdHVn7BF3UOjhKmSMZzVdkb8DDWWe3EOposHKsV0lmlxSjvp/NOC1MPnAzx0nbZJe7YyzY7MGPRumrAkirJY2NkzcwgiaOd3CxN/cPRaih0tqWr5cbxSqoWeP1VkcgA22xgcepkkikme1pt4SXce/ElFLgZQ369vNhfUWDRB3G99MXPYUi5+630udpzvQ88S5Fv30kdH/gwocNyp9I7sG5ffyP5eHkdDGl2lRdg3Cv6YQjVG28uqrM7DtMvabgumTsR91UjXhY1kkbx3dTIukjZQkAICAEhIASEwCYJsDfgZMQiColY3STKsi63o6dgWf0B6V0Xk9a5t6xrqpHIqZPWyy8tAPOyrVik8vUcVxiPI1yYLkfDXNUYnTlzjmgLbUiscl4sMNMP3+mtjjjDwBJLmLfJgpSFqZHdIn5DgYXqa6+oB/q0EHWIVZVhWkWmz3FEWrxnNSwfp5NBsjsgF2pRpHGIV5Wj+id9qcrDcZ5jedkWXjPVs3NXNlHBltccXf6nLzaHJbWg5mhtIkGRe75N3gsvJt8VVxaKdqTV0K/Bd/0cJV9+gVLHj+GCDOOifOp+iHtFOz5GBMdKjfJOnBWjmqardx38nUh/V/h7kX5RwyMJTL6vkylU1cTHolQpJ1Z1B1j7AkWo1p6xlCAEhIAQEAJCoK4ElBdgEat1Y27Nv0DWuUfJCG7HA2+wPuXigbbsAF2gtEHukgJVlcmmWDw406TPLUeW6Sy0KmcTgmWVH6qdwSlGdX7w5uG6SKOG7LIgzYnQjChFHF9TzWArofqqEqqcdUGrck3CToGQzJzInc/UKHdcLHb5fO6kSqzkbqk8+Sw7FoKIC7zxzSrtin8gPmI/vI+SL8ELbZMGXrpm+V++Qsb2HWTAq3FxYO+/LNTN05PusariS6HNzSgP0fYg3sDUMPB3oPD7AUGK7wcv3+hR3w28rOGtQ5yWqo6F+zseT9IC1jyOxzFXHsetHESotnLvStuEgBAQAkKgbQmIWK1j16cWyZr+Cel915LWfXkdC95oUWuJw9XOcTwsq1i6Zmp6hrbggTocDitLqBKj/JCNh3F+0FZWUjyAp62luhKqG61pxddB9CWPHFKXZcW5Gr5bZC9OJ0jr1YIycvozt5MRuxA1EKIqNouIDzhkjjO22XQc/8sVYKEOx0n+G9+I+6M/f86xlzo+TpG77yI7VXpdUkdS9+6Ce+roYYo/9giF3v3+lfUEh+Bt76HYTx7GcPmTK883KgaWSX3iJJlVEKpZS6iaT40XNdl51bkXNRkh6vyeVPqihvMKBv3k9XloaSmiRjokEk1836zT7yJU1wEkp4WAEBACQkAINCsBEav16zlr4WWI1cfJ6NgFC1q4fgWXU1JWWK2ZlhPlE2atP/xgnP6wdRT7eNi2LFM5V+rt6aKe7rCaQ8qi1A3BWphLrzuarUxOb2Z2io+z6YospKWic5eWOpmNW5mIDAhU/1XXZFMUbiFmow/8APfOVGF8Ex7xUjqJp5+gwE03Q5SvtKoawyPku/hSik5OwAGZSyyBEKraadTniqvLIr7ye5F+QZMVpepFDX9nlCjNnqv+d4OHyXd3dZLP56W5+UVqVbEqQrWs21ISCQEhIASEgBBoTgIFYtWXFyLN2RoX19qMQiA9SBqsqnoP5uk5RF8tal3NnsxZfGAFTQ9HzIrT9AN3TqjyA7iaS6dRAmuVTk1PUyDgJ3+Xe4R56sRxsmHdKxiau6pGxQkersup1SYzxDcnNldeqFLkzqsr0xfnOgR2VWSWHdGsebyYl7qb9BLDYflqc+IUJZ59Rs3z5OOmDmxVHTtC8WeeouDb3lWyKb5rXk+xB39EPETbDUEzYVE9M7miKk7hqb4fuPedIwTS34m8EOX0lVpHVxRaYQSXF8T3jx0ssVjlOaytFkSotlqPSnuEgBAQAkJACBQRYLFqwxswO+ww2BuwhJoQsJcOkT39GFHHbiJv6XUya1LwepmysCoK/KCd9qqbdmSUfRjnh1+2CqWFFs8v5fsl/RCOf3MCjLNbxJqfvHSNrm/FUjHuEKvmqRMQqiVEkBKXDoXp2HWiyUUX7xQfOy9y7HOyrEjmfR3L6Pgux3I0qwQeCmudPYNL0leukqxpok28vEg8tx/zUbFWbIl7wnfp5aSFgmQvuGS4asokA96IebmjvBWUPUuzCMX9zi9nsvs8okB9H9zTHVmxmkI75heWWm7OavVt0e7pO6mJEBACQkAICAEhkCHA3oATEKvKwipUakPASpJ59kGyoxhKWOuQNdlVVE5abbFlKOD34QNrKIYO8vIvHMfW1GKrUF4/lRZSS1hn9fjJU5gvt1xRTWqVOHUS8x+50qq6petcWHYmTS4pzzRdLyBFQaLiY1yPOiiJj7VTvZfDwq7qVHAR2RD6yeefJWt5ab0Cm+c8htKaJ45hfeGxknXW4P3Yu/eikucaEmlbZMSWqdujqWHs3eFOzL3ugGfrIOaCBtT3xId1e/nljdtEapYXC2m2rPr9JZY9yiZq0q1YVJu046TaQkAICAEhIASUpatzO1nbP0SmtxeHa79/5oFhKcOmoC9BAU+i9QCmImRFj5N15n6MqYw2pH328jis1+NYquYCmNPKfHDckOgsr3lKeEIxcREsnXgZGH6oZQ+8bCFdL+Srtnra5YxYPW/HNuoIhdbLsqbnTZ7/CFHIkjC95IzaS5eJXTuzDo1qTe5UZqfMY06maPJ4YSeW3PV8FqcgIDy79pAxgPmaLFSzIQM1NX4Uw8XhRtklw2Cz1dvs1pyYUEsEeWE9LQ4aXoj4rrmO4pjL6pagJZNkLM6Tpxe/oU0avBDTAZ+PYrEEbjXHvdak7clWW4RqloRshYAQEAJCQAg0GQG9azfFL/q/KeEZxAM4/0l3PjWv3pgkhv8a3SFlUVs9VROegXVEt2JkD7yJki/+CQRAA8S4nSJr4QBp/TeQ5qvdg295PZ3vQ352zYpUHuJYbuDr8mJ19auUWD1xis7bvpVCDRSrJoZxFgR+Zl8ByxHp2FXXZY5z0cU7uWNODUGcm49acEJlBTMc+a5kJz2ogALJ0fnKJA+/Stb8XDptC/1rLS4Si3B2rqR3dRe2DC/TmAmvrZqeS1x4uhFHNuZ2WrOzRDt2NqL4qpTJL6R8EKoejI5IQni3ShCh2io9Ke0QAkJACAiBtiNgXfDbFPeOKktZJY3HMnw0u5iiXh1iNeCr5FLXp9WoQ4lEfcvbyJr8XkPqa8/uJ9q+QFRDoVpRw6Ch+EGW+1oN73WIpfXyKSVSS8WxTFuOROgYxOr2baMYOtmxXtY1OW/OzKQd9Tgr6VTbOT3JVteMTVnF5U5k6pU+Lo51VprPZWynmWjEOK2sEGOe8/eynE2XxPXAvgqwoqYOH1JiLnNx62zwwogt2/xZKVQx77m3j4yRUUqdPOGKNvOyQK3wwoBfRHnxaSWhWv4rNVfcSlIJISAEhIAQEAJCIEsg4duqHoGzx5VsebH42bkFDBWLt9RQMcUAQ2610HmV4KhqWmsRTpWi8CSKB/ZyQt7GVk7qDaRBATwXlddvZJ2UHhhbXj5KW6mkGYGF/XycIw9E8pBDFqsnTk4oR0v1HoJoLy+THY+nZXi+uplKOiIyuyu5Z07kkqbnmWa1pYLHuRVfn0tfVBQ7LxsYTKdnaJxObe20kGMnSspDcea6FtqYM9OrL7kDAa/3g4tbAubV2gt4sdTkQb2Ecpmzp80iFaG6WYJyvRAQAkJACAiBRhGweRmOjYd4AmIVyxqwaK23qNh4rcu5EorAjJeTsEZpLLJmHsfQ4xrOk3VaDNdpBTuC4fUW1RheqLNy5qZms8wXs1LWZdOobTYh0EeiUTo1cRpiFcKRhVmdgrW0qOZ7pkt0/osKqMN8XXgvK9iLTqVri0hnmvT1fEqlVpvMXjoud5DbIQ3DMHU4D8rllBGpLFbNmSnMZXaHAypuVbWDNTtD5iprwypPulhb1jUB6wLbkebvCx41Ucl32zX816iICNU14MgpISAEhIAQEAJuJuBZPIgHk/yD8UbqyiJ1bmGRYi0kVu3UEtnzBzaCo2rXWDNPYg5erGr5bSYjXoZGBSWUWDaVf8+UrTMdCTl/tqyePnNWiVXLKr+8zbTTjkagAR1WbEed0u135J6rUm4no0Edx47ka+3mrnDuYF/v6kq/HOD43DnOCYQwJ9JCfVs12HhJYWGpGhvr7a4IWK9X76vd/O0V5a0TwcsZWXi50uxhnVdJTdk8maPalN0mlRYCQkAICAEhgAfh8a+Q1+ghM7wPD8RY+D1r1aoQTiIeo6UFOCLq9JJPPRkUPFVXmFsDk8ORESUXyJp6lKw5zBNtYLCXXiVKwFGOrw+1qP4jZCU5qmU1+N5QH65N+VeXfUuVSLgEi+GZs3BuNERqzmqtl/ewcR+TQxSru5jFKurG++lW5/c4kqeUplWkk0kmTS4pS29Qcxyr3HLH6Txyc175EFfoymqIRKgDJ80HDffnHKx4rStUuf3W/KxagkfDnNSCgCHoeq+bLKoW2YlGjsAooCMHDgIiVB0wZFcICAEhIASEQFMRWD5G/iOfIWvoTWQbHfxovOHqsx0qasD64U+RUeaqKhsurFYXssff2BkMu30Sz8kOy1qtylsrXwvz3mKTpHXsUkJpraS1PsfrLK4QaVUvNK/anEKYvQGfPTuVFqudHaSXELRVq0oiWXKocYFIVNXM13VF2cWncFwgZnPnMzuODYszpzMldhqkFKpSuIUlWcsYpoz5tCxi1QuEwtMtcWTH8H3E9IIVAV6nDTctBcN9gHmqEtxHQISq+/pEaiQEhIAQEAJCoEwCeMBaPk762D+VmX7tZDzjNYqla3wdeJDEVsLmCNjJGWSAPmpwUNpQVaMxfbrE8/9gWOV6sDdgtbZrDZjYmGuohF9x3sVikFlkUeT2M1ZTvlbF5U44cnOkUbGl0mSvx4iH7PzUtNJ15INdOFHKDVNeJZvCC5rwiNeHLbFGrIahvyusrI1sHt8fLerUqpFYq1H2xl+9VqN0yUMICAEhIASEgBBwFQEzaVNiGctLYCthkwQSWJvRBUI1r8o22Z41L88qP27xynuHLatnYFnluas1c7DExbLo4E9xWBGnEqdT8SVqr/i6zHHBBgcFyUocc16cBs6U0mk5Tfqj2s77rNpz1mU+pyrQWv+wSF3BPd1Ezeu2YRut2AHNfzuJUG3+PpQWCAEhIASEgBCoKgERq9XBaSchVFd5UK9GCXlpWGZuNaxLOUqLvQGfPnNOeQUus8aVJcNyMLlQoq0FUqTgAFc5jtVu7jizkzvOlLDGcVao24sY3ssZq7o4thyn6ursweIMcy1p3h124lVquRTwsBQblzSNu8F577ikWlIN3D4CQQgIASEgBISAEBACxQSyYtVKteADdHFja3Ucd4dFNWe4y+3UosF50eWco1pcUjQaU2KVt9UOvBxM3kqJ3IvFauY4d0erndyRqk5WZGYOCquYS5pJ5TjOps9FIYKXaFF14MjsCWxVNQosqplismkyh82+0YIh+HgLrGgGD9G25/i74ZLAfcH3jgTXERCh6roukQoJASEgBISAEHAHARar8SUs3SBidUMdUtM5qqWEznq1ZIWk/i9fERVrvVWLcCRcb2hvWqyepWisup5WNb8PhrG8YFZ1ddSrZN0VigyPHJbcTuYSx3FuN7OTOy7OHWulwuutEqiKOxJmtxwZDNLK4a+cpjif5j3WOztJC4VWNgBDgi0sz+OaAGvqyr5wTe3auiIiVNu6+6XxQkAICAEhIATWJiBidW0+a57lOarrCaU1M6jySSVuYXTMeRJaP3++pDiUiiuwZJa4pjiPKDzC8jqrvK1W0ALBtEV1hdjLR6TniBaVmD+9UijmzuV2HFrSaX/NnHdslBjLilN1H/BJfLBvYOkaPQRP3Svuj0wGRVVsukMM+9VYqPr8K6sOoWq6yKKqsVfsEpbflRWXmHoTEKFab+JSnhAQAkJACAiBJiOQFau8lVA+ATs5X37iClOWoQVX5qhEE0ul8vsxr6Py1+TjHEU4I/NJHQkKd1mgxZRYxZzVSLSEYCtMX84RCyOCR9kVYUV9VkTgEkccdgsYOU6pvFc9zpzIbpaXsK5vMpM3IplR5mMMbSEtHF5R1ZL5l07l6lgd3p31zq7CodjZGptsUZ3OHjV+y6K6o4Tlt/E1a/saiFBt+1tAAAgBISAEhIAQWJ8Ai1TxBrw+p4IURglrUkGCOh9syqK6jjR2mFnLXX6GNRuL1TPnppSDpZXWxcr4aJ1hUvNUWXTi/4LAhTlDcRJ12pEmc5yLKXE+fS6TIpcwX4jNlsPpc0qbqvrkhCqRMTBIWld3xgJcIIuRATIrkV8+Z/fv6f0DpA8Olqwoz1G1prC2rluCx0iLarfUR+qRIyBCNYdCdoSAEBACQkAICIG1CBSI1SZ/kF6rndU6p3n7SluUqlVApfkoocQaqPzOK9Z3qxbpSFip4GSxOjU9s2mxyiJVD8OqqkIJseeoY0kxWISl6DCfb2ZvxUZdkLmKN1ibM3n4EHb4AJ/MhpWr1tFJxujW/JDTgrpxzpy4eYMxMkrG1m0lG2BjiaLUyRMlzzUiUjM8eGkA668E1xEQoeq6LpEKCQEhIASEgBBwLwElViOwFLGDpeZ+lq49ZD+EagXzQSuqkMOCWfZ1m7KorlOKsz7rGF9L5cQOlqZn5jYtVnnuZ36+7Or3aPrWdf6brZXjpla7jjSOUyp15nil8M9ckzQp/tx+fE+Qgj95pYrLbfJddDHpvb0FX6OCIgoOsvVrgi1eGBij28gYGl5ZWViZk6+9SvYSL93jkoA1XfXuHpdURqrhJCBC1UlD9oWAEBACQkAICIF1CZgJDAMWsbouJ2VRLVOoVqrtKk2vKrvCarduEypIkFdVlThrchbAjpVmZlmsxpSwc54rd98YHHII1RJXFTNYcczX5NuS3oUFNJuVOlV83nGN4xRhiGvytVfIjiynr+ZzuY9N3vP3ku4U1qhLrhx1BRI780vn4vp/jd4+8uzYWdqTLoRqYv9TrmqD5oFQ7cULDgmuIyBC1XVdIhUSAkJACAgBIeB+AgVi1f3VbUgNNZ/Lhv46rZ5VJ1IosTaaPVtWZ7B0SSS6MQdLxvCoKjqv70qIPYc4Vekcx0UX55qxmtU0lwA7uTTpTBFhk7WwQMkjh9V+WnXyyXSdWKR6dpwHz7i+XDYrh02rzHLnm2HH2LaDvBddUrKqdiJByeefK3muIZH4TvASOnqPWFQbwn+dQkWorgNITgsBISAEhIAQEAKlCSixuoxhwOINuDQgH1tpyhVw5aYrXZSbYnOCbYOVYrE6Ozuvlq5ZKdzWztSz/TwkKGaZFoZrXblSDuZjsjo21678qXSWq2aPK2JRSrz8YlqoIiPVHpVhOhP/dTeQzk6VELLZZstLZ84nsmdyMa7d4bm3XgxpNraVnp+aOnGMzLNn3FN/ePzVBwYKXha4p3JSExGqcg8IASEgBISAEBACGyaQc7DEc1YlFBBQFtUVoqkgSf6gWFvlz5Teq8g6ms28ln1U3bzVMGDMWY3F4qXbv0qs5zxYKA083q6oTlFEsfjDcUGKggMUhmOmmEulzhcn4kpl4rKbeJySrx5ML1PDcUUfHyyPPASY4NAnH1jQ5o+aac/YMky+a65DHzjbk29B4tlnyIaTKbcEdsDlwXxaCe4kUPoucmddpVZCQAgIASEgBIRAMQEjQDT0BrKD27GGZGP+rKdQJwtF+71R8tNccQ3rdmynMFw0egpzAscgCKy6lVuyIPSFFhiBusmKxJKp6hxZy7rk897oHNViGHEME53GMOABrY8CgfKW+jGGR0iHVc9KzCC7fJ1U3iz+nFGsBp39U3ysVGX+gtLaMZOpY8NqVDHgOF6KBRZE9nLr2bVbVSNXF65OIEiBt72DEi8cIKvAwVAmw+wVK+qWPeGereb1kWf3+RDe+0pXCm1IPPu0YlI6QQNiMT/V2IbfTgmuJNCYv2iuRCGVEgJCQAgIASHQXAQ0XxfZl/wJRbw7iTy8YH1jB0rFDIt6w14K+MsTFVWnbUMymxEyT99L5viXkD0/7DcmaB3nE/Ec1QJlVL265OVTBXmy2EF9bC0jpMq4tGx95EjIw1vLXUt1rSpwPvF4Rqz295HfMZdzteu0QIA823dQahZCFdfbPAcxl7hI/OXiC3dyqdRO7ih9O6nMMnG5U/lj1r185AwmxHbsiZ9S585duWgb6VRWiPFdegV5L7yI4s9AxDlfsOTyz1zmYJzLyEU7vG5q8K3vgPjGy7MSIfnyC5Q6Nq76pcTphkRp8PjLjp8kuJOACFV39ovUSggIASEgBITAugT0C36DFjuuJpOMddPWI4GJB+u5uI96g+HGiVU01IBAtBdeJmvmiXo0u2QZeu81GP5Y+oG95AX1iGQVpf7PSqT1C+VLygrOhOVes07GWbGrxOrMLPX39a4vVnWDfHv3UQwOe5RgLBZ3JcUfVyRdaTWH1NkWld5xEXZZZLIczVlNHe3l4jir9BBhpOD08PqbwDI1KcxH9ezakxGynJDTITkEeOh9H6TESy8ibSRbFZWHU2bnL1B7rvpHC4YocPMt5L3s8tL1grffyHfugnOp+dLnGxSrhOrOXQ0qXYpdj0BjX72uVzs5LwSEgBAQAkJACKxKIN51pWtEaraSLCpmZxcqnluYvb4aW83bTVpXaa+j1ch//Tw0LDtyPRRzcP2kdUiR012sotT/aZFUTtFKeKmE61yTT5hWX+Vkvk4aJRozaRK4r2YgVmPYrhU0XSfvxZcQb3Mh3ezMYcFBOq64ac625DLJ7zh0aT4yIz+zEek0mYwtm1LHxyn26ENEqSSS5AtkIcvBu+8i6njvBzAoQqlcFcd5OBmoSMe16WMX/AvWnvMvoNAHfhFL0uQ9GDtrFn/8J5R86QW0nycKuCRwvf9/9t4DXI7jOBet2XwiIgGCRCKRSBCJAUQiSIIRFEmRVCBtWpJFyZL8Sb6W7c/33fv8/Py968/vu77Xz9e6DnKWbFOSlS1KosQokiIpMecIJoAACQakg5M3zLyqntQ907M7u2f37OyeahzsdldXV1f/M+dM11R3NS5VJiObUzIRkH6Lk6kga8UIMAKMQE0EaFKBb2vpAUih7y0MXmFN4jl8eCaf+KYy0Us4QajgQ7LGJKRmf8zACCQEgYqVTYgmqhqTxRIcPXYco7bi715bft9wip9u3+TT6F2KR14sRs9YCz3dnvWpYl+1RG3ED5lA8ZLfjd/Gp0kyJKLrCZVqG8rKcsieoz2rR4/ZAZYi7ysyVE9bqxhMXgAkTwvfUPRIAQPQlS84/Q/BLm5ppJFctUqUhB3q5Gx+/LTGxmHy6Seh+Bwaa2i4imXJKEhIIIEYfKjnA1dDbv0mvEaINbE4yrm6OEXR1ssnIJPCc1MHPvMFjF48qNXGGj4OY7feApXDh7T17SIa6H3Prl3Xru653xgI8NLfGCAxCyPACCQEAXqokwHqGJ7gGJ+Chg9Cc3hYLLES9WSUkvFKcyucABi5PL41LYggG8bAIH734SvsHNKzYkJj0J66fEF/QHlChs9qMAJBBNLFd3Bv6nwk+0ZEkKddZTJWjw0Nw+xZtAwYf9ckQ6blOpklsEZebnk3UR0Ys8/E66KftEe1qZfe0BUXFtbU96iSmNDllIhkWDXjeuvkCI/90BDMmTUIefy7HeoHFUvPngO5FStg4oXnfVgl/QSRrEAZxGAZq5X+Rb3KRM1tUxVzapVU9ivKb+6DSdyrmkXvo9HfL9RwlTBo3zAGger/xKdg6M//FCoH3xbSHSZSRgXdF+uxtCOT6h+Avhs/IfbYavvH5/DEfT+D8huv289kLVObiBgdOndGxFLlNqnE3aoIsKGq4sElRoARSBICGMLexP069DaWoiGaQ8eggpETK28dgMq770Dl/fdwD9phNFCPo6e0vnD3FOwhhZOZ1PwTIH3CQkifdBJkMPJfeuEiMYGghy8ZtMKATRImrAsjICGQOfA9yCw/ASq5hThPlmfdElMbs2RUDKGxCtNlrFoVsMqjYB17EsxDD7Rn5GgopWZtACPjGiLtUUPbK1mX4if+vRIySLWCkSgzxhcfJU3QQ0ao000RX1IOHR+GwUFD+xKE/m7n1m9UDVVsq9p6aOnJEY2oR5WBKKr9KYxD30IkduXXLlBPjd39rIIRV/0UMaBQ8ZknIXfuVrxHaJmsbepSX4aBS1GXLkNj9SYYvflfoPz2W6Ke6iip3TnCm4S13UN9nwY+J3s+eB30XH5lZMMyPq9pyTM9q5OWUviMz6xcmTS1WB8JATZUJTA4ywgwAu1HgJbnmhit0Tx0CI3Rg2JfT3nvG1DBQ8LpgVevQRo1IloWXHnnoPhPO4ZEogkmPrjSGDEys2y5CHqROXmJOAycDNpUL3ph5X1Pbjv+ZgTahcD7v4QeXOJamn0OVKyUmMi2S5WofmlH2uihHP5u9UAml1bm9VFtGqVbJq64GH8bzIM/wr8V442KmVI7I38ivuxahSs5EhZIiUalMcRqDdZvoppJoXY+I17j5lhPikfT6dDthpYBHx8ewZ760bOqeuwNXB2T23QWGN/7Nm75wDvQUydo3GnG5HbgDjBUxgpPHuaFCElOoEysRHJTef9+GL/nbkjheaNZDKwEuPxU8AhDH1834Uqf/FmbAXBFwui3v4HPvf2iqS0WP+UXAgHT1e1jOr5Ts2ZDASP89n74BhEMStcnGafjP/4BlPa8ZN97OqY20rIbNvHL6DbiH6drNlTjoMQ8jAAj0FoEcGkQPdDonDkKXV9+dQ8uE3oNyvvfxEknRkCcroQTEopIaGLABxH0AScQqblzxWQis2KlCLpAxx6kF+P+M1oqzIkRaDcC6EGEg3dAlv63W5ca/Y/nDMj1pnAlPk7L5Yl+jXYdVY17UlMLLrb3pyZRccXIiaeg36TGRfMZ4wluAhd5Vo+PjMCggcYqRs71PLCZDP7dPhUyaAyWDuALTmHluR2iBxPdnJ66rhUpD09jnKrOV0WgJ96jioxdIlF0v7tLhA0kFDEisVjR85EbcBXPiaK9/YF8yEvPlzx6XC08qmbsR/9hH+lCMRhIFLZXjtxx5Lujm47v9EknQ89Fl0HPVdeIl7u6Pq3RUbHkd/KB+4DySUy5s85BQDlcTxKvjasTG6ouEvzNCDACbUGg/NorUHrpBSi9iP9ffxWX9e5H47Q9npAQAHRQ+6H3YZL+P/oQpObNF17W7Oo1IlhHdvVpaMjOCzVjAiPACIQRqBRxgg6mbaxmZasgzNupFKN3OaROOB+9YnNaPwTP0mp9V/F68C0mMsqa5VWt1TcZq8NorALu+ZSNVVodk990JpRoJQ6p5qhHX14hlBeE0IcwMmVLVchyBBI3ZulsWmGRhlrb9fbLGZuHgvtNPPQgpOfPh54rrsIgRLNEK3n5vtHTA4Vt54m68dtuheKzeNwOPhtJguoZF51Hdi0EN+sD77nsytXQc+U1kN95QbSRiuMrPvEokN60RSeJKTVrFmRPx0BKifs9SiJa7dOJDdX2Yc89MwIzFgFadlt65WUo4Vvl4rNPQ/n118A8diTxeJgYsbBI/598DPez4vLgVWsgd+ZZeFj7BkgvOjnx+rOCjEC7EXCN1XxfClLkWe2mlMpCesGFOHk/bVq8NI2hV8WY6uBrUcRlssMjo7jk2hDLgGkoFGMgf/ZmGL39p3bE96AbX4FCY+wFvaqupevipLTXEEW9jsmmWWhcj999B77YWACFHTvRi0pLxbHOvbDk9UVa/syzIT1vHvIugokHcK/n0aO2oep2Kb51/SgMUy+gl5oCD9F5r/TcizzSBVdIlfa8CGM/+C6U92IApYSm7BnrBa4JVY/VchBgQ5VvBUaAEZg2BOhtcOml52HygZ9D6eUXp39pb7NGioGbyvveQP33QQnfck/iOWy0hCi/eSsGZWKDtVkws5zuRICM1Un0rHabsWr0rwFj3naMJm57x5J59VwrqBXa+bKb5U31lvLGUNc2VnHPqjEAOYrmjkuBM8tXQBa3axTxZahIij2HflIyBj21sVL2mmr6pD2z1MBrIhuvnmwvI+xOXKuLkrCF+iWkV3C1ztgP/wM9qyegEbgeI0XbfILV7YTO+sRx9F2HHkD0Zk7ccycU6TxS9FpOyxJgHG967nzIn3c+FHZdKs5LJWyjEu2pHfv+d3CVFEZcJrwSmvLn0u9q9DgSqvaMU4sN1Rl3yXnAjMD0I0ABksqv7IHxO38KpeeewQBGb4sjZqZfkyb3iG+ORfRhnGyQ4U3HDuRxqZZYroUTD06MACOgR8AzVvvRs5p2Z+R63o6gYuCk1Pwd6E1di+omcTyOTsJwmPrxNNprEvJAarnqIuqCKVUTUMLASRRgadZgP2Sz5OFeIDySRToaxRm72t6xHj1ioKwbk0wT7FIbJytRHMnBCmdxNJLpyJqRb94MAzd9VhiibsA+uwVdN8zhF209IWMxg0fbFJ9+AsbRwC1jQEA/IV8NQ9vnjZczenshv3WHMFDpSB06L9XVLyQBcam89y6MffdbUHzsYYy+TWHUkpnSi07CZb9nYNRlNoOSeYV8rfgK+VhwjhFgBFqAABlyFPVvEvfjUJ6W/XZdQg8rBYMq4vE5tOe2iPtZC5deIZadGRQpmBMjwAiEEKiU0LM6gp7VLjBWyUBNzzsPPWKN/75Pi3lL7kPxE7833+MYuoQqQWJs1h5VnUdV6kbt3ymVyiU7wNLAAGTRsMrh0tnUHT8V52wLo88x5sgQpOTarx4itoVoV7oMmk49Ni9jN7Hl2kTx6dV7GeFcFboIT6uFS2VfhuNf/ksY+PVPi2jFtiS6UNiG9HUSGVaZJcvwSLUFkFu7XsROmHwQVyhhEEIKsuTJdBs0+o0BhrJrz4C+j/wKZNacDmkyUNPpaGnYN60wGv7HL+OWnqcT/5zP4ZLwFC6nltzp0WPjmrYiwIZqW+HnzhmB7kXAwvNP6ey08Vt/iMtkX7cfXOJB2r1jpqNzaP/Q5GOPiOBQxa3boe/6X4P0yYuj30J3MRw8NEagKgI4r+4GY9XI49FVCy8FY5C8qQlPjlVGgX/kBazVtKYmtp1GhpBvNIXa+IxNs5d0HlW5m5AODqGEe1aFZ3UAPat41FgOja0J/LvsJWko6F8WS4D9oWFlLc9kSAlJoJf1MioeEtnTB1fnlN/cC8f/7q/ROLwBei65DKsQa+RVVSH88doV8Kgn3HKSxuPTei6+DIpo6E7i85ai1dN546KtYKXOaiTHCDdyeQwWeCpk8fzZ/KazUf4KPA94Nr58qWEqIBYUEPH4X/xPjNz/JrqIk+tJJSRonIVtO4HOSueUfARq3H3JHwBryAgwAslCgJb5Vt4+gIeVfxW9qL/AIBZFfK7GeFgmaxhT0wYf1DRZGL/rDgy89Dj0XvsR6Nl9JRh9/fiUrDLRm1qv3JoR6DwE8E+DWAZMntVODLCE+1FTJ12HBsOH8GXUFA8IqvNPQ53s9r1Bf3/ET/zW/p8sv41Pk245iajzhEqcsbM6OVI3jhxfL08wMpVxGfDw8Cj0YaA7Oupl4qknHCOKnkd+G/vppNKEhSjxiGeY1LFo4xiroqX/YauAZdsRKio8tTy5HtlfAkx1tHR2BJ+d5J0kb6aIBox7VD12SRKk8FVDoYBBjTA6MAVk2rINrOKkfTY4Gq4UsLDy9lsicr2IFoxR7EmQgZ5RakNng6fnL4D0iSdCZsUqEcmeIuGSYWqk0TyIcWY4RS+efOgBGPnyX0Hl6GGUT5omO5E3Nb14SazxJXskM0M7NlRnxnXmUTIC04KAOXwcJu/7GYx+7V+ggkthZ3yqlMXEY/gf/kYEker7+KcxOvAi8UZ3xmPDADACEgLuntUcGqvinFWpLrHZdA+egXkppJffhMsiexKrpqKYMCSmvkfVsdEU0bIxp/OEqszxSjo54b61Zpww7kr40nAEMFrtaWshj8eKTb6AAX5Ewjaqq1IYcbJtKo/HbkJWnm/gOoL8L70a0fUev5MRX3iWNz5Hx2/7CR7rcgj6b7gRA/SdhNF/8f4Sx306/btLgkWgJuwCjUo6e5WiBKcGZ0N2DXr3SVfH2KTzyM0jR0T0Y4ogbJA3sdpYfK31OfEy9qhYMTWKgZOssWSekxpUnoJA5XGlk1j2G6zkciIRYEM1kZeFlWIEOgwBfGiJIAoYjn78Jz8Sb3U7bAQtV3fi/vtwCfQ+6L3+Rsjjm2/33LyWd8wdMAIdggAZqx1zzqqRwcAy6Jk55TMYkKXxfalTujSNGBrURvw4Bk8MBWJ3IzPGF19Vg3ge1aoioIwvDI2TFkNq/SaAV18R0XK9Fp6xSHYqFtB4lYchrF1lLFIDFEKGtNJALhMrJTQmvYXWorkvw2UhNp+KeVyJNPnIL6Fy8AAuA94t9tlmFp4ojFHA/aO2u1ZpQR2hDOckVtKZqnFJsTBie3rR69+LhCkmsb3liAgeOPbjW0TUe/KqdkqiI2my+NKClv9y6gwE2FDtjOvEWjICiUWA3qSWnnsWxm75LkYifBKN1M55aE0rqDiBoSNtRtC7Wjn4FvRcfiVGpFyoTnKmVSHujBFIHgKesZpkzyoaCrQfNb3i87j0clHyQKymERlSZNC0eI9q3P2v1VSlunge1bAUapeSLM4yBbVbtxEyuE+VAt7ZSTb0CBWy7VSasPZkz2uwmgTJxqkQrGXCGtnidXg81mAZ2XGpLr3cpKXAWQzQ13PBReIc09TChWBgRGMloY5Cd3fMnlzkCumntIxXQIOXtrPQcuLJn98jtvWYx4fitU0IFy13zp2zBVdBdNjvbELwa5cabKi2C3nulxHoAgTMYxg4CCMOjt3yPRFiX7y97YJxtXIIYu/qD74nlmH1XnUNpJedEp50tFIBls0IJBwBMlZLKROM3mQeXWMUcE/fKZ/GVRHNDp4kGzItukhkyIif+H25to9qaGn08xk1ldNL0npiV60GAz1qxoH9eDyaG30eLTrHECXbTiQ07JTzSYPGay3DTxiJvqVI7K6NqlY5FfhFKuCnzeh8EYUSvfwtPvs0lF9/FSMCn40rcrZjxN8zMPIvvuhMi/XANiPJoLZRqZHrg8qbI8NQRk80eXgnf3G/2AMrjN+ofhJKz65aA7kNm/DvShM8ywkdYzeqxYZqN15VHhMjMA0ImHh26Phdt4k9KnTsDKf4CNCDf+Jnd6Kxegh6P/ghyOKbfgqKwYkRYARsBCqTZKxakOtB35w0F283PkZhIaSXfhwD0VzQblUa7D9gBTUoRd/Ml03evWZ5VfV91ab62ji8gxj4avMWSKHRV3njNVWAxExZ12b0mKR6jyZlQp5fwa82Eh5PxWKVBWBefnfgNXUy+GWOjsIEHUXzwnNiKTAZXpklS0Xk3/T8+dgep/RimbEkipq7qQ5DlfbJVtCgL+9/E8qIVQlXS5UIM1z624mJ9uSSN5Xw4tRZCLCh2lnXi7VlBBKBAO1HHb/1Fhi/8zYRUTARSnWYEtbEOB6K/giYx49D73UfFVEpDdxHxIkRYATQTsAJdnnCFLFgMgVcaitP4tsEkDGwBqOFXo8B0a5CSyBB1nNdeCQAyLr0bS5z6rQzIIVetco7BwEwwJCdfGPQNxYdmtc9lpUlwFSPKXBjKq1EwaF4FV7GMYbDZc/E96q8jOjSPHoExn92F3o3H4DM4sWQxnNVyQDLLF0ujpdJ4ZYSI5sTvPZ4nPbVPMHkOaVzwN/cJzy3dFROGc9mreBxM3TkWqen7MrVkDtrM3pT27SfvNMBbKP+bKi2EXzumhHoRARE0KT/+A5M3H0H0NJfTo0jYGEQqtKLz8MonjkLeKxPfsdOPDaAjdXGEeWW3YSAhXFgSuM4bcdlwJl8Gw1DNEpT87YLIzU1bxsGp0lGIBbd8tb2Xn/fCG63N5Vw8LXxUTH6+yFz/oVQQa+qiUtpfdtNNQapRchLSm5W2Vj1xYoc8Yte5Y5lsXJeaetXUM6xYBUOmezyWBMTUHr1VSi99qpYkUNLgdMnLsJ7dT6k+vrEETR03qp7hA09W+hoGmpHS5/FN74wFd/j4+J5TqujKIaCOTxMAIR16EBKanAQjdRzILNsWQdqzyqzocr3ACPACMRGwDx8CMa++01ctopGKnoCOTUBAZwMlPe+DiNf/1cxx8mfd4E4YqAJklkEI9DxCJgVjASM73GMtNWeY2vQKBVH0Cz5VTAGTkMjNRDEptMQ9i2z2JrHbhKbMXbXLWBEE3rVaZA551wooVfVwuW0fkLDLGiIkq0mG57ELNNCY5YrXckSTWSDZZdPlu0snvZYnYxXltpQMzQ+xTJdXKorzlels1Ap4FImK74NLAN6WQ06k7VcEufJ0reFZ83igbPi2JpujTGRWX2a/RIYj+7h1HkIsKHaedeMNWYE2oIAGaaj3/q62JdqjYy0RYdu7rTy1n4Y+dd/xslEHvLbd+B2ow6fEHfzxeKxTSsCZhmN1RET8oPo2UwFrYYWqpIZwDMsr8Wlldejd2oxWcst7KzVoh3cAktV4/Qau4nE2Kw9qjqvsdRNVfUjbDo8miQH2d1XQeWxh9GrivsukdG3T5UCVtllv0+SSkm6D4PGKpaVYEzURJx3arcRRdnaDdS7+ngWsd1A9Cp/CN2EaIriTBo5jNQ/rtCh/yKh8pK21EJR32bqzs/U3HmQ37wVMng8EafORKCNa2k6EzDWmhGYiQhYk5Mw9q2vwfgdPwE2Ult3B9C+qZF/+Gtx3E+3LLtqHVoseSYhUCmhsTpKE/FpSGiQGrPwvMXT/xDSp+I5qb0YgKWjjVQJMzKqxE98LKmJnbyMS1C/fUbbxlJrGyrZy2nVpnI3ao1f0rWza+0xGItOgsy5W70gdsKO9JsH9NeMW0OSmwubUSbI/E5eJsm6yc3UvNpQNj59/ZFHEix4QoBJDGoH3VVKZyC3bgMULt2N0ZE7+SVTd12WekfDhmq9iDE/IzDTEMB9lGPf/zaM/fD7YNFeSk4tRYD2AA9/+UsY1GJvS/th4YxApyFQKZpABmvLEkZNJc9pZuUXIbvhzyG14BJcNjkbu5NNgpb1Pj2CyTUofuKPyfcm+m18mqS2RNR5QiXO2FmdHKkbR46vlyuY2oWpVEtU/I9LYLO/+ut4rdeAhV569a5SS0ImklRqgCAZgzaf/Ckk4IcvwWb3y1KVzexVORlN2SNhYz/v9hUQo5JRlagWQcbOLWcWL4Geaz8CKYz4y6lzEWBDtXOvHWvOCLQeATRSx++6HUb//WYMvjDZ+v64B4EARV4c/tu/FFEYGRJGgBGwEaDgSsVRs/lzbDJQ8WzU9JIbIXvOP+PZxh8X5aTvR9UZcTXvFTJQxE98Q0Vn0+ho8oWJ9mjW1FBh0MkJ960fi55K4p0aDDSU+/Tn8DzcQZumNMBC1bKipl0IKhYqE5siVC2LKqneyUaboXa39icye03lvMMT1IXIHr/D00VfZJwWLrwYcnhuLqfORoAN1c6+fqw9I9A6BEwTik8+DiNf+XsM1DDeun5qScY33xRSPj3/BDwW4mQMbHIipObM6e49nIh96dlnBPZ0nh0nRoARsBGg4Ep0bM2UEy3lzc5CD+oSPBN1J2Q2/m/IrPl9NFAXocOti5cJkjtS/JBnMV4KezAj2smM8cVHCLPJOmNc7qZqY22lb52RnNTadZC7+HIRZIjYQ/acw25/yZ+Cu6qxJ7gdgY4YlR+JRHcNUY9HsiBdmuBxC04rl80jOyrRl5AsVVDWNfp9sp8TTbrlA5f5ZtdvhL5f+3W815t0I3YLNh04Dg6m1IEXjVVmBKYDATrsexT3pbbTUDL6+iF7yqnioZNdsQqX4i0URnPl4NtQfPxRKO15CSrvv9exh5BXu45WqQiTD9wH6QUn4jmrH+Hz36qBxXUzBgHyqpYnMQIwHhOZqteeJM8pGqeQnyeW+KZmbQBjzmb0qJ2OE9r2T4emZUotDCcKvuOd1lnz3qEm9nyfDJsqWvqMyFWFr2aPPgMZV0FjVerGZwzkqF2qhpFCcmhguU98CsrPPAGVV14J6O2M1xk/lcg4VMUiwY/ERNZgkEFopqJBkhyKlBUUqSwa1lWOZibZVEtJ0SVCX5uzMz/T8xdA3/U3dqbyrHUIgfb/ZQ6pxARGgBFoNwLmkcMw+v1vQemVl9tmBKbQg1rYeSH0XH0dZE6miJvS43XT2VA4fxdMPvxLGL/9J1B6+YWu3D9LZ9mN334rRh49WYyXA0K0+zdjmvrHW93qZq/eFGGkKMDkVc314qIw6c+CViziaOTQMCVPaS96TwfXQmr2JjD6VmCAlR5tk24ken8+KSN+agHno+C1rQW2z+g3nmIuaKTGFRfdzh83qStMu0IB8r/ycRj/0v8nXswaaHj6tqdt/LlGnu3ZRDPcF4Mq2TyebkHjD8vhKMAet9OcJDvmvRDn9ugqSfyBfgJluwVK8ZXHJkhFZV1ptlc1qL+kS4dn6Uienis/CFle8tvhV9JXnw1VHwvOMQKMACJg4cHf43feBpO/fFDk2wFKau5c6L3iKnzgXAMUXl6XyNtauOAiyCxdjsbcj2HyFw9A5dD79oNZ16BDaeQxHr/tVsgsWYaBP1aydFrqAABAAElEQVR16ChY7foQMKBiNPHMP5zrmukBnCyrj3x1sh3UEBspk/FQERsEGIgSJgUF20w0c5Z4pazDj5Qw0SOZ2QxYPYOQyqGxiZ0aNDY88xQJ3n8jjeV0Pxqly4WBavSdguXesD4dStHAE2MkAeBjtIjP4suWzK74zTWcOo+qhk1L8rXRVtvLfAlEjA6bOWsz5HZdBBM//iFYJrZUGlMZGT3AlUpbeJAkGatU5RqMNnO4TOzyETYen5Sxu8BP0oWSq49dYdPcT5km6WJXy5VICdW7QjrvO7thE/Re++HOU5w1jkRAfWpFsnEFI8AIzBQESi88BxP33gXm0SPtGTJOOnPrNoqQ8lFGqqcY7kUh461vzschvXQ5TNzxUyi//qp/fpzH2MGZSgWXOL+ILw9+An1zPxZpuHfwCFn1AAI0jcSFiwFqg0WczI4VVsPx3jPBTPWiQWd7UwyU7xqVNg0ZxY//7Xql5G+7DclAPkziCz9ECb9puaU973X7seXZ2lO93cj+cuuovc3vKOHLt5Xy6r1+01mweuegU3TQ3l+IBqpBhmoaDXxhsBZwqynm6X+3JgF+vYMj5GdyChhpDhQGBt/J7r4ayi++AOVX9iBVg5PcFPOK7apatlqAqTklX7Is0K7zPr0qJ6N+eWz0l0JI9PiJ4vlmfT6pXhBDZSQ0dD/5XbQ7l5o3H5f8/hquoMC/A5y6BgE2VLvmUvJAGIGpI0B7P8fvuRNof2q7EkXry6w5XQRNiqsDPaB6LrsCMouXiqWykw/9ApcCj8Ztnng+a3RUeIwzp6yEwiWXdXcgqcRfjQ5SEGfEY4U18N7glTCaW40T6yyeykEGIf13DVW7nKKgZYJO81Wk0T/ipX80gcUfMkJtHvp22iPNpSNR5Akhn89p79CofxJG9WIPIYkmuUo/ti7Up02nJs4yX+QVCb/NDE5h8nncq9oko96WzJ8NI+BcG2xP163dqS4N8P5PLV0OOVzFU/nqP4A1NBSwRFXLjozBgKXq2YzeuMUbGyo5mgQ9l0KkJFeIlIxMqUrIdMqib0LYq/cqvK5kfpEP6BvLmLUbdsxnz6VXQPY03G+O15JT9yDAV7N7riWPhBGYEgLWxARMPng/FHHfZzuPojHwqIA0Bk0SE9M6RmTkC5DbdBb0f+wm6PuVj0H6hAUoo66pSh29TT8rLQGevP9eqOzbO/2dc4+dhwDe+qNopL478AEYya4CE+z30jRXbmbS/YbF6qKWIjrBAcVNjI5N/znVgUAt3OsQFWaNdeXDzaaVor+xxBLjnh7InHMu5M4+Vxja4dEgRSHaZZ8UrMeB+ZX2KGvgT9rZhqgDimgfFEJ1Ds2rCpcFxat32khle7+q04/4wkqpXq5Jej63bgMUdl2Me9Fnzr7zpF+TZunHhmqzkGQ5jECHI1B+4zXcl3p/28/uJIMzhftPG0pomKbpkO+rroGBL/4+ZFef1pCYRDai44KefwZfJvwczON8ZE0ir1FSlMLZ7mh+Nbzbbxup5Eklr+V0pVo91a+KXiJNtCu4ND484Z6ukba3Hz0qNXSqH/waAuVqXyPF2JJZ6sxP5b6tZXPJULj9pE5YCDk8fzO9dClq6hhuQUFSOTxOp408zoBxqhRJlkQQWSFC6kTIcsoBsigGaMRuk5zPYL1UDv/uSJWi3+R/pGbPgZ5rPozPfrxm8kVNvuqsYQwE2FCNARKzMALdjoA5dAyN1AfEcS9tHyvuOwVa0qdJlXffwf2zdysPdg0b0PLhPL4VH/y9/yqWBHfLUiBrbAwmMGgUvVTgxAhEITCWWwnv9F+JntSVYrkvTUbDE9Kav0ZR4hW6blqro8mNpHm5TFbzJMQT5GVCPDPaq9rIpJzAFz8RmKoIi5J/vWq08Rmla6cRWAdJd9/GaR7dzh+Doq5bwGdP5sxzIHfRpeids/c3+y2oZ7UkdHHbxlHMbqDhVOWSye8ZwWqVpAItE3aS4HEYA/wej2ANVLrt5e8YLDJ7u/P5bTtElF+K+Mup+xBgQ7X7rimPiBGoG4Hy3jfEHsh2LvlVlFafrF4VGWoj//z3MPw3XwKrllcRDd7M8lOg/9O/CYNf+B1IzZnryenkTHkfXqsH7wPz8KFOHgbr3iIExnPL4ODAVTCMRqq73FfsAXWMGtm2kfOkTrAcR0Xdr6qOJssS/dTqDIV4LF5GlmLnTYzQyst/w7hEUghL8VPrKvkSfPj9Nj7N5/MvGF07n1fiqDurkxMWHe5L3PM1epPlKP309UF2y3bInr7OMRbRclOMN6cs0+Q8MStlVCRgzIqXR7J+gt9vFGB35Pn1bheuMevX+DkST/WCRyFjQS5jZ3JRVKoEWdNE5el89cKuSyEdcTpAopRlZRpCgA3VhmDjRoxA9yBQOXQIJu77GZT3v5n8QeED1Tx2BMZ/+mM4+v/8ARSffgKsYjFab5yJkIFa2H0VzP6jP4H85i0YERCPr+jkhEsdJ+6/D+jlQnDy08nDYt2nikAKxnLLYf/gR+F4BgMnAa5MkJLrYZInwHKeWINlqXld2WpzXOpD9BOjM4/Fy4TVoCl2BZfFu+MLczBFQYCwFD/VrpLSQntfaC+JRGzW9dDJkbpxFNWPRU/1jVpZTrCfNG4bSV2yG6wTTlTBiCgFTT0HZJVb7pBqggpqy0HJQSapC68KM15eV+/QHB7xFeIPESRBycjSftQCer6zp63lAErJuCQt0YIN1ZbAykIZgQ5BACd4lX2vozf1fnywdUhQEnx+WqUilJ57Gob+9I9h7Lv/Diadn4oGXFSiJUF0APjg7/8B9N7wa5CiQEu0xLhDE3lTx+/8qX1ubIeOgdVuHgIWRsQdRSN13+ANMJw+Feeo/mTc7UXxGDlE2aPk8jXjO9y7L5X6jNuvx+dlfDleDv8edM/y32rIeSOeWkZcAHKqxu+rGvyKMjJjfPGKiGAh3n1bT2e+AaaoKxdICYwca569DawNZwGIJaVoMPpNkcEpyDRkkIuCRyUgSSbYeZniyXWAUDhURo/V7tWplHnkPGqmFAMlV5jCo+jqKJSUL3x+59Hrndt2nrdEOymqsR7NRYAN1ebiydIYgY5CwDx2THhTzSOHO0pvV1nz8GEY/febYfgfvgyll14AilxcLZF3lc5ZG/jsFyC7dl1HP+CKjz4Mlf37qhro1bDgum5BwICx7DLYN/BRGE1RAJhwIm9R0GNEXM2YhyoTW6drHU3WKla/KMTj8zKyFD9Py39rsPjMXZSrxzzzhk1AiZ9aV8lrIWFbo418EWqw+tKr5+Ldt+HOqF0tfBR15YKjkjUwCyrnXwzWKSscjx32o3TlFOQvjZyqI8S2ip5CliOQGkpZIUdTr7T3Ogs2dCoUMhbksg4zud6T3f5M5qSToYBBrzIYPJFTdyPAhmp3X18eHSMQjQB6U8tv7QcyeDo50b7aifvuhuEv/2+YwDNghdFdZbJg4LmLhQt2weBv/id80F0Cqbm4dzX4Nr0DADGHj8PEz+4Cc3SkA7RlFVuFwGh2KbzZ9yE0UqMnbGK/nnOPy7e6nG9UP90kWUeT5Yt+a3VOQjxBXkYW4+fx993qlBUhvtZTz9XCUNcDtRE/NTCV2vrd1GjjM7Z0j6qkWmRW54m1mf0xSOpG6mvRPlX03FkYoM9N6uNFY8kpJCwoZZQiCSA/p2uMe2xexulRiNAQ/WovJ7g8VtFQ1Nkk59Orpyq/QDlXF9FIfPj1Pq29OQOPEcqdswWy69a3VxHufVoQYEN1WmDmThiB5CFgTYyLJb+VbgjKgw/+0isvw8hX/xFGv/0NKL+OUXEr5SqgG5BZtQb6b/os9H30RsicurIjlwJPPoZe1bcOKBOfKoPmqi5DYBQDJ+3vuwaGU8tqjsydgEpz5JptWsUgdKhLkdqTZdurWpuvVWPqGLl14V7vqHz8yQBrRnLv20Zk1dJAhqJaP5Vzt4O5Co86E9HoI6RKZDF2qSx0D5ZrDkjXwKEFqxSyplIhKQVbCw1JUa9WvcLc4gIuyaZj52hvamrW7BZ3xuKTgAAbqkm4CqwDIzDdCOATmgzUSTzqpJuSeewojP3w+zDyr/8EEw/eD7WiGKfmzIFePH+t/5OfgcL2nR0XaIm8x3SskFUqddNl5LHEQGA4dyrs77kSjqdPqbkioNokPEZXVVlaNoetUzCNsZXjrApCJ1XKbsSm6+17K5suumkC9TdWtAcWO164CCwKrDR3PprfNEaUoYhRCkJT1VCn+gCPbCVTLZVlmmCX2jhZjxJRLzqnD4/RoSjtsRBRL7jpd8lpZn8Rv0pRqqexQGem5necD1l80cxpZiDAhurMuM48SkZARQCX/ZaefhIqB99S6d1QKpdh8qEHYfRf/gnGvv8tjBJ8rPqo8C15fss2NFZ/A3qv+yjQg7CT0sTP7wELlwFzmjkIDGdXwIHCZTCUXoETyvqCgrXUTqnnEjTZpjFDk+t6lOl+3um+7vUEa2oV+s28xcz1m8DCs7khJ53VqdhuZMwFRlKzHGSo3t7mltqIrFR2mntmplflZLyyxyhlsFJmS4hhKiNCEftzG88Uhmq3nI0uj4/zegTYUNXjwlRGoKsRsNCYm7j/3q4eY/nAmzD6nW/CyFf+HkovPl9zrOkly6D3+htxOfBn8Py8M5C/mdOcmt03zFB5+y0ovvBcw+25YWchcFwYqZc4RmomlvKyt6jZ88+Gf0uCk+bgSOoUbHukgkK6u1wnRA4YtYCfCmatlD0VveS2etRqeuPRSDIvvwoAnxN2TAPPHJSFe8aeTUQeBRIsKGW1qSiFXrhIDbys1LegORVevUZuiCTJCNU5hKAu6mCiWrWMnsKzUnsuuwLSFLWf04xBgA3VGXOpeaCMgI8AnZla2vOST+jSnDUyDBN33wEj//S3MH7brbWjAg8MQuHiy6H/07+JD8TdYOQLHYHMJC5zDsyIOkJvVrI+BI5nT4UD+YthKLUCTJA8OzHEyMZqDPaWstTv3dMbF0ElY0y9g00SVY43yqmq3MpefNnNuhZTuW9r2W3yfRinHwsjzZqXXIERgNOOvYmjVDpRCh4PXTG/xs+JK6kKsPlkmmCX2mDWLqk0767wyE5GKWOBys6XlHGaexW2avQp66IrC85p+CgUIENLfs/YMA2dcRdJQiDe69gkacy6MAKMwJQRKGIQHmt8fMpyOkEAnblafP5ZqLz3LpTxOJfeD34Y0gsXRqoulhfhmavpBQuBvKzjP74FKu8ejORPQkXxmSdxifNRoON3OHUnAsN4BM2B3EVwLIXnpBoZxd9P3qBqE23XWxTkoTmoPFlvBDma9/rmiS1BR5Nli35lgi5PQigJ4bUkIh8Jpf+cIhBwrpLAyMB7iHZbBq+cvqm4XnGug88obKCY4vWdOtRa93ZU4+h2/r3kQCFERPOrPZjnbgPj0YfAeOIRr0Iethi4hWDJ0GKXyu+Zr4ItQxXgy8VcHNgdIS63hz29LBDXONif14MuUxezTkDzaQQePouzV13X0UfKNR+YmSGRPaoz4zrzKBkBBQFxJI14Sivk7i3gnlwyVMdvvQWG/+rPofjUEwBIi0x4mHh64YnQe/W1MPCF34Hchk3OOXqRLdpaYQ0PQ/GZp9qqA3feOgRGMktgX+5SOJpaCaYR9qQGDdCgJlTv8sgTZjkfbBO3LM/H3TY6mltH36LfWp2jEI/Fy8hSwnmaYtN/TlUQICzFT62r5Mvw4a/Rxmf07jdfSmM5976VW0vdyGQlL+55hRIuyHJ0/YRb4P2Fx9RU8CxuGHCPq4m44yQyGYzq41aqdDtRGZA/yCOVKSuKJNlJTlkpaqucFt4XZpy821aUZRrqoqijFLxWrctks1DadTkYuPSX08xDgA3VmXfNecQzHIHym3uB/qtPnhkACj1sx8Zg8vFH4Pj/+h8w9oPvgjk0FI0DzmKM3j7Ibz4XBn/vv0LP1ddBanCWNHtODma057j4mP+GPzmasSZTQ8CAkcxieCN3BRw1yEjVL4IKT2rDvbo88hxTzodbNE6R57hBKdSn6DdG5x6LlwlKC5SJLy5voGkiijXswJCOsqUVqowgCIwQppB1EsGP5NiQSozu/RYtNV6NTo7UTVUh+vvQB1mWo+tHKxwx95YAG+4Umu47mVspOM5VlRYLVEVBkh+QIboM0mqVI+QozZSC6CXUt47F4Wz2V/nMzVA+a3OzxbK8DkHA/S3rEHVZTUaAEZgqAuRNrHVsy1T7SHT7SgUq77wNI//89zD8N1+C8quvVD/eJZ2BNO5NGvjs52Hg878NmVNWgCHO00vQKPHM2OKzT4E1OpogpViVqSBgQQqG0ZP6Wu6DcMSgPanR0X3jeIN0PI3YOXHG5JsCYW7qM26/Hp+XCcvzKMgjz+s9OmdUBAhL8VPtKoWbqJSIayhfp/jig6KVcrz7tp7OyMKy+RV15YKigaZQ6AETYxlYa9d5lcLwR9G+/eYUJBoZw8o96jPbcpRKR7TU3hbuN5LZBZU+3IyTF0VXK7vg6eySqZHbTKl0+L1mcofUwqvwWzU7Z/X1Q3H3BwHQq8ppZiLAhurMvO486hmMQOm5Z6obZjMEG9q7OnHvXTD0p/8NJu+/F8zjdMRL9JPXyObwkPHLYNb/+UcYHv8CSHlLv5IBGO05Lr1UO7pxMrRlLaojYMB4ai68kr0KjsAyvCurT8RreYPsCXL43lbmndUViqwNS632W4R12CBWvzJfnAbIUwuHyEHMpApxAega6a6cHggffr+NT5PayESfVWKoP6u7pnI3tsRwZ9Qu+rfG5pfl6Pqppq2Fy1DNyz4AgIaUmwjT6D5d89jlpu+w3kFavOukkyP3I+U9VieDX3bOL0vcbqUgEYeMmVKpNGpSAY3T4hUfBBO34ejQa1IvLCbhCLChmvALxOoxAs1EgDxu5ddfA0APHCcbAYqAfPxLf4Znrn4bgy3tDz6JQzBllp2CS4H/C/Rc8xH0tC6OcC2EmrWcQF7y0p6XW94PdzAdCKChas2BY7BU7PWrNvklbXReJ1lLqnd5ZMeRnJf568nrdNPRXJnUp+i3VuckxBPkZVwx4W9PcLiqWykxUAkPXeBE0MZv7V+qGm18Ru9+CytQH8W9b+VWUjcyWcnr2tkM/hhkOdH8ili/kMmCddoZYJ6/CwDzblINXtX48wxChyzakOWnlJGqWoOi7LGIjFcSbe2SSvOEemTJ5JVort72t8RjK6dW62ieLA3rFEnl1adDecsOBd8piuTmHYgAG6odeNFYZUagUQRKr7+K0X7HGm3ete2siXEY/fd/g5F//DIUn8al0RMTVcdq9PRA/8d+XRxjQ4GWjHy+Kv90VFrFSVzGvCc8yZmOzrmPxCPgTqCDc+BWKF5r7ip0qEuRWhJxFHXJa8Wo2yBTtrTidt8ATrGbSIxBkyeuekE+976V6VI3MjmUr3XXyHJ0/YQEBggWRlk3z9sF1rLlao3SsVIQfGFswjyqQCzJLCIvEaSs2s6p8Oq9TBU25FHYgmXSRWbQ1KvSGypZs2ZDeedFYPW5QasaEsONugABNlS74CLyEBiBuAiU970xs/enVgMKH76Tj/wShv/6SzB+x08wSvA71bjFWXqFHTtx3+oXoXDJbki1+xByDKhEe2/p7FhOjICMQCOTcLl9tbw8ZXX5fJ+VS2ngWye4lhhlAl2LeYbWN2Dcxm4Sm7Gd2OtvrLo9qs4QrJMXg7ltJ0D/oDQojfEW7LZmuQaDppoMYI/sZWy1/KLD4xGcjFcmfuRRyoJkC1KzDi3I7LE2lsG4EOVzd0Bl1WnoTdUHkGtMMLfqRATYUO3Eq8Y6MwINIlB5cx+Q541TBAJ4ZA2dtTp681dh9Jtfg9ILzwJF1I1MqRRklp8K/R+/CfquvxGyp60FwKNt2pVM3KdaPnCgXd1zv61AIDRjbEUnbZDZFGu2DXpzl1UQ8A2WepYWVxE4pappucUwMrx1zlYwxRFm9t9+GwX89OBwMl5ZmIJSPQ1T5neGHfzdr1UWzZRObLlED6vg0USzwAeGJpMoct4hx9JFElFH1ly6DMpnnyuOAqqjGbN2KQJsqHbpheVhMQIhBOgs0bcPoKFaDFUxQUIAH8Dm0DGYuPN2GPnqP8LkvXeDieeURib0IqQwsEbP5R+A/k9+Bgo7LwRDCrAR2a4VFbhkuXLwrVZIZpntQqAJXqpGvUVxhtywMaCZ+yr9NSK4CVgpOnRloRbwXTloaVCN3FhSc03WOvFEsLZsB4uC/jjwuij79lygAuVI/k9HKvK4DTX9EMmX5zJIDbysl3GYnLJH9jIR9Y4aChsWlDLpEiK4SjX8bQ0MQmnreWAmKP5Dw4Phhk1BgH3qTYGRhTACyUfAPD5knxuKBiun2ghYkxN45MvTUHn/fSi8uVcYotWCJxl4ZEFu45mQxiXAGdyzNH47Lh9+F5cPBx/mtbtumIMCKtVcstywdG7ICDQHAWFPBua41SXHMC5moJEaAxUNrI210gjSkHzZZIQ1w6s6lZcsdIv5GmnUbRYplQZz3UYwztkDxl23AThxIGxDVNZAo1GIhAQL27jN6Pnh3NvEStaiG1tYsIj24sMeDWbl5sK4dGXZzYVs7/p4TZ2M097tiYT5v1q+cOJ2k9PSLU7pu7JmLVTWbgQLn6ecGAFCgA1Vvg8YgRmCgHnofd6fWu+1drzQ47feAuW9r0Pvh26A7BnrwYg60w2X/aaXLIWeq6+DNEYHpkjCpZdeAKi2fLhenarw07LuyvvvVeHgqo5DQJqoRulOno1qE/qo+hiio7r06LpJqo7mNcCM6FcmaPKT5Ul4f/wIvDd2GIoVWgViT5jtSbM98yZDSIwbi/Q92DMA8wfmQBoNhyQm0jGbzsJgrg/6M71T19O3IOIPl8BH3CwjviHp3yc1rqzPGF+fGpxR926NZsLbp/+d0I+h0X48PdATaKIn0NjzEhj0N5/uSawUxmrQcqxa9iT6mQCuBpYt+dqLIQXHJZWD9U6VZ6z6PSm5SP3FyBxW1EXBOaCrIrBGwVx0Ei753QLm/Pk1OLl6JiHAhupMuto81hmNgHnkCO9PbfAOoKW/xccegcpbb6ERei30XHYFGLg3KSqlBmdBHpeCZdBoHfvBd2Hi7jtqRhKOklUPnZZ1m4cP19OEeZOOgDwhjdBVmShqeKje5ZHFyXlNs1gkmswGk44m89j9GlC2KvD8kT3wyFtPweGxYzBWHIOJ8gSU8MVOGY/QmkQDlf6btAoEhYblqpSe/l4YmD0IqXRSdzXhNgEcfCaVgTwarP1osM7ODcCswiDMzg9ArpSDUwpLYEFhngxXU/Li+rsXXGCpYletE7eZ7goo7XxGvFbx5SsyAgX3vpXJUjcyWcmLe16hhAskh2w2Srp+7Jr4nxbGKzC37oAUrqQxjuLzVoIgyn6z+7dfGjj2I3aIOcWYVXUQbYIC/cb2oMTYgi8jZCZJpkcWkr2+nZJgVLrz+O2u6M0T4eeRFWapnypZK1+A8pmboXLGBozzwKZJFahmXBXfDTPukvOAZyoCFdx3CaXSTB3+lMdNQZXKB96E0X/7CgZZeg4GPvsFSM3DN78RsybyumaWLof+T30OsricaeQrfwfmMbwGrUw4QaDjh8zREUi1a59sK8c3Q2V7E8CI8cfxBrk88hxSzkeIbogc1temkA6TlRI8degFuP/AI/Daob0wNHYcxkq4t9qsgGmZ4r/thaq/65zZAz2p4QQbqv6YyIZJGSn8j6swMChbmvL4L5/Kw0m9C2HDrNVwxuzVcHLPicK4bZbhZ29FaNSj6uuvzUk3lHu/afnqIOrkSN3UIcll9a1HkuPa07p+3Baxv/FvvnnRZZCiY8IeelA6r1zqSAjDctAQDbIEjdVYgw4J8VUPVnllJ+OVnSZKOWjw+mIJTWKl5COLhVj6imbiw1y6HCobz+Elvz4knHMQYEOVbwVGYIYgYOEeVavEgZSmdLnx4Wvi8S8T998LpddegcEv/C5k120AI5fTi0UjNoVLwnouuRzSuKxp6E/+b3ufsJ67KVTyqloU/IkN1abgmQQhygRQo1Acb5COJ+Idi6aH+kiyviWzDEPF4/D4+8/BAwcehTePHIDRyVEooceUDFMyEGZiolFXcPz0vySFDRiGUThSPAYvDr0C2f13wLz8bFg7ayWsn30anNK3GAayfWjU2kubZZxjY0gXXfzEb627T3Q05aVdfPFVVY933+o7I4z1NTaVxuDefbp+qioWVdnTC5WLLoX0G6+B8TYFthOvXcQ3Gae+fYo9+wWbSyrb4qNHQPXidwcHQaMRnP6H+z7C6d15zeHVU2t78HaXziJgrzsnI31RD4anH1ZQHpPz5fweYz82WdQJJRSCTQ5+WhiMsLx1J1RwuwwnRiCIABuqQUS4zAh0KQLmyEj1o1a6dNwtGValApX9b8Kx//aH0Hvdh6H3qmshhYe/Rx5Ng2fB5dCgHfzt34fjf/0XYOKysJYl9Jqbo6OQzF16LRt11wqWJ6NRg6zlDXKNweBkvE6nh7Z7b24r1VJ/JVzWe3jyKPzyncfhp6/dA+8N4R55k7g51ULA9SyXoAxjY+Owf+wg3H7wfljWdzLsXnQ+rJu9BmZlB4QYsgvqQlW8GGjUo+pfbe29IxN91lrDrVqvu7flbuzG4c6oHS2zDicXLcRAaqbrJ9w2HsVafyZYF1wExi3fA8C/xYq1LPVpXzlZR6XS7kwmBQYurr1D86Qo/Ng1VtimsscRGoTcJFSJBNGPW+ExexlRY/OoNLdJ1W9c5ltZuQbKZ23GJQZJXbJfdQRc2WIE+K5oMcAsnhFICgLWxDi+wq8kRZ2u0MMaH4Wx73wThr70Z1DE5cC07DYyYaCl3Llboe/GT7R0D44495WuNaeuQICMy+gppj3EoAEaHLiQ4Uza5bm7nA+2iVsO6kZG1vsTR+DBg4/B/3r8n+DfnvouvHv0PTZS4wJahW/f6Fvwlde+Aze/8X14bmgPHC+P1mekkmy66OIneOWiO/bvkxptfEbspgZvdHdKjU5OHNG6drZgXy9ZTjS/ok7sgrnrMgCM/i4bX65vVRFCtp2csKySAgRhXdsNXD76dvOiRuaRKkTWY3YqvC874zf1KjzZpL9DdTTGks/mKaXw+AKdNtIXXgDz5CVQuuASsHgFkAQMZ2UE2KMqo8F5RqCbEcCIsLgRrJtH2Jax0XLq4mMPg/nuQRHtN79tB6TnL7AnhAGNDAwYkcOowenZs6Fy+FCgtklFDDxj8V7kJoHZGWLieINcHpo3yhP0Zo6wgl7UZw+/DHfuux8effspKPGZzc2EV8gqWWV45PAzuDT4ddg8Zz0sN0+CpdlFaHv6BljVToXh0KhHtapkNFr8m4tMmtg6VRHr3rcyi9SNTA7lyWCqhooDhWin6ycksA6CNXsOVK7+MKTf/iuAY0clRVArbwktCaxVdniUkagjC0UBFnqqPNRP+Ho4PN5XkCcoQ6ev6Ez6wDbK+LBKJ4bIuEy6fNa5UFl1mtSes4yAigB7VFU8uMQIdC0CVqmMTwY2VFtygdE4LO/bC6M3fwVGv3EzlPCIAtorqk0YcMOYNUtb1RQiRUidpuNwmqIvC5kyArW8QTQJb1VyJVN03gfefhT+9ql/g1/sfYSN1FYB7sgdLo/Az97/Jdxy7G54buIVGDFxiWmcRG8pxE81E04VFPvFRmxGVf70ltw7Vu211u+Qyh2vZJ6DR63gf5GUbpUCVmPZIdlfdtkhhdtThfQ7Lfikst2APh0J+CWauGWpyue1c8JnajM7VV5jlZV4RHLq/RLmvEqfGiTR2bMYJbl07naHh78YAT0CbKjqcWEqI9B9CJCnTfsw676htmtE5tAQTPzsTpj8xQNgjVVZBtzKCR1eY4uMVU7dgUAH/M4enRyC2/fdB1995ltiL2p3AN8ZozhcOQa3Hr8PHhx9Et4px1mlEbQYmjlOX3bYe9fMfuLJim+Kx5PXCJeJR5lZGEhPTgIlHyq/SqIFF9kK40+q9xtF5IK8wbLXzKmIrPcYpUywDZaD7YPlAIPV3w8lXB5tzT9BkstZRiCMABuqYUyYwgh0JwLiqZ2ER3d3wkujMnCfDe1DzW06E4xCQTtQMmAr776rrWsKkS4xX+amQJkIIU14qdEKb5GLzd7hA/Afr94G33nxx+KoGZfO39OHwLg5AY+NPQf3jjwCbxQP1Oh4pv9xmN7xW0uWg3nF1QC4kkbYap4Bh6aol6dLphTsa6gyhK9roJ5eRCskIVKSi1kygD2Kl3FEe2WHRyoLDqdsf0ly3EqP3+lHUQaZ3HoMmlTecBaU1+KZqZwYgRoI8B7VGgBxNSPQNQhgMB+asLrPiq4ZVxIGgrimFyyEwiW7obDrEsgsXqIE0XBVpL2jlYNvg4URmFuWUBcDl1VxYgRaiQBNU586/ALcvvc+eO7dl2ECz0Ll1D4EilYJ9kzi9gNzDHb0ngWr88vFOa3Tq5FvBNL90W6vKj3rfI2mFwmvN4z4bm3eBtaTj4Hx5OMe2X4O46eynzNYRnZlELp6pCkvswLIi/aSkBhl+8gZp43XVC1TSSgX1L8q4tgK+c2Fi6B08W7beBdy+IMRiEaADdVobLiGEegqBAx6o5tq+2O7qzAVg8Hw+tnVa6D3Q9dDbuNZkMJASVHJxKAaE/fchdX2Yz6Kb0p0CvGPkyNOXYIAeSWUiWj942p2oBgTdXr43Sfhh6/dAfuOHYAynonKqf0IUDCrA8V34G7zIaCjbdbmV3hnrnraNXA/+U3o71aVZ4jP6HXXrkz0Pa8fQzT/1EdAgZXM3VdDGo80g0PvB4xPjXxFRTQ80bjz/wRgpWIcYvsg7qEy8siXTchXOiEhHhOxUil28pvKYkTzoCrEUD5vF5iLTo4tnhlnNgK89HdmX38e/UxCIIdLUdnT1tQrbuRyUDj/Qhj4T78H+W3nVTVS6Xig0a//CxSffqKpOoSE4TU28vkQmQkdioA/Q214ALSSwl3+O1VxNCclTyot933j6JtspDZ8VVrTECMRwPvlI3Dv8MPwanF/2OBo4Abwm8jWjkZ/nxHtohq8muY6knvfynVSNzJZyYt7XqGEC7IcXT/hFg1S8MWhuWoNmNt3agSQISqTnYLypTAgM5ZDpCBBlkl5tV4pKQXkdMrkm6V/flOvQhIuOJSyzx/ut7J2vYj0Kx/bIzXmLCMQQoAN1RAkTGAEuhOBVG8PGBleEtqsq2v09kLfJz8DA5/7LciuWAVktEYmPL929JtfE4GWrInWLpE0cFJkFHoiVeGKzkMgMI8MDYC8QbWSyyOzyvla7d3614b3wa1774b96Ek1OWiXC0uivsl0OFwZgruGH4T9xbcd3RzDkS66+Kl9z7iDin2fSIzu/ebKaPRbJ0fqpgGxvgEty9H104Dw6CYYPMjceh6Yq52jWBT47WviN3YqJR5ZV5+vSg4bSM0dY1OiYFbhEFVqvSLdq/IyUjXSFHKg7OhCZ6UWL78azDlzpbacZQSqI8Drw6rjw7WMQNcgYOCDEjK4/JfT1BBAj2VmyVIY+PwXIbt2XXXvJU7kK7jUa+QfvwyTj/wSWm2kioHR8Td9fVMbI7dOFAL+1FqvVhxvkI5H9ijpJavUg+PvwU/33gMvvrsHj2TmyNIqOskqkRHyfvko3DZ8P1w9eBHMN5wtCXTRxU+tu8ofj+4+0dGk9anqUlNfVN25ePetfixkO+lrbCqNwbWvdP3UrWy1BkYKrFNXgkWBlTBOAQwfF53TKl43kTHq4xrU3i77VMxVWQJMfO6SYK+Nl3F6FCKCe4mDTEIQ4oQ+crnKydOXm0L6O/q5PKVtO8Fculwbv8GVwd+MQBAB9qgGEeEyI9ClCKQGZ4PYp9ql42v5sHAGQQZgz6WXw+w/+Z8Y2fesKkYqThPHx2Hy4V/AsT/6LzDxwH2i3HIdsQNa9psaGJyOrriPaUCAPD3uRC+qu1reICGDZpGBpCEFOOwitTxaHII79t8Pv9z/OJTNipaPiclCgO6ct0vvo2f1l3AUPay2ZYRXU/yE74co7f37xG/j06RWMtFnlRjqz+rubbkbW2K4M107n9fml+VE89evc2QLDGhorTkdzM14tirm1RQeg6iXyZiX7FqsRoJcTw3kQQkBmjYO3f9yhIRk+RxqRzr+YGO5LUBl5Woo77gQ6FgaToxAPQiwR7UetJiXEehgBFJz5lRfntrBY2u16rScNn3iSSJgEkX1reqxxGW+5EUdv+1WGP/JD8E8eqTV6vnycfJDRmrU0Tg+I+c6BQHy9KiT07DmtbxBQobjqvE9NrL3JixTpkxUJuDnBx+BO964Dz2pbKTK2CQ9T8bqq8V9MC89C3ZkN0LOwC0KeEPVvqv8kfn3TI070Wf09kT7UhrL6e5tqZtIodG/NzQGexwkxzWvdP1ECp9ChXXCQrB27gJr3xtgvPaqrYCnB2qjeElJO6dSqIxXE+vV8Ts8EToJAxwbiObEI9ilNl4XjmdVU08quYnsYLt/SYYjVggP6o9lC7cdlcmbipHxXexdefzNCNRCgA3VWghxPSPQJQik5s4HyHGQnXovpzgbFZf49n4Yo/puOLNqRF0KmFR6ZQ+M3/I99KbiUt/J1u5HDY6F9smm5uF15jSjEKDJaK2JtsvjTzRxWulNOqPhqlgmvDT0Gtzy2u1QKbORGo1UsmsenXgOTjEWwbLMycJItXAdp2S+VFU+zn0iBEiMZCDHlV+tc/e+lXmkbmRyKK+aUnK1XUNyXAtO14/copl5c+UaMPDIGuOdgwCjo47xKPUg6SVXElkuey0UfmJBgmzNhsrIIxufniA5ExRKPTs0uUrOu81lGsbFKJ+9FSprN/L8w8WHv+tCgA3VuuBiZkagcxFIowFj9HCQndhXEI95SZ+wAPLnXQg9V1wl9qUqD39ZEE4E6OiZyYd+gV7UW6C052V7siDzTEPewBcRpDOnmYVAXCM1iIo8lw3WueWR8ij8eO9dMNHiIGBuf/zdGgRMfOFw3+TjcH1mPvQahbqMyDj3idA6NmP8Mda6t+NLcjl9K4rUpRKl5vdjy9V+FgpgnrsN4PVXIPXEo4Chsz021xiU7EisQy0lT6VrYPs8ar0nLJDxR04VUsnLOq8XpLKwaL1yoJ0rI1AvXlE4NBNXIlXoHFl+gRq4GlyMiwAbqnGRYj5GoMMRoOWq6bnzoERnbJb53MNql5P28mYwki8ZqPkd50NqcFY0Oy71LR94Eybu+CmM4xmpJi77bVcy8gVI42HqnLoIgaA3ZBqHRuelPnX0RXj50GvT2Ct31SoE3qscgReLr8PZhbUt6sK3WJrhTSUlG/Z0oiq17GbX4GsRGFXFWkuWgbX9fLDe3Gt7Vh1DlIxPYaxKhmlYEA5OQO2bqorhSQ2CfzdCZeSRoyORSCFOCA6Jc21STxeHLchIZJdm9fZBedNmMJedUvtieII5wwioCLChquLBJUagqxFIL14i9qlabKhGXmcDH675rTug5/IrILt+Ex7pE/1n0ioWofjkYzB++61QfPRhXOo7GSl3Oipob2r6JD5IfTqwnrY+as22Yygie4vqETdUPg637bsHzApH+I0Bc+JZKmDC48UXYWVuKcxJd3nANdmGU65MZIXCNR0Fa91GsDZgUL5jPwNcsiDsO9tYtHtXbUvHkpTV94xFR9tQGQnOLzxVucarxyYyXsm1Lx1hxI//A8YsLRl2/K4SPzK6ijv6Wdhv5ZSVUDlrMwZQGvBlco4RqBOB6BlYnYKYnRFgBJKPQHrpcjRU82CNjSVf2TZoSPs7ez/4ISicfyGkT15SVQML9xaN/+RHMP6zO6D8OgXFoKd6GxNODFKDgxj0iT2qbbwKie9anfxGq0t38z3vPgRvD70TzcQ1HYfAcWsEnpx8CS7qPbcFuvtWlGfMtKCXuCLpHvY1ittq+visWbPBvOhSMN54DQMr7QEwUWNHaeFV1WkvDSqMMbXHEcuDDvzCi8eUUh8ASciXOhFwBMsSRl6Vl7FbzJ0LZTRSzZMXS8ycZQTqR4AN1fox4xaMQMcikF1+Kh5fUuhY/VupeBbD5/d+9FchvwWDXKBXtVqq4Dl4Y9/7Fkz8HL1NuDc1CYmWKwuPOe9DTsLlaJ4OgYlmI4IbWT55uHgUHnrrCdw+5++fa6RvbpMsBEpWGV4tvwkbK6thXsY5W7VpKqrGStPENiAo+p7X6xjN30DndTSxlq8Ac9sOSL/jnK1quywdYxN1VQxPVXeyN8N6B9uQMnK7WvW28p4R7DV1MlI5HC7Llm3hEWnltRvA3HQOn91uw8mfU0CAz1GdAnjclBHoNATIkKFjatzlQJ2mfyv0paW9hYsug4HPfxEKOy+sbqSi0VB89CEY/pu/gPE7fpIYI1Xggp7yDBrbnLoMgXrW6sYYelxxDx96Co6O4EsY4YKJIZhZOgaBYXMUXiy93gJ9fVdds/aoysvW61GY2vna6FvKvwuN9qOXXAcVjxQzd14E1ulnqBHlySDEJL6cvEehMv53yWSsVv01dRltAa5UrxTsRJYlmgbbY9kmORWBegsD+lXO3Q7kMebECEwVAfaoThVBbs8IdBACBr7pzKzCg7dfewWsUqmDNG+NqqnZc/DYmRugcMFFkKYz3jDSb1Si5dLjt96CBupPMXjSfjzBPFkBqejaZvEweU4zD4GwV0XFQJ6E0yTUnaDLeblFxarAs0dehslKUSZzvksQmLRK8Fb5fTQ2wj4x3RCj7pMQr8RY654MtY0gNEuOL943X0ld15ptfj9+jzVz+BwyL78K0i+9AHB8yFdKNHSukqRrUJ7wrArT0R2bhlm6NrZYlCsb88EmoiwRg+WgEg6rhUEby2dvAXPVGv8PTZCXy4xAHQhEz8rqEMKsjAAj0DkI5M7A88wy2c5RuEWaklE3+Dv/GXqvvg4j5Z5Y1UitvLUfhv/+r2D0W1+HMkZpTJqRSpYHRXTOrFjZIrRYbJIRkA3RKD11PK7BGmyzd/xtODx8GEyTgygFsemGMpk+R80heL10INZwdPeJjua9ASGprs0Uq4dopnj3rb4zsp30yeaXx6DrR9+2NVRz7Xqo7NwFgB5W118pbE/RHV0xOTkl9ctr5jRRy0QUlrktRzSVyhLVzhK7yDmdUN5uJKiuQqSZx2GkwMRoxuULLuYzU22U+LMJCLBHtQkgsghGoJMQyG06Eyg6rDU+AwMq4cwkhW988/gg7f3Q9ZChCLliYqC5gvgQpyi+xccfhdFvfx1Kr7yc2GN9jHQGspsweiS/gNBcyM4mkaeHDAD9VNweWy1vkJCBrMHJuCNaAYgmnY8dfgaOTR5X6FzoLgSO4/Lf19BQXZldWnNg/n1Cd4d9J/o0qblM9Fklhvqzuntb7saWGO5M187npZxh223OL1Y0v92q5Z+4BcW84WOQfv4ZgL26Zdk4xir7VW39ZJ4wJtoxyGwiLxGkbFRbMvZtUxXxxKW+5Yt3A8yeq2VnIiPQCALsUW0ENW7DCHQwAin0vGXXbRCT3w4eRv2q40Qgs3Q59N/0ORj43G9BZglO0KKMVAwgU3n3HRj9zjfg+F/8Dyi9+HxijVQBRDYDeTpAnlPXIUDGZTUjlQYcNECDIAgZNKMUvH6tQ/IJmButjMHeof0wWeZlvwowXVagoErHKsMwadW+zv594t+JPk0CRiLWuielVlWzOjlSN05bXy9XmLjn3YLyTby634WwDKXZdBQKPVC59iOON5KsREz05WRFUcpTWakTRZkB83JR8KsEyR9KtRp+m+i1Ehmv5IvPpKF0zlaonLl55s0tBHD80SoE2FBtFbIslxFIMALCqAk/7ROs8RRUw3GKs1Fx38zAF34Heq66RniUoyRaRfSivvAcjPzT38HoN/4NTLFnKIo7GXTx8mHtumQow1pMOwKux7Raxy4PeaPcJOdd2lsT78LQ2HGweNmvC0nXfh8zj8P+cu3jh3T3iY6mLC3VMtQPpXvfyi3DoqWbWmLUU4nBrpHl6PqRRE1PFp9V5lnniijA9jJqdQQho9I1E1U2+TLYYw3UBxiwGGLQjFfiEVmn7HyZ806AysWXR7/81UhkEiMQBwFe+hsHJeZhBLoMgdzGM/HMzVnJilrbCozxwZ/GCIT5nRdC73UftQMmVenHPH4cJh96UBw9U8az7Tol5bedh8t++c95p1yvZuup8zrJfdBEVMeje1e1b/gtGJ4ckZtzvksRGDXH4XBlCFbVGJ/uPtE2kRmb5KDU3bfavmMTybLyPaqOnaX9/YgtspmMuC2HAiulXsTASu/RSwRHX09tzOASYH8VcLCelPGYHc2CZYcsfbl/IwSn/yE4hB2LkLlLfP1La8u1sjkooc7WiSdJEjnLCDQHAfaoNgdHlsIIdBQC5IHLnY1LdLo54aQpu/o06PvVT0D/Jz9T00itYCTfsR98F0b+7i+hk4xUA4+lKWw/j5dbdeu9HPJ2tHag740egvHSRGs7YemJQICi/45YzYxVQIaLnWovWHc5q3+HvX3V+d1aYXi5hYhv+Ver0X4iRDdOxoBE1uKlULnsAwC4FFiXyGA0fKiRRSnYTYKkUDlIsJt5Rmiw2is7Ga8MYK4+HSpbduhUZRojMGUE2FCdMoQsgBHoPATE2aF4JAt0qReOgkXlz7sA+j/1Wej5wNVAR7dEJatchuITj8HIzV/BqL5fA3Oks7xJGTwGIHNqLZ9I1OiZnngEZC9Vg8rKXqlq4kyowEhxFMpmso5eanDY3KwGAnS9J80imPivOckzc5ojbgpS5HteFaPXMZpfbT0tpZ5esLbgOaTedo6wcahdBiwZj8L/GSiH7FnZUqeBYVlpEiiFxo7M1py5dgAl1JkTI9AKBHitWCtQZZmMQNIRwPNCyduYXbkKSi+9mHRt69KPjpopXHgJ9Oy+EtIU1bfKzNwcOgaTD/4cxn/yIyjh2bKAQZQ6LRUupKMA+LihTrtu7dKX5qZRvxIj5XEYL09o9qy1S1vut5UIkFEyZk3AiDkGs1ODTe1KeP1qhgFrapchYTQ+vVkaYk0cwZo7H8ydF0HqzX1gHHoP9XNGIw9KzrsjkNi8Nm6dK8MrqxlqKvavun8ghCypE0+2c/4u8pXRk0oe1WpnkKu9cIkRqA8B9qjWhxdzMwJdg4CBx7QUzkcjp1tSKi2iGfd9/Cbo/eiv1DBSLSjjEQCj3/w6jH79X/HomT0daaRmli4DcdwQHk/DiRGYKgJHysdgpDTKhupUgeyg9sN4TM1Rc7hJGgtTp0mypldMYpb+usPO5cBavxGs7TsBslEvIvF1gAK5UrAlIUmhqg1sw9TtU/r22oiMV1KEVU5ZgUt+twP0sjdVgo6zTUaAZzdNBpTFMQKdggDtbSQjJ7P8VGG0dYreOj0NfFDmd5wPPVdcDdlVq3Gpb0HHJmhWqQTFJx+D8R//AIrPPgPWaGct9ZUHRsub0/MXRLvIZGbOz2gE4ixtPFY8Ljyq7QSqN9cDS2edBLMLsyCF+/XqSWYOoFTAyXtKmljXI2AaeMkgGkOvNZ1TW6yUpqHH6l2MW5MwhkGVmpOa77+Mc982qjs5Dt07pZX9NKqfRQEPz9kCxkvPg7HnJRRD2jpKe1AjzY+sZPNULRMLtnG9plgMpVr12MDsH4AyrlyyTl7C3tQQgExoJgJsqDYTTZbFCHQSAvigSp24CA28nR1tqNJS354rr4HCrottoy3qbFS8NiKq7z13wtiPb4HK2weAjNZOTZnFS8TZqUaPPuBGp46L9Q4gEGPSKALHVJl4ut6i4GQ8KHoUl/5OVmqfqxnQsCnFQiYP25eeA5efugsGc/2Qy+RwSu7NxmP1kc1noW+wB1Lp+gzcWMKbxoSrOcwKHBx9D378+r3wxHvPt3VPMJ2nSkGVqiX/PnEMpShmn9GzqaJY49Jr3dtRcqLb+WMgdd1bLJo/qodpoOPvtHXqSjC3XwApPNfbwK0qHrCoO9mjlMTOUizo/gTQEN020nBdkqi1DVfK2gJFG+daijb+h8OPC5DWbQTz9PUAVV4K28z8yQhMDQE2VKeGH7dmBDoagRQu/81v3gqTv3igoyLdCtBpn+2a06Hvxl+H3LoNQEuZIxM+dMsH34Kxb39D7EklgzWwZiqyaVIr6EiaNL/NTurlaZ5eutlnQHrQAA1U4wSWJrH2JFQWJ+epzWRlEspouLQjrZm3Aj658QaYV5hbt4Hq6tuLL23mzh7EGHFpl5TY71VzlsPcwmz0ro7Dc4dw60GbEgVSquC/asm/T2q8OPAZvfutmtw4de59K/NK3chkJS/ueYUSLpAcYZRhla6fcIs2UNAQtDBCv/Xic2A8/KBWAboq7jhsBiwpXlWbQcUtwEMCql1eUW8zmSctBnMrRpqfP9/ujj8ZgRYiwIZqC8Fl0YxA4hHApXXpJcvEstnyvjfQ5dis6I8tHDk+TMkopaW+/WikkkcVqnhRrWIRSs89A8P//HdQ2fsGelHb4zFqJiIZfMuex71LqcHmBkBppo4sq3kI1JxD4ouYWhNt12OErMhr6ybniUJLUStt+BtAS3xnFQZhfmFe80BLuKRsKgPr56+GpQMnwfOHXrG9Ym3Q2cSbgO6Nail4n0TySozu/RbJG7NCJ0fqpqoU/e+Nb42JYXu/C7V/h6p21sJKa8GJGFjpQjDe2g/GgTexJ2dk0pegaYxT1/i0va70u49eWpRgD9vPCfUDwNr3BXK6kCG7hXtnK+duAxNfEgPHRmjhVWfRLgJsqLpI8DcjMEMRSA0MQH7rdig98yQUn3kq2SigFzWNh4r3fuij0HPJ7ipBJnAY+NA1h4Zg/PZbYfTfbwZrHM8LFDOTZA+xlnYGBtYo7LwQMstOoVlHLXau7wIEal3lWkYqQaDjCd4+WSML6Tr3hTYD3lw6C3N7ZjdDVEfJSBtpoLEL46FNf5voHtDdGzKQwfuE6nQ0hVjrppU7qJLX6Rbuu97ObH6SQ6YaJV0/dk0CPvG5Z23eBta+vWDc+gMM1TyKSjlGpvPlaemVvYyoohEThT4U/FQ2rK/CgHqYp58B5tlbAPr6hVz+YARajQAbqq1GmOUzAh2AQGb5KcJDWXp1D1hjzTwAvoHB04PSpKdnOKVmzYJZf/jfbCMtXO1RaO8p7UEdvfkrMHH/ffbD16vt4AzOMLIbzxQvFox+nih08JWMrbrwauB1t6fW+mY6r5PMaXtGwpPx4Jy0P9uLhhNGJJrmNFGehHdG3oeKVUFDOfnLdpsFz5HJITg6MYR/7tq3kiUDacjgv2rJv098q8anSS1los8qMdSf1d3bcje2xHBn1C6lWGRu3+6zBT2LUjNdP26LRHyTkYjbPQADK6WefxafkdJybWccwmuKyhqeZxUrKI/J+UI71ab50EggCE7NhyPGmncCVFAHC6PNc2IEpguBJEccmC4MuB9GYMYjQBGA85u3QJ5CzWeqT1paDlZxEo1lemMcTik8XFx4EsNVHsU8PgTFR34JQ//9j2HigZ93j5GKI0zNmg0FXPKcXrrcGy9nuhsB8vRUM1Jp9LW8QUKGMzP1J6gBzwrK6c/ikvo2GKo0hsPjR2DP0deFsUrlbk5lsywM1Hv3PwyvHNvb1qFmjAzk8H+15N8zNe5En7HmPVmtP7lOd29L3cisSl7XzmbwxyDLieZXxLa1YOE2HesCPFJurrtEnozM+pJjtqqNgmKEBS+zIAMu+TXPWA/m2g34IJo5L5NkFDjfHgSq/3Vqj07cKyPACLQBgfTipXiu6i4gr2plP+2DaU+iQEeVQ+/X3znurau8dQAm7r0bxn/yQ6gcOdxVRqqRx5cJuEQ7d/a5QMt/OTEC9SDgeozC3ihfymC2HwrpvE+YxtwbR/fDzU9/F3Yt347GcmM69PT2wKyRAdyyntyJNHm0xvF4mjeGDsAvSZW1cQAAQABJREFU3n4CDo68N40oh7vK43LvglEd72r3jCJRYqRx1n7ForTWFtz7Vq6UupHJoTzZX75ZGqruLI+qo76J+0ON554C48H7wRDxFpxRSoMV2MteVUJBYhOisOyxOF5WBSwJZIu8uWgkl7efD4AvizkxAtOJABuq04k298UIJByBLEbPzWM0v/GjPwRrpD3ni9K5pmQoU/9xl7daExNQwiVR43jszORjD7d/+XILrnMGl1sVLsQjePBIIU6MQD0I6Cb7uvZ96R7bUKXZPU1spzGRl/Gpd54T/xvtNtffAz2z+xJ+PE2jo2tNux40UvtS1Y+4kj2PVbWIzVhVSosrJYtO6qkTPKpC3Z5eMC+7CtIYGBD24X8yKN0UHJpXdjJemX69seBbqighWHaFYg3uR62cdS5Yq05rmqfcl845RqA6Arz0tzo+XMsIzCgEaGlpz2W7Ibd+ExiZ9njtaH9pkQI7PfU4QLn2URnm0SMwcfftMPKVf8Bjdu7vSiM1NXsO5M+7ELKnnTGj7kceLCIgT0RbDEhPuiD2qDbDE9ZiVVl8kxDoS/XCrFSz9ruTJWSnJNxD1byprp6d+G2tWgPW+RdhMEHcTy4g93G3yzQqiSYGGSxH8Mhs9LcHtwKZK1eDicGceCWPAJI/phkBNlSnGXDujhFIOgK0B7Rw6W5ILVjYNlXLb74J47f+EIrPPxN9nEylgme/vg6j3/o6jH7za+hRfQGsGIZt2wbVYMdGoQf3Dm+DwsWXgdHb26AUbtaxCDTBSxXXW5RP5SBHL6ia0GfH4j2DFCdjkpb95o3pD6DVPpi7w3w18cWlteY06XdVtjBtM9X2mspII4/KpnkPpjJY/YNQOW8XAK/kkYHk/DQiwEt/pxFs7ooR6BQE8meeA+VLLoPx//gumMPHp19tjGhYfO5pML8yCoVdl2Cgp632klfae0YGKp4nV3zsEZh86BdQ2vNSZPCl6Ve8yT3i3qDMqSug56prId3GFwdNHhWLSygCKUhBb7YHMvh7VmrDeaoJhaVr1aJASgV8OdG85BuBzdqjGvcli24MZHL5Guk4Opdm4V7RynU3QOa1VwFGMfigGKgz4sDApe2mOGCsDC75DaLktkePbQXnAtaGTRhAif1anXu3dLbmbKh29vVj7RmBliBg9PVB7wc+COa778LEPXeBhZF4pztZk5PCS1p5D3XAAEm0LDl9wgIRJMnEYEuVd98B89hRfOjSU7U7U2rOHOi95sOQXbGqOwfIo6qNgDrL1PLX2oMaVa8TvWTgZBH992jpmLYvJnYPAn2pAsxL1T6/1r9PXAsmAgOfMYKhfnLUvVtLUnQ7/Rii+Wv11L56a/XpUNm+E9J33iYeg7qFEMKrGjREhVtVMuFDkBABAygtOgnKl1+FK3n62jdI7nnGI8CG6oy/BRgARkCPQApD4Pd9/CaovHMQis8+hee2teGsP5z4mIcPif+0HJGO0bEo0mE7dNHD1DKq0dMDvdd+BINb7Wj/kUEtGyULromAbvYZaFTL60T1Lo8sTs67Is+YvQruLzwER8fRUKX5KqeuRWBuahYszZ5Yc3z+fSIZN7pWPiOaRjV4de01NPe+laukbmSykhf3vEIJF0iOe4vr+gm3SBgFj4yxdl8NFm57MShSvzA4xYeTtx2t4cBJOA6HzR4RcqCXVcE1nYIy7oM1Tl6csEGzOjMNAfblz7QrzuNlBOpAgJab9n/6c5CeN7+OVi1iRaPVmpyYEUYqnq8BPZdeAb1XfwjIYOU0sxFwJ9NRKJA3qFZyeWRWOe+2n5+dDfP65kCaz0p0IenK7xQakhTtd8Co7S3T3SdaUCRG937T8tVB1MmRuqlDksvqG9CyHF0/bovEfqNlaS06GczdV0l7VUlb5+8Bfsl/GeTxihqfzWvj8pdPPwOs8y7gJb+JvfgzRzE2VGfOteaRMgINIZBdfRoMfP6LYBQKDbXnRnUigEZqYecu6P/UZzl4Up3QdSu7P7XWjzCON0jHo3hQHNEpIwWr55wKvRl+QaJHuzuoFERpQXpuLM+n7j7R0RSXXK2bNiaM8e5bfWeu0RXuyuaXx6DrJ9wugRTyquLZ2ub282xjNXLQugqkyWQnb+FS38oHPwIwMCuBA2aVZhoCbKjOtCvO42UE6kUAgyjktmyHgS/8LqQGButtzfx1IEDh//N4oPvAb/8e7wuqA7duZiVPjzyX1I21ljdIyFDdKUKMhiToZ85dB7MK/Luuw7obaLQs94TMHFiXWxlrOP594t+JPk0SIRN9Vomh/qzu3pa7sSWGO6N20earzS/L0fVDhqxszNav/fS0sOYvAPOqD6F39ST7b4UYnoOJ8oUF+0ermPhLgy9KyxddBoBH4HTE4LUjYWI3IcCGajddTR4LI9AiBAw8S61w/i7o+8SngPaucmo+Aga+Gc+dtRkGPvsFSOEB65wYAUKAPD36CbePTy1vkJDhzLjlibec96UBzM/NgYWDJ/DyXxmULsrTcTSnZBZDP56hGif590mNO9FnFPdtHNm1eHT3ttRNZHNdO5vZH4MsR8efwygumbTPH9lZAirISDVxJQ4oK58UK9XX0iHbBNVyrZyyAswLLwHAeBCcGIEkIMCGahKuAuvACHQAArRXsgfP8uy7/kYRfbcDVO4YFWlZde6cLdB/02cgjRMOfpPdMZcuEYrqvEFBxVwe2YsU5HHLNDXffuI50JuLZ8i47fg7+QjQa49ZqX44LXtKbGXj3DNCmMRYex1AvO7d+1bmlrqRyaG8Yo+FatG5KDHo+kmhJZvqDDsVoH8ALFz5BGest58f0tjkodvXRVOJJAtlCCMVPbT8DJJR43w7EWBDtZ3oc9+MQIchYOCDrHDJbuj9yK/YBlWH6Z9EdekooPy286DvYzdBZvmpHLwiiRcp4TrpvEGyyrpJuFyvy589+wxYPf9UnKjzNEGHT6fSMkYavaknw7x0/P2Hsuex6rhjM1aV0uJKjZGGPdb6HWqxUk0Rb520GPeqng/WPGfVkxgqfZAVGuhCKWMBl/xWzt4C1sazAPLsTQ2gxcU2IsBPoDaCz10zAp2IQGpwEI3Vy6HvozfahhU+4Dg1hkBqcBYULrgY+m78BJ6VivvF+FD1xoDs5lay22cax5lGg+byJedDIcdB1KYR9pZ31Wv0wPqYe1PrV8a3fmovWK9fer0tOsUZWu+4Ivlxi465biNYuDpHa2z6lwdFoG9VKpsn4dJhPJMV5vDWnkh8uaItCLCh2hbYuVNGoLMRoKBKhV2XQN+v/Trk1m3A801znT2gNmifmjsXCpddgQb/r0JmGS7D6whvRBuAmuldNuG+aNRbtHrwVNh04hkz/Qp0zfjpSJpT0Zs6Pz2na8ZU/0C63HzFo+TMreeBtWSZDQ0Zo8IgdaxS9ct2tlKU3y14XjfuTyXPKidGIEkIsKGapKvBujACHYSAvWR1B/R98jOQ33EBH6VSx7VLn7AAeq/7qPif5gPV60COWacTgTQu+71s8fnQW+C9qtOJe6v66k/1wabcaa0Sj3J9I7BZe1Snoqxjk01FREe2tVasBmvzVrBmBZd3a6xUHGFlJfKfdS4eR8ORvjvygne50myodvkF5uExAq1EwMDIgLm166D/4zdB75XXgtHP0Wqr4o1Ls7J4kHofnpHac8XVHJSqKlhcKRCQ1+c1CEkje1SpK1q+uaR3EexcvKUr9vA1CF9XNKO9xltz6+GEVP3eVP8WrGH6+Yxtxyz6ntePIZq/7UOpXwEMfGiefxFY+KwhD6kYsTdsLyPkmvNPAGsrelOXLOVVPfUjzS2mAQE2VKcBZO6CEehqBHBpYnrxEgywdAMM/MbnRZ6XsYavuDjiB6MmD/7u/wGF8y4E2p/KiRGoiUCTlv66y3/rFZdP5+CCRVtg8RyMRs2pYxHYWFgtIv02EhzLv2d8j6kWCJ9RvOTQ8tRJdO9buZnUjUxW8tSuhrbKY0rXjyKwwwoWGaDbdoI1f75ec7RXrWwOKpvOBvPMzXwcjR4lpiYAATZUE3ARWAVGoBsQSM3BPZcXXwqz/q8/hjy9oeXkIWDgkqq+T/4GDHzqcyIAlcFRFT1sOFMbAdUHEuaP4w1yeWSnl5wPS7UpNN1f3LcIPrLiShjoHYhiY3qCEViUPQG29G6E3hQGxqKLLn5q3VX+gOLcJ4JbYnTvN19KYzmdHKmbBoT65qssR9dPA8KT0wQNdfPsc8EiI9Q9W9W75HaGogRbm7eBgc9uToxAUhFgQzWpV4b1YgQ6EAEjX4DsqStg8Iv/GQY+g97VExfN3Ei29EYfl/rmt26HOf/vn0HvNR8GMuY5MQL1IuBPrfUt43iDdDxxPFPUIx1psmne6XDjmmugJ9+jV4KpiUSA9qXu6t8CCzJzbS8nXXTxU+uu8oeju090NNVF6befSi7efasfi2eXhRSw+eUx6PoJNes0QgGXAO++CmDhQn/7sAOKhdH7TTp3dS0uD5aB6LQxsr5djwAbql1/iXmAjMA0I4APPYpo2/vh62HOf/9z6MGjbChKMKDRNiMSjl8Y7KvWwMDnfwcGf/8PILvmdEGbEePnQTYVAfL0RE+47a5qeYOEDNl95GioIUXqnk1lYcvCM+HaVbv5yJpIlJJV0ZfqQSN1M5ySWwwpcKK50kUXP7XuKn8s/n3it/FpPp9y3onPKjHUn9Xd2+G+w53p2tm9E6/NL8uJ5q9f5yS1sE5eAibGj7DwGeyiZKVw3yoGXDJ3XojH2PDxU0m6XqxLGIEZMnMMD5wpjAAj0GIE8GGYxofkwG/9LuTxgTj+0x9B6cUXwBw+DlAut7jzdohHA7WQh/TCRZDftgN6PvBB26PcDlW4z65B4P9v702DLUmu87Csuve+pd97vU53T8/WPUvPiunBYAYACVDEDCVIpmjZEi2P5AhZZtCUHV4UYQdDEfohBxl2+KfD+uGwZUs/HKIXCYggwyLtkAgyMAAIkiBmATDEDIAZzPQsPUv3TC9vf3f192VWVmXVzbpV976731Pv3ZvbyXNOfplVN0+drCx6evz+oqSJRd4gzSPymkSBruzGE275sSOVFfUXzn1W3di/qb5++dvqoFnPJ5aSiSHA8bIRrqufX39aPb76kFoKarAhIzOFna7/i0ZVon4yTgrqJIRw0hXQJux7xnx8yrDWY97LmXoZ3cjHGm8+Od7qs5aJRrZ/4a+o4JXvq+Db30SDcePrzFnV+oUvqwC/VXIIAtOOgBiq095Dop8gMOMIBFh+tIwlRrUHH1EH3/1TdfCt51XzzTdU6/on2Be/NeOtg/qYCLCNlXPn8E7ZJ/S7UWsPXMSSZ3kf3ex37my0gEZI0UTb0tBesRN9N162paeWj6tn7/6CNlZf/vCHqi7GalnoxkLH2xpHK+vqC0eeVJdgpC4HmXdcs9NB0wkwZiKDrUix0uPEIeQ6gLL8e8m349alccS42V1x09KubGREGDgEPjm+mrOa1/5F3Dj90Q9VZ2tLtT7zNJ5dfbpk789qi0XveUFADNV56UlphyAw5QiEJ06o1b/8i2rpyadU/eUX1MF3/kQ1L7+pWlfeU6rdnnLtPephts8lzRVs689X9Cx94ee0MR7Uah5iyRIERodAWSM1q4E1WLP5Rel71+9S//Z9X1Yhnl39/kevqt36blEVKR8DAjQMj8FI5cZJT/iMVOrATtf/xqtYRq3S46Q0YRmphqZobJfnZCkT65TqMsVj+HIM32n57uBRlPaXf1EF+N3tfOkvyaMo09IxokchAmKoFkIkBIKAIDBMBCqnz8Bg/atqGS8kb7z6ijr4s+8YDysM1vb21jBFjYYXZjcVLJ2qXrhP1S49oZae+IyqYRIQu6lGI1W4LioCZd1HY8bn4eP3q42H1tRtqyfUN9/5U3VrfwbO3TFjNE5xIZ5CPVk9pp5e/ZT69OrDapU7/HqPxFDzFh8qM+E9DG8qVRnY0wlViuxmnloL41YEGJ2/+IuqfeacCvDbJYcgMCsIiKE6Kz0legoCc4YAd8Bd/uKX1NJnPqcaP/mRar7+YxO+/ZZqffCB6uzvTVWLqW/1nguqeu99qvrQw2rpoUfxDO5dxbOhqWqFKDNzCBTNtks0yPUWDYFdLPGutdvVX3/gr6hTMFZ/96d/oK7v3EiehYypJDJqBLgr84WlO9WTq4+qi8vnu5f7phQo70lNVSuVGCXvUgokRLmq5BYkdec01jmGd3f/3JfmtHXSrHlFQAzVee1ZaZcgMCMIBKur8Eo+qZYee1y1Pr6mWu+9o5rvvK2ab/1UNd++rFrvvqPauzt6E4hxNimo1lR4+rSq3n1eVS5cUDXcha4gXsVS32D1iBio4+wMkTU0BIbtoD2+dFT9W+e/pE6uHFf/8se/q67c/GBougqjYgRWwmX15Moj2DTpQXVH7QwchNNhiA3rGdViBPIpFslhmo+ClAgCs42AGKqz3X+ivSAwPwjg9TV87yo/NSynbd+4rtowXNuffKya772rmjBYW1fehbf1imrfvDn0dmvD9OztqnrnnXq34upd92CDpDtUeOo2bbCGa+tinA4ddWFYiMAQLMuBl08WKmee8VuqLKmfu/Ozan15Tf3h299W37nyMjb2bpSoLSSHQeC25ZPqC6ufVg8t3avWQ9w8K3MMYTzli5ke0zB/zPt1zKfPb62UCAKCwOgREEN19BiLBEFAEOgTAW5IxOdA+eGxtLurX2vDHQv5HGv7xieqdfUj1b4GQ5YG7c0bpnxnW3X29lSnXledBibK+iEkMKhUFHkGKysqOLKmwvUNFRw9prjBUwWGaOW2MyqEkRpuIB9lNpSNkTT88jVJBIa5VhftGDK7GBl68j5922PqjvWz6unbL6l/c/kb6ifXfop90mZwo7S4VdMZWcVrgp44/rD6+TOfU2tbeBa1SeOr5DGqAaDFJ97cYXl23WXrJVtoNEE7E238NQmFRW5QOX7OkisICALDQkAM1WEhKXwEAUFgZAgER46oCj4KxqQ+Wk3VOTgwBimMUgWjtMN3szKfr7zh5JhGqjVUOSMJQxXgoypVFcB7q/AJlvDqhtoSdkBcNvGRTuJGBo8wXnAEirxB7iTcdai58UEhdI0Byjl35Iy6beWEevDkfep7V3+ofu+NP1Afbl6VZ1cHBdipd6S6qg3UL55+Wt1z5JzisusrOx+oOv5KH/qaOIbX00COO+5K65chLBrbGfISyWTERlDoOsOXU0IVIREEBIFCBMRQLYRICAQBQWDqEKCxeYSftalTTRQSBMaNQBmDwEczqvsytbCm7lo/p84euU09efYx9YfvfFt97c1vqc097AysrYNxIzTb8tZooJ54VH0JHtR7jtyh1qq4cRfgplsfR9zXjOj/xGArYhPXdQh9eSl3fXn2DtfuaLlx6xfmX+RLGYaebRCPajfmkiMITBMCYqhOU2+ILoKAICAICAKCgIMAPT00APxTcUNY5A3SPECanfRHrB1p/Ud9xgDzaEYt49nVezbuUH/nkV9Wv3TfL6g/++j76k+uvKjevfE+3r26pxqthmq1sQJCjhgBGqDVoKpWK8vqwvrd6tMnHlGPHruoTi+fQD42eMtYiL3GRcxURyLKyI3YCeDx7DmqktrJOEl6O8lL6PRNCKtfQuoQ9B/1je1u2d3CfPWMdNLygFfZqZZPb6jlWxAQBCaDgBiqk8FdpAoCgoAgIAgIAoUI0LgsMkayBmiWqeYRGRDWjiCNG8/WKZv26ZbOC9RSpQbv6mn179z3ZbzO5i/jkcqW+pMPXlTfeue76u0b76qD+oGqtxswXJuq2W6qFspbWL7f7rQXasnw6ZWT6oH18zBOH1WfOv6gOl47WrYbytOx0/V/upd6MUjGSVInyXNqOplFY9Kp1TPq4+OIieomellmeszbRCokraEnn9hs7WaaqiUJQUAQmAwCYqhOBneRKggIAoKAICAIDAWBMt4gS+N6o9z4UBSJmDiOKi9begx//o7Pq2fu+lnVUm318d519e7WB+qDnavq2u4n6sbeLbV1sK326HXFs+fUnUdsjsQRk6uT+Fo6sqJWNlZVUOlvWaxmPsAX9dqsb6v3t69q43oAFnGVEJ7UX//Ur6m7l87FeaUi/RpYGsvDP6PqHTtOph1vpdrQg8jHxxET1fSPOH8uq5iSCArNwycnYi6BICAITBABMVQnCL6IFgQEAUFAEBAEDouAz+vk8sybhPdr47g8bdxnDKTsSEvohFouK+LgUtfb106rc2t4BygL8E+jzcbpT9beMZTpYl0ZZEzHZSZdxQZpS0vY3TuiMRJG993EsuUfXHtN/W+v/Ev12nW89/mQy5iPVLErOXRnf43sIDb6H18lj9JwuoTl2ffUYvh9mYxYqmuRHr6cns2SQkFAECiJwHhuO5ZURsgEAUFAEBAEBAFBwEFglEaLI2ac0f6bZM2JcWpZLKsaVtSl0w+r//hTz6lPnXpQVcPx3/vv2x7sH/xiIGKKpJ94E2EYx6BGu745U6CAC8WgcgpESLEgIAgcEgExVA8JoFQXBAQBQUAQEARGhoDrpRpQiOstGgK7lBYDmyNFigzCuIhnSvPhJGicfvrMI+pXHvtldem2hyZgrPYJ1Egx6lOX4XSBl4s75tMEfh3z6dO1JSUICALjRUAM1fHiLdIEAUFAEBAEBIGJIeB6kSamBAVPjSKHR6EGY/XJM4+qv/vY3xi/seq3uw7fqENy6MSLag/J6BDVE//uIZhIVUFAEJgoAmKoThR+ES4ICAKCgCAgCPRAYAgG3SiXNY7MGBiE8RCw6tETPYvoWX3y9CPqP4Jn9fGJeFZ7qucUDgKsU71ndJS8ewruKswfCn4dR3mOdCknGYKAIFAaATFUS0MlhIKAICAICAKCwJgRGPJSzSGzGwgMrUNfikyp2zDTer0MGMbq33v8OfXYqYuqgmdYR330j0z/Ncq3IeE9rGdUB12Sy+GVaONvgTsEB5Xj5yy5goAgMCwExFAdFpLCRxAQBAQBQUAQmAACRd4gdxLueprc+KBq+4wBv88qkaDlFgkn45hRHEmY+GKu5eErH0MeN1j61G0X1X/x6b+jHj35gN7VeAxiy4sg7vq/JKbgXNRVsXCHsGhMxnUKIsPik4hJRqyj7mh3Wk6ES0wQEAT6REAM1T4BE3JBQBAQBAQBQWCaEHAN0Ty9fDSjsusSU8CvTVm5MV0c8fPTuWVoelQfZlElqKhHTt6n/sHTv6YuHr8w2tfl9Ntu0uv/ol5KECktwiUszz4R5IkdZtzmm+JGuZS6bsKjh2QJAoLAZBAQQ3UyuItUQUAQEAQEAUGgEAF6lPIn3KZ6kddJ83DdR5FUT1ahPlkCn26+PFuPMkvJdenKVCjN2Goy2pDvgr3v2N3qN37276v7j90D23BIltth1dY40anaq5fSQhL4C+okhNprm+YyWMo3tl0xeVx99Qwt22Da4fLJp8+TIPmCgCAwDgTEUB0HyiJDEBAEBAFBQBAYAAF6lIpMHJ/XyRWleUQeI9dx5MZJn027PPLiPt18ebY+ZWg5RcIsHSsW0VqaMnRWkTGExP3uo+fUf/fF/1o9fPL+6VgGTIz0P75KHgmsSZ0kz2HiZBaNSadWz6iPjyMmqpvoZZnpMW8TqZC0ht7l45OTqiYJQUAQmAgCYqhOBHYRKggIAoKAICAIlEOgwI9V6vk66zFKe5HS8t2ydEl/qV76UoaWU0JYTBJHeuhRhqZH9VEV8TbD3Ru3q3/0M/+5evz0w4qvshnm0W2iFXAnTvq/Vy+lefig9eW5rnI73tKc+k/5+HTL9rfFn0sdTInLxyenf22lhiAgCAwbATFUh42o8BMEBAFBQBAQBIaIQJExUsYb5KNxPUpDVLenB5gyy8pN6IoQiLR3LY9hNuiQvGis3rNxh/qvPvMr6jNnH1NLldohOQ5Y3YLPPujZS2n+ST+k87tSLmHJLuvikckY/rhNzNeUum4io4MkBQFBYHIIiKE6OexFsiAgCAgCgoAg0BuBIRhf9BaNymOUTPuTZvjyktLIo+pm+OJgkvBJYj7SWcgLYQjxWdX/5PG/rZ4++7harix1qz2IcdeHgaXZDzCekioF/ZAQ9mUIdwOR5PjGrSMmIczEWK8ITpePT06GpSQFAUFgAgiIoToB0EWkICAICAKCgCBQCoE+DJE8fq5XagjsUmJ8xoAvL1WJiSJFSjHJcC3imSEfd5LG6oMnLqhffexvqs+fe0KtVJbHrUIx7h6NElgLOiUh9HAZb5Y75tOS/W3Ip0/XlpQgIAiMFwExVMeLt0gTBAQBQUAQEAQmhoDrRZqYEhQ8NYqMFwXuBvzQyXvVf/jIX1efO3dpMsbqyJqceFz72VV4VOok2oxKgvAVBASBUSMghuqoERb+goAgIAgIAoLAoAgMwaAb5bLGkRkDgzAeAlaDdlM/9WisPoz3rNJYPaxn1e8f7Eeb+aTNHwqDDKz5xEhaJQjMAgLD3X5uFlosOgoCgoAgIAgIArOCwJCXUw6Z3UAoGh36MbH6oR1IpbFXssbqrzz2y3gWt6O+++ErY9dh+AKTfupns6Zeegy6JJdjLNGmlwQpEwQEgWlGQAzVae4d0U0QEAQEAUFAEChAQG8c08MCdSf79DRZUjdeICK32GcM0Gfly7dMtFxulWQVsQVumGJQxDGq2Iufy3tK4jRWLx6/oH7t8edUGFTMbsBsaj9Hv23WrsZAdQJuNpQCOVdq6XFSmjBXVFdB0djuqlCY4W/z8OUUKiIEgoAgUAIBMVRLgCQkgoAgIAgIAoLAtCLgGqJ5Ovpo+rVx8nhn8/2mQEKl5ZYwyBI+SSzhkomNqjEZMcNOsl/uO3a3+s8u/QeqWq+pg936sEWk+REn/V8C06hmaWgdQnqJyxrCaQXTqcOM26LbG1TXDkOfnLQmkhIEBIFJICDPqE4CdZEpCAgCgoAgIAiUQICeHjuZziMnTa9D8/DQeLJ6sfGW+ST78mxlyiwlN0XXi2PEuTRjq8n0hDTo7tw4q5bC/t+vWt7cdHGigVYCU6eKiRbUcTu2gLQs+r6x7YrJ4+Orl22DyyefPk+C5AsCgsA4EBCP6jhQFhmCgCAgCAgCgsAACNDTU2SMFHmDNI/I2+U4vbpW3rplZVX16ebLs/wow8jpRQVq0rmVbDwvTBjnUUx1fnEvD0l9jROxjdEtZGz6i2RJnSTPqe5kFo1Jp1bPqI+PIyaqm+hlmekxbxOpkLSGnnysPe2Tk6omCUFAEJgIAuJRnQjsIlQQEAQEAUFAECiHgJ1M51GX8QZZmrQXKc3RLUuX9JfqpS9laDklhMUkcaSHHmVoelRfmCLipP979VIaDR+0vjzXVW7HW5pT/ykfn27Z/rb4c6mDKXH5+OT0r63UEAQEgWEjIIbqsBEVfoKAICAICAKCwBAR6PYXpZmX8Qb5aLo9U2m+g6Z66UuZZeUmdL04Olq6loeTLVEHAd0Bg3pUHT6+aNJh1mnpo+orb/jjNjFfU+q6ib40FGJBQBAYJQJiqI4SXeEtCAgCgoAgIAgcBoESxleRN4jlRTSDqphM+xMOvrykNPKouhm+OJgkfJKYj1Ty+kCA40n/l8c0GYIFdRLCvpYW99LeN24dMblVWa/o9obLxycnl7kUCAKCwNgQkGdUxwa1CBIEBAFBQBAQBPpEoISnx+d1cqW45SXYuVUL4z5jwJfnMjI6FFClilMJl1USJ9NhNy7hPj8xjdOgHtWCfhgB/u7Y7acT8uv525BP349UoRUEBIFhIyAe1WEjKvwEAUFAEBAEBIEpRcD1Ik1KRa1DX4oUePLYkL74TarlI5Dbl3HoN9KGp1XST/3sKtxL/mE8nYk2vSRImSAgCEwzAmKoTnPviG6CgCAgCAgCi43AEAyww0z2i8AfmTEwCOMhYFXUXimfdQQGGViz3mbRXxCYXQTEUJ3dvhPNBQFBQBAQBOYdgb48ZsVgDJldscA8iqlRJE/B2cjvy0faF/Eg7U8E9PP6m0EklamTaFOGWmgEAUFgGhEQQ3Uae0V0EgQEAUFAEBAEhoSA+/zdsJ2OPmOglM+qSBEf4yI8xPgtQmigJdJFXRULLU0Y15hAxD+wRrnqYAKNFJGCwNwgIIbq3HSlNEQQEAQEAUFAEPAj4Bqrforh5fpNgYR/WXsy4ZPEEi6ZWFmmmWpTlSzRzEPrOwBOpas4hMN6RtU3bh0xPeEoumHi8vHJ6clcCgUBQWAsCIihOhaYRYggIAgIAoKAINA/AvT0FE24i7xBmofH2+XJ6ltBn26+PMuYMkvJTdH14hhxLs3YajInoWttFTRJ28EaJ76hpgSmDrQmWlDH7dgC0gJV42Lf2HbFxISZiK+eIUkUc/nk02cYS1IQEATGioC8nmascIswQUAQEAQEAUGgPAL09BQ52oq8QZpHZNC4do0bp0bZdBktfbr58iwvyjByelFRGf1vqpVRLGFsRS1EWIBiNwYaJ2JbvmYCf1InyXNEOJlFY9Kp1TPq4+OIieomellmeszbRCokraEnH2u2+uSkqklCEBAEJoKAeFQnArsIFQQEAUFAEBAEyiFgJ9N51GW8QZYm7UVKc3TL0iX9pXrpSxlaTglhMUkc6aFHGZoe1RemiDjp/169lEbDB60vz3WV2/GW5tR/ysenW7a/Lf5c6mBKXD4+Of1rKzUEAUFg2AiIoTpsRIWfICAICAKCwMIg4E52R9Xobn9RWlIZb5CPptszleY7aKqXvpRZVm5C14ujo+U4OsMRN+xoyVYeTqzugEE9qgWikw6zTsuCCsXFwx+3ifmaUtdNFKslFIKAIDAmBMRQHRPQiyyGc4dWu73IEEjbBQFBYA4RGMu1rYTxVeQNYrmPpgTrwl5Lpv0JqS8vKY08qm6GLw4miX5FHH0MJM+LAEHV/+UxLd0PCWFfS4u9ekaZg45b1isy/B11vedHL72kTBAQBMaDgDyjOh6cF1pKG0Zqs9laaAyk8YKAIDB/CLQ7Y7i2lfD0+LxOLtostzQuOzfu0vcT9xkDvjyXp5FbQJUqTiVcVkmcTIfRoITjbMT6bbPGaVCPakE/9KtLCYTtuC1BmiLJr5e0gepacz2fPsVWEoKAIDBmBMSjOmbAF1Ec72w2Gk3VbImxuoj9L20WBOYRAV7X6vWGas3Idc16plwv0qT6RevQlyLWnOihcV/8evCZsaLE7JoGxZN+6mdX4V6a23HbiyavLNHGT7GgQ8YPhuQKAlOKgBiqU9ox86ZWE4bqwUF93pol7REEBIEFRYAT6N3d/Zlo/WEm+0UNLDIGiurnlg/CWCyPXDjHUzBdZrO/zYMMLD8nyRUEBIHRIyCG6ugxFglAgN7Uvb0DWQIso0EQEATmAoF93Hjb3z+YubaMYHXmYBhMjSKDqT+aWrNg6JVreT+vvynHsX+q+UGz/7ZLDUFgXhAQQ3VeenLK28E7+nv7+2pnd0/xmVU5BAFBQBCYVQT4KMOtW9uqPSMePPf5u2Gr7DMGSvmsihTxMS4aMIto/PbV5kFALQLdLS/V826FCcT9GIxy1cEEGikiFxSBWTgD++0aMVT7RUzoB0ag1Wqr7Z1dtbu3LzvsDYyiVBQEBIFJIsDVITdvbamD+mw9yuAaq6PGz28KJFL7sq10tSKOIOqfaaLQDMdKIBO3bvTPUyfaDOsZ1cOM26JJuztkDiMnBlgigsCkEcANwGGde5NuipUvhqpFQsKxIGA9EVswWMWzOhbIRYggIAgMCQFunnT9+i19s21ILIvZFHkewaHIG8RyH00J1oX6+YwBX57LqJRcMokZxRGXTTpOpqUYp6stUqrRbKoOVzQRqgTcQggSWAv6ISF0+q6QfU8C37jtWSEqzK+XtCGlrpsoI0BoxopAC2+O4Nsj8vt1rOpMrbB2G2c2PvN0yOtp5qk3Z6Qt/LG8eXNL75h5dH1NLS3VZkRzUVMQEAQWEQHeVONKkM2tHb2D+VgnS67bJwf8Im8Qyy2Ny86Nk3U2nSMulZ340JJsX15SauUUUKE4piirWFk6V5kFinPcHuBmy+rqcl/vOU1gjXvEP1YSwni8HRZeO25dPo6YKDvRy9LpMW8TOSH52Cm9T05ONckeEQL2VYZNzBH5aWjjlGFThUGo70Ntbe+oShiqarWKTyX6IF6pqAo+3WNjRMpOKVuuXORnng4xVOepN2eoLbwg7ezsYdfMPbW6soIfzhUYrOZiM0PNEFUFAUFgThHgnelGo6G4aRI3gtPeqAl5XTiZ7p6KJ8DTACmaaFsaNsFO5tw4uWXTiYT+Yr30pQweAU0Eq4jJ6vqO9YkjXSRzlTHqGyDkX8fz1TTP+LsbVsotqvPB78tzB5Adb4ftIB+fbtn+EefPTc4k8rEnlk/OYXWX+rymEGRzbeG448HHJ/gmCF5T9asLI4OUS9NJYeuQ1sZpiLKc9A3kB/rRC9OX+hvXEl5OaMDWPEZsGJLK0JBvOm5y5uG70WxoXOehLbYNYqhaJCQcOwK8APEaxg2W+LFH0YTL0kkoCAgCgsCoELATpFHx74evmY7l1yhzzfTRFNiJ+QILSnrpS5larpmz9uQU6xdHepLPfKF5hrQEME5Lff3qFHdFOa452e909rWxWoGxWsTDB78vL3Xjodcg6NIqP8OnW7fsfoQRX0NPPhZtn5x8raTEImDsUPNogZ3TcVk5l5g3GzAstWeUHlIu3TWe0mFsQmflUg/dhyYDxmzd+ypEjnPrha1V4BSpwRsbe2GNAWvGgBu3rZyNkJ7Uer05d4/ViaE6G+NvobScpgniQgEvjRUEBIHpQ4ATsO6ZeUpPXjN7TbTtNTVLU4J1So4vkUz7k1JfXlJKL4k1FdzcTBw0mg9tirKKkm6GjxYm96NvAs2IQHu1dvb2tPepVjNeqBBLKrHGsgvBBH7dI7o8yXPI3cyE1CHoP+ob264Yw7FbGOuFhecNakfN9cnpX9v5raGffQSmxMn9aO+oNkIbkTEKg7TVnLrlp2ZJLI3YdB/xmkiDlcuIK1xKbOMIeT5wDJHGfHgpNvE0l8mn2CcHfGVatoGTV+3QGoihemgIhYEgIAgIAoKAIDAiBDAxKjo4eep1uJMrl9SN96rfq8wn2Zfn8jByC6hSxamEyyqJk+kwGpRwHGtMT6Qx4R/9gYk2hPCbk1u79LJRaeqJOp/zq8BYbbermKibSTkn7BxDJqShwnyPpk5m0Zj01PZmDconv16iONW1tzby6b1qzWUmx4MxSNs6pOeTnlH97CiX62J80utP41RvboSQdWb50OdA5PXNtoPjXXtdIwNWe2V5fiDN8WKNWHOewKj13OTJ8hxFmj3A83h7d1eHo5AxSZ5iqE4SfZEtCAgCgoAgIAiMAQFOyDi54rzSsSfGILlbhNaBJkJpRTgVSwyMbo7ImfEJM70hw1gS6cUmyjQmqp+C3tx2A8s1MWkn1mHY0M7VUBuunITDw1QxxmqFcXqbaMjiw0ddddzpU5g42hj2Syufa8dt+RoJZdGo0UMmGlaHkZNInP4Y20ljtN2B955hZIgyNF5HGKJtGqIYCzBE2zRKUbaIBzGps+3Yq8A9eB2l0crzQIc8R5hGyPNAnxugsecIw1HdCGF/cjf6TWwyxb0U5vEQQ3Uee1XaJAgIAoKAICAIRAiMchJeZAwM3AmDMJ5hY5WbdnFiPE2H9qjhuTejVyuabHMCbibq3ImVhiq9TpyMpybq2pClMUsrFstweaOh4F7DeNruH1ijMiTG06ZuKdoYpcGJMcX+a0f9yLQ1SG3cGqO8TshRjABx0s/cqu4VEMZQjQzX6JwwRq05F2JDNj4/zKqFYqndFOzHPexGzz1e9vbn00hlq8VQ7e57yREEBAFBQBAQBOYSgdJOzFG3fiqMllE3shx/eq7q2MV0EEOhLwOrEHMSuERuPN0WbfzQg4q5OifMPPRSSOtR0l5XelujCTo8TjRsrSeKOw6zDDbuyI587UcmcqyMOV7YD1yOq43PyCBt0UB1DNPESIWHdKbfsTn9hrS5QcBNo9JDgeepNVL95wTOD+2VNeeLpUlzSVK8saXfnAFD1WzClpTNW0wM1XnrUWmPICAICAKCgCDgIOAaM3SaDNNYHdgY4JyzV+VeZU7bUtFhNizFeLQJvqaNu6POxmGNhaiDnH7SJZHxxAk7x10T5Zii6zFnlgk7yyNpqEYGrTVc9VLjyIh1x+3wsHEUdpjS6BuNPEfIgFFjdBrjUz8fag1ThPqGAQ1Udxkv2jLITY8B1ZNqJRBgf7Ra/LT163XcKhx3dpkwDdRA39yJbvLoc8GcM4YHl+fjxhaWI3PJ7yL0sxiq7miRuCAgCAgCgoAgMIcITNMkXNuT1t4phbXfuEhVnVEjVXtGdukVGcOy30LMLYHFm2kbt2gznc0zWZ5cW4m+V4UHI+GApVFF+rbmwkk5u47eVnqc9CQ9ipvlxMaYrVS5sU1Ve2T5vGyZ7vZpHytEFSA3bnEZhm7lIcf5vCg9oXpJqTZEYZhyN13kaY8ojU9rkCJOA3URjJQ0zL1GWJpyllLGAOWNCDwOm1FcG7AYm7x+87lve0MiQzbXSTFU57p7pXGCgCAgCAgCM40AJqVFs3JOdHoZonZCm6UpwboQOp8x4MtzGWm5boYvHlsQLCziSBLQ8DNDB42SbWyCQs/IWI5onm9Q8m92ZJBOcOTkmIapqermR3mmWHcRNgM2dDaPjdJxNyPKsg1GkZ6Ac3MfMtCeWMzYkW+MVhqv8Dhhos5Ju47DQ8uNnfheTD4Xa18posuR1pN6jIXseDciE130cLGY5NJbRYcQQnRijNp3i9JAMR+zyy6MEejCc1YbplrJIcgWFjOJgFliP5OqD01pMVSHBuViMMKbsvATMcKHShYDRmmlICAICALlECjh6fFPyBP2LLc0Ljs3nlD3F4vm+alKvjyXwMgtoEJxTDEMRV0FpiBOz8j2zq7axU6d9kbCuNQirtb0TMtkbox6RGVzbH66tu0ahpYiiWgWaRHZLF0vrpmmpbHGHHhg8SIU2rCIGAM0wJpivpIDAzv2xjJunns13le+G5PGrA5pwMLgdQ/qrPkj054fbvmgcRoXvAlBA5RLuvUrgBDnjsr0nGoj1Bqj0IC26LjHwKBtk3qCwLgREEN13IjPvrz0lX722yMtEAQEAUFgqhHgZLrXhZeT3KKJtqXhpJgTdB5u3OQM57uXvpTJgwvZYkVMVtd3rF8c6SJJZ9iGpXOnLkUjdQdG6tb2rl7COT4FTceb/unupcgsdMYajSgYqrpa1HGxsi4vM5ZYMSa1Ee1mNbRxVTcCtp3AeHcp35jFpE/kxZrqcWCKSEvDFVVVG8uIWcTajDfhoD7g63XIjcYpGmA9sjRca9p4hTHLkB7ZWlXzYTt7nUeuMcm4MUSNAUojtNEwcXpNWU6dqJUJEbMRZsshCAgCpRAQQ7UUTEJkEei0KzXzS2RzJBQEBAFBQBAYJQI9pvlabK/JtdXLRzMqu66XvsYYgFZ6Em+184exfnHET6dzy9D0qD6OItopNG62sNyXHxqs4zxsv5jQphINmJMtS2C19AkVa9pcRty44Woz2c641BTZbyfb+m5tUVeolYkwc+pl6TQFwI4ehYVoGIuoqz2dwL+OHVO1vpqfMWKN5zUyXuGJpTFLY7iBTWv4ChDrFWXYgCXM50fd996KEZrtBUkLAsNBQAzV4eC4EFyef15Vzt+nTi1EY6WRgoAgIAhMAQJ6AowJdY95ufbU+AxRq76dRGdpaDjpubolHCD0mSC+PMvaOpV6tUfTgonmQ8IyipLGMrfCpijkZkkHMJA2t3bU/sHw3nlYiKODAfHkYULrvTR5ST5tOEtJSBHX4y/JM7RmTMZdg+L8Z1QTGVZOrDfZxglLl5ZlNAaRFhbRpOrRpGS55RVHTJ5la6uClJ5YUnFdcRu+VxqkXEkMX6z2qrJ1bPuNW7f0eypZIocgIAiMHwExVMeP+cxKfOihreN7jc4v9Z4yzWzzRHFBQBAQBKYOARqXXfP4jJZZAzRTbCbekUXqGqZuPFunbNqnmy/P8qNMI7cXFahJZys5sTgrG0kYZ0smljY7dHI5aEu/83AHr6FxvXBDUayPTrSkxNU3qpiXzkcKlUw/2N5I1455IttSJBEtyH7FzY3pmKPrpXIiemNGGpKoXAtDvq7DEnsYHWkom8PqjBTz7CdhGfPQBi4r6boxA+bIIQgIAlOAQMpQxd0je5bacApUFBUmjEBw+fLl2tLS0umgcvC3q63KfzphfcYsvmO2ix/DO+7c39AxN7JLHC8A1GdYF4JpaltXYw+Z4UyJNGot/QoBLjqzrc5Bkd4BHnFxHDH5tjyPxi3P0mTLDMe0rDwaSzui0KKSUtmR5Zb7aLLlTlUNZa9yl3Yo8S4M0YeZbsyTcwCvmvV05tHwuTe9kymY8lm7Tpv8zeYxdAnxuTvmU6R5zQfizOOHf6QNMRadtMljmV4YaWjBgHIMHQNT1w0pRMuLeGnamM7UgRKahp6veMdWKsfK+p/6MELtIpksRZp55M9Dy9XtYpq6RHmIaFpL44RgrGrwWnJ5p6VB8XgPtFuPP4Q0SGmg0ovKV9BQr4kfhSdHloBp0yfJ9cy2wuRbCjRXk1pqS1UYop59RjWP1sqIPaq6TqJZdz3jYe2pS8Qj/qXTQvjFw7YNfHTDTK58TxYB6YvJ4j8p6dpQ/frX31rZOLn36Evff/UJnJVHJqWMyB0PAsvLy+r2M6dUWKv1EIgZEXaI59zh2IlTK61W8wHsHv+rqyuYzCzQwclGvb6tnydaoGZLU/tAwJ0U84d0/2Afk1Mu7UtPerwsOQnScyLPlMqdIMXFcQTsLf+Ic1xkIu6PeqYIFcyytqxOmg5fWdYuHWkykt3i6Y7bhtmGerQlbkmxjsVULqbMTKeNQRUTHzJCQ29vv64NO2Mg8n2T1mgzRqU1HClKG7SRIYcAhzHqtGEYxXU+vuJ6pNdlvNa7/E27TR7LmDb8GNUGpc4zZVovlKNAb2CjRwj056+FKdORiAfzqH/ChzrEaVCBIDFadRllkwR/NHQ5AJnWedTb1OGrSmp4vtDoS0bjPTi8OCb4mQrDdODmE1DfoYFGgVMe9YXuD18VnWeJDEEq5bBKqpPCc3hpSWcM06SGhzCHZdwWTxX92phpuMGQNGxhY+y+Zotrs5cXFoNFbXj1hRdeqAW1zS8Eneo/xED4Aq7wa4sKxqK0m5d0biwQ8Fc198BVO7JJef3mi7YX8a00fK7ITuByoZICQcBBoHuS2us8Q0VdXIaGQnrQxUVxJNYqzumOxDQx95gmVRQnCopjuqmO6EbktyQpzqdh+0ZpEBnJNHqwCQyu1fSAau8pCrSdx3xex/mBLpqGXtFIL52PMrNrKylIa3TW139dV1NrHmFIxuRl6Aw95TLN+qYu41oupUb8WKppcH+TZZoWGwVpB7DmF+VpXiQ2NwOoB3nwy/B141EdLcTw1+Rkqg9qynx6jE25vnGgsbI0EekcB7rNJdvH3/7eh0bUIfHhSB4YV/rbIUUG1+R11egidOpEUdOT3flJTgkmCbGOdemRKS+T5NLtfvAtw1NoBkRg5m/+DNhuqaZCLOlcV53K38Nl4MvAQ4zUBRgUXI7Ed3nJUYyA3SWwmFIoBIEIATm1ZCgMAYE2difNPQYZY5mZeybpFdWTJlsYpbPZXsbI9DehbO08rouXT69f2WN4uwxHqw4ouKjLdHmaKJ3yaZ+lyKazdbpN3a7xVcQiy1LSgoAgkIPAeB9jCOv16gruuP6FHG0kew4RoPFVxw53chQj0ORW9nVsZS+HIFACAd59L/ZalGAkJAuNAMcRn0/1W3OYcWcm3ZyUd03MD4lgRsQhuaWrk/co+aelzXdKj5OSTSw2arMjyTeqcnoP2YP2aflr5qASSgIkZIKAIFAWAd/FoWzdvujwmCLeUNxR1/uqJcQzjwBfNi5LWnp3Iw16boRBD7QcgkBZBOS8KouU0PVCIN8AwfzA/HdV7znhz0wrMkkPL2v85gqL6kSc0oEpyxHCbFtkQ5OTpLoUYoYttqGXaLEyy9505u8ZPwSR8OWNFQNtAjDp3JTtpzjPRhDaaBLRguwXQnPEdEymEhFBV2ZEhBs4/iPSMS6OI1bdHDnkZnn7OdvinFLJFgQWEQHnBBt988Ot5eU9PG3wjdGLEgnThMAuXmBdr4tXtVefmFcKiEHfCyMpSyOgPaq5k6k0raQEgV4IaEPV60BCJv+dMkbNx8nMMHfpWWQo8+jt858RZUTmp0YuC3w0vgpQJCaP4qZunEuhyRFla1b6C0UIbTQhXLwYx0ixl9Tg0oh3rrf4+xBkGfNtmUmblM03YUwRRTi+bF4ScVg53RPTMU/XS+U4lCZqdCJtRKfrpMl0ScwmrmGUivOdOjGPqFAH/IrSljSTtNkSCgKLiAAs1Heb7eAj7CkwNmO1eqLVOtitqD/GPgr/5SKCvqht5p3VG7c21dnTp/QmFouKQ167+YqRre0dLJGWZb95GEl+NwLaUJXnv7uBkZy+EOA4auQ+noH5gf7HZDyaRNsZg2eLm1guaZI5Nz1QnMync2Ni5OvyTLFJRpmaGeM4vLvomKKub7RNb6Kk6zEOvTJykjooIAGOFF2UnbQnqbFIsf0SrziyePARlvRqj27Q2es8ElyRA+DNODNllp+lslz0/Tn2JQlsJiO64xKOSf0opknsaDSh4UIm5kjYIUZWrIPAjv9IakTNgC1xjFVmRfUY1Qd5uMpqhvziYfVlaOO6QL7GjUBHXcUF4sVOp/1ys915O+i0dzAmayrsnO2oyiMYoJ/DOLgPasl2wGPoG5wNLy2F4etjEBWLqD766KONl19+5XInrHBNSLTPa1wukTlGYA9e1Zu3ttTxYxtirDr9zB/zXbyYfXNrG9dAp0CigkABAnp5Hd7jJIcgcBgE6ClrNPgqBhzZeTJn5/hks/3EJrebja1PLgknm8s8nZsUaUYmmWQaKqR1VpKfSPXFEilGDi6yuVUj3lEQ09l0xD63uk/8HOXt7O6Wbk0dj7HoH7TYuutGjTkmNymLyV3w47ilNxXjWm4kjueo6pS7I8NLrZWJfpSdet20PQptkQ1tW+J0wi1pe5InsSEggM3BwYUXuCZuUrXQo0xv4x7vj3Dz7CV0yQ8rgXpHhe1PKktL12vN5q1Go97o1Hdab731cGdj48Xg2LFj4draWgVztfVarXZbS1XvRL0Hg1b7M9gW/fOI3wmefJkV9zOvIqwirCDP09PIlaMcAm31o+3d6++UIx4OVZXu2z9+8cVrS53w+4g/ORy2wmUWEKBBtrm1hVfPVNTa2qp+D98s6D1KHWlocFn0J9dv4jc9+kEcpUDhPTcIcLxwGZ55DmxumiUNmQAC+/v7kIrrj2emzHHGmZZ+xUxGN48fKabg5SxhZygpw8zazHdSPyrXdfClvWJaI9AjzVo6O6qvXVOaWJfGQr0RK5uFjINb/MU8owtjkRATQ3bsedWyE0pdXVMtzhdvZuzt7ZX6neJ7nbkxIHEymBPjLp9jVE5cDaWmjgaOm2e4GLMyHldun8QdYjPjDFbV3ONedorc8acJ46+ISAuLMp16cZvivDhihMV8ELFFNrQZcTohNnMAFsgxMALGKOUFzXzwavpOu30VA/ePg07lRYyDn1YrwbXWkcqNO9fXtx577LF+l7Hxbs1VfF7F52vos+AHH310ZOVW43itpk61Q3VX0O48qsLKZ+CKexTdfQwyabQugX4Fo30Vg6KGuBwFCAC3bbwd7D04OHcKSIdajJdj4p001eoHzbb654iKoTpUeKefGd8T+smNm6rRbKiN9TVVrVYX0rtqjYwtbDJ18+ZmqR//6e9d0XCcCNBANS8kH6dUkTVvCPBatL9/gGbFU/lUE/0DlWEAACAISURBVLV5AIszMToNJal7eaRc+oTSL8OWmzqgichM4CYYxycKqCijPaf21J2EPHQc1DrDYWJKSWDKoiCuaNMx3eJFtnd2St0U43iioYqlk3rMWPPU6YUYPOal85GK+4v9w4NhQmXGCHLcPrGkpNXxOCPmoCP80iSm3OGKgmQUxflamBkvWY5Mm6W8hmlczoj9JCwjuZQSUeogipMFDrbdVNZJ+SpGYA9wboEMy9HUHsIGbi7doKcUTtPvqU74o5X16ruVRuPjL37x57bpKCtm2R9FxJOGFD9X8PkBPv/fC51O7dTly3wF5/FmGJ4KmuF50D7aCdVTcLveg36u4SRZhv6kOYoPQ1llChDsAbf3/9tpBf9qFP1mZfhCbaheunRp7+VXXnsJ79Zkp9JdLscCIcAJ9q3NbXWAZ1horOLdutrLGkYvj59nKNh2Guts+zaeSd3DBJE/7HIIAv0iQG9q2Y1N+uUt9IuDAD1lNCz0RD3yZKZbj+sT8nmVskaCvWLF3s50BZ3S9E6+SWdzLUHk28oUZ5IRMXK9eqLYVwHX1/LPqEY8GMBmyPOoRoosTMDrzE5JQxW/461apXJrubZ0q93p1NvGNFvGfO8IAONkHF4lvSQSQeThRswcbgcy7h7GqLMU+meTfUQSm+mS58VB20HHxsaohy5mRyEUoOtEsjz0ph29OCY8YmW1EH7xiNpmkyZTvhMEuBPnLXxuAKtNYEijdA8LeN9uBeqVsNN5PayG71WXwxtwV27C87/77LPPRs8yJEzGGXs6CKjzzehzGSGee+38zuXLl5dbzdbRIFg+BmP6bBhW7se15hIurg+C5gSusesYb8cxIk5hOPB8MYMDkQU7ruKM+uMXXjxHO3GshzZUYR23X3rp9dc6Yf1foAd+fawaiLCpQIDG2d7egd4JeBmG6vLyMryrXM4/3wd39qWRSg+GLNmc774eZes4drgqQcbQKFFeDN58Pt7cLMubDyHf/HsAyasDUj0RT6r0oNREReUJv4gyw78XE5e3G0+0y4lZYhvmkM179tb2dql3oWNut7W+uvrqmZPHv9ZotX+0u7+zub+9F27v1Y/Wm/WzGGf3VSqVewDnCQyqI6rT3oDXhJtW0KPEzWkySDNpPxHKtt+RHRPHkYgmGSw2Iwm7aFlEpp7DelS7isyNlSTbwzSHZay1p0rSoITzgsV4f2gbzvgP4Xn8GPFNfOg1vd5ud97A7YWfhEH77ZqqfHyw3Nlsnz6981cvXuRdtpk4Is+gXZbM5cOv4/NHOC+qMGDXm5XKqYqq3hG02/e3VPAIfDfnUY4bPAGM1/ZJnCenkT6Fz7xv5IS3w6h/1WyE/+K554Kxv69RG6oAWT355AOfvPj9P//9jgr/GhTinQQ5FhABehf5jCY/cggCgkA5BOjhaNrNb8pVESpBoAsBbqC0u2deiYVJVDyHThNixp3xYIKy7zk15+2sl3fYeX0vGl9dW89XZvPyaYq0shwWN9Srf+BNLV69EdyqVqq/X6tWf2t1dfkbly5ehOcrOb7yla9U7r7rrmOtSuW2+kHr+MH+3vH9vb2TgQrvxC3qB4JaeAd642inxWWQneOoCWMWRqzq6Hljlw8UxJlhaYR5ujSbVeD7BB9bg2G5IzVubfVyVROq8uKSOrMd20OTP8LKjCvYkOgjwH6j3W69h459DWtg3w7bwbWDWmuzcuzY7q1Ll/afC8ZvtIwDXlx76f213tefIv4tLh0+8eabR7DicB0P0Z4Mm5072p3wPtDeh7GG12fA29runMBmL7djvN6FOvDQzsnS4Y56vak6//f995+9hjaN/YgNVYDdfuGFH78U1Jr/DwbnPxi7JiJQEBAEBIEZREB7U/EqkVZ77DcaZxAtUbkXAlzO2cx9LY1TMzULd/J7RTN1MklvzZ402cIozaDM/D5b3Sjgz/Uqt4CZXBa+ubmJlU+F+81s4fG/r2Gr0//l4GDnTy9efIJLM1PHc889xwvW9egTl73++uvLn3yyf7zZ3D++39w5urPTPN5uNW6r1irnYVDeCy/SbfA40Xg9iY6+DVNxepS4jNjfeTo3XZROxaKdCCncUVRUw5i6bg2HmdEsyzJF4E/k3ivyk89SLg2xa1hI93YQBu9jGfgnQYhnS3UYvqXarXcq7eDDpaX29Q8++GAHY6VwwM1S4wfRNVo6zOXO/HD56ys4D8L33nuP3tTVer2yHoadk+1O6w488/oAbvjcBXxvw82bO7Cy/TzOjrtBx6XDM3YEH+DGxf968+MPvxVcuCv3FBtlo2JDlUKeeurBT/Cqmt/pVCqfwzXiS6MULLwFAUFAEJgHBDh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"}))),I.createElement("image",{x:342,y:60,width:51,height:83,xlinkHref:"data:image/png;base64,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"})),I.createElement("image",{x:370,y:82,width:51,height:83,xlinkHref:"data:image/png;base64,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"}))),I.createElement("image",{x:342,y:60,width:51,height:83,xlinkHref:"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAewAAAMYCAYAAAAJttRGAAAABGdBTUEAALGN5fIAKQAAADhlWElmTU0AKgAAAAgAAYdpAAQAAAABAAAAGgAAAAAAAqACAAQAAAABAAAB7KADAAQAAAABAAADGAAAAAAFC7GMAABAAElEQVR4Aey9CZgcV3nuXz3aRvto3zdrtSRL8iIZ4xUbYzAJtlkdMNsFshBCbsgN/8sSCCEk/EPuJZAAIQm5cBPAQBwciFkMNhiEF9mSbO37Lsu2bLzJtiRL7vv+jqfk1ng0M93T1VXV9Z7nqeme7uqzvOfUec+3nO+UIicjYASMQA4RKJfLJaXyAw888N+ffvrpIYcPH37vM888M2HPnj3RI488Eh06dCh6/PHHo+PHj0e6N+rXr180fPjwaOzYsfF106BBg24aqDRhwoSvKK/H4zxzCIerXAAESgVoo5toBIxAkyKwZcuWzz/xxBN/8OCDD0Z6Hz366KPRww8/HD355JPRkSNHoqeeeip67rnnThC2uDlqa2sL14wZMwJxjx8/Pho9evTPROa36/1HmxQqN6sJEOjbBG1wE4yAESgoAiLna++9995o48aN0YoVKwJZQ9RI1RB1TNbAIwk6amlpCZK2JOtozJgx0bhx46Kzzz47uuCCC17Wt2/fcbrNhF3QsZSHZpuw89BLrqMRMAInEJCqe7wk6a9KDX7F8uXLo9WrVwfpGgkbFTgk3VWCuPv37x/U5g899FAgcv6XOn3+9u3bnxgxYsSbRo4c+cOu8vB3RiANBEzYaaDuMo2AEagZAdmoX3LgwIErRK5Bql6zZk2k/yHcbsmaQrFnHzt27IS6fO3atZHIP/z/7LPPDp09e/YP9u3bd+nkyZN/VnMl/UMjkAACJuwEQHWWRsAI1B+BgwcPvlq26pb777//8lWrVkWowtetWxft2rUr2Kwh4p4mVOZcclSLHnvssQhJm9/zmUg7Wrx48TeU14Se5uf7jEAjEDBhNwJll2EEjECvEZAj2X+JrKMdO3ZEv/rVr8qbNm0qicSDdFwNWXesCL/FoxxJ+9e//nUgfzmmjd+/f/9fT5o06YP6Xlr0Us9XAx0L8P9GoE4ItNQpH2djBIyAEUgUAdmtj2/bti0SUUe7d+8u7d27N0jHqLd7m8hD28OC8xplSOIuyxb+J/Iyv9Jk3Vt0/ft6IWDCrheSzscIGIHEEJCUO10Sbwl7NRcqbLzBu3Mwq6ZCsYoc4paqvYTKfefOnV8gD6TsavLyvUYgCQSsEk8CVedpBIxAXRHQ9q2fiqRb8ATfunVrkKzrSdZxZVGPs5d7w4YNEXu2hw4dOlU27i9Jyv69+B6/GoG0ELCEnRbyLtcIGIEeISBHs9fJi3sUW7YIioKdGekacq13Ik88xvE6l6c428Ra5IQ2r97lOD8jUAsClrBrQc2/MQJGoCEIiDDPlaPZ34uo24hmRuSyo0eP1lUVXtkQpHa8xpHkCWV61llnUd7xynv83gikhYAJOy3kXa4RMALdIqBtVxdof/X42HsbyRoHsSSkaypDvmz1Yk83dnJCnErCfrbbivoGI9AABKwSbwDILsIIGIHaEBB5PotNme1cvMKdSZF1XMM4f0ibbWNSj1+g8m9V+Yvje/xqBNJAwISdBuou0wgYgR4hgIoa4sR+jTocT+5GJcpGwhZRD5Fd+2W6pjaqbJdjBDpDwITdGSr+zAgYgUwgAEFDnPEr0q88thtSN8qiXKT69oVCYwpuSOtcSB4RMGHnsddcZyNQMAQaRdIFg9XNzRkCJuycdZirawSMgBEwAsVEwIRdzH53q42AEagCgdgRrYqf+FYjUHcETNh1h9QZGgEjUG8EYts1qvFGkmdLS0sUX/Vuk/MzAtUiYMKuFjHfbwSMQMMQ6NOnT8RFEBNeG2nLhqhbW1sJTxrClKoORxvWcBdkBDpBwIFTOgHFHxkBI5ANBCDN/v37B8LktZGEzSJBx2xGo0ePXj958uTLVPaD2UDFtSgqAibsova8220EcoCAJNx+w4YNi8aMGRMk3VjKTlIt3rdv3yDNDxkyJBo0aBDS/f0m6xwMlgJU0SrxAnSym2gE8oqAyPNHI0eOXD9r1qxo3Lhx0YABAxJtChI8ZYwYMSIsElgsiLQ9TyaKujPvKQIeiD1FyvcZASPQcARGjRq1XqS5XWrpQKKDBw8OdmVU5UmkmLC1SEAVHqR6qeIdMCUJsJ1n1QgkM+qrroZ/YASMgBHoHAFJ1leJqA+gFp86dWo0adKkCOKGXOudyHP48OHRzJkzo7lz5wYpWypxH/5Rb6CdX00ImLBrgs0/MgJGoFEIyF5dEllPnDBhwvHZs2dHp512WoSqGnt2PRNkjaOZpPro9NNPjxYtWoSU/ZCk7VfWsxznZQRqRcBOZ7Ui598ZASPQEAREpGUKkl25DxI2x1/u378/4shN4oxz9TahYh84cGCQrrGVT5w4MRo/fnwkx7OHe5u3f28E6oWACbteSDofI2AEEkVAUvYXL7jggveOHTs2EPbDDz8cTvE6evRor0kbyZp8p0yZEi1cuDBavHgx7/9Fi4T/m2ijnLkRqAIBE3YVYPlWI2AE0kEAtbjS7+tc6vdycpb2RZclZfNZ9Nhjj0W1kja/ZxsXW7gg7OnTp2MnLyv/kl7flU5rXaoR6BwBE3bnuPhTI2AEMoSAiDWoxaW6XqRtVy+7/PLLPzdt2rTozjvvjNauXRs99NBD4dzsatXj2MGxWc+YMSM699xzo6VLl0bz588vicTf3L5ICOVmCApXpcAImLAL3PluuhHIGwKyK6994oknjsgZbI0cwxZhd4Z0d+7cGR08eDCQNtI2UvixY8fCRZAVJGku7iViGq/stybsKFL1kiVLogsvvBDP8H8RYb/LZJ23kVGM+pqwi9HPbqURaBoE5CG+RfbrL4twP37mmWeORZ29d+/eaNu2bUE9/sADDwTb9qFDh6JHHnkkkDdq75ig2WMNUeNYFqvBFyxYAHE/JZv1rwEqluibBjQ3pCkQqP9GxqaAxY0wAkYgqwhUSr8HDhz45qOPPjpGUvdlq1atKks1XoK49Xkg6927dwcpG6mavdsEQ8HTHKJm65b2dJe1Tayk15skvf+xiHpzZf5ZxcD1KiYClrCL2e9utRHILQKx9NtOrL9FQ/bs2fN1/f9mkXfYjoV6XCQePfjggyck7MptW9itsYHL87wkqfqb2uP95hiQOP/4f78agawgYAk7Kz3hehgBI1AzAvfcc08/qblf/cwzz7SImD8q8j7z2WefjY4cORLOz2afNRfbtyRtl+WcdoO+/6aimj2n7Vs/Ekkfrrlw/9AINAgBE3aDgHYxRsAINAaBxx9/fKQczgZRmgg8FIp0HSedAHZcB3o8KZI+FH/mVyNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI2AEjIARMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI2AEjIARMAJGwAg0FgFv62os3i6twQgoxnSr9twO2Lx5c5nzk7lI8WvH6hC2svLS2chHdTCE9+h2BMr/GwEj0HAEHOms4ZC7wEYhQCQshaq8XtdVnOL01FNPRcSX5jhGAmpwOER8uhOHQWh/bkSwDf0uxJ0m9rS+/4X+f6X27D6/obdRlXc5RsAIGIEOCJiwOwDif/ONACStFI5EVGjKt4igrySe9H333Rdt2LAhHBKhM5UDeUPOnOpEgrAV9SpSQI1IsaU5tSlSBCxiT18kgv+0bvnDcKP/GAEjYARSQsCEnRLwLjYZBCBrndb0Lc5HXrly5Vlbt27tt2nTpmjHjh3Em450ylNQhx8+fPgEWcc1efrpp4NkDZEjiXMCFJK4iPu6FStWzJk1a9ZfSl3+y8pFQfxbvxoBI2AEkkbAhJ00ws6/4QgoNOUbIWfZraObb745QsLmIAhU4pyVzBWrwisrBzlD1lwcy0g4S6RxneQ08tWvfvUrJW0vle37Bi0Kfqfyd35vBIyAEWgEAibsRqDsMhqGwP79+/ciUaP+5nXjxo3hxCbs1bH6+1SV4XuuWMLmDGV+h4OazkrmZKdROoLxzYpP/Q2R+W2nysefGwEjYASSQMBe4kmg6jwbjoDszDN13SJpetpPf/rT6KabbgrOZZyLjPobR7JqkyRpTnYKl9Th0Zw5c8J17bXX3r1gwYJl1ebn+42AETACvUHAEnZv0PNvM4OA7M9v11nI0yBoSdnB/gxRx8cr1lJRSB71OVK27OLBMQ2p+8knn5wjqfsjOkf5syL1p2vJ278xAkbACFSLgAm7WsR8f+YQkGQ9Y9++fVfIMSyowbdt2xacxiDaWiTrygbye9TksouH85Sxa2/fvn24JO+/wDFN339GpH208jd+bwSMgBFIAgETdhKoOs+GIiCb8nxdy9i6hc069u6GbHtL2HFD4nOVH3zwwYgFAZL24MGDPylJ+2u6Z198n1+NgBEwAkkhYMJOClnn2zAEJOkekjo8qK3ZzoXk25kXeG8qBPHjjIanOV7n2La1X7s0bNgwP0O9Ada/NQJGoMcIeLLpMVS+MYsISOL9hOzLH2OfNZLvrl27gpNZEnVFNc7C4Pbbb4+kgg8OaEOGDEmiKOdpBIyAEXgRAibsF0HiD/KEgBzLPibSJk54icAnOJnVW7qO8UDKxi4um3kEUePUxuVkBIyAEWgEAi2NKMRlGIEkEFAwlNHskZYTWAnPcOzMkHW97NYd6xyrxQnCAmmzOHAyAkbACDQKARN2o5B2OXVHQNLuy7FX4wiGqhrpN+kEaSNVszhoRHlJt8f5GwEjkB8ETNj56SvXtAMCIs/BldJ05fsOt/pfI2AEjEDuETBh574LC92A6sOXFRouN94IGIE8I2DCznPvue5GwAgYASNQGARM2IXpajfUCBgBI2AE8oyACTvPvee6GwEjYASMQGEQMGEXpqubt6F2NmvevnXLjIAReAEBB055AQu/yxkCOnSjf0tLS9SvX79wkhbHYTYiUU6jykqyPVroqBmlXDvuVbZB76fokJaPaLvdRXrfqusEfLzXWOmjGPCr2traXq92Hz/xpd8YgZwgYMLOSUe5mi9GYOTIkf+wcuXKL+mYy2jo0KHhQI4X31XfTyBqFgjEEu/Tp099M08oN5FVX4Vv/SMFevlriqANOnVslV7PTqjIhmWrNpQVP/4+HXm6aPny5SHWO3vzO4t4x4Etiv0+dfTo0cfWrVsXibi/OmnSpPcpj6caVmEXZAR6gYAJuxfg+afpIzBo0KBo1KhRTMQNIVAkesg6y4R9//33D9JiYoKCuzxHD23YsKFNZPXXBw8eFHeXSyw0hg8ffpaiw+1VDPZFY8aMadOip6R7jgrLfen3avc1kCQ9UgexYNL7kQh7EYe+cFobke/iA2AIbMPihISE3draGo0fPz6aNWtWGDM6vOUd+vwdipS3VONnv3A4EG72HyOQUQRM2BntGFerZwgopvemyZMnz9NhHGWRdwnp9+jRo4mEJ2XyJ39J9pEmd6TUQAI9q2lj7nrkkUfOE2n9N2HwbmKrS/IMUqfIuSySC+xFO9BISMKcLKL+NTUjcptI6xkR+JskgW7R+82NqXH1pegI1VHSGNykNp67Z8+eiLC0+j/asmVLiHgnDCLaXhlTHsIeMGBAuI+oeGhl+I3aWp4yZcrdWsTcr7ZfIxJfUX2N/Asj0BgETNiNwdmlJISAzqS+QgT6KZH2dUjbECqnaiURNhSiY9KfMGFCpPIC6SXUrKqyRWpW4gCUv5A6+H06sWz4vffeW1bI1hIkRihVEViJhQxtIMVqfTQTLD4mTpwYnX766QMXLFjwPWG6XHm+VvcerKoiCd4ct5EipAn43Jo1a86lbVu3bg3nnyNZE+MdVTgX/Q9JVyY0C9IyRJA8iy1IW4Rdmj9/frR06dKJM2fOvE0Ll8v13fLK3/m9EcgKAibsrPSE61ETAiLrPZKuvypV53WoO5GemLwlTdaU36l+BNFhA+WUrhkzZkSa3MOEjyr5VL9p1OeQtaTFb4mI3gghya4f3XPPPSWOAMVWG2scKgmM9nCJnKNx48ZFkjKDJM5iR+17qYj/buF4nfDNBHnRRrXtVrVzoNr1krvvvjuSpB2kapkAwhno4F3Zxsr3fEd7SZg1IG9U5NIyhHazgNHnrVqQ/VDEf4UWMreHm/3HCGQIARN2hjrDVakNAU2yrUhLixYtCrblzZs3B2mr44RdW+7P/4pJHuk6LmfevHnYQbeL8FJ1WBKpvkrS89+KtObccsstQT0sm2wkKTssXlB1Q8JdJUgeiRTJ9MCBA9Hs2bNbzjjjjGmSuH8pcpwrbcKWrn7fqO+k+r5k1apVJdonDUKQlB9++OGg/u6ujdQxHg+oyuNT3fgMKZ3+JQ8tboZILf6rTZs2naY+3tmotrkcI9ATBEzYPUHJ92QaAak1V0iy/sFZZ511JapOvIRRl8aTcj0qjwQWO7gtXrw4kuoYCe39sgU/VI/8a81DmoRLZKeeA4ndfPPN0c6dO4OGARsuBNQdkT377LPhqFBs3ZC1VM2oxgN5g+XChQs/LZvwO7Q4eaLWOvb2dyLUAVpEHFHbnrv11lv7bNu2LZA12hS0B5W26p6WxW/4LQsVFjdoZPgMPMDs7LPP3qFyWym3p3n6PiOQNAIm7KQRdv6JIyBV50GRzXckFZ4nAm2TVFlCamRCx7GIibnWhDc4kjXSu8grqMKlMn5CquI3SD1+c6359vZ3kgxbJFX/ibQJH/z5z39elsNUSVJhhMSJzTqWJrsrh/tiYo9xQuJEfax8ylqUXDNnzpxr5Hk9Z+zYsVu7y6/e32vxNV6q/h/fcccdi37wgx/g8R5s1rG9urflQdJoFnBUw3zAwgXSxkwwbdo0tr1ZNd5bkP37uiFgwq4blM4oLQREOuKX0lf1ukbkepecrfqKwCJsuNizYyKqtn6QFoSNnRfCvvzyyyPt22Xr059D1u3lnuzZVG0hNd4vMp0i6fDTkojLP/nJT0p4SrNIoa2QcG/SY489FiRtqYmDQxaOaVKLb1Ce/RrdZhHp29SPi7BXsyBhAUb9kIzrmcgPDFkIYPaQtqY8ffr0X6m9ozUOHqlnWc7LCNSKgAm7VuT8u8wgoAlV82og7VWSBH9X+2z/mQlXxBo8o5mIUZOjJtZ93apQIWnUwVx4g0uyjORJXJajWUmSdV95Vf8vGk+5jQahvZ1lkdfXkKalzi2hyuY9ZF2LerhjGyAv8GI/M6p2nLNkEjgmovxttfkfO96f5P/SlhxG+qUPecUmjwRc78S4IF/aTTlaJJT27t1Lef+qsq6Mca93uc7PCFSDgAm7GrR8b2YRiMlT5PqV9evXr3rNa16zSmRTlhdwCbssNm28iSEjJmYm6M6S8glBWCBptjudd955kVTC5SVLlpQkVS8ViafqZEY7RSiflL36YrUzeEpjf0WtWw+yrsQESXb16tWBwKRlaBUmv6XvG0rYtAstCdoSiDQJsq5sMxhiSkGiZ0eA/BUuEWlfJtxvqbzP741AGgiYsNNA3WUmioAcwlZLOjxTUuHq2JbNZB8TGx7BTMyVpA1RxxdBRSBr9lvL6QjbdUlk9Tmpw1frnq5drhNt2fOZi7Q+iPQn6TqowbFZd+dcVku1kGZZ5JB3u2khRE6rJa9afwNB0z5U1bzWe1HSsV6MCdrNuEH9Lq3KQJX7e7rPhN0RLP/fcARM2A2H3AU2AgER7L0itFcrIMZNqLXPP//8MAlDPEiOcQQwCID91XiBQ9SowZGuCSSCLVOOR0+KvJ/VPX+fBbIGO6m+W3bv3h20BpBKve25cf8g3ZI/pgQkXGG2TIuetyqsKWrihiVItPJKsmDKYWGAiQGTAG0XviOSLNN5G4GeImDC7ilSvi93CMhp6AeKN32lPH7fJ5LrJ8LpJ0ntEtl8USuXUH0iwWGjxRMcqRoHKzyEpQ79sbZxHRZp/41IfHmWGg9BQyjYrtEaJCFd016wYXEDcYOV7LtDhOHlIrXrtXipvyE5IyDTbmzZFVJ95/aTjNTX1SgOAibs4vR14VoqYhGvlH6ohnOFJOel/5CEeA1EF5MdZI2jGVGv2Gste+0XRdq/H/+mPZ/MTNpoBZD8tBhJxHZd0e5A2pgQIG6kbWkh3qLv/4+un8X3Ndsr+LIo4uI9UreTEcgCAibsLPSC65AIAiLrF820+uw6SdFXSAV+RAFXxmsy7isJdZ9U3i0Qt66SJO3vV1aos3wqv0/jPVIgkm9S0nVlmyAsJGyCsmgx0yKNxCJ9dptwabhNu7JeSb5X25LM3nkbgZoQMGHXBJt/lFcEZJt+WnX/LvUX6TAra27OF/FAoBA1F++TTpSBRA9pI2lL4n6T1MXYscNJX0mX7/yNgBF4HgHOk3UyAoVEQESNzrxppcR6dioSPd7TELcI/DypiwfXM3/nZQSMQPcImLC7x8h3GIHCI4AtN5bqCw+GATACKSFgwk4JeBdrBPKGQCPU73nDxPU1Ao1EwITdSLRdlhEwAkbACBiBGhEwYdcInH9mBIqEAFu7+vTpE86Npt146hWp/W6rEcgCAibsLPSC62AEMowAqnCiwREFjktcvUH/423vZASMQAMRMGE3EGwXZQTqgQDCbXzVI7/u8qAsQrfGAWZ0/3Z5jdd+yHh3Bfp7I2AEOkXAhN0pLP7QCGQbASKzQaCoqZNOEDYnV40fPz6EbZXE/e86uvSJpMt1/kbACJyMgAn7ZDz8nxHIPAIQKPHPFXUshFTl/yQS+bIgQLoWQUdTpkwJpK1yTdZJAO48jUA3CDjSWTcA+WsjkDUEsCePHDkyHFZCvGuCmvBa70Q5nGDGxclllKmFws90/aLeZTk/I2AEukfAhN09Rr7DCGQKAanD++rYzxAmlIAmnCqVBGGjcufkMo4bnTp1KoejrNAZ4a+Q5F3/1UGmEHZljEA2EbBKPJv94loZgVMiIMn32yLOoKJGVZ2ULRu1O4SNKhzpWh7iT5msT9kt/sIIJI6AJezEIXYBRqB+CMjhS5xZetPdd999pSTeIRyxuW3btnBy1+HDh+t6GIiOIY0WLVoUnX766dFpp532rFTjt9WvJc7JCBiBahEwYVeLmO83AikiILIOx3Odc84543QM6FM68rIsKbiESvyRRx45cYZzb6qIoxle6Mo/mj9/fnT22WdHkyZNOiwC/0T7giH5I8J60wD/1gg0KQJWiTdpx7pZzY3AwYMHW+S9/b05c+aUzj333PJZZ50VHMPw6Bap96rxOJnNmjUrWrBgAUQdPMSlhv8QmcYLhl4V4B8bASNQEwKWsGuCzT8yAukiIEewQ5J236Azqr/x2te+9nVbt24NTmh79+4NrxyFGZ+w1V1NCTvKhVc40vX06dOj8847Lzr//PODOlwE/hpJ29+3dN0dkv7eCCSLgAk7WXyduxFIDAFJu0claf+7CPWaQYMGtdx///3R/v37I6nJI4j7ySefjPR9OBbzVJVAImc/N/ZqvMFxZps9e3a0ZMmSsuzXJX3/CpH1T/i9petToejPjUBjEDBhNwZnl2IEEkFAZHq9Mr7+gQce2HXFFVdMg6BXr14drVy5MpC3JPDgiCbpuNPy8TDHA3zy5MnRGWecES1evBgHMzzDS3Jqe5vs44GsO/2xPzQCRqChCJiwGwq3CzMC9UegXVU9/eGHH75z4sSJZ0m93Q87NtIyh3XgPX706NEgaUPcfIf6G+l62LBhQbKOCRuvcOWxTST+OpH5mvrX1jkaASNQKwIm7FqR8++MQEYQQFXdTtovefDBB/+HHMbeJPX2OY899liwQ7P1i/e8krBXExsc5zJJ0SdeRd679f9PpV7/rsk6I53rahiBCgRM2BVg+K0RyCsCsX1ZKuy/efrpp7916NChqyVVH5WX9zFJ2X8q1fi0xx9/PDQPwsZmLXJ+UmFNf0/fD9HVVyS+UmR9Jze1LwA616PnFSTX2wjkHAETds470NU3Ah0REOnu1Wd/F3/+61//+i599iNFRZvEZ6jERdD3a6/1eVJ974nvq3yNFwCVn/m9ETAC6SJgwk4Xf5duBBJHQKS8Tp7jsxRq9ETcBW37ek7HZR5OvHAXYASMQN0QMGHXDUpnlAcEpOrts2vXrqEir9lSDX9WxHU+25/0eTiyEkcsonxpP/Lnjx8//kVplXfPmDEj98TWDG3Iw/hyHY1AkgiYsJNE13lnDgF5Un9ApPzX2HNxwpKTVrRv377gQc0RknhN44yl1/dLbfx+Sac3qBGvr2yI7buVaPi9ETACjULAhN0opF1OKgiIlCc89NBDV4uoh8iWO2j79u1/pr3KZX1eIhoY+5YJOCJp+oTHNB7UBBOR3TcaPXr0ZTfffPPfyUFrl7ZJtYjEn5Z99wupNMaFGgEjUGgETNiF7v7mbrwk4aEi409Iin7Pli1boh07dkS7d+8u6/9A1hyYwR5leVWHMJ4EEUElHl/tB2C0yVnrfTpsIzhrSQp/Vvn+p0h7X3Oj59YZASOQNQRM2FnrEdenbghoK9NFIuT3IEGvW7cuWrNmDYRdInznkSNHAgETbxv7NQnv6fiV98TV5rxpVOWSrMOrPu+nU7H+VvedpCYPP/QfI2AEjECCCJiwEwTXWaeDgAi4n0j56IYNG6Jf/epX0caNG4N0ja06DiCCCry7FBM4Evjtt98e1OecXnXRRRe9bv369WVJ5K9VkJLvdpePvzcCRsAI1AMBE3Y9UHQemUJAcbWPSqoui1RLP/rRjyLZrSMFEgmqb9TgSNU9SUje2LmRxtul83DcJEFHIPGFCxd+S9L2aZLCrR7vCaC+xwgYgV4hYMLuFXz+cdYQ0ClVl4iwo02bNpWwW3Ny1YEDB4JTWU+JurJN/IbriSeeCF7lEP7mzZuDGl0HZPSTg1q/yvv93ggYASOQFAIm7KSQdb4NR0AS8TSR9E9QX3/ve9+L5B0eSQKumaw7NgDiRqV+2223hYUAp1rJk9zhOzsC5f+NgBFIBAETdiKwOtM0EBBB/5W2bhEYJbrvvvuCOjv2AK9HfVCR41Uux7VIMbgDefO/kxEwAkagEQiYsBuBsstIHIFt27b9D6mqf0uq8AjCJjBKNfbqnlYQ0iZfCLsWFXtPy/F9RsAIGIGOCJyILdzxC/9vBPKCgIKgTJTX96vwAudCDc75zxArBOtkBIyAEWgGBEzYzdCLBW+DyHmWCPtinM248AhH+jVZF3xguPlGoMkQMGE3WYcWsTlSTw+RrboPkczYwqUwpEWEwW02AkagyREwYTd5BxeheZKuy0jU7JnmQhXuZASMgBFoNgRM2M3WowVsDyFEY/U3r/H7AkLhJhsBI9DECJiwm7hzi9S0OIxokdrsthoBI1AsBEzYxepvt9YIGAEjYARyioAJO6cd52obASNgBIxAsRAwYRerv91aI2AEjIARyCkCJuycdpyrbQSMgBEwAsVCwIRdrP52a42AETACRiCnCJiwc9pxrrYRMAJGwAgUCwEf/lGs/nZrC4iA9qX31WEoUxUR7qSzu7V//YGRI0c+XkBI3GQjkEsETNi57DZX2gh0j4BCtL78ySefHL1z586ZigT3ScVcL/Er9qz369cvamlp+c7WrVu/09bWtnPMmDH3xDmK4HVLyaemxID41QhkBAETdkY6wtUwAr1BICZZpOlHH330z5944om+Ohv8vXo/mMNQDhw4EOn/EAWOyHDDhg2LRowY8YZRo0a9QZL3rj179vyotbX12JAhQ34psv42dYnz7E29/FsjYATqh4AJu35YOicjkAoCMbGuX79+xV133dXvscceW/LQQw9FIu1wEIoORgmv/E+SZB2JmMMFcUstPl3E/bt6jYYOHXrd6tWrPyCp+1si7s+m0iAXagSMQKcImLA7hcUfGoH8IID6+sEHH9x05513zkWSXrt2bbR58+ZwEArSNWeDHzly5MT54Lo/6tu3b1CLoxrv379/IO8pU6ZEM2bMaJs3b965S5YsOVfnit8vIv9WfpBwTY1AcyNgwm7u/nXrmhwBnf/9KZ1Q9uFt27ZF9957byTVdrRmzZpo06ZNJ4i6JxBA2vv374+QzJHIIXJd199///0HJk6c+Iue5OF7jIARSBYBE3ay+Dp3I5AYApKq/1hngH9YavBIxBoIW1JxJBIPZK1jR3tcNvfK3h1xprhs2hGSuf4vX3LJJbfJw3z28OHDt/U4M99oBIxAIgiYsBOB1ZkagWQRkOr7z+UB/lGk6VtuuSVIx7t37w5qcFTgELBs2z2uBPeLmANRx7ZvvMqnTp2KpP11Sd5fGjt27Fdje3mPM/aNRsAI1A0BE3bdoHRGRiB5BGLCPHjw4J9C0NiqtW0rqLIhWqTjWhOkHRM3ZI+a/Z577oHEl82fP3+ZFghPxx7ktZbh3xkBI1A7Aibs2rHzL41AwxHAwUzbs34XqXrjxo2R9lEHskY6hmzrkY4dOxY8zHft2hXdfvvtwS7OVrDRo0e/W/mHLV/1KMd5GAEjUB0CJuzq8PLdRiB1BKSq/vyWLVsibeMKZCrJN3iB16tiCrISbODYtLdv3x5pm1i0cOFCXvvUqwznYwSMQPUIOJZ49Zj5F0YgVQS0RauEGhwJm21cvVGDd9UQbOE4sVEGjmxaGFy6b9++f+nqN/7OCBiB5BAwYSeHrXM2AokgAEGzBQuPbtmyw/7qJAqiHKRryoCwRdZl2cnfKY/0a5Ioz3kaASPQNQIm7K7x8bdGIHMI4BAGmXLxPsmEepxyIG2px0vYtbXv+yKV25pkuc7bCBiBFyNgwn4xJv7ECGQeAaKVcTUqPfXUU5H2fQcVuRzQ3idV+ahGle1yjIAReB4BE7ZHghEwAt0iEG/5QtqW1N1Xi4XCzB2NXBh12xG+odAIFOahK3QvN3nj4zjZqG9JSauJmxzOwjeP8RNfLFTY5uZkBLKAgLd1ZaEXGlQH7dmdqZOYBsnrt4wHMFfekybUsqJ9TaMtxMCGvJlkTdp579n06h9va8MMoDPFIwWoGfKud71r2mmnnTY0vVq9UDJx37kGDx4cXl/4pj7vyJsUlzF+/PhIh8D0mTx58n31KcG51IqACbtW5HL4OzkL/ae8ixdwQASTEZJD3okNdSXBQ+S5XJbXdIk9yRC3kxGoBQHImmcDr/jDhw8HZ7tf/OIX5+oY0l1EfstC4qQ1jkeFsAcMGBBOXqun2p7jVzn8hbx1zGo0cODAUJ6erYU6fnV9FjAoah1M2AXpeRHzWZp4Rvzyl78Mxy8qWlaQsPNO2HH3aaItIWXTnmZpU9w2vzYWAez0LGZjRzvitWcpIfmOGDECqTcaNGhQkITrRdjkA2FD1iwIJkyYEOYJFglatHxLz9ZS3fNMlvAoUl1M2AXobR21eKnOSL5RkuhQ7aUNoSyRRGObbwEgcBONQI8RiBd9PB9ZtF9DqJh+CEcLeRM2tl6ETdvJnzwhbSR5AvToiNVo0qRJC/T/qhtuuOEpnZteGjdu3DBJ4mfqUJhDPQbXN/YKARN2r+DL/o/1AA6RKu86EfXQOMwkD7vJOvt95xoagc4Q4NmN/TX4vl5kXVkWecYXB8xIFR7piNVo5syZ884555xA6qjNReD2yKsELuH3JuyEAU47eznNTBJpvxObHJfs2HU7JCLttrl8I1BUBJCEGyX9s8DHTIBNH5KGuJG+JYmXFyxY8IwOiBn00pe+1GryBgxGE3YDQE6zCBH0McJL3nfffdGqVatCqEm8qJ2MgBEwAj1BgMUBpM0CAS0dseW12yRatmxZSeekl5cuXfq0yHxma2vrjp7k53tqR8CEXTt2mf6lHjJptEpl2biuhbA5xKHZHM0y3QGunBFoIgRQw8c2fZzxIHDZrnFKK02ZMiXS1q+b1dxZTdTkTDbFhJ3Jbul9pSBrbXO6UVu4fhOnEW17Ck4qrJJZMTsZASNgBKpFIJ47sKFzxKsOgwnbyuS1Pl5zzX/Ipv3xkSNHrq02X9/fMwQc6axnOOXyLq2Er1LQh5a9e/cG6dr261x2oyttBDKHAAt/YsuvW7cuELdU44NF4tdoa+XCzFW2iSpkCbuJOrNjUx599FGORAw2J9RY9gzviJD/NwJGoBYEkLQhbS5MbTi0sjdc28EsBNYCaA9/Y3B7CFTebpN3+D4k65UrV54IlBKrs/LWFtfXCBiB7CKAf8yGDRui9evXY2v7N/2/ILu1zXfNTNj57r9T1l6BUfoRWIHVL3YmtmWYsE8Jl78wAkagRgQwtUHa0uiVcEbTPONtKDVi2d3PrBLvDqGcfS/V9wSR9cflANKmuOERccOxNZmwc9aRrq4RyAkCCAZycA1hUg8dOtQUhwplFXoTdlZ7psZ6iazfJIn6d+QEEk4aYksXK2AnI2AEjEASCOAxfvDgwTDfMNcgZTslg4AJOxlcU8tVq93hbOFiywWStR+e1LrCBRuBQiDAHINgwAV56/VwIRqeQiNtw04B9CSLFEmXOW4SZzOczggn6GQEjIARSAoBpGp8ZTg7nNcxY8bcKTu2hcEEADdhJwBqmllqtdsfmxJOINiTHIY0zd5w2Uag+RHAmZV5Bj8ZHFzlfDZO27yubv6WN76FJuzGY55YiXpwJumw+atwNNMJXcGuxBnRTkbACBiBJBGAtNHm4eiq43zLEhq+o8/6JVlmEfM2YTdRr+th+a6czhYSxAAnEEvYTdS5booRyDgC2LKZe3bu3FlCPS5b9scyXuXcVc+Enbsu67zCIus9IumlCkUanD94eBw3vHOs/KkRMAL1RwBtHqY4TvNqj/3wlvqXUuwc7RjQJP2v/ddTUEfxsEjKDmTtUKRN0rluhhHIOAKoxLFhQ9iclQ1hS2Don/Fq5656JuzcdVnnFWY7BWooLrw2eYCcjIARMAKNQgCtHvEf0OxxjgEE7lRfBEzY9cUztdw43IOHBdu1CTu1bnDBRqCwCKASR2BAeICw9X+QGiQ86LTfkiWIOowM27DrAGIWskANjjqcYCk8ME5GwAgYgUYiwNYu5h7U4Vxyem3T62+YrOvXCybs+mGZak48IByliR0badsq8VS7w4UbgcIhwJyDOhxJmx0q2to1RNf/ksZveuHASKjBJuyEgG1ktnpQhiFhow7H6cMq8Uai77KMgBGIEUDKhrQRIFCPi7Dn6Lo2/t6vvUPANuze4ZeJXyt2uCIC/jp4h6OScnSzTHSLK2EECokAkjYSNj41o0aNihTMaUAhgUig0ZawEwC1kVmKqK+ErAm8z6o23n/dyDq4LCNgBIxAjACELak6mOg4iEgCxHki8HHx936tHQETdu3YZeKXUoH/k86i7cNq1gd9ZKJLXAkjUFgEIGsuIp4pRGm0YcMGbNpXyEy3tLCg1LHhJuw6gtnorETQl2vlOoBVLBI2tiMnI2AEjECaCEDYzEecaYAjLGY6BXHypuw6dIoJuw4gppWFyPoarVxHsZpFBeVABWn1hMs1AkagEgFs2DjAckHYmOqceo+Anc56j2FqOcg2NCTezuXIQql1gws2AkagAwLx1lI8xSFvXcc73OJ/a0DAEnYNoGXlJ3iDY7dG/WTv8Kz0iuthBIwA5jm2l0LcXK2trW+TJnCwkekdAibs3uGX6q8JUIBkvXXr1rCFwmqnVLvDhRsBI1CBAIcPIVDoBMGy/GzeqvnqHRVf+20NCJiwawAtKz+RhP0s6iZCkiJl24adlZ5xPYyAEQABhArZsUuKwChftPLfG5XeIWAbdu/wS+XXihd+mqTpz8gD88x4O5c9xFPpChdqBIzAKRDAWxzCZo4SYZdQjTv1DgETdu/wS+XXeghGS6J+Ldsm8BD3udepdIMLNQJGoAsEYsLmQKIRI0YE57MubvdXPUDAKvEegJS1W/C4JHY49mscOyBsnYiTtWq6PkbACBQcATR/mO0w2SFtO/UOAUvYvcOv4b/eu3fvQBH019esWRMpwlk48IOVLJeTETACRiArCDAnIVDs3LkzBHWCtPXZQAkXz2SljnmrhyXsnPXYgAED+mjQz2XwE5QAu5BV4jnrRFfXCBQEASRstIHMV7xqX/Y3abrmMKsEaxgDJuwaQEv7J2zfwi6EDZvABBA2q1knI2AEjECWEGBeYr4iTgSkresqbfF6j6RsT1g1dJQJuwbQ0v4JextxNoOwOanLEnbaPeLyjYAR6AwBCJvtpqjGIWwEDM1fH+zsXn/WPQIm7O4xyswdqJHGjh17CMJGFY6KKXY6y0wlXREjYASMQAUCkDaqcQibLV5ylh2oz+ZX3OK3PUTATmc9BCoLt6FGIgDBqlWrgv3a8cOz0CuugxEwAl0hAGHjIU5ExoEDByJxT1L6sH5zXVe/83cvRsAS9osxyewnBw8e/BKOZhA1NiFUTcQTdzICRsAIZBkBzHbMW5jy2gWN57Jc36zWzRJ2Vnumk3pJBf4OtnJxxizqcNuuOwHJHxkBI5ApBGKVuASOaMiQIdHUqVMRNkzYNfSSJewaQEvjJxr0gxSA4ChkTexwbNdORsAIGIGsIwBhowl8/PHHYxs2hD006/XOYv1M2FnslU7qJLJ+q0i6lVUqKiWfzNUJSP7ICBiBTCKANhBHWRzP2lXiV2su+81MVjbDlTJhZ7hzKqsmz/A3Sw3en61c2scYBj8rVycjYASMQNYRgLBjL3E8xbUdtUVz2vdE2mdlve5Zqp9t2FnqjS7qInJuQarG6QzVkiXsLsDyV0bACGQOAUib7V1sS33iiSdiocNCYxU9ZcKuAqw0b8UjnEFOXF5WqATUdzICRsAI5AkBbNnscMEPBwe0MWPGjM5T/dOuq1c3afdAN+VLsg4xdyVRH4Ow2RZBtCBL2N0A56+NgBHIHAKY8XCYReggvHJra+sf6bMBmatoRitkws5ox8TVIljKtm3brhFRn4U6HJUSg97JCBgBI5A3BGLnM/xwFASK+ewVIm5renvYkQaqh0CldZucM16lrVz/sX379jDAUSmZsNPqDZdrBIxAbxCAsNEUbtmyJcSRwHN8xIgRPrmrh6CasHsIVFq3aQU6JFaF42XJgJfUbdJOq0NcrhEwAjUjgMCB/83evXtDmFLs2WxVdeoZAlaJ9wyn1O5igEPY2HvYvwhhW8JOrTtcsBEwAr1AIFaJow5HLQ5hjxw5cmovsizUT03YGe9uHDQgagY3R2lC4E5GwAgYgbwiwByGKpztqUjXOhhkvQSSV+S1PY2stwm7kWjXUJYG81GIGhUSg5vBbgm7BiD9EyNgBDKDAGY9HGjRHO7atQvP8R9mpnIZrogJO8OdI2Ie3L9//99gGxcDO7ZhZ7jKrpoRMAJGoFsEEDo4cpNtqpyPIGHk8W5/5BsiO51leBBoEA9VwJR3oxLHjo163NJ1hjvMVTMCRqDHCBAMCkEE7aE8xXv8uyLfaAk7o70vYi5pQB+QOryMZI3Nh5B+JuyMdpirlTgCHvuJQ9ywAuhLJGwinu3evTsEUmlY4TkuyBJ2RjtPavAJeFLKvlNqP93G519ntK9creQQwKsYSYzIfvbfSA7nNHKOCbtv377RjBkz0qhC7so0YWe3y74GYaMu0ildlqyz20+uWYIIQNI4J0HavDo1BwJI2PQn0Rvlp4PHeB99NkDOaEeao4XJtMIq8WRw7XWuWn2+HGczLtThSBpORqBoCDDuka5ZtOLDAYE7NQcC9CX7sPHPkblvsDSJ/ykCP685WpdMK0zYyeDa61yZpLDv4EHJgLb9rteQOoNeIpDGGOQ5iPfr8hxYyu5lJ2bo53EQFXx05KvTR062V4jAv6B+npehamaqKibsTHXHC5Vh9ckEhf0aZzNL2C9g43eNR4Dxx8W4hLhblBpRC2majjP+0TJB3mksGhrRziKWQV9i6oj7l/lOfXym+vqMIuLRkzbbht0TlFK4B/Xfnj17QlABJAwnI5AWAhA1wXs4i72trS2ophmfSSapRoepjMc3bNiApqmsid0HRCQJeIp5ozVBMOFAkKlTp3JGdoq1yXbRDVklZxuC7NROK84wKem1Hx6U7FHE8YyVpyXs7PRT0WrC2GPRyFhkTELWffr0OZwkDprEFxBUg0WCxn/J4z9JtNPNG0mbA0Ew/+GzYwHl1P1hwj41Ng3/hrOv5RV+zdatW68nDGnsaOPJquFd4QIrEGBCRSXNZAqJtl8fFpEmZmuULfNGyHrTpk2OoV/RF834lvkNoYT92Mx7JuxT97JV4qfGJpVvtNL8bR308UrOv05a7ZhKA11oLhFAAkLrg5lm/fr1ZW3Feb9OWbpMjVlYrwahYWLRqnLOWr169Vhd0e233x4OvqFsp+ZEAL8Itndh/hg1alTU2trqrQCn6GpL2KcAJq2P5TH5DPYcVpzYdpBunIxA2ggwqeL0BXFLCiqhvpQGaEFcL8g2fl/raztZL5Uk/1/YzJHoubxwrRXRfPyOOY4FGZI1WsXRo0efno+aN76WJuzGY37KEjVw52lCnMvpNQRMwXvShH1KuPxFgxFAdYkN+2c/+1l0/fXXR6tWrYo2b95clkboBsi2t9XRWO+/du3ar9x6660Tbr75ZiT5QNhM5jYL9Rbd7P6evmUhiI8EGpxBgwb9hcbZXo2HXi8Cs9vq2mpmwq4Nt0R+JbvdhZKq5yJVoCJiojJhJwK1M60RAWzZ2JbXrVsX7dixI0yykoyu1pj9kMbslFqy1RgfKq3SNEnt31A+Z2zcuJGFQFgcEFgD6d6puRFgexdaRSRsJG2Ns8kaCxOau9XVt8427OoxS+wXGrRjNUH1IWDKQw89FAg7scKcsRGoAQGkIVTUTLCShkMOU6ZMaZk7d+5fDh8+/C9F5r8zffr0b0jiPoSE1J3kLU3SW2S7fKXG/XVIV+SJZI2GiYnbZF1DJ+XwJwgm8djCHILAMmDAgJ+qKfNz2JzEqmzCTgza6jPWgD3KQEVywUZolXj1GPoXySLApIrmB8Jevnx5hDQ8bdq0aOnSpdHkyZOjZcuWfRmnSUnMPxdZf76r2kiC+qiuT2pXRHT33XeX16xZU4Ko8UKHrCmD8pyKgQCkjUYFgYX9/kOGDLEtu0PXm7A7AJLWvxqsg+TMsxCVI/YcLjudpdUbLrcrBGISJaQkpErQM3mMh8l26NCh0YQJE64ZPHjwq6U2f71Iu18neeEJ3ke+GstYmLJ1S7siSvExi+TLYtWpWAhA2CwGcbrl8hh4cf+bsF+MSSqfaFX5KpH129iHiEoICQPCdjICWUUAr3HGKJMsY1bbcSI5jOE0BIH3nzRp0oX9+p3M10zKsbc5i1NJ2GHvbWy/jKX3rLbZ9UoOAcYG42Dbtm1hEYjGxulkBEzYJ+ORyn9SAY7VavLfNXmVNWBLrCyRXBjATkYgqwggaTNGIV7s2pKmgxMa5xsjcaPa7oywGd9okHhlguY9ecX5edxntceTrRf9zjhi3AwbNiw4oCVbYv5yN2FnoM9k8yNWMo42JVRBSBmetDLQMa5CtwgwTnEMq3QOg7j5H0laIUwDkVeOZ6RyvmPMx1J6twX5hqZHgDHC4g8/hoEDBwYtY9M3usoGmrCrBCyJ2+URfpyJC2cznM4qJ78kynOeRiBJBJh4cR5Cgu6YKom78n3H+/x/8RCIxw1SNoSN8KJ58Y3yFv+2vtM6sPd7/fOOqvdhZ6AHZ8yYMQvVIGTNCjN26slA1VwFI1AzArGau/KVSTm+as7YP2xqBBgfaF/w45H28VuSuN8DWSsVPpCKCTsDQ1+ryf9NlB+8ZFlV2tksA53iKhgBI5AaApgFiapHAB359vyjiPvdlrCjyISd2pB8oWCtHM+PgwWgSrSE/QI2fmcEjEDxEEBowRmRXTNEP5Nq3IdkaxjYhp2BZwH1D56RxBC3DTsDHeIqGAEjkCoCEDZb/pgT2S7IKV5OlrBTGwOxPUZBIs6JtzLgHelwjKl1iQs2AkYgIwigEoewiVtPmGbmRScTdmpjAHuMpOl3yWb9E2w1eNSKxFOrjws2AkbACGQFAXbKoBKHtHHIhcCdTNipjgHZZv5EK8g2YiljpzFhp9odLtwIGIGMIIBKHPNgHK5WPj5PZaRqqVbDTmcpwi+SPoztmmD37MM2YafYGS7aCBiBzCCAhI2pkF0zqMTHjRv3OZkPz8pMBVOqiAk7JeAplj3XrCLxhOwsyESKVXPRRsAIGIHUEEB4QYhB88hhMOyi0Q6aldreNTq1SmWgYBN2ip2AjQb7tSXsFDvBRRsBI5A5BCBs1OIIMpA2UjbELcm7LXOVbWCFTNgNBLtjUXg+QtYETWH/tVXiHRHy/0bACBQZAWJSoImUZB2EGx0s0+UZ682OlQk7xR7WQOyPhI0d2yrxFDvCRRsBI5BJBBBiIGwEG0nZHJL0qkxWtEGVMmE3COiOxUi98/9rMM5lywIX6h9L2B1R8v9GwAgUGYFYwoaw5esTjh4uMh6OdJZC70uqfotU4B/Eho19hhWkT+hKoSNcpBEwAplGACEGc2E7YQdHtExXOOHKmbATBrhj9lolniWi/jcNwLJU4SXIGunayQgYASNgBE5GAAkbwo63v+L3IxIfrMBThdyXbcI+eXwk/p/2FT7HFgUFSynhSGHbdeKQuwAjYARyigASNnMkcyZRz9gCK1s2cUoLyV22YTd4IHPQB6pw9l+jDrd03eAOcHFGwAjkCgFIm9CkSNrtPj8lSdwfyVUj6lRZE3adgOxpNqjAid6zZ8+esFokOICTETACRsAInBqB2PkM1bi2wbZI2HnLqe9u3m9M2A3uWwiblSKkjaRtZ7MGd4CLMwJGIJcIIGVjwyaAisKWPpfLRvSy0ibsXgJY7c8hawbcvn37gk3Gp9BUi6DvNwJGoGgIIGGjDmfe5EAQzaMLJPR8rmg4mLAb3OMaeC2sElHtIGVj03YyAkbACBiBUyMQb+8iKuTevXuDpK25c8ipf9Gc35iwG9CvGmwlitFWrjeMHj36TRA26nC8H60Sb0AHuAgjYARyjUDsLY52Eo9xTvKSw245142qofKFdI2vAade/UR7Bssi6Su0JeHbDDRJ12WpdEpI16h6nIyAETACRuDUCEDY+P8QQGXUqFFBPV5EYccS9qnHSF2/kTT9hXgfoQZeCdu1ybquEDszI2AEmhQBCJsdNWgnMSUi+GgOLdwWGxN2gwa4Btrw7du3R1u2bAnOZo4d3iDgXYwRMAJNgQASNWZEnM8IOqVYFr8jbeVyCUKTmqKBPWiECbsHINXjFrzD2/cQhoAplq7rgarzMAJGoCgIMGdC2kjXSNmKeNZH+7HPl+Q9tigYmLAb1NOoc3CY4GKVaMJuEPAuxggYgaZCAO0kUjYmRl5F2sebqoFdNMaE3QU49fwKr/B4DyF2mCI6TNQTT+dlBIxAMRFA+EElvnnzZqKeIfycFu/EaXZETNgN6GENphYNshZImxUhAw4nCicjYASMgBGoDgGEHc5h4DwGNJaDBg36Y+XQr7pc8nm3t3Ul3G8i6SWytXxeA6sNG7behz3YVoknDLyzNwJGoCkRQOBhLm1tbY2mT59O8KkLpL3s05SN7dAoE3YHQOr9rzb5v1ED7ELU4FxI2Y5uVm+UnZ8RMAJFQQAbNpL1gw8+GIKoQOCQdxGSVeIJ97IG1jDOcCU6DwOLweZkBIyAETACtSEQe4ojAGFiRAjq06fPmNpyy9evTNgJ9pdsLG+Srfr3dZRmGVU4hG3bdYKAO2sjYASaHgHMiey0wY7N9i4EIs2ru0Xeo5u98SbsBHtYBH2EFaC2H5RYDVq6ThBsZ20EjEChEIglbbSXXIog+W/NDoBt2An2MBI1q7+tW7eGE2bY8G8JO0HAnbURMAKFQQBJG1v2jh07ohEjRkT9+vU7t9kbbwk7wR5mBUjAetThDCyczUzYCQLurI2AESgMAhA2QhDbu5CwFU2y6R2ETNgJDm8O+MDOAmEzoEzYCYLtrI2AESgUAgg/hHxGi8mF81mzJxN2gj2s1d9xJGui8TCgIHBL2AkC7qyNgBEoDAJI2JA08yuRzyQc9dH8OqyZATBhJ9i7AwYMeCUDCqJGPW6yThBsZ20Eyk+55QAAQABJREFUjEChEGA+xU8o3t4lDSY+WfOaGQQTdkK9q1Xft0TU75U6vIwdm4HFitDJCBgBI2AEeo8A8ykmR85oQMqWcDRYu3Le2fucs5uDvcQT6huR9Bu1/xpns5JV4QmB7GyNgBEoLAJI2MytbJdlP7bm2ha9ntHMgFjCTqh3cTJj1YcNG3W4kxEwAkbACNQXAaRsnHlxPmv3Fj//wIEDn6hvKdnJzRJ2An2hQCl/d9ddd51E2LZfJwC0s2w4AqVSiTCQUUtLS8R7Lt5rD2x4X0uFeDaYeCtfWeTGVy15+jfFQCCeV+PtXezIGTx48JBmbb0JO4Ge1YrvfVrtlWVbCRHOLGEnALKzTAWBvn37RkOHDo3kUBn1798/4v8hQ4bEgStqIm1Umvh4VF44a8ZxolNpqAvNFQKoxiUoBXv21KlT36Oxc+OwYcN+matG9KCyJuwegFTtLSLsslTiJbYa4MFowq4WQd+fBgJIy6RKybmjJK2zh6PRo0cjxUQDBw4MpyQRZWrSpEnhfZxHT+uPhARhQ9A4Z8YXNklOYyK/OKRvLIGTd+zAyWv8vqdl+r7mQ4DFXmyGlLQ9VATelIeBmLATGLsMnvYY4sG2YsJOAGRnWVcEIEZU3ai2IWKk5uHDh0eSUgIRxwQNYbe1tYV7kLC5kLjHjBkT3ldL2DQCQkalGV+VBzvgAwKJI0HxPe95vuLPeM54H5N6XUFxZrlBgPHBIg+VePvYaMotOSbsOg5JSQCar0plJhwmEgYQzhCWAOoIsrNKBAGkVwibc4VHjRoVTZ48OZo+fXo0Y8aMoO6eMGFCNH78+PA9pM69McmjFoe4JY0/qHyO1FDBqSxqeU545YKAcSaCpJmEeZ54feCBB4LWSo5FwaGTnRg8Y07FRoCxwuKOMcJ4aNYFnAm7juMcstZEsnnTpk3Bds1Kz2RdR4CdVa8RgGQhWIgZSRqJGWkaSZoLqRppGXKeMmVKJHtgIGypwX8tEh8lQu7TRSXKXXzX1VfP6+I7uUP1PS5S/pYm5Ddio4zJG+JmDy6EHZueIHUWy5ihmLSZxLmcmh8BJGwIGzMK40JamFrHYqbBMmHXsXvkZPYDTSRz2vdf23ZdR2ydVe8RiMka9TYSM3bnadOmRbNnzw6SNEQtdfcxkfn9ciprEaG36vXrkrj/O6W3a5Aaukexvcw3qfg3aQF8ta5/kvQkXj7M8/WciHm8iLo/oX/Xr18fCP3ee+8NRM7EDYk3q7TV+xHRPDmwMGOhxoIOW7a0QP9TGs7b5F/xWPO0MopM2HXsTU0eTzForAqvI6jOqlcIQNKor/Hq5kKqxkkMlTcSNCrvuXPnRqeddhq26FtGjhy5QqT94c4KVV4Nl1oqy9RC40bVi+tEklT1ERHz+ar3KzVpl7CnQ9K0mecQIsfmjWqdST1Wu5/IwG+aAgEt7IKfAws5jQfMki+RmeZaNe4fmqKB7Y0wYdexN0XWbOUK57MyWWCLczICaSEAaWFbRtUNOUPSSNZI1Ki88fYW0aEW/4VI+raJEyd+jLq2S7UNJ+dqcWqv56f4nVShf7Jo0aLf0+Jjxumnnx7IWtqu8s6dO0sE1Iil71jqrrYs3599BCBtFmcaC6WtW7eW582b9yXV2oSd/a5Lp4ZaxQ/AzhbbUUzY6fSDS41CMJPYGQzCxha9cOHCaNasWeVzzjkHSfS/pCr+G0mtA2XDXifS3hfjVinVxp9l8bWynuPGjfuMyPgmabmmSIOAqvzokiVLfr527dqyIg6WIGrsnDyTbCGzb0kWe7T3dcL8gS1bi7WSxgRq8lnyy9jW+5yzkYMl7Dr1g1Z3i2+77ba52FDwYGVSMGHXCVxn02MEkKrjQCbYpFF5I0nPmTMnqL0laZckdZ8v9fgavW+qA4S1CNkgoLhC2r1794L58+evBwf2k4MBDmo7duwIanPex8+pCTxGLb+vSNjMudiw6Vs0SpK4f6wWzcxvq06uuQn7ZDxq/k8P/oUaMIGwYw9WTwI1w+kf1oCAJM6gAh87dmxQe0NQF198cVB7i7SOS/3dImecK+VEdnsN2efuJ3Ko26Bnsk0q8a/o/eskeR3ftWtXH0ndkcg8uuOOO0L4YOyeSN9M+E75RgA/BQgb06S0SfTraflu0cm1N2GfjEfN/2klN7Td4SGs3nlvwq4ZTv+wCgSQHlF/43CFBzje39O1h5oJS3Y8Plsjsv6AJO9bqsi2KW7VIuZxNeT1NEZq8d+XduHvsXOiieBwHp5RVKg4i6JO5TJx57fr6U+20+JDxKsk7qZahZmwezk29XCXNDgWalV3DZI1Di54qcYeqb3M3j83Ap0igDTNBVGzlxq190UXXRTIGqLG61uOZE9Jmv6mJO73kAljVampJrBOwenkw/a2f0H+JeecccYZpwujc9FA7N27NzikrV69OpiyeH6Rtp3yiQALLvyISLw2W1+asHs5LrVtZLBWcd+RSnwuK3VsYmwh8Sq9l8D656dEICZrpESka5zK8P5etmxZNHPmzEje3mvlcPNZkfnDkqy/H2dUVLKm/bS9nbTfqdep8jV5tcwEkPbb2eqGGpXJXc5qJ7Z++RmOR05+XiFsHAxJCE4xYbf3fe4XqybsXo5Fqdf66yGfy0od73AeeD/ovQTVP+8SAaRqHKmQotmupe1MSNbl8847ryQP6SdFQFdLqt7RZSYF/DJesOh1j5r/JT2n/yLtxJ0veclLvoSWgq1fW7ZsiWTnDjZu1KpO+UIgVonHkrak7JLs2d9Rn7+hGUjbhN3L8cgqDpJGHY4KBtu1kxFIEgEIG4kaTc7SpUujSy+9tKx91WxdGiCpu4+k62eSLL9Z8tYkfkST+D9q8fMPsmeDYVnYlfCyB1ueayZ+7/bIT48jLMVOhGg8mZPVv6+XJvRf1d9vzU9LOq+pCbtzXKr6FCcWiLvdyaGq3/pmI1AtAiJs4nqPRR1OaFGRd0nbtIZqr7WNr1WCqUn8uXbJq7RmzZoy6tR4ooesec+zDWnbibRKcFO6nX6KiZv+xEwps1FrStWpa7Em7F7CiXcp9i/UaHidQtpORiApBNrJZZxUt/dIAnxaNupDut4lT/Cm2lOdFH6d5SvSDrZNmRgWKcjGV7R3ffq55547ZuPGjdHy5cujFStWhGfc0nZn6GX3M+ZmTJX4KBAgKLs17XnNTNg9x6rTO2OVOKTNe7zDnYxAUgjE5CJnqXOSKqOo+UoVvlZtXyY16u/Jw/6LaDDYz8tinOcaSS2W3oqKUZ7ajeYTDQmXFrqna327TL4KK/LUho51ben4gf+vDoGPfOQjT+KcEkvY2L2cjIARyC8C2gr3r5LKLiTm+oUXXli+6qqrogsuuCDEX+cscC2a8tu4gtQclTjqcKJOsuiSXfs0XWfmvfmWsHvZgzfeeOOXvvrVr4aBgYODH+ZeAuqfG4GUEdAzjHlhuZ7nyYpHvk8mh7ICrpQIO4y0htnLmrSUO6kHxeM4yJxMQCGkbfXZ0B78LNO3WMLuZfdIon4XqnA8E1nV2TGll4D650YgIwgoGM1+qcY/pC1zJbbQLViwIMRmhwAsaWekk7qoBoSN9hNzJYSt5H3YXeDV9F9JDV7Gs1RbBko4pDgZASPQXAiItD+tBfmNUo2vVLjXQbqi7373u5GO7QwqVyRtFupO2UMApzNixuPhj3pcpJ37jrKEXeM400PaTw/y05C1t3PVCKJ/ZgRygIC2BG3StrnXa5/2UU4/05nb4XAVqcpDpLkcNKGQVUSIQvOJpM0crahnMzRv988zGCbsGntPqpbPyj7Sum3bthNbPmrMyj8zAkYg4wgoctwPtTXoNTpT/Nnf+I3fiK6++upA2rJtZ7zmxa0eknUcbhZbtoj7fdrmNSfPiJiwa+w9qcLOl8qlhXNXsWE7GlKNQPpnRiAnCCgK2o8VsObP5IgWnX322SfOGmefL3HdnbKFAKYKTBbYr1GPY8+WQ+H/p89zG0TFhF3jGJO65Th2ETxH2Z9pwq4RSP/MCOQIAanB/1Lx2o/pgJXo9NNPL59zzjnhwBVikTtlDwFIG9U4QhVnPYi8r9uzZ09ug6iYsGscY6zc8D7EqYGVm53OagTSPzMCOUNAtux+qvK0iy++uMQebU5Jw57tlE0EEKYQrIiVwbY8BcTJ7UZ678OucYzJNvIczgys3FC5sJJzMgJGoBgIKCpaX2IuaMsXx5lGUpeHw0JQvcZHOhYDiey3kq22OJ3RNzihMWfnNZmwa+g5kfO8tWvXjkOylpd4IGurxGsA0j8xAjlFQCrwg/Ic/4j2Y0/USX2v0zUeW/a6deuC5s0L+Gx0bKwSZ56W02Aga5k0slG5Gmphwq4SNK3O5uqQj+/Ifj0V9QrStZMRMALFQkBHNj6pFv8lrb799tvPUACl8ajFsZOieWNe8CI+/TFRSdg4BiJly1H4ePo1q60GJuwqcZOD2TIR9ULOv8bZzMkIGIFiI6BjTdvY3oVa/J577jkhYcfRD4uNTvqtZ+GEvxGqcdTh8vAfpVo9nn7Nqq+BCbtKzPAMZ08fhM17JyNgBF5AQJLmeZIu58mxZ4gcMV+pSXKupJyJusK+J9l9j+u7tTrT+zuSeFbql8dlD96ozw++kEu+3smOfabU4ZMkYe8ReQcHJ8gBW7al7PT7kj6AqHEMxoyp8fafqtUZ6des+hqYsKvATBL1eBH1H3DuNSfAoF5xMgJFRYDQvCxakSTRNkFQP/jBD4I6GJUwqmE+4wQ7VJNcHFkp++8y2RGXIZWKuKPW1tbo1ltvZSINF7G6RYDvV1jQv8sDtlpsPKd67tW88InFixd/nDrfd999wdEJDJzSRYBxB1kzFpmzRdoLJXRdpfEFcecqmbCr6C51+Bh1/FIImyvP3oZVNNu3GoGAgIh5prxtTxNJH5N0vHDHjh1B08QkiFMPnrhxXAIInOcDwuJ7JkwShI3TD57VOAFB2gr9GSmSWLhkGw6qZU2mn9eieLgI/Q4R+Ebde3/IIMN/JGn/+c033/xxFiGoYIk3brV4NjqM8cciknGpvilrQXijapa77V0m7CrGkwi7TKczOXHOqlXiVYDnW3OHgCQTCY+lsgh3lsb7OzZv3vxqPQNLHnnkkUDO+j88CzwHfAY584pEzcXkCGF1fE6QoFFN4lXNqVdI1jqD+qQLBy6R9ychcEngt+qQnZ/zfsKECZ/MKpCbNm0aLLxC9cCGNsVbiqwaT7fX6IdYNa75u6QxdUTj8lKNxVvTrVl1pZuwq8MrrNIg7FiSqPLnvt0I5AaBdrL+hEj3tfJ+XihCihSLOdq6dWvw40CChpDj8I9MiPyPNMPF53wGiaGWJPE+XvTitcuFRKroU0E1rq1S4RXpG8kbRy5FE7t02rRpl6I6V8osYetQkKe1sPmQjuL8qylTprC4CO1GFQthxBjQCKfGIhCPQRaPzN0yywzQOGQsmbAb2xWNK02qlCdR++F0xsV7JyPQbAhoXE/UxLZa0vKR1atXj9f7fthkN27cGAh7y5YtQZKGhEiVRHSq9zFGfA+BQeaViQmVFL8ieRPuE1U50vr06dPDRPurX/1qv6TvFqnVV4jM3677H6vMJ833qgvbhT6tBf3A2bNnf2zevHkRCxC0CbS5Eps061nEssGei624mDPR7mhxOSBvWFjC7mGPrV+/vr/UJ7ukmgv2KWxUPIRORqCZENAe1UGa0PYrMFBwnLr33nuDnRrSiVXcLFQ7Em61GHQkr47/sxhASkdiR5KHvBUSNNLhGxOJLnbRRRe9RqT4qKSlPxWp/0W15Sd5v+zvf6u6XvCyl73sUqTs7du3B7NAvMBJsmznfWoEGGOYbPAtYCGVR4dAE/ap+/ekb2SPmsPqjNV+rAa0XeokiPxPjhHQRDZZaum+WoRej38Gqm+OjsUWyxZGxjyEE19JN5XJNVaxY4LCWS22i7NgYPuUpKSy1Omf1CL6GyLJ43Jk2510vXqSvyTtR7XgOQBZx7Z6nOtoT8eFSU/y8z31Q4BxjFqcMZTH+duE3cOxoIlsrbxWy1KFl+LJq4c/9W1GILMIiIxxlFqkxSjR+yYR+AOVN2SNrRryTms3BOTGFUumsVYLSZ/PRdSlRYsWlXVG9Xa14ZAWHedqYb0hC2BLbf8z2dzfLCmuJK1AWPBgRovrnoU6Fq0OjBnwpx8YS9IS5e4ACBN2N6NWnay5oFSmo+V4U4onjW5+5q+NQC4QEKnM1di+HUcc2asJsxnImhCbjHUcxLKSqAtqcp5FVJvYuSXBlvhcpD1EZP0h1fWtade3fc74iur2iBY7/y5Juw9SHSpYtAR5lOzSxrRe5dMHaEpZhIqwn3ecqFfmDcjHx2t2AzJkrc69mg7GWYHVmR1IugHNX+cCARHLABH1D+WfEa1YsSLYrJGsZccOqlwmtyyRCxJSbNdm0uVZZC84fiVoBvR6hV5XS51/JaSZVicwZ1C2HONu1Ja157C5I2UPHz48qPbTqpfLjYKmgwUfCyiN7UVa+H0gT7hYwu5Bb2ni+hwrelSF2PZQiTsZgTwjIOn5zSK7r0nt3fenP/1pIDzU34xzpECIEYLMaqJuXDgQQd44dul1jGzbYy655JKbVO9BulIPM6btaSVt9wpb1/AFYP7IOrZZ7fN61AttDOOFS0JYP4319yvOwHItplbUI/+k87CE3QOENTH0h6SZzIjaZO/wHoDmWzKNgCaqr4u0+6I1QkrFXo1kjSaJSS1LknVXQFJfFhq7du0KbYC4eU5V/yXSHAzp6reN+E7OZrsI+MJFgBic55zSQ4BxzfzNfI7jmcb6ND0L89OrUXUlW8LuAV5sYYGoCe4Q2/WyLH30oEm+pcAIiOAeR4V81113BXUynuDxQjQvRB13H88hEzD137BhQ1CTy2O8rAn5dsKfajKerC1h++P7G/mquuH/MlsBZzbJo30uW+QgbeYTFkVO6SDAmIGwWehhptB4GZpOTaov1cu9LjDjgeNrvAnbnc6CKoUHzskI5BEBLTg/LAl0IEFQli9fHt15553BNyNPUnVH3JmAsbejLcAGry1VpZUrV4b3em5TO+AhtmUrgMo82bE3EgSG/eRs8XJKDwF2HbDIwweifcvgWzROxqdXo56XbAm7C6x44OSU84eaCEaygkeFwsosb1JIF030VwVCQER9uWyoH5CDWYhctnv37qAxgvDirVN5hYM2sOjgFY0B77kUJnSK1OXfVqCMv5G3dmp2StmtP6byv4PzGYm5JO+Y53msIHRhOoGw9f5czesj1J4Hst4mE3YXPaRV1xnq0D/USmwAARAga1ZmTApORiBvCGjROVdOWqN+9KMfnVAf4wDVLInnkucTKbt9Io503OVYff4GqT6/o3amRtiyXT+JHXvmzJlh/sC8Rn09lzR+9MXjBMJua2sL87qeg3Bee+NrU12JJuwu8JKa7fVapc9od04IK2I/YF0A5q8yi4C2sSzUwvOTEBl7rPGShdyaLfF8oh4nxRIUdkppxVJdmWjPeAl1OHXhYBNp75oN+ly1B+2G/BvClScTp23YXQwzEXVJ0c0iVIfYO+hkE3YXgPmrTCIgNfgZ69atW/vjH/+4DZs1HtUQdp4mqmqBpW1Isd/97nejG264AZXYf8i8dUG1+dTrfsiaUKULFiwIUjanlDmlg0BM1owPtgWy0yAvi1cTdhdjRirwFjoTz3CczkzWXYDlrzKLgKS5NVp0lokjwAIUfwzsu81sQ8XPhOAYqMd1ldiyJrXnL9X2WWl0lGzoz3EOOI5nqGHZ3mUpO42eeP50OUxBLForTZ3p1Ka6Uk3Yp8BLarWpeqAuQYVI2EaIG8I2aZ8CMH+cOQQ0VoPeFYKWVF3CMxxPaqSJZiZrOoLnFCmbSZnnl/3ZLFgUvOTbaXSUPMNXa2vXrUjZijNusk6jE9rLjMcGZM34aHckTtVk0lM4bMM+BVJsplfHno+9j/16dCwd7WQE8oCAxqrWm6WytENfI/QohCW1eNAUNbMqvLJvkKLQjGHLZo82RypKsj2z8p5GvZd0fVBzyHtU3ve173c+KnGk7GZfODUK32rLYdHKnI72FFu2/Apm6ZnZzDNTbV6NvN8S9inQVsf14YFHskZCiR1ZTnG7PzYCmUKgnaw/I7K6Di0R0gTjmMmpKCTBAhvVOBIUUjahQZmk00pShe/QomE/9mwuFhC2ZafVG1EIEQtxt8cV/z7PDAvd9GrUfcmWsE+BEQ86JI1jAqEbmfCKMtGdAhJ/nCMENBEtVYCt37rjjjtasONKJR72/kJiRUq0l0UK2gU0C+PHpxsfQ6R9kzzFL9R+7FakOxYSaAGcGo8AYwPCRoNKaF6ZTIJaqvE16XmJlrA7wYpVlsh6LA8Sqy9ecdJxMgJ5QUBj9m0aw5NYcGK3RlPEIrSIi07ajXaBnR7tJPmUNA/D0uhLSdafkyr8MbZ3Eaa0b1/LTGn0Q1wmzwPbdtG8MNdnPZmwO+khrbhGabL7HCq0mKxRjzsZgbwgoAVmX0iKqF9I1xB2UROTMlI2kzLPtDzmB4m8N2u726I0MMFjXHbssCcbtbhTeggwNpjjWcjl4RkxYXcyVqQmmaMHeigOZ3QmahNW6U5GIC8IaMwehqDYZ8pWrjxID0lhy6SMhowJGXs+mOj5Hq/PXpNUmV3lC0njKY6U7bjiXSGV/HfM65WELe3q0uRLrb0EE3Yn2Ckq0V/hWcqEB1nzwDsZgbwgIGnyXI3ba5CwIWpUfkXxDO+qj5icwQLVONjIGS2VUG+owYl21traapV4Vx3WgO9i7QuLOYhbr+9rQLE1F2EDSifQ6UG+CNsfwRZwPHMyAnlCQBL1TC04p6H+ZeHJZGSTTjh1L6g+cSLVnugg4abRrxA2wVOGDRtmCTuNDqgok0VcbCrBIVEL25dXfJ25t5awO+kSpJF4xcV7vAmdjEAeEJCUME6E8H4k6/aAEEFD5DH8fDAVNGZIUti003IkZSsX0jXqcEc8S/epQsLmOUHzwqv+zzQnZrpyaXUlHYd0wpYLHmpPdmn1hMutFgGR0ViN2XOx1bLoRILw+H0exVglDjbtKvFq4a3L/RA10jVqcXuJ1wXSmjLhuYCwWcChiWJM8H+Wk1XinfQOanC2wrA/j/ee8DoByR9lEgGR9XNMQNhpIWyP3Re6CcJG88CzPX369NQc8eQjE9TxVom/0DdpvcNUhEocjQee4oyRLCdL2J30DlI10jUrcUvYnQDkjzKNABoiSJtXE/YLXcXkHBM2EhVq0DQShM1BIEQ7s4SdRg+8UGalhB1rpF74NnvvTNgd+kQT3RlI1UgoRDfD5uVkBPKCAOM1ttNaO3RyrzE5Q9IsxpGq0iJsSBp1OI5vJuyT+6jR/zEm4j36LObksxQclrTQzWSIUhN2hxEiqXo1DzOrcUsnHcDxv7lAAK0QEjavWbfJNRpQnun4ShMbxa32iV2N7vwuymMssMCV5mWCAuv8mLjiXdye2lcm7ArotY3rfpF1H1QjJuwKYPw2NwggXceEjUqc5IVnbrrPFU0JAZ4Rnhu0L5K0XyH/pc+kVJUuizVhV8AjFfjjqMKRsLPufFBRbb81AicQYBsipI26F4khTSnyRKX8xghkHAEImwUufkvt5pJMBuAwYbcPJHVYmzpqIOFIcUhh4nMyAnlEgIkHe1wsYeexDa6zEWgkAhA2cz5+S2hYs/rsmLDbR4WkkbdJKhmLSgT3fqvEG/m4uKx6IYB0jXaI8csrE5GTETACXSPAc8KzE4fzxREti8mE3d4rsl8sVScNjE9tsUo8i8PVdeoOAYJyxGE32TaEc5OTETAC3SOAhI2zJmQNeWcxmbDbe0W2vj50FoTNKsvSSRaHq+vUHQLs8WXLEKdBsdeX0JdORsAIdI0AEjZaKUxJELZV4l3jlfq3OOdA1KjEsWHTeU5GIG8IIGFzfOOQIUNC9Cbqbyn75F60meBkPPzfC3HmmfuxY6e1R7+7vvDyux0hJOpYws5DxJvuOtbfFxcBDpeAuHk1Wb94HMSYpK198MLhxX2T5ids64Kwmf+zasN2LPH2EYKETSfRWRC3VeJpPjouuzcIED0LKRvSTpuUetOOev8Woo7DgqYZZSyea1C72lem3r1cW36x01nWvcRN2O39y4MDWaMSp9P8INU28P2rdBHo6HSWbm2yVToLmeHDh0eTJk2Kxo4dG2z8adSQ/fHxbpSs2krTwCXNMuNFlM6Sj9jaq3gcmTy2yyrx9lEign4OlQhStgNOpPnouOzeIABhI0UiQfI+Vv/2Js9m+S1YgAsOedj4eZ9Gwj8GG2ksYVs1nkYvvLhMvMRxOkPDOmLEiNnql8EvvivdT0zYwl+rqk+qk95CZ1myTndAuvTeISAp8lgsRUJMqMRN2s9jioSNBz3SdVtbW3jfO7Rr+zVzDEIBAoLnm9owTOpX9AeLKY2RN0vT+oci7UxpoQtP2AoffqEenI9qO1fZD1BSj4HzbRQCY8aM2SyCfsOECRMCKeF45vQ8AixekKpHjRoV1OFoINJIqF+Za9jry3unbCEAacs8WhZxf0oxxSdlqXaFJ2w9OM+iApF0XeIBsnoqS8PTdakFAUnYG1pbW9chRQ4bNsznLreDGNuwp0yZEqRsNBFppDhAB5Ic752yhUC7BqRE/2iR96ks1a7whI2jGapwAr7bASRLQ9N1qRUBSZAbFDTlZ6h+Ywcrop4VPeE5P3HixOjss8+OZs2aBWmnMv+hDpdmD8cmzzkZG5QIbCyi4IV2retbslTFVAZslgDAAQTJmoeI95aws9Q7rkutCEjd2xf1L9HOcLBCuix6im3YsUpci5iH08AktpPGc04adXCZp0YgNllIgCtlTQNSeMKWY0GJlS6u/HiI26Z06oHsb/KDgAi7PHr06OjMM8+MTj/99GC3zU/t61tT7PgsWnDCQ+sg+/7j8gJ+rYj7n+tbUve5SSAYKKLuK9toOMrRWr3uMWv0HSyk9u/fH+3evTuEqm50+V2VV2jC1sNTkgQyI1aJ4whiwu5quPi7vCAg6fHfdN0/ffr0aPLkySfiihfRYxxnMzQNIungiDd+/Pgr9P67PP8p9OfF0uQNxASH70xWD5lIAZdMFImGNVaJE/UMO3aWUqEJWx3RV5PaMuwVxBGHsJ2MQDMgoHF9h6TKJ6dOnRrstjhYoRKGvIqW8AbHls/iRV70bHO7C7JWavjZo5prXiFCGAQRoNHDDOeULQQQ2lhIcWG+yFIq3tNbgf7BgwcHKMjE2/Ua1FNWiVeA47e5R0Be4n+8cOHCaO7cuWVJlWHfMeRVJCk7lq4xDZx33nnRzJkzQ7+mQdYULKI+R/NMHwQESNuEnb3HrMKGnTkv/kITdlhil8ttSNZx1CGrxLP3ALlGtSEgm+1NUgUv0WsJdTCXJO/CEDYLExYotJkFCx7i2LHTSgpH+nbZR1+KYID0ZifXtHqi63JjtTi8kLUFVaFdR7X6LtE5OBlwZc0jsOth5W+NQPcIiKTv27lzZ7Rs2bKg3tu0aVN09913B3Vs97/O7x1I1tqLHo0bNy6aMWNGdM4550RLly7lOW9Lo1VybL1MhP3V7du3Rzt27Ah9wdzjlD0EEN7QurL1Dv+mLKVCEzYdgY2CDoKwee+HKEvD03WpBwKSMD8j++2foIZFuoO0kRxYoDbreEe6JgwpQVLUdjzDj+maqM8frwem1eYhibofkz/e4TgzNSvu1eKSxft5NvBrwjGQ5yVLqdAqcTqisnMgbj9IWRqerks9ENiwYcOHJWnu0vauZ7HhInXiNd3Me7PZyoUJ4KyzzgraBUm1rxZZH6wHnrXkgTAACcSnAWJ681xTC5LJ/oY+YSEb7xxCkMtSKryETedwnCarXjrHD1GWhqfrUg8EXvaylx3TuJ4pkv62JqLXrV279oSEHdvommncQ9ZI12xnu/DCC0NUM7U9FVV43H9yMDvCPMPxjahb7SsTI5O9VzgB6drburLXN4Gg6aB4D3YzTVwZhNtVSgkBSZfPyaa7gu1N8+fPD3ZdHLCw8zbTASFoDXAumz17diBqnM20pe2wCHxfStAzxwwUzm+GBCol7LTq43K7RoDFFJyQxW1dhZawRdJIHoGsUYfH0kbX3elvjUA+EdBE9EXtyz5w1VVXfR6Jk0UqExOqWoJ4NMNilQUIDmZs45o3b16k9t6n/eiXaItbahEwkKo1+b+byFlbtmyJHnjggSBhNwPe+XwSuq41hA1ZV3qJq6+05m38vv2ONS00YWsP9sshajubdRwW/r8ZEVBYzkNq17+KQP5MzlhtOGNB1pqIwgQFeedVVatnOUQxI044XuGnnXYakvazOq3sBknYj6XZn5L6n0a6xukPvG16S7M3el42Ahy2bIWtPqJnZEDPf5ncnYV1Otu7d+9IPTz/yYksdIxXu8kNMuecLQQkbf4ppMZWLy6ioeGExmlWeYyEhkqfo0Q5hQubNa9Sie+XV/gmkfUnkY7S6gFN+GPxDEfKjv1kkN6cso8AghyaJ2lE+u/Zs+ehNMdRjFZhJWxNTI/I+aPM4R9IFk5GoAgItKv2viFb6rWyYf8mB4SQmJxQ1cZHPiJpZ3kRi1aAxCKDxQYLkMsuuyzYrs8444wj8oafzPft7U1tw7Mm/Ac3b94c9l6DL9uEPN/QM9lPsWc/Cy6NozEyt3xPtf7NNGteWMLWQ/OEVrrDYjVglienNAeIy24uBLDDtZPYa3Qi0Re01eu9RANDyl69enV07733BuKGWLDhZTFB1jiXIVmzTQ0HM64lS5aEdmjf+R+pjS267znam2Yb5B1+THbrvtiucTizn0yavVF92Yw1EvygRWzqUVQKS9isnlBNQdh0hpMRKAoCMYnJY/z3Fc3pe7L1PqFDMb4oO/ASmYgCUfNMxIvZLOHCBIraHsmaC8JGrb9gwQLe/1zS9idU7xW677m06i3sVPzzCwURdnnbtm3B2QztBfOOU34QaCfqmLBTr3hhCZuOqLxS7wlXwAikgIDI+scUK2n7sFTJgazZv7xr165IdruwZxgnqXhhyzOTVho4cGCIC44KHKKOz/uGrOVEd0iLji/ru5+nVb+4XMhaqvDLZLP+O0nW/VCFs/carUVenfrithXplb7igBZ8Dxh7BOJJOxWWsNMG3uUbgSwhIGn7PDlG7ZMn+RhFBet/3333BfX4unXrgioXooa000oiwUDWRGljn/Ull1wS1OBS5R/VXuuyiPoGXdenVb+O5Ur9/U5pK07H/glZEzSFhY8JuyNS2f0fbQhOZ/Qh2wUZe2mnwhI2kw9burDTpSk1pD0AXL4RAAE9A6hxg6OWpO2DUjePhiQhGDywUZWzxYXnheeG5ydJ8kHtzVat+LQtXgn6QmzwadOmBTU4+6z1/xDVM6wk2tuQngqgfSipHgPuuuuuEdqJEjQVkDUTv9Xh+XrWGN+MdbbiIWlnwaejsIRNJ7BywhHE2yzy9SC5tvVHQKR3guikup0pr+s1CjgybdasWUE6xGkKFTnqXdTl7CtmMktqsQtBs6caqQaHMqm7A1njDY4qXFL2Vi0q/qeQOFHvyjbUH6Ge5yiCfo/mlys5FY3TuZjsnfKLAAtXriykwhI2K16cQFj9pqnqy8IgcB2MQCUCIsgnJE1fqXCel0rN/LTI83LZta/duHHjiaMh8dLmGYo1VEgjkHd8kV93ZB5PhEjT8Xte+V/7pwNBx05leLEjWasub1cwlKGq289F6Osr652F92rzYAXauI5FzZo1a8Iix7brLPRMbXVgDDO24/FdWy71+1VhCRv1FFsseK2cZOoHrXMyAvlFQES9QbXn4vm4XiR6LXY8BSMp6yqhIkc7hZoc2yyvaKqQJlkA81x1pwKG9CXFh4M6YvU3h3ao7Ig45+326rJU3yXZ1pG479br/80qqvK4HyQtxFYJAhMIQ8p5yggFDsyU1R7rul6QNAtStEksTrMg2BWWsDt2FSt7iNvJCBiBkxHQs/G0zEdjFy9e/G/at/0KtlFBzjt37jzhUIV5iUkNEkeiZHLjHia9zhLPW+zIAzFr73Qgb95rQRDU4ZC5rpKk6o+I3D+v0J7peb111ogOn0lb16I6TmAbV4wN4Ui997oDUDn5l7HLWGZxignVNuycdJyraQSKjoBIlLOkr6jEQU7lvxZJlTWZDZEk2T+e3LBtQ9aQdleLYPZRY4/m4j3StaToQyLtNfrs/Mqy8vBeE/ooji4l+MzWrVtD7PAsTPJ5wC6LdWTsMo7RHsWL0LTraQk77R5w+UYghwhoMpOQXBpJ1UXaHxBJv1Eq8MNIk0gmXLzHHt1Z4nsilUHUqMN1X4uk6CekCn+r8n20Pf/Mq7ziekptepaulcuXLy/fc889JdmxMyGRdYa9P+s5AoxhFl3xArTnv0zmThN2Mrg6VyPQ1AiIVE+QqQJK/G81lqtuqTL/umWaQEbUU+rvq0TQN8qDvqzXEnZrJDIWJU75RQAJmwtfjKz0pQk7v+PJNTcCRiADCMh+/1ECzch2XcI7PHbAY7J3MgL1RKBzfVU9S8hJXn64ctJRrqYRyBACmjf6y9Gu/4YNG4Ltmm2iXTnbZajqrkoPEIAXKq8e/CTRWworYaPmwCkGGwUdItVWlw4yifaCMzcCRiBXCIikZ8tuv4UTzpCqOUITz3C2tWVFfZorQDNYWfox3vnAQgxnyrRTYQkboqYDcCigYyBtJyNgBIxATxCQvfo4+3MJJoNXuOKvh33XSNdOzYEAQh3mDYXqDdsXsxCxrrCEjXQNYXN5RdwcD5hbYQSSRkAT+ELZrOfIsSwER9m0aVOIZsZ+6yxIYEm3v0j5I8Qh2BEYCMEOAk87FZawecDiY++8Kk57GLp8I5APBDRvfFmnb72UKGY333xzeeXKlSWCxRBYw6k5EciS9rWwhM2Kia0XPvKuOR8yt8oIJIGAbJqTIWvs1jqNq8SJXNg5szSpJ9HuIueJf1NWUmEJmw6wKjwrw9D1MALZRkCEPFy2zG9q+9bUX/ziF8FmDWmjBjdZZ7vvmql2hSbsZupIt8UIGIHkEFAY1g9IHf4q4oTjZMaxmXgOY+N0MgKNQsCE3SikXY4RMAK5Q0D26SFyUH2TyPqjkDVS9SOPPBLU4Pi+WLrOXZfmusIm7Fx3nytvBIxAkgjIM/iftH3r2jvuuAMnsyBdczIZvi9Z8BpOsu3O+2QEtDhL3U3ckc5O7hP/ZwSMQIER0KR8wsNIccH/QUcrXivHsjLnW7PXGrLGWdWq8GINEvyddNTrDI2P4Wm23BJ2mui7bCNQBwQgGQ6hqENWhc8CHGWv/rDU3u/QqVuziWSmIzNL2KwhbUvWxRwimD90oNx5Ghf/rj3479Dxr/vTQMKEnQbqLtMI9BIBeSyX2ZoogvmQrk/3Mjv/vB0BSdBv14T8KezV69ati+66665o1apVEVHNkKotWRdvqGhBHPpdmpUWRTt7uYh7slAwYRdvKLjFRqB6BBSkY/SKFSsiEUs0adKkv5JE+HGdLX3OhAkT1lefm38BAlu3bh3Q2tp6GClaccHLOsyjRBQz4oPjDc7iyKmYCEDY+CugXSE86bBhwxbrs7u1UG74+amFlbAFNiqOcOgH752MQF4QUHjMgz/84Q/LsqmWFi9eHF188cWtEydOXCfp8GXjx49fpfHssFs96MzYlKDXkZKozycuOBL1mjVrSlKDExglnLxlqboHYDb5LYwByJoImSLsL8u34Xo1ueHPWWEJe8CAAdHQoUOjwYMHB+Ju8vHm5jURAtpmdEwTRl9J1mH8jho1KtL/5fnz5/9MgTy+LaL52pQpU37QRE1OpCla2JS1bev1Iuu3yMHsapF0WZJ2iF7G1i2cy5CunIwA4wDHs/hKC5HCEvaIESMiTWpB1aGHNC38Xa4RqBoBVvsQyp49e4JanL3BkrBLl1xySTR16tQ3Ssp+RpmasLtBVmT9Di10vnz33Xf3v/XWWwmGUmIRhL0aacpk3Q2A/rrhCBSWsGWvwk3/hISNWtwPaMPHnwusEQFCYhLDGhUdRwBiZ5Xnanjft2/fN0jKvmLgwIF36LPXUoTGtoa4PcnBQoudb0h6vkwYDVVc8P5SgZ/YXw2ODogCSk5ZRKCwhA1BV15Z7BzXyQh0hgALSy4cYSAXpEGdIMX2o3ACnQh8kCTHQePGjbtGUnh50KBB/0dj/b91lleRPhNmo+RE9vD69esDXlu2bImQqHEuk0o8LIA4dhdsnYxAFhEoLGFnsTNcJyNQLQLY1PBe5cJrHN8MiHv27NnRwoULI3mPR7Jxv1MOaYv69et3ld4/KvJ+utpy8ny/FjMTtYg5LpX3JkgaxzKCoCxfvjwQNgsfcGQBb7LOc083f91N2M3fx25hQRCAdCAfVOWc0QwpyT4LYWPbPltbwPZJHfyP+u7rY8eO/YXISRzVXGryyjZJy/AqtXe61Nx/JNKerb3rARN8Vh544IGgmQCvmKTj14IMFzczhwiYsHPYaa6yETgVAqh0cUiDtNlTTNAPbNtz586N5syZU543b95vn3nmmW+UNP6vIuv3nyqfvH7OAkTpUpH1lXIee78c8vpxfvVtt90W9lSDDWQdmxJY5DgZgbwgYMLOS0+5nkagBwggJUJGXDijYduO7bMi6RKxsEVmbfIk/4Pvf//7Z2q3RN/hw4dHI0eOHNrW1vYKbXO8vwfFZOIWtTVoCNTOhVqgfF2aA/mSPX1ch3TMlIlgPCYCIpahbVCY0QjiZqsWl5MRyCMCJuw89prrbASqQADyjlXA8oyOCBAiwua6QGryaPr06fEWR8It5iaKENK0vOSvlNT8H1qEDFCEsqBdwE5Ne/GgR6KOnfJ4RQPhZATyikBhCVsP+wkv8bx2nuttBHqCACQlQgsX9zP2x4wZE0h7xowZwVkNFTqSp+zeZTmuvVvbxtZIOt0g6bsPNnCSJPCy1OvP6PfHwgcN+iPJeJDINsxVELC0BscWLVp0ppzo/kxe8C9Hg8CF+h8NAk53eH/THocUbVAnuZiGIFBYwtbDHjxqeSVEqZMRKBICkBnqcpyuFJs8OGNBzFwi838mCiDviVWAZMorkrp+82Hh9FeNwkoLiXFSbd8i9fcC6kldUPvfcsstIcAJbWAxgjSNyhszAKpwiJq2sTjhficj0AwIFJaw2e7Sv3//cPFQOxmBoiAAgbENDAImqhdqcp4FBVoJl+zZkfZw41kezZo1C/t2kMiJDiiJ++OSXsco8NBOLXZ5cDAkH9bVousZ2cB/otcDPcVSdZktUj5bv+kjgm3T7/rwW7QCIt1jkpyvFCkvQLJGeubiPR7wMVlD0hA07cGJjNfY+9tk3dOe8H1dIaDxGRZ/8fkTXd2b5HeFJWwineFsg+QAeTsZgSIhAKFxkSBvEs+BoqQFiZWIX5A5rzrsAHV4eNXzMkCS9x9B7kjhED2pfUJ7Tr+/84477tilia27VbC4tDz49ttvn6afz9f7Fl19qROhVyFdorkhVSM584rkzMV7FhnUD1V+XP9QEf8xAgkggCZW472sxSvj+kN6fSqBYrrNsrCEzaEfSA5MRHSGkxEoOgJIp5AlJAhhombGFszzATFD5pXvicXPojeWOmT7blFM85fq2Xppd4tgkXMoA9LlolykalT1EDCv1INXLuqDFM3FfbEDGe9ZLJCfkxFIAgHGFwGJdHxtST4cf6sFa2rnzxeWsJmAFLIxqABtw05imDvPvCEA6cWSdyzdnqoNTGIdCZtnSoQdtFbdPVOUBdkiwSMxx1J1TNbxKwTtvdKn6gV/3ggEYsKWKeZ6tEuNKPNUZRSWsE8FiD83AkagewQgXPY3Q7hxgqQVqCRI4vFnXb3GpA1x854rXjBA0rw3WXeFoL9rJAIan6mrcQpL2FLvDUS9h5ovVuk1svNdlhHIOwKoqbmQQOKEqrry//jzU73GRM338XzY8fVUv/XnRqBoCBSWsKW++6DUG5/F89U27KINe7e3HghY+q0His7DCPQcgUJuQNYKviRb29/icCav13J3DjI9h9N3GgEjYASMgBFIBoFCErZUdsEWISn7G9qeUrJKPJnB5VyNgBEwAkagfggUkrBj+CRp/zb7sbG5xXaz+Du/GgEjYASMgBGoxicjabQKTdiyXUsb/nywCJzPstQxSXe88zcCRsAIGIGuEUD7io8TF+/TTunXIGUEIOo4khOb452MgBEwAkbACEDQRMJUwJQQV59gW2mnwhM2EjZhFgmiEm/xSrtTXL4RMAJGwAikiwCEjcmUELyQNYGB0k4mbBE2nRHHRbZaPO0h6fKNgBEwAukjABcg0KF5RSWeBW4wYZuw038yXAMjYASMQMYQgKDRuiJlI11D3mmnwhN2rPZALU7nOBkBI2AEjIARAAH4ofJKG5VCE7ZO6zqq7Vw/HD9+fMTlozbTHo4u3wgYASOQDQRiCRv/JtTilrDT75djOnjgboiaCzuFkxEwAkbACBiBmLBRh8MNJuyUx4Q65JjO/L0LsmZrFx2D+sPJCBgBI2AEio0AXIB0rTMnwrnv2LLTToVnJx3h9wge4hC292GnPRxdvhEwAkYgGwhA2Pg2yXQaCDsL/FB4wpZUXWJblyXsbDwkroURMAJGIAsIxCrxLHmJF94tmtUT3uGHDh0K6g+rxLPwqLgORsAIGIF0EYgJGykbc2kWdhEVnrAlXfehYzgXm5UU752MgBEwAkag2AjABRA1Gli4wU5nGRgP6ohtUodfr/OxD+N8Zgk7A53iKhgBI2AEUkago9OZbdgpdwjFjxs37sFJkyb9lkj7Z/HWLpN2BjrGVTACRsAIpIgAEjWSdVtbWwhdrfep+3ylXoEU++OkotU5j8bOZ7jyZ0H9cVIF/Y8RMAJGwAg0FIEKlfhz2ou9p6GFd1KYCbsdFNkrSniKS+IO++4gbNuzOxkx/sgIGAEjUAAE0LTicIaELe3rFu3H/mDazTZht/dA3DlI2Zaw0x6WLt8IGAEjkC4CCGwIbpD2/2vvTMCtOutzvzYzIQkZmQLhBAghEAiEABnIZEbNja213mv0sbfWaG/V3lZtvR201evQ2kFb2+tQo9bWeKPG9vo4xDjHmJhAEiBhnmcOMxwOwwEO+76/L2eRAznAGdbea3rX8yzWPpu91/q+37f3ftd/+P6frOvl6bbmpatbsNtGgdJzWNhDhgwJGeNZSOHPwgfEbTABEzCBMhJAsDHeJNif0VoTv5YFBqWf1hUPggYnuMQvuuiiSOVKHcOOwfhoAiZgAiUigFUdG3C4w5WU/K6sdN+C3TYSGqTDuMOZj02p0jiGrdW8sjJWbocJmIAJmECNCTB9i4JarOCIFmRps0u8bTSUsv8d7XsZKFzjCLY3EzABEzCB8hDADY51jQ4QHrVgZ3TsdVf1iDIBtzBIrM7iGHZGB8rNMgETMIEaEmAq1+DBg4O3lYSzLG12ibcbDQl135aWlmjjxo3hLou7LW8mYAImYALlIYCxhpcVK5uksyxtdom3Gw3dVc3DumaguMuyW7wdHD80ARMwgRIQiBPOyGeyYGd4wOUOfzuCTWZg29w7F0/J8Hi5aSZgAiaQJIE4ho2FHetAkufv6blsYbcjqMHaI6t6GAPFgFFb3LHsdoD80ARMwAQKTgDvKl5Wql466Szjg81iIAj16NGjmX8XllZzLDvjg+bmmYAJmEBCBPCujhw5MrrsssuCcCd02kROYwu7A4zEMMgSxMrmsQW7A0h+ygRMwAQKRiB2iVOTAw2Qh/WeLHXRgt3BaJBoMGrUqGBhs7yaBbsDSH7KBEzABApEgPUkcIdjYZNwJtFeoN/+J7PURU/r6mA0GDDc4Tt37gyDZ8HuAJKfMgETMIECEYgFG4Pt6NGjz1188cX36re/OUtdtIXdwWho4D4WLwJCmToLdgeQ/JQJmIAJFIQAv/GEP0k4Zt+6deujem5r1rpnwe5gRA4fPvwFLOzhw4dHdol3AMhPmYAJmECBCCDYWNYkm5Edrvh1JheRsEu8gw+dUvmP8DSJB1jYTO06cuRI5IVAOoDlp0zABEwg5wTaW9hM5cJQy+JmC7uDUVHsYrME+mbc4iy3STEVpnp5MwETMAETKB4BBJvfeKZy4V0l6SyLmwX7FKMil8hgpnUh2Eyix9r2ZgImYAImUDwCJJzFs4NYVpPf/CxudomfYlSU3r9X2eKHNL1rwLZt26Jjx45F8fEUb/HTJmACJmACOSSAhc3soBEjRkTysBLD7pvFbtjCPsWo6A7rCf3X58ePHx9deeWVYSC5C2NgvZmACZiACRSHAL/teFFxh8urukOC/VQWe2fBPs2oyMpu0sAF9wjuEov1aWD5v0zABEwghwQQa6Z0kWDM77wez5PB9p0sdsUu8dOMisR6T0NDQ3To0KFo3bp1FuzTsPJ/mYAJmEDeCLCEMkYZbvC22PVBJRk/nNV+2MI+xchoCldFg/hJTaL/cdu8vDC9y2tknwKYnzYBEzCBnBGIk80QbXZZ2HuUbPwlfv+z2BUL9ilGRe7vMHFebvH/o9jGfgaTCjjEOSzap4Dmp03ABEwgRwSoscFsICxsft/lDg9e5/j3P2tdsWCfZkS4y1ICwv/TQDYxoCQkcCTW4c0E6k0gvutvbW0Nl9bf9W6Cr2cChSFAThKCTb0NQp9UttTf67LcQQv2aUYnvsuSld2XuzAGlPl5+tvx7NNw83/VhgCfR00tfFw5FX20OIE/g7XB7LOWiADeUoqk8NuOMaYKZ5/Ncvct2J0YHc3P+xoJCVOnTo0uv/xyF1HpBLOyvEQi2q9efd21a9e7d+/ePYN6AKp3H2oD1Ovavo4JFJEA3tLRo0dHU6ZMicaOHctv+5ey3E8LdidGRy7xP8LCpjB87BLHneKtvARwR+/du7equ/Nle/bsGVMPEgcPHrxp//79A3XdUNu+Htf0NUygqARwh1MznNh1vDpj1vtqwe7ECEmcj0iwd3InRsZ4VgvDd6IrfklCBHBJy+KtrF+/PpJgz03otKc9zYEDB442NTVxPQv2aUn5P03g9AQQa+Zcs9AHa0XoxnuLPKkfPv270v9fz8Pu5BhIsN+kwvCPbdmypSq3SYWJ9qzgRclSb+UigHVN4teOHTvC/Hx9+etSeFiCHe3cuTPavn171NLS4tXjyvWxc28TIoB3FMFm5g85SXhNtfDHe3V8mMRObZnN5rSF3ckPge7CfijL+hsaYDLHw10Zd2jeykkAwcY1rYXug4jWgwLWNTcJumm0YNcDuK9RSAIINgYXljU7q3TJ+FpAZ7Ms1rTPgg2FTm6aZP9xsglJPKPGOMLtrZwE8K5s3rw5Wr58ebRhw4Zo3759X6s1CQl167Jly6KFCxeGmwV7d2pN3OcvIoG4WMqYMWPCbzkxbBljvfPQVwt2F0ZJbpRW4tfclSHWFFHxVk4CWNjEkhsbG8MuV/WNuNPWrFkzIEkinFMu8PGy5H+sjNa3cD3c4mSJ45r3ZgIm0DUCWNhkhzPzBwOM33FZ3LmIbTqG3YWxlljv175TS25eiHsSC8tb+QgglFjYuKYRTr78EupL9fgYsTAR+VRPqSDUuOfWrl3bXwluy7DiFyxYUNV1Kps2bQr17Xt6Db/fBMpIAAsbN/jEiROjCRMmkHz2VYU6N+SBhQW7C6Mk14l+L9d84Iorrvgs7kj9mIbiFdyx2T3ZBZAFeCkWNpYuVjbxsDlz5oR5nA0NDbfLCh6ou/ePd7ebsVhLmH9frvbBixcvjl544YXo+eefr6xevbpuMfPutt/vM4GsEqBQCrlHeElZNlmhzXWysP9Ov+H7strm9u2yYLen0YnHsqYekRvlrbJ6Zs6fPz+4U/jxJmvXot0JgAV8iaZ3RU8++WSwuPWDcK+mh9y7cePG2zRv/87udBfLWtb7h3RD8JfcFOtx1lwAAEAASURBVOhmoDpv3rwKK8aRKe7NBEygewSop4ErnOm5PNbv+Y81pSsknHXvjPV9lwW7i7xHjBixQxbQDRLp+RLuq5jHx48orlFv5SPATRqi+vTTT4fpVuQ2YHHL0r6jszRii7r965ubm++UNycktOnGsIIFTxjGN4XtKfmxCXSeAJ7QWLCJXUusF+r7+o7OnyH9V1qwuzEGGvhWFcxoYf4emYbxNBssbW/lIiCxDXOyJbAh+Wzu3LkUVEGwo5/85CfM2Q+VlDTn82eKm/033eBtP5kQFrXKjX5L5/gN5lir/Gj085//PFq6dGk4J8VZuCn05+tkcv7bBDpPAMHGFa56GpHykHCNt+i5XCSbxb20YMckuniUFdULwR43blywrqnv7K2cBLB6CYnwGUCwiTNzI4f4UpgB8dbft0l0Z6i06Hwo6Yeil7wy/STSezX74HuaXz2LJMZFixaF98ulHo6INzcAJLl5MwET6D4BBBsPGN9NBFsJxLmbJWXB7ub4y53yfcVBpigBrTc/zCtWrOjmmfy2vBOIrWyJccSOcCtZLIg4go3LXKJb1Z399+TW3qn+kgI+SEI/ECHmtRRgIfub5LJVq1YFyxrPjVbmyjset98EUidAZjjVzWKv6KWXXhop1+T7qTesiw2wYHcRGC9vizl+QFnifz5p0qSqimeEUqXdOJXfUkACCDjZ49zEMW+f2QRyiVeIn8lFfiFdjkUe6xzRxuWNWx2x5712gRfwg+EupUYAsWZZZBlZVWWGV+Qa/7pupj/Q9luem4IGFuxufIRkHYUBVkxylubxPaOsw6pikxXcl/wAs3srLwFizSSIIcC44bi7Z+cx+8kb4h3vvJfPD3/7c3QyKf9tAl0nwFQuckmYey3LuqKs8Hc2NDQ8zJni3/KunzWdd1iwe8Bd8ZA5KmjxdrnFv3D11VeHAhq4MbGQvJWbgG/cyj3+7n02CHCDrPBlmHONG5z4tXJHvqHnd2ejhV1rRe6C7l3rXu1fvXLlykeIU8ZrZSsuUvuL+gomYAImYAJnJIBgM82Sedcsj0zdcN1M58YFfnIHbWGfTKSLf2vh8164WiZPnhzijtR6xsr2ZgImYAImkB4BwlDEralsJhd4+I2WcbVVzx1Nr1U9u7IFu2f8yDo8rKShZYqTXEHc8sUXX+zhGf12EzABEzCBnhJAsHGHk+yp6bfVadOmEb9+jYpfNfX03Gm93y7xHpKXYO+XG/wNygKei9uFeX4sjE52MO4YbyZgAiZgAvUngGVNuFJeUAyrin6T/1PJZ2vr35LkrmgLu4csJcrEQ17UXOzvajL+DFZ/IVucObW4x12ytIeA/XYTMAET6CIBjCWEWtNuQ2UzcoxkWD0hY2pXF0+VqZfbwk5oOGRZf0PJDQuxsikuT1wbl4w3EzABEzCB+hPgN5jf4ksuuST8Hkuwcxu7julZUWISPTzqjm6p7t6WXnXVVZFiJZHiJCE7sYen9dtNwARMwAS6QQB3+OWXXx6mdCmW/XXFr/+pG6fJ1Fss2AkOh1wwz+ISR7BxwRDH9mYCJmACJlA/Ang2mcqFYI8dOzas96DHS+rXgtpdyTHshNi2lbj7hGLZe+SG+RyuGNzjlJg8ePCgq1YlxNmnMQETMIFTEaAEKStysewxC3wQx5bn85NKOvvwqd6Tp+dtYSc0WiSfIdr6YHyeDwmreOEeR7iZC+jNBEzABEygdgRINCMzHC/nzTffHFF9ktCk1r5+X+2uWt8zW7AT5I1oczolN6xSvCTc3VHD1slnCUL2qUzABEygAwIIdmxhx5Un9fu7qoOX5vYpu8RrMHSysMepgEqVVZjWrVsXqd54cIuzoIM3EzABEzCBZAnEcWtKj2pth2jWrFkh4Yzf4mSvlO7ZLNg14i9X+M9Vs/ZWuWOwuMPKTQi4NxMwARMwgWQJINhxVTNi14i2Es3OS/Yq6Z/NLvEajYHmZd8toZ7PHECKziu27Vh2jVj7tCZgAuUmQFY4YUhyhqg2qb+/or9zP+/65FG1YJ9MJKG/FU85rP1/kfhwyy23RFOnTg0rxTienRBgn8YETMAE2giQFc6c62uvvZY1r8kbYgnN/UUDZMGu4YjqQ3Q+d3tKgKiSOc68bBZT92YCJmACJtBzAiSasRN25LeWrHAqnGnBj9aenz17Z7Bg13BM5J75ulzhn5k9e3aFYiq4xZl2YNGuIXSf2gRMoBQEEGri1ljXWNUzZsxgOleT8oZ+R3OvHysiBCed1XhU5aZ5186dOxv0gXoNCWh79+4NRVSam5sjZ43XGL5PbwImUFgChBeZNkuhFAT7sssuo274LxS7/nJRO23BrsPIKlvxD7XPVubiudwVtra2RocOHYqcNV4H+L6ECZhAIQkg2HgtJ06cGIpUSbh3SqzvK2Rn2zplwa7D6EqkV2g7ev3114dypVjXsrqDcGvqVx1a4EuYgAmYQHEIxO5wLOtbb701rN+gxwOL08OOe2LB7phL4s8qCWK75gdecPTo0SDaTPBHrKkzjsXtzQRMwARM4MwEyAEigVdey1BNUqHGqvKFKnKPf/TM7873KyzYdRo/fchuVyLEhUo+W7B06dKosbExlCzdunVrEO06NcOXMQETMIFcE2BtBhZWImY9fvx49oqyxKfKJb441x3rROOdJd4JSEm8RFMONqko/Qvbtm27lg8aOx86piN4bnYShH0OEzCBIhPADc6Gdc1v55gxY8KKXDKEtijZbKn+v/ClJG1h1/kTrrvAFrLDSTrjuGPHjmj//v3R4cOHnTVe57Hw5UzABPJDAMHGHa7w4vF64azMtXv37hsaGhpa8tOT7rfUgt19dt1655VXXrlYIv0XSpD435yAxUFIQtOHLoh2t07qN5mACZhAgQkg1sy5po4FiWbMuVay2ZaxY8e+Rv+3tsBdP6FrdomfgKP2f+jDRVr4NzTZ/0ty4zRPnjw5YicJjQ+lNxMwARMwgRMJEDakkhmlnhFrWdSszfBD/WbOP/GVxf7Lgp3C+Gru4DK5xt+mzMb18RxC3DwW7BQGw5c0ARPIPIFYsDFupk+fziIfm5Ql/g+Zb3jCDbRLPGGgXTmd7hj7sQwccZn58+dHTU1NwT3OVC9XQesKSb/WBEygiAQwYnCFy8AJibr8Xo4bNw5r+5BqhpfKumZ8Ldgpfsrl1rl87dq1e+XaOUePK3v27Im2bNkSYtnM1/ZmAiZgAmUmgGVN+VGWKSYrnGlc+q1sUpb4uDJysWCnOOqyonUDWRm8cePG7+mD+Jp9+/aFcqUcKaZiKzvFwfGlTcAEUifQp0+fSNO2glizfCaiLbEe3PbbWU29gXVugAW7zsDbX05iHT5wDz744OtVtvSg7iSrqn5W2b59e4SFTa1xi3Z7Yn5sAiZQBgK4wrGuqVPBkpmscz1lyhSyxB+i//FvZxlYtO+jF2duTyOlxw8//HB/ifWNWjO7QZXPqhs2bKgwLxvBdq3xlAbFlzUBE0iNAHk9TOEiM/yqq66KXvva1+IK/xeJ9++k1qgMXNgWdgYGQRmP+7UYyGsl2p+cNWvW25iXzfzsp59+Otq1a5dFOwNj5CaYgAnUjwBrXDOFS7+HYTUuTXv9jOLY765fC7J5JQt2RsZFWZBNcn//g443aurCBFxBixcvDpnjWNveTMAETKAMBOJEMxVFiWbOnBmNHDky0rKZm8rqBm8/5hbs9jRSfqwP5EJt85VcMQGXkFb3CrFsluKkfKk3EzABEygqAeLWuMGZwjV69OgwfYspXKpRMU/P/aCo/e5KvyzYXaFVh9dOmjTpgyoI0F+Zka+TeIcPMJY2tce9DGcdBsCXMAETSIUAlrWKSoXCKEzfwiWuuPXDcoffn0qDMnhRC3bGBkV3mSvVpN9QDLuqmrlVCXWFpThZJKSlpSVY3BlrsptjAiZgAj0iwPQt5luzxrVEmiIpYY1ri/WJWC3YJ/LIzF+KYb9h9uzZ31SiRfXAgQOVvXv3RhRWYXfmeGaGyQ0xARPoIQEs63iutUo1V2+66aaKwoIVFZR6Qw9PXbi3u5Z4RodUU7wekYX9mFb3qhDLVtJFuAMlzuPNBEzABIpCAOuarPC2amaV4cOHf0fTt6ZKtB8pSh+T6oct7KRI1uA8srLvV7LF1xXLuRMLe8WKFRFH4tkuqlID4D6lCZhA3QjEhVFUEzwkmN1www2h9Kga8O8yUBbUrSE5upAFO8ODpfjNbs3D/sg111xzkz7UA5iXvXz58rB2Nm5x1xvP8OC5aSZgAqcl0Ldv3xCzljcxVDF79atfjaV9TJnizad9Y4n/0y7xjA++kjCeUDLGmzTN4QhTHFithqQMXEjeTMAETCCPBHCDU2uCOdb8rinJ7Khc4T/TFK5fl4A/msc+1aPNtrDrQbkH12grcv+fWiDkdaqn+23Nz+69ZMmS6NFHHw3ucSeg9QCu32oCJlB3AtSYQKyxrG+++eZIU1kjxav3qwzpq2hMWRf26MxAWLA7QynF1yjJTJ/fsKrX97QoyM9UV/cO5mPPnTs3GjBgQIhlE8/2ZgImYAJZJ0DcGlc4cWumcMmyxsKuKuFscNx2fvPixz6eSMCCfSKPTP4Vf4B1B/pqWdQ/OXjw4M0kaGjaQ6g5jsXt5TgzOXRulAmYQBsB3OBUMmPWi1YnpJpZVWWYmQVTUSLtAIM6MwEL9pkZZeYVEu6jsrZvl0W97LrrrhtDoQEs7bVr10YScYt2ZkbKDTEBE2hPQL9dwSOoRNqIGuF33XUX1nVFVvZ9Euzvtn+tH5+agJPOTs0mk/+DaKuQyp2U8NM87ZC0oWQN6u1G3MF6MwETMIGsESBujVgPHTo04veKXWLdrLDeiqy1Ncvt8XrYWR6dU7Tt05/+9EHFgpboA3+9hPocXOMknzFHG0vbmwmYgAlkhQBijTdQFcyiG2+8MZoxYwZrXG9SdbPbJeCLs9LOPLTDJlkeRumkNsrKPqSnvtbU1LRPq3j9h1xMfZRFHq1evTrM0eblzh4/CZr/NAETqDsBXOH9+/cPSWZUMiPJTMdjMjQeVHGUF+reoJxf0IKd4wHUh/47mzZter3uWr+thUGqWje7QlLHtm3bgnAr3p3j3rnpJmACeSaAWOMGx6Imbn3LLbdUKbUsy3q1nv9wnvuWVtst2GmRT+i6qrlL3d3eWjO7VRZ3OCtflLh8KdXQLNwJwfZpTMAEOkUAq5q51vptCstlTpkyJZo6dWpFwk0Ytrd+o2xNdIrkiS+yYJ/II3d/tX3wqy+88MJBrXQzkOldzMuWtR2sbETcJUxzN6xusAnkmgBJsYh1g6oyqigKCbJHNPf6W/q9OqaOsXvrBgELdjegZfEtuoM9S4VV5ij7cpi+GKO4w125cmW0Zs2aKLa8s9hut8kETKA4BEgwY7aKjIdo1qxZoZTytGnTmhS3fkBu8G8Wp6fp9MSCnQ73mlxVd7UzZWl/QvXG30/GOJZ2c3NzcInHLvKaXNgnNQETKD0BZqtodcGQYDZmzJhgWVMnXLk2/2axTubjYcFOhmNmzqKyf5/Vl+SAqqJ9QNO++vAlWrZsWaTkNCeiZWaU3BATKB4BCXOk9Q5CJjiVzFQj/JBc4K+Wt29j8XqbTo8s2Olwr9lVlYW5Vif/sJLQJsjKfmMcwyamjZWtbPJQEa1mDfCJTcAESkUgTjCjGIp+f4I7fPz48RR1eo+Mhp+XCkaNO2vBrjHgtE4v19T9CxcufKNEulVFVnozL5uEtM2bNwfh9jzttEbG1zWBYhGQNy/CBc4cawRbHr5Whed6S6w/V6yept8bC3b6Y1CTFrSt8MVSXyP0RdrE+tkkg1AJbceOHSG+bdGuCXqf1AQKT0Cu7oiVtziSBX733XdHWNVKfm2RYI/U8zvafoM8fSvBT4MFO0GYWTqVvjDhiyKX+CFZ2z/WYiF3kMGJW3zDhg1RY2NjiGl7yleWRs1tMYF8EGCJTKZtUb2MlQOVCR7WNVBt8LsRa3oR/wblo0f5aKUFOx/j1O1WqqrQLrnBH9BSdvfqC/T+LVu2jKagARnkcXy72yf3G03ABEpJgJt/xagjGQIslXlMM1OeVCz7U/ptWVBKIHXqtAW7TqDTvIzuhNfp+p9RvfF3q9pQKBeIOxzreteuXWHqF0lp3kzABEygIwK62Q8ucEofywgIlvXVV19N9TLi11UVSLlHrznQ0Xv9XHIELNjJscz8mXRHPHHJkiVHVHWojwS7qr3CWtrr1q0LFrdLmGZ+CN1AE0iFABY1bnAqmE2ePJkpW+xVxawrWnHr7yzW9RkWC3Z9OGfiKm1JIH0XLVrUb+bMmS1a6Svii7hv374g2PGULwt3JobLjTCBTBAguUyx6VAQZdSoUcGq1u8HyWYV7f0l1nbP1WmkLNh1Ap2Fy+iLFWds9pObfLvWp7142LBhERnkTPdavnx5pBh3qEOehfa6DSZgAukSQKxxg5MJTrlRdixs5cTsVqGUVVu3bu2rFlqw6zRMFuw6gc7SZeTKatYSnDfoi/geifUdShYZj1v8wIED0Z49e+wez9JguS0mkBIBxJqpoFQwQ7C1PGYQa7nFN+q53x0yZMj3U2paaS9rwS7p0OvLtlJdf5cS0b6t43gVOQgJaBRXYdqXFhKxpV3Sz4a7XW4CCDUljbGsKYpCMZRrrrkmWNeq6dCiuuC3yUXO74e3OhOwYNcZeNYupy/n/crwXKTEkQYVVakqA7Qyf/78aM6cORELiJBN7ph21kbN7TGB2hBQ2Cwkl0mUI93UB4v6jjvuiCZMmMASmaxxPUJivas2V/dZz0TAgn0mQgX/f8WyD0iQx6qbVc3NPnb++eeHBBMqoik+FWLaJKdheVu4C/5hcPdKSyAWaqxqXOCUGWWlLSxr5bpE8sCN1m/DJr2utbSQMtBxC3YGBiHtJuhLGBaUlyCfoy/rVt1ZV/VlHcQKX4888khYVxvRptiKRTvt0fL1TSB5ArjBuVmXty0iE3z27NnR9OnTwzQu5bncIMFen/xVfcauErBgd5VYgV8v4W5W9wZpbvYnlWzyHgok4AZj2pdW/zpeGQ1r25sJmED+CSDUzK/GsmYta6qXtVnXx3R8Uf//IYXLfpX/nhajB5VidMO9SJLA7t27b1HZ0lmyqD/61FNP9V29enW0ePHiaN68eaH+uP7fS3QmCdznMoGUCDClU1O0gvtbx6qs6gr1wWVlr1Vm+CTdxBMy0+H4lNCUWurLQsAWtj8HryAg19jjevLx5ubmx2699db5ZInqi11VTJvVv4LF7WS0V2DzEyaQOwKDBg0KFvWMGTMQ7YoE+2lN8/zw2Wef3YhY0yGLdXaG1YKdnbHIXEv0pV2g6V2XKDHtQypB+HbN3a5qyleFuZnM18ZVTmzbmwmYQD4ISHzD3GqEWt/rYF1TD/yqq66qysLmu/0+WddP5aM35WulBbt8Y96lHqtIwma94R2Ka/9cX/CH1q9fH7LI16xZE+EqJ5scq9vJaF3C6hebQCoEiFezWh+JZffdd19whWulraoW8ECs36zvu8U6lZHp3EUt2J3jVPpXKXv0a6qEdlhf+K/IdXYWiWdY1yzRyUpfFu7Sf0QMIMMEEGo8Y1jWcXKZhDoaO3ZspPLEPxo+fPjdGW6+m9ZGwEln/ih0ikCceKJs8Q8+99xzo/X32xYuXFjVQiIVrO4FCxaE0qYsIMKynd5MwATSJ4ALHLFmzQCEWq7vaNasWVXN/iC57GFZ1PtVzexBZYk/nX5r3YIzEbCFfSZC/v9AgMSTNtH+CE9okZC9SkR7L3fs7Ig2G8loLrISUPgfE0iVQByvxgVO1TLc4IpXV++6666Kpmx+QdO13pFqA33xLhOwYHcZWXnfgGjHvdcd+ccU036vRDwINkdW+lq1alVYXxsXOdY2z3szAROoPwHWB1BsOljW06ZNC4ItC7uiZNIvSsB/t/4t8hV7SsCC3VOCJX2/KqLt1sIh/TVXc6LcbQ8pw3SiEtOixx9/PMS0WTwES5sdq9ubCZhAbQlgUbNRDIV4NRY1K2xR/OjGG2+MFKfeopvsEbqJ7tP+5ru2rfLZkyRgwU6SZonO1faFZx3c+Tt27HhaRVbGa+/Dj0PsHse6ji1tx7VL9OFwV+tOALEmVo1Yy4IOXi8KoFC5jF0W9c42sdZLK04yqfsIJXNBJ50lw7G0Z5Eo8wNQ1bzst2le9lvkFj9LBVZmkIRGdbQVK1YEN/muXbsowGAXeWk/Ke54rQjwvSJOjQucfJLrr78+rK41fvx45lkv1cpb+5UNfm38Xa1VO3ze2hOwhV17xoW+AmLd9kPwRXX0izxet27dT3v37n0r7nBNBYtUcCUUWdH/Bfe4XeSF/ki4c3UkgEWt71oQbJbERLQnTZoUUblMFvZKTcG8Q9/RTTSJ72odm+ZL1YCABbsGUMt2yvY/BDxeuXLlb2nhgB/rbn+8po2EGJqei4hxb968+bibvGyc3F8TSJKAvmuIcqhYRihKQl1V3LpCKWHllWxWfYRb9BoKH3krCAG7xAsykFnqBlZ2Y2PjRXKDt/br12+dRPrspUuXRk8++WSYr83KX3v37g3C7SlgWRo5tyXLBBDo2KKOi6CwXvXkyZODWF933XUV5YxM0nzrQ3KRH1DcujHL/XHbuk7AFnbXmfkdZyCAla2XbOdlEuevScDfcejQoVB/nB8d1tlWnDvsxLadkHYGoP5vExABRHrAgAEhqUzFTkIxFMR6woQJTN9iutb/1OOl+o55WkZBPzG2sAs6sFnoFpZ2m3gTx/6lluUco4zyC+Ue76cqadHcuXODxU150ziuTZzbmwmYwMsEuMll11TKUABF4aZIq2pRsYyVtloVu94qi/ovFX568OV3+VERCdjCLuKoZqRPsVjTHLnnZiPg+tH5iaaf3IYrHKHWEp6RRDxY39Qk19SwYHFbuDMyiG5GagQQaaxqMsCZqqXvUJiipdoHVQqgEKvWc38twf5Aao30hetKwBZ2XXH7YnKR36X49gQJ9j+y2pdi22HJzvnz5wdXOS5yRDy2uE3MBMpIALEm+xuhplqZRJqFOqo6VvhblQa/qUIoT2mO9T+092SVkVWZ+mwLu0yjnYG+KiHmh2rGD5V09rSmoHxc80RvR7ixqInPKUktWNxMB8PaRrhtbWdg4NyEuhAgqYwCKP379w+WNVY107RwgU+cOJEFOx5RBvi/6LszV6K+x2Jdl2HJzEUs2JkZinI1RIsPzFGRlQf0g7QGQeYHiUQaCq2o5GkQbZLTSFZDuC3a5fp8lLG3WNUINSKthTnCTpUyCqCQWEapUYWUFuum90cxH73HSR8xjBIc7RIvwSDnoYtylT+oGPb9L7744lkkpC1btizUJSebnHW3EW02C3ceRtNt7AoBhJqNeDV1C1iog4QypmzJDX6QZDPdzF4rMV/clfP6tcUjYMEu3pjmrkcSYf1mvWQpyMLeJMt7BNY1cW0WEVmzZk2olkaSGgJOcpo3E8g7AVzflBLVMrXBmlZ4KAi2XN/BotbfeyXUl8qibsp7X93+ZAjYJZ4MR5+lBwRiseYUWqDgtYpjv1s/Yvv1+DrN0Z7+y1/+MlqyZEm0YcMG5nVbsHvA2m/NDgGSyignSrUyrVMd3XbbbaG0qL4P/yj39wUKG33MYp2d8cpCSyzYWRgFt+E4AVkcz+mPt/KELO+hyhqfr+pNw7BEiHEzHYxMcs3pDpXSsLZtcR/H5wcZJSARDslkWNUkVyLUuLqxqtkl2FXV/6b4yecl0n+Y0W64WSkTsGCnPAC+/KkJ6Eduq0R74hVXXLGL2B6uQqa2yE1eoeiKFhkJFjcC7tj2qTn6f9InQPY3An3++eezLnWIT7NQR0NDQ5hbrSSziizuGzQrYmH6rXULskrAgp3VkXG7AgGJ9u5Fixb1Zz6q3IQzVTDiCeLbWNWINDvztrG84ylgFm9/eLJAAKuaDbHGqkag46zve+65J3iM5DV6WKJ9v3I3+uv/WrLQbrchuwScdJbdsXHLOiCgoitvlIv8ISWk9SIZjSlgrAKGi5zHWpM7CDgi7s0E0iAQu78RaaxqpmTJzR1WrSNerXnU0ZQpU1pU/GSL/v/PJeRfS6Odvmb+CFiw8zdmpW+xBPsWWdnvlUgP0JSvu1SbPFRLmzNnTqQM85BRLmE/7ia3xV36j0zdACDWWNTkXMQJZffee28Qawn1aonzCiVU9tNc6+/JPf73NEyfT73N86nrNkg5vpBd4jkevDI2ve3H7XH1nZ1pXt/Uj+BvUmgClzjrbWNp82MpYQ81yjkyjxu3uTcTSJoAAs0cao5Y0rKcw/QsPpMUO7n22mvDUZ/Jf5eF/aGTr2+xPpmI/z4VAQv2qcj4+UwSOPnHTSVM36Ifwmdk0XxMVku/PXv2ROvXrw9zt7GyKcKCiOMyp9wpmy3uTA5trhqlz+Hx9lJOlxwLhBpx1nREVtGqKkGyQtwa0VZC2Tsl6pTl9WYC3Sbw8qeu26fwG00gXQIS4N7KHL9Y9cn7KJ69b9y4cXsQbbLIf/WrX0WrVq0KrnKyySl1Gpc7ZR1urHJvJtBZAsydZmoWR9zelBKNs7/x6tx+++1M06oqbl3R/92pz9dSPX9MyWWNEnl/2DoL2q/rkIAFu0MsfjLvBLT+9s+VeHaLhLwqC7uClY2IU6s8rpxGERbE25sJdJYA4kytb8RZMxZCbBqLmkQyFTrh2Cpru7dE/U16/H87e16/zgQ6Q8Au8c5Q8mtyR0A/qrfSaFnVf6y44kf0d39+UNniuuTEtGWVH19cJJ4WFl7kf0xABLCk2YlPc2QeNeKMaKs+QNhxeatOwCqJ+Xq5wF9lcCZQKwIW7FqR9XlTJ9CWoPa3il9vU/zwdlk+90u8+yi+GKxtMspxm1PylGlgiDcJao5xpz50mWgAsWksaUSaPY5Ha950SCpjjWq5ujfqNYuUTHYPjW77zHkFrUyMYPEaYZd48cbUPWpHoP0PqKztN0uQz2tpafkvSk67hwIsL7zwQvTUU09FJKjhMic5Lc4mt3C3A1nChyzKwc1dvCs3ItJe1d8VzbH+V8Won1ei2XxZ1k+UEI+7nAIBW9gpQPcl60egfVa5LKSHuLKE+Euyrn+hH9trqZiGdc0UHKwp4tr8zZKe8f/FVdXq12pfqd4EcHlT6GTgwIEh25uEMqZojRkzJsKiJpEModaRet/X6fOyWJ+tffVup69XbgK2sMs9/qXtvdzgAxV7PCgru0r2OCLNY1nYldWrV4fkNCWuHT/GVndpgRW847i/L7300jAFS9OxQnUy/mbBGerYY23rNZ+SSP+VaoFvLzgOdy+jBGxhZ3Rg3KzaEkCs29zl4aZVLvFXyQL/CfO4+fFmyhdHXORyoYdscp7DTR4fa9tCn72WBCS8x4udkEyGIJNIhkgTmyYDXJZ1s57fofj0ZbVsi89tAp0lYAu7s6T8usIT2LFjx28pg/wPNQXskFzmZ2nK12hZ3ecR31b505CcRmIaiWpOTsvvxwGxZnoW07GwoMnyxqpuy/ZuUmhksYT7bLnDb9Zrd+e3p2550QjYwi7aiLo/3SLQZm3/m97MjiXdT4J9v+Zxf0IiPZTEtOeffx63eSh3ioscS7vttS7AEkhk9x9Emjh1bFmT9Y1IE6O+5pprolmzZpEJvk9C/lblNvxHdnvilpWZgAW7zKPvvh8noB/yE6bi6O/D+s+vyLpeph/yL6ty1QRc5Ag21hlxb2LcuNBJUJOwW7SP08zWA1zezMFn3JiaxXrUsqxDNTKsakRbVvWXNL5fklg/ma3WuzUm8DIBu8RfZuFHJtAhAQnzMAn1OfrPS/TD/zOqppFNPm/evGjp0qXBVc5jYt1xqVNi3d7SI4AlzcaRjG/i0kzPIjZ9ww03hBkBEvDHlGz4pzoeHD9+/Ea91muypjdkvnInCNjC7gQkv6TcBJSM1CgC7Cu0Otgw/fA3YrEh2qy/zbQvXKxY2pQ6xV3OHot3uenVv/eINFY1O2KNZc20PWLWcbxaU7h+qmSy+/TaI/Vvoa9oAt0jYAu7e9z8rpITUILaX0u836pjH+0XzJ8/P6wKFq8OhpCToGZLu74fFOLUcmuH1bNGjhwZzZw5M4j1lVdeiRu8UVO0jqr4yZs039rFTuo7NL5aAgRsYScA0acoHwH9+P+Jev0nEuQr5SL/lizsK1kVLLawse7IJI+tbQt37T8jrEnNzhQtsr9xgc+ePTtY1Xq8UBb1NbFFrfHQwxPzFmrfQl/BBHpGwILdM35+d8kJ6Ed/iazp18vVupikNIRZwhBpmc9o2bJlUVNTUyRL/Pha3CXHVZPuawxClTK4E6ogiYzHxK0Vm+a5rcoveL1ed9z9bbGuyVD4pDUmYJd4jQH79OUgoHj2ncoYryqJqY/E4Muatz2MtbhZWOTZZ58NR6aBeUWwZD4PiDQbFjUlRSl6ElvT1113XVViXVHm/k2aTz1IpUQ3yQNCKVGvR50Mfp8lJQK2sFMC78sWi4DqTv8o7pGs7GslzOsk4L1Jeopj2UwFw+K2aMekundErBFq4tVM00KssaiJU8vTUdWylxUlmo3VcXX3ruB3mUA2CViwszkublWOCUhQNkm0L5ZVt0sJaUclHH2eeeaZCIt70aJFIaucNblxn3vrOgGEWlZzWKgDq5qiJ6ykpUIoRzWfWlreZ4qywi3WXUfrd2ScgAU74wPk5uWTgESbkpa4ZW9jChEJaBRawdpmx9Jm3jZJaRbuzo0xVjV5AqyoRfETptJNnDgxmjp1KglmTZq29ZtifdzT0bmz+lUmkB8CFuz8jJVbmkMCsgZXyj37XSVCzbzxxhuHsITnihUrIjLKmceNkDOP29uZCRBeIPub1bOmTZsWFurgOGzYsGck4t9HrHXzo3slZ3+fmaZfkUcCFuw8jprbnBsCim1vUGPvU4nTBxVrfRsZzNQlx63L1C9KmsZFVmxpdzyscREUhDquWHb33XdjXR/TnOs/k2X9sER6ncW6Y35+tjgELNjFGUv3JMMEFFN9l5p3nxLOhkh0qrKsK7h4JTRBsHGPO679ygFErJmqhXXNzc6kSZPCoh0NDQ3EsX9frvHPxO+yZR2T8LGoBDytq6gj635ljsCaNWsGyA3eKpE5jGX93HPPRY899lhYBYwMcmLbWNveXibAlC2yv6ladvPNN0e33norLvCfKBv8DlnUvSTSnqr1Mi4/KjgBW9gFH2B3LzsElBh1qK01ldWrV98kS/sXxLJZ8QtLm3g2u7cohAtIMCOxDGuapTDZdbOzVOKNWAuZxdqflXIRsGCXa7zd24wQUPnMg4ppz9XKUUM0l/jSJ554orJ79+4TVvzKSFNTaUb//v1DDXBEGstaVvVcFUE5JGbvoUESa8+JS2VkfNE0CViw06Tva5eWgBKonlXnZ8pSnCTL+xeKX1+gxLTgEmfKV1ld48T1iVtTDIUpWzNmzKi+6lWvqkyZMuV1EulNfGCcXFbar03pO27BLv1HwADSJCARWqS1tvuzTjNCrb+jF198MSzVmWa70rg2mfMs3IEbnBrgrLSlqXAVJZd9Ue3ZFbfJ1nVMwseyEbBgl23E3d/MEVAG9ChZkLuUYFWVeFeWL19eWsGmghlFUVQdLrr++usj3ch8Q+7wBzI3aG6QCaRAoFcK1/QlTcAE2hFQMZXdEqp7JFQV5hlPnjw5LA3Jus5Y3GXYsK7JCCcb/Nprr42mT59OnfD5mg735jL03300gc4QsIXdGUp+jQnUmIAKrDy2cePGv5dl+T7VHw8lOLXyV1hfWzHbGl893dMj1sSuuUHRdK1QG1yW9WoJ9irdsBxNt3W+uglkh4AFOztj4ZaUnIBKlP6TsqLft3///qqW4qywpjZxbUqXFlm0STIjdk2i2ejRo6MJEyZEChGM5ePgBLOSfync/RMI2CV+Ag7/YQLpEZBrfI8Srr4rd3CFDGm5gyPVIY+Y4lRk1ziWNVXMrrnmGuZaV+lzvDnBLCbhowlEkQXbnwITyAgBuYD3SrDfItGaqfWdF2FtkoBFWU7cxkXcYuua5TGJW6uqWUWx7P9RxL66TybQUwLF/BXoKRW/3wRSIiDB3iOBnqtYbpOKqkR33HFHSEDDyi7aRtya1cu4Mbn66qurmsLF4h5L9fxXi9ZX98cEkiDgGHYSFH0OE0iYgIQ7uIn79u0bLVmyJCShUba0SLHs2LpmFS5VMavopuT3tPLW5xJG6dOZQGEIWLALM5TuSJEIaJrXYlnZ0yRiA+QeD+tAU/0M0VZCWiG6yjQuCTVzrSMJNdO4+haiY+6ECdSIgAW7RmB9WhPoLoG2zOgHtILXfLnHX6f47qu00lekpTmjrVu3FkKwSaKjSApzzmfPnk2t8I3KFJ/fXWZ+nwmUgYBj2GUYZfcxVwTIjEa0ZXH+s0T6rVifFBS56KKLgms8V53poLG4wgcOHBgy4CXUwcpWpvg8ZYc/0cHL/ZQJmEAbAVvY/iiYQAYJxNOZDh06tIsM6paWllBcZNu2bRGreuV1I9udrHemrJFspulcrVr8ZJzWCS+Gnz+vA+N254KABTsXw+RGlpWA1oTuJUs7atCa0BRSwTJF9HCP53Uj452kOjwGOlbVp7V57YvbbQL1JGCXeD1p+1om0EUCsq4PaOrTByTYh2SJtuIaxzolYSuPG+5wlWENiWYkm1HdzJsJmEDnCNjC7hwnv8oEUiEg1ziu4o8ppv3ZhQsX/kAu5Bl79+4NNcYpWZo3S5u510zj0rzrsISmHvs3KJVPli+aRwK2sPM4am5z6QhIuHdJtJfgRsYqzVshFbU/xOBx6cd1w+mHXP7/VLrBdIdNoJsEfHfbTXB+mwnUm4DmY/fZt29fxHxs1sxmVa+8WNi4wqkZjjuceeWqaNYq6/oBZYl/pd4cfT0TyCsBW9h5HTm3u3QEyK4mAQ2XMjFsRDAvG4lyeAWwrvESNDY2/raKpvxrnA2fl364nSaQJgFb2GnS97VNoAsElGx2DJEmDsz8ZYleWH6TKV9yl3fhTPV/aVzVbNq0aaHkqqztXfVvha9oAvkmYMHO9/i59SUhQCEVbW/R8X0S7M2K//YePHhwdOTIkbDjJs/yhnVNhjuVzVRyNVjZWW6v22YCWSRgl3gWR8VtMoGTCMSuYx23yVrtxSpXuMeJC+dh6U0WMeEGg6ptxLFVltTGwklj7D9N4EwE/KU5EyH/vwlkjIAyq7dpetfQ5ubmkIBG5TMs7SxvxN9Z6GPq1KkUf3lGFvezWW6v22YCWSRgwc7iqLhNJnAaAhLsP1MM+4taHCTatGlTZpPP5A0IbVN7Q7IZHgFZ10tV3ex2/d/+03TR/2UCJtABAbvEO4Dip0wgywQkeF+aOHFiNH369FCPm/gw4pi1jTaRFT527Niwy42/U22fbbHO2ki5PXkhYAs7LyPldppAOwKUJyUznCleFCMhczxrbnFi67SNuDW7BPuwxHpnu274oQmYQBcI2MLuAiy/1ASyQkDu8P5K3DpKIhdWLGtLZ21eNhY2gs3NBVXN1M7suQGyMqBuhwl0goAFuxOQ/BITyBoBucRblS3+R4ghc7IpRkImNiKZlQ0Lm5sJlgfVwiXBG5CVtrkdJpBHAhbsPI6a21x6AhLmVrnElyKITJNimheCnaWNmwfi67jtuaFQpvj5WWqf22ICeSNgwc7biLm9JtBGQBZ2bzKvSepqaGgI5UqzNCcbweaGQu2r0ka57Ed78EzABLpPwEln3Wfnd5pAqgRINCN2jUu8qamJla8y4xLnxoGiLm0egIpi7X8gK3trqsB8cRPIOQFb2DkfQDe/vAQkiKtkZS+k5Ofw4cMzI9gkvyHUZIYTY1c7D0msP13ekXLPTSAZAhbsZDj6LCZQdwIS62WyXL91xRVXBLc4Fm0WMsVpA6562jVhwoTo61//+ti6w/EFTaCABCzYBRxUd6k8BJTI9X2J9k4EkilUuMXTjmNzfUqRMpWLZLjrrrvOyWbl+Ui6pzUkYMGuIVyf2gRqTUCu5jkSxplyQe9GsMkUz4JgY+3jDldlsxBnrzUHn98EykDAgl2GUXYfC01ArvHV2g+11eoO1m2aHT7ZwiYxzpsJmEDPCViwe87QZzCB1AnIsq4w3xmrFnd0mgVUuHbsEsfC5rE3EzCBnhOwYPecoc9gAqkTIHZNcRLixmkLNklntIEbiHPPPZfyqYdTB+QGmEABCHgedgEG0V0wAQQbgdy7d2+IGadhYXNNxFru+dAGKrCp0tkH1K6VHiETMIGeE7Bg95yhz2ACqROQYAeX+L59+1JL8kKwuXFAsGVZY2X/Stb1Q3q+mjogN8AECkDAgl2AQXQXTEBCeYT49YEDB0LRknpb2FyPymskvtEOjrK2n1O2+EaPjgmYQDIEHMNOhqPPYgKpEpDr+U+xaknywsKtt2DTebLDqXDG3Ou2Ii69U4Xii5tAwQhYsAs2oO5OOQnI9fxVifV+LFvEMo0NC5ubBtrAVK40bhrS6LevaQL1ImCXeL1I+zomUGMCKgV6iURyD6KJWLJrCc4aX/Wl03MtirZQ03zUqFHH1+euy8V9ERMoCQFb2CUZaHezFASaFMt+CuuW5C8ytuu54RLHusctTtU1/V3fBtSzs76WCaRAwIKdAnRf0gRqRUBJZ39DHJs52Qgnlm89Nq7DDUIcw1ZM/bBuGpbX49q+hgmUhYAFuywj7X4WnoBEs6ppXWuwbnGLY+1i9dZjw/XOtSTUIelN4r1D07qeqse1fQ0TKAuB+nyby0LT/TSBlAlINFuJJZMpzrFeGxY2gs1NAi55XXuXrv1sva7v65hAGQhYsMswyu5jqQgg1PFSm/XsOIJNSVJlrHM8KBE/Us/r+1omUHQCFuyij7D7VyoCJJvhlkY42xK/6hbHJoYdW9g6DiwVeHfWBOpAwNO66gDZlzCBehHAumYe9OjRo6OWlpaQCHb06NGaXx6XOPOwqR+u2uFH5Bb/dM0v6guYQMkI2MIu2YC7u8UmgIWNZd3mlj4+H7sevcYlzrVl3R8ZMWLEF+pxTV/DBMpEwIJdptF2X0tBACs7TjrD8q3XxrW4YdC1D9Trmr6OCZSJgAW7TKPtvhaeAIKJa7rego11zd4WP3914UG7gyaQAgELdgrQfUkTqCWB2MJGvOuxcYOAK5yEM64tl7inc9UDvK9ROgJOOivdkLvDRSaASFM0hSUuWTWr1uVJsaqZd33JJZdEI0eODKJdZL7umwmkScAWdpr0fW0TSJiArN0jiDbTunCLE1euZRwbwcaqpiQpNwpY295MwARqQ8CCXRuuPqsJpEJAMeSdEuhHY/c0jajHil3xTUF8TKXzvqgJFJyABbvgA+zulYuArNydra2t30CwSQCrh4DGVjaWdj2uV64RdW9N4GUCFuyXWfiRCRSCgNzhzQh27BKvdadilzhu8VrHzGvdF5/fBLJMwIKd5dFx20ygGwSGDx/+iLK2P8kymwgoFnCtNs5P0hkJZ046qxVln9cEXiJQu2+yCZuACaRGQO7wgSSfIda1dlMj2ljz7LawUxtyX7gEBCzYJRhkd7GcBBBr4tjstRLS9glt7R+Xk7h7bQK1JWDBri1fn90EUiPAFKs4+axWgs1NAecmjh1b9Kl12Bc2gYITsGAXfIDdvXISQKwRUBLBiDEjqLXasOCJl1OopZbXqVX7fV4TyAsBVznIy0i5nSbQBQJYvVjXSkAL87APHDgQ7d+/vwtn6NxLuQ43BKNGjYoaGhqIY/9O597pV5mACXSVgC3srhLz600gYwSampouVPz4FSY0yWa4rGudeBa7xRFvrcHtOuIZ+3y4OcUhYMEuzli6JyUk0NjY+Gv79u37ro4/OLn7JIEdO3YsWNi1SgjjvPF12q7V/+R2+G8TMIFkCNglngxHn8UEUiGgWPVyWdIrdPFftW8AVi/xZOp7S9BrFlvGiuc6ipVXBw8eXJFob2rfDj82ARNIjoAFOzmWPpMJ1J3ARRddtEQW7gO68NH2F5d7usICIMSwDx8+HG3YsKH9fyf2mBsDltbUdSoS7I/ouD2xk/tEJmACJxCwYJ+Aw3+YQP4IyMo9fHKrJdgrlb19eMiQIf2am5tDUZOTX5PE31jYZKLLyv68xPpj+vuEG4ckruFzmIAJvETAMWx/EkyggAQknHy3K3GMudZdlKXNzb8NgFqD9vlLTcCCXerhd+eLSkCCfURifazW/cPCZtO1/FtSa9g+f+kJ+EtW+o+AARSRgJLNvquEtM0UNFFsOZQnTbqfFEwhTk4MW9te7a9wzSd9TZ/PBMpMwIJd5tF33wtLQJbvSgnprgsvvPC4YMfWcFKdRqiJX3PUuVt03takzu3zmIAJvJKABfuVTPyMCRSCgBLPqlQ7Q1ApVZrkhvhjYcfrbh89enS7nqu5Cz7JPvhcJpA3AhbsvI2Y22sCnSRA5TGWvERYeZzkRjIbYo27naNuCKpJnt/nMgETeCWBZG+7X3l+P2MCJpASAUSaoiZY18yXTmqLXeucmwVGOCZ5/qTa6fOYQNEIJPctLhoZ98cEck4Awca6TlpQsa4RbXaE2mKd8w+Km58bAhbs3AyVG2oCXSMgIT07toBr4RKPLW0E3JsJmEDtCViwa8/YVzCBVAgotvw2lr7U1KsqVnaSG2JN7JoscY5Jnz/JtvpcJlAUAhbsooyk+2ECJxHQXOynxo4dW7n44osrJJ/FbuyTXtblP+PzqI55NHr06EjnD7HsLp/IbzABE+gSAQt2l3D5xSaQPwKIdS1c4u2ndSV9/vxRdotNoPYELNi1Z+wrmECqBEgKwypOOtaMSLNzbieepTrEvnhJCFiwSzLQ7qYJ1EK0k74J8CiZgAmcmoAF+9Rs/D8mUAgCCDVzsUkMS7LimcW6EB8PdyJHBCzYORosN9UEukMAwWZ6FyVKEW3+TmLjPLEr/NgxVyVNgqnPYQKnI+BKZ6ej4/8zgQIQQFQRapLEWluTW5+Dc3IjwB4LdwFwuQsmkFkCtrAzOzRumAkkQwBhPe+886IhQ4ZEmuqVSMY41nX7aV2y3l09JZnh8llM4JQELNinROP/MIFiECCTm+ImiDVu8Z5aw4g1O0VTWG9b5z4kK7uxGLTcCxPILgELdnbHxi0zgcQIxCKbxAnjZDPOifjr2KTndiZxbp/DBEzg1AQs2Kdm4/8xgcIQaC+yNeiUNFvq7c0ETKCmBCzYNcXrk5uACZiACZhAMgQs2Mlw9FlMINMEamwAH5MF73ldmf4EuHFFIGDBLsIoug8mcBoCiDWJZxRN6WnCWfvLcN628+1VJvqO9v/nxyZgAskTsGAnz9RnNIFMEUBUyQ4nq5u52ElZ23H1NJ1vmzLF12eq026MCRSQgAW7gIPqLplAewJY16zYxdrYCHdSgk3BFKaL6bhN53SWeHvofmwCNSBgwa4BVJ/SBLJEAAsbyxqxTqIqGYLPzvkGDRrE3i9L/XVbTKCoBFyatKgj636ZQBsBLGyElWpnSVrYnE/Vzpargtp9hm0CJlB7Arawa8/YVzCBVAlgDSPa7EkmnbW52uek2jlf3ARKRMCCXaLBdlfLTQDhTnLjfLoBcEnSJKH6XCZwGgIW7NPA8X+ZgAmckYB/Q86IyC8wgWQI+MuWDEefxQRMwARMwARqSsCCXVO8PrkJmIAJmIAJJEPAgp0MR5/FBEzABEzABGpKwIJdU7w+uQmYgAmYgAkkQ8CCnQxHn8UEMkugLZs7TOliWhd/J50xntnOu2EmUCACFuwCDaa7YgIdEZA4b9HiHKHaGfW/kxDr+Bxapat3R9f0cyZgAskTsGAnz9RnN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index 344f3b4..b892610 100644
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similarity index 100%
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diff --git a/assets/js/runtime~main.dfc58ce0.js b/assets/js/runtime~main.82dd65bd.js
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It had only 2,700 members the first week. Fast forward many years, and LinkedIn’s product portfolio, member base, and server load has grown tremendously.</p><p>Today, LinkedIn operates globally with more than 350 million members. We serve tens of thousands of web pages every second of every day. We've hit our mobile moment where mobile accounts for more than 50 percent of all global traffic. All those requests are fetching data from our backend systems, which in turn handle millions of queries per second.</p><p>Read More at <a href="https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/en-us/blog/migrated/data_centers_pops_0.png" alt="A Brief History of Scaling LinkedIn"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" 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Scaling the Wall of Real-Time Analytics</h1><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2015-06-10T00:00:00.000Z">June 10, 2015</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://github.com/kishoreg" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://avatars.githubusercontent.com/kishoreg" alt="Kishore Gopalakrishna"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://github.com/kishoreg" target="_blank" rel="noopener noreferrer">Kishore Gopalakrishna</a></div><small class="avatar__subtitle">Apache Pinot Team</small></div></div></header><div class="markdown"><p>LinkedIn’s real-time analytics infrastructure, that we built to allow us to slice and dice across billions of rows in real-time across a wide variety of products. 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<link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">Engineering Restaurant Manager - UberEATS Analytics Dashboard | Apache Pinot™</title><meta data-react-helmet="true" property="og:title" content="Engineering Restaurant Manager - UberEATS Analytics Dashboard | Apache Pinot™"><meta data-react-helmet="true" name="description" content="Restaurant Manager is a comprehensive analytics dashboard and pipeline for our restaurant partners. In this article, we discuss how we architected this analytics platform and its robust data pipeline."><meta data-react-helmet="true" property="og:description" content="Restaurant Manager is a comprehensive analytics dashboard and pipeline for our restaurant partners. In this article, we discuss how we architected this analytics platform and its robust data pipeline."><meta data-react-helmet="true" property="og:url" content="https://pinot.apache.org/blog/2017/09/17/Restaurant-Manager"><meta data-react-helmet="true" name="docsearch:language" content="en"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="default"><meta data-react-helmet="true" name="keywords" content="Pinot,Uber Data,User Analytics Dashboard,User-Facing Analytics,Real-time data platform"><link data-react-helmet="true" rel="shortcut icon" href="/img/favicon.ico"><link data-react-helmet="true" rel="canonical" href="https://pinot.apache.org/blog/2017/09/17/Restaurant-Manager"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/blog/2017/09/17/Restaurant-Manager" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/blog/2017/09/17/Restaurant-Manager" hreflang="x-default"><link data-react-helmet="true" rel="preconnect" href="https://BH4D9OD16A-dsn.algolia.net" crossorigin="anonymous"><link rel="stylesheet" href="/assets/css/styles.f0793de6.css">
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UberEATS Analytics Dashboard</h1><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2017-09-17T00:00:00.000Z">September 17, 2017</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, we use data analytics to architect more magical user experiences across our products. Whenever possible, we harness these data engineering capabilities to empower our partners to better serve their customers. For instance, in late 2016, the UberEATS engineering team built a comprehensive analytics dashboard that provides restaurant partners with additional insights about the health of their business.</p><p>Read More at <a href="https://eng.uber.com/restaurant-manager/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/restaurant-manager/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2017/09/image4-2.png" alt="Engineering Restaurant Manager - UberEATS Analytics Dashboard"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber-data">Uber Data</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a 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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><article><header><h1 class="blogPostTitle_d4p0">Engineering SQL Support on Apache Pinot at Uber</h1><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-01-15T00:00:00.000Z">January 15, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber leverages real-time analytics on aggregate data to improve the user experience across our products, from fighting fraudulent behavior on Uber Eats to forecasting demand on our platform.</p><p>To resolve these issues, we built a solution that linked Presto, a query engine that supports full ANSI SQL, and Pinot, a real-time OLAP (online analytical processing) datastore. This married solution allows users to write ad-hoc SQL queries, empowering teams to unlock significant analysis capabilities.</p><p><a href="https://eng.uber.com/engineering-sql-support-on-apache-pinot/" target="_blank" rel="noopener noreferrer">Read More at https://eng.uber.com/engineering-sql-support-on-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/01/Header-SQL-768x329.png" alt="SQL Support on Apache Pinot at Uber"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" 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and help our engineers understand why these anomalies happened through a dimension heatmap</p><p><a href="https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/02/datacube-1.png" alt="Analyzing anomalies with ThirdEye"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" 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For the step-by-step instructions, please visit our documentation, which will guide you through the specifics of running this example in your development environment.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7</a></p><p><img src="https://miro.medium.com/max/4728/1*eR64jBH1ZvC3uNfPP56p5g.png" alt="Using Apache Pinot and Kafka to Analyze GitHub Events"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data 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href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><article><header><h1 class="blogPostTitle_d4p0">Monitoring business performance data with ThirdEye smart alerts</h1><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-06-25T00:00:00.000Z">June 25, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Explain how ThirdEye smart alerts and automated dashboards helped the LinkedIn Premium business operations team monitor key metrics—such as new free trial signups—for the timely detection of outliers in business performance data.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/06/thirdeye_business_performance-3.png" alt="Monitoring business performance data with ThirdEye smart alerts"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" 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Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" 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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><article><header><h1 class="blogPostTitle_d4p0">Building a culture around metrics and anomaly detection</h1><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" 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As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Moving developers up the stack with Apache Pinot"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" 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The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data 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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><article><header><h1 class="blogPostTitle_d4p0">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</h1><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col margin-top--sm"><a href="https://github.com/apache/pinot-site/edit/dev/website/blog/2020-08-08-DevBlog-IngestPlugins.md" target="_blank" rel="noreferrer noopener"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" class="iconEdit_mS5F" aria-hidden="true"><g><path d="m34.5 11.7l-3 3.1-6.3-6.3 3.1-3q0.5-0.5 1.2-0.5t1.1 0.5l3.9 3.9q0.5 0.4 0.5 1.1t-0.5 1.2z m-29.5 17.1l18.4-18.5 6.3 6.3-18.4 18.4h-6.3v-6.2z"></path></g></svg>Edit this page</a></div></footer></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Blog post page navigation"><div class="pagination-nav__item"><a class="pagination-nav__link" href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis"><div class="pagination-nav__sublabel">Newer Post</div><div class="pagination-nav__label">« Deep Analysis of Russian Twitter Trolls</div></a></div><div class="pagination-nav__item pagination-nav__item--next"><a class="pagination-nav__link" href="/blog/2020/08/08/DevBlog-PinotMonitoring"><div class="pagination-nav__sublabel">Older Post</div><div class="pagination-nav__label">Monitoring Apache Pinot with JMX, Prometheus and Grafana »</div></a></div></nav></main><div class="col col--2"><div class="tableOfContents_vrFS thin-scrollbar"></div></div></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" href="/who_uses">Who uses Apache Pinot?</a></li><li class="footer__item"><a 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<link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">Monitoring Apache Pinot with JMX, Prometheus and Grafana | Apache Pinot™</title><meta data-react-helmet="true" property="og:title" content="Monitoring Apache Pinot with JMX, Prometheus and Grafana | Apache Pinot™"><meta data-react-helmet="true" name="description" content="Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain"><meta data-react-helmet="true" property="og:description" content="Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain"><meta data-react-helmet="true" property="og:url" content="https://pinot.apache.org/blog/2020/08/08/DevBlog-PinotMonitoring"><meta data-react-helmet="true" name="docsearch:language" content="en"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="default"><meta data-react-helmet="true" name="keywords" content="Pinot,Monitoring,User-Facing Analytics,Real-time data platform"><link data-react-helmet="true" rel="shortcut icon" href="/img/favicon.ico"><link data-react-helmet="true" rel="canonical" href="https://pinot.apache.org/blog/2020/08/08/DevBlog-PinotMonitoring"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/blog/2020/08/08/DevBlog-PinotMonitoring" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/blog/2020/08/08/DevBlog-PinotMonitoring" hreflang="x-default"><link data-react-helmet="true" rel="preconnect" href="https://BH4D9OD16A-dsn.algolia.net" crossorigin="anonymous"><link rel="stylesheet" href="/assets/css/styles.f0793de6.css">
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When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" 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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><article><header><h1 class="blogPostTitle_d4p0">Monitoring Apache Pinot with JMX, Prometheus and Grafana</h1><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" 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Merchant Live Monitoring with Real-Time Analytics - Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><article><header><h1 class="blogPostTitle_d4p0">Achieving 99th percentile latency SLA using Apache Pinot</h1><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" 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Merchant Live Monitoring with Real-Time Analytics - Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><article><header><h1 class="blogPostTitle_d4p0">Achieving 99th percentile latency SLA using Apache Pinot</h1><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" 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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><article><header><h1 class="blogPostTitle_d4p0">Utilize UDFs to Supercharge Queries in Apache Pinot</h1><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a 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href="/blog/2020/08/08/DevBlog-SLAApps"><div class="pagination-nav__sublabel">Newer Post</div><div class="pagination-nav__label">« Achieving 99th percentile latency SLA using Apache Pinot</div></a></div><div class="pagination-nav__item pagination-nav__item--next"><a class="pagination-nav__link" href="/blog/2020/08/08/DevBlog-PinotMonitoring"><div class="pagination-nav__sublabel">Older Post</div><div class="pagination-nav__label">Monitoring Apache Pinot with JMX, Prometheus and Grafana »</div></a></div></nav></main><div class="col col--2"><div class="tableOfContents_vrFS thin-scrollbar"></div></div></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a 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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><article><header><h1 class="blogPostTitle_d4p0">Utilize UDFs to Supercharge Queries in Apache Pinot</h1><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. 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The world’s best research hasn’t seemed to hit the mainstream yet, which made this an excellent opportunity to see if I could use some open source tooling to surface new analytical evidence.</p><p>In this blog post, I’ll show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.</p><p><a href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843" target="_blank" rel="noopener noreferrer">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png" alt="Deep Analysis of Russian Twitter Trolls"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" 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Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to enable our community operations team to take corrective action. Having a real-time dashboard of different events such as consumer demand, driver availability, or trips happening in a city is crucial for day-to-day operation, incident triaging, and financial intelligence.</p><p>Read More at <a href="https://eng.uber.com/operating-apache-pinot/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/operating-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/10/1224-5.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a 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architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/03/pinotincubator2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a 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Merchant Live Monitoring with Real-Time Analytics - Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a aria-current="page" class="sidebarItemLink_zyXk sidebarItemLinkActive_wcJs" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><article><header><h1 class="blogPostTitle_d4p0">Change Data Analysis with Debezium and Apache Pinot</h1><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-01-08T00:00:00.000Z">January 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’re going to explore an exciting new world of real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Change Data Analysis with Debezium and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/debezium">Debezium</a><a class="margin-horiz--sm" href="/blog/tags/cdc">CDC</a><a class="margin-horiz--sm" href="/blog/tags/change-data-analysis">Change Data 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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a aria-current="page" class="sidebarItemLink_zyXk sidebarItemLinkActive_wcJs" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><article><header><h1 class="blogPostTitle_d4p0">Real-time Analytics with Presto and Apache Pinot</h1><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-02-02T00:00:00.000Z">February 2, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://www.startree.ai/wp-content/uploads/2021/05/team-xiang-150x150.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" 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Typically, upsert is a term used to describe inserting a record into a database if it does not already exist or update it if it does exist. In Apache Pinot’s case, upsert isn’t precisely the same concept, and I wanted to write this blog post to explain why it’s exciting and how you can start using it.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93</a></p><p><img src="https://miro.medium.com/max/1400/0*So3GjHjLY7DJAiaP" alt="Introduction to Upserts in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/upsert">Upsert</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" 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class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><article><header><h1 class="blogPostTitle_d4p0">Automating Merchant Live Monitoring with Real-Time Analytics - Charon</h1><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-29T00:00:00.000Z">April 29, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, live monitoring and automation of Ops is critical to preserve marketplace health, maintain reliability, and gain efficiency in markets. By the virtue of the word “live”, this monitoring needs to show what is happening now, with prompt access to fresh data, and the ability to recommend appropriate actions based on that data. Uber’s data platform provides the self-serve tools which empower the Ops teams to build their own live monitoring tools, and support their regional teams by building rich solutions.</p><p>For this project, the requirement was to provide merchant level monitoring and handle the edge cases which remain unaddressed by the sophisticated internal marketplace management tools. We used a variety of Uber’s real-time data platform components to build a tool called Charon to reduce impact of poor marketplace reliability on the merchants.</p><p>Read More at <a href="https://eng.uber.com/charon/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/charon/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2021/04/deliverDoor@3x-768x263.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing 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class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><article><header><h1 class="blogPostTitle_d4p0">Introduction to Geospatial Queries in Apache Pinot</h1><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-13T00:00:00.000Z">June 13, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Geospatial data has been widely used across the industry, spanning multiple verticals, such as ride-sharing and delivery, transportation infrastructure, defense and intel, public health. Deriving insights from timely and accurate geospatial data could enable mission-critical use cases in the organizations and fuel a vibrant marketplace across the industry. In the design document for this new Pinot feature, we discuss the challenges of analyzing geospatial at scale and propose the geospatial support in Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9</a></p><p><img src="https://miro.medium.com/max/1400/0*1xrDSs9lLZ5dD3zK" alt="Introduction to Geospatial Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/h-3">H3</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" 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class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><article><header><h1 class="blogPostTitle_d4p0">Text analytics on LinkedIn Talent Insights using Apache Pinot</h1><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-16T00:00:00.000Z">June 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn Talent Insights (LTI) is a platform that helps organizations understand the external labor market and their internal workforce, and enables the long term success of their employees. Users of LTI have the flexibility to construct searches using the various facets of the LinkedIn Economic Graph (skills, titles, location, company, etc.).</p><p><a href="https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/06/ltipinot6.png" alt="Text analytics on LinkedIn Talent Insights using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg blogPostDetailsFull_xD8n"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/text-analytics">Text analytics</a><a 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<link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">Announcing Apache Pinot 0.10 | Apache Pinot™</title><meta data-react-helmet="true" property="og:title" content="Announcing Apache Pinot 0.10 | Apache Pinot™"><meta data-react-helmet="true" name="description" content="Learn more about the release of Apache Pinot 0.10 and all of new features that have been included in this version of the product."><meta data-react-helmet="true" property="og:description" content="Learn more about the release of Apache Pinot 0.10 and all of new features that have been included in this version of the product."><meta data-react-helmet="true" property="og:url" content="https://pinot.apache.org/blog/2022/04/04/Announcing-Apache-Pinot-0-10"><meta data-react-helmet="true" name="docsearch:language" content="en"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="default"><meta data-react-helmet="true" name="keywords" content="Apache Pinot,Apache Pinot Releases,Apache Pinot 0.10.0"><link data-react-helmet="true" rel="shortcut icon" href="/img/favicon.ico"><link data-react-helmet="true" rel="canonical" href="https://pinot.apache.org/blog/2022/04/04/Announcing-Apache-Pinot-0-10"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/blog/2022/04/04/Announcing-Apache-Pinot-0-10" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/blog/2022/04/04/Announcing-Apache-Pinot-0-10" hreflang="x-default"><link data-react-helmet="true" rel="preconnect" href="https://BH4D9OD16A-dsn.algolia.net" crossorigin="anonymous"><link rel="stylesheet" href="/assets/css/styles.f0793de6.css">
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You can find a full list of everything included in the <a href="https://docs.pinot.apache.org/basics/releases/0.10.0" target="_blank" rel="noopener noreferrer">release notes</a>.</p><p>Let’s have a look at some of the changes, with the help of the batch <a href="https://docs.pinot.apache.org/basics/getting-started/running-pinot-in-docker" target="_blank" rel="noopener noreferrer">QuickStart configuration</a>.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="query-plans"></a>Query Plans<a class="hash-link" href="#query-plans" title="Direct link to heading">#</a></h2><p>Amrish Lal implemented the <code>EXPLAIN PLAN</code> clause, which returns the execution plan that will be chosen by the Pinot Query Engine.
This lets us see what the query is likely to do without actually having to run it.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">EXPLAIN</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">PLAN</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FOR</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token operator">*</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following results:</p><table><thead><tr><th>Operator</th><th>Operator_Id</th><th>Parent_Id</th></tr></thead><tbody><tr><td>BROKER_REDUCE(limit:10)</td><td>0</td><td>-1</td></tr><tr><td>COMBINE_SELECT</td><td>1</td><td>0</td></tr><tr><td>SELECT(selectList:AtBatting, G_old, baseOnBalls, caughtStealing, doules, groundedIntoDoublePlays, hits, hitsByPitch, homeRuns, intentionalWalks, league, numberOfGames, numberOfGamesAsBatter, playerID, playerName, playerStint, runs, runsBattedIn, sacrificeFlies, sacrificeHits, stolenBases, strikeouts, teamID, tripples, yearID)</td><td>2</td><td>1</td></tr><tr><td>TRANSFORM_PASSTHROUGH(AtBatting, G_old, baseOnBalls, caughtStealing, doules, groundedIntoDoublePlays, hits, hitsByPitch, homeRuns, intentionalWalks, league, numberOfGames, numberOfGamesAsBatter, playerID, playerName, playerStint, runs, runsBattedIn, sacrificeFlies, sacrificeHits, stolenBases, strikeouts, teamID, tripples, yearID)</td><td>3</td><td>2</td></tr><tr><td>PROJECT(homeRuns, playerStint, groundedIntoDoublePlays, numberOfGames, AtBatting, stolenBases, tripples, hitsByPitch, teamID, numberOfGamesAsBatter, strikeouts, sacrificeFlies, caughtStealing, baseOnBalls, playerName, doules, league, yearID, hits, runsBattedIn, G_old, sacrificeHits, intentionalWalks, runs, playerID)</td><td>4</td><td>3</td></tr><tr><td>FILTER_FULL_SCAN(operator:EQ,predicate:league = 'NL')</td><td>5</td><td>4</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="filter-clauses-for-aggregates"></a>FILTER Clauses for Aggregates<a class="hash-link" href="#filter-clauses-for-aggregates" title="Direct link to heading">#</a></h2><p>Atri Sharma added the filter clause for aggregates.
This feature makes it possible to write queries like this:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> nlHomeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'AL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> alHomeRuns</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>nlHomeRuns</th><th>alHomeRuns</th></tr></thead><tbody><tr><td>135486</td><td>135990</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="greatest-and-least"></a>greatest and least<a class="hash-link" href="#greatest-and-least" title="Direct link to heading">#</a></h2><p>Richard Startin added the <code>greatest</code> and <code>least</code> functions:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> playerID</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> least</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> greatest</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">hits</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> hits</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token plain"> </span><span class="token operator">AND</span><span class="token plain"> teamID </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'SFN'</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">GROUP</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">BY</span><span class="token plain"> playerID</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">LIMIT</span><span class="token plain"> </span><span class="token number">5</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>playerID</th><th>homeRuns</th><th>hits</th></tr></thead><tbody><tr><td>ramirju01</td><td>0</td><td>5</td></tr><tr><td>milneed01</td><td>4</td><td>54</td></tr><tr><td>testani01</td><td>0</td><td>5</td></tr><tr><td>shawbo01</td><td>0</td><td>8</td></tr><tr><td>vogelry01</td><td>0</td><td>12</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="distinctcountsmarthll"></a>DistinctCountSmartHLL<a class="hash-link" href="#distinctcountsmarthll" title="Direct link to heading">#</a></h2><p> Xiaotian (Jackie) Jiang added the <code>DistinctCountSmartHLL</code> aggregation function, which automatically converts the Set to HyperLogLog if the set size grows too big to protect the servers from running out of memory:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> DISTINCTCOUNTSMARTHLL</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'hllLog2m=8;hllConversionThreshold=10'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>distinctcountsmarthll(homeRuns)</th></tr></thead><tbody><tr><td>66</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="ui-updates"></a>UI updates<a class="hash-link" href="#ui-updates" title="Direct link to heading">#</a></h2><p>There were also a bunch of updates to the Pinot Data Explorer, by Sanket Shah and Johan Adami.</p><p>The display of reported size and estimated size is now in a human readable format:</p><p><img alt="Human readable sizes" src="/assets/images/human-readable-sizes-b8c4009dd53d23da3b8637963827a8de.png"></p><p>Fixes for the following issues:</p><ul><li>Error messages weren't showing on the UI when an invalid operation is attempted:</li></ul><p><img alt="A backwards incompatible attempted schema change" src="/assets/images/backwards-incompatible-99886dcd0be55a8100a7d6c5f3da3bda.png"></p><ul><li>Query console goes blank on syntax error.</li><li>Query console cannot show query result when multiple columns have the same name.</li><li>Adding extra fields after <code>SELECT *</code> would throw a NullPointerException.</li><li>Some queries were returning <code>--</code> instead of <code>0</code>.</li><li>Query console couldn't show the query result if multiple columns had the same name.</li><li>Pinot Dashboard tenant view showing the incorrect amount of servers and brokers.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="realtimetooffline-task"></a>RealTimeToOffline Task<a class="hash-link" href="#realtimetooffline-task" title="Direct link to heading">#</a></h2><p>Xiaotian (Jackie) Jiang made some fixes to the <a href="https://dev.startree.ai/docs/pinot/recipes/real-time-offline-job" target="_blank" rel="noopener noreferrer">RealTimeToOffline job</a> to handle time gaps and proceed to the next time window when no segment matches the current one.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="empty-quickstart"></a>Empty QuickStart<a class="hash-link" href="#empty-quickstart" title="Direct link to heading">#</a></h2><p>Kenny Bastani added an empty QuickStart command, which lets you quickly spin up an empty Pinot cluster:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">docker</span><span class="token plain"> run </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">8000</span><span class="token plain">:8000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">9000</span><span class="token plain">:9000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> apachepinot/pinot:0.10.0 QuickStart </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -type empty</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>You can then ingest your own dataset without needing to worry about spinning up each of the Pinot components individually.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="data-ingestion"></a>Data Ingestion<a class="hash-link" href="#data-ingestion" title="Direct link to heading">#</a></h2><ul><li><p>Richard Startin fixed some issues with real-time ingestion where consumption of messages would stop if a bad batch of messages was consumed from Kafka.</p></li><li><p>Mohemmad Zaid Khan added the BoundedColumnValue partition function, which partitions segments based on column values.</p></li><li><p>Xiaobing Li added the fixed name segment generator, which can be used when you want to replace a specific existing segment.</p></li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="other-changes"></a>Other changes<a class="hash-link" href="#other-changes" title="Direct link to heading">#</a></h2><ul><li>Richard Startin set LZ4 compression as the default for all metrics fields.</li><li>Mark Needham added the <code>ST_Within</code> geospatial function.</li><li>Rong Rong fixed a bug where query stats wouldn't show if there was an error processing the query (e.g. if the query timed out).</li><li>Prashant Pandey fixed the query engine to handle extra columns added to a <code>SELECT *</code> statement.</li><li>Richard Startin added support for forward indexes on JSON columns.</li><li>Rong Rong added the GRPC broker request handler so that data can be streamed back from the server to the broker when processing queries.</li><li>deemoliu made it possible to add a default strategy when using the <a href="https://dev.startree.ai/docs/pinot/recipes/upserts-partial" target="_blank" rel="noopener noreferrer">partial upsert feature</a>.</li><li>Jeff Moszuti added support for the <code>TIMESTAMP</code> data type in the <a href="https://docs.pinot.apache.org/operators/configuration-recommendation-engine" target="_blank" rel="noopener noreferrer">configuration recommendation engine</a>.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="dependency-updates"></a>Dependency updates<a class="hash-link" href="#dependency-updates" title="Direct link to heading">#</a></h2><p>The following dependencies were updated:</p><ul><li>async-http-client because the library moved to a different organization.</li><li>RoaringBitmap to 0.9.25</li><li>JsonPath to 2.7.0</li><li>Kafka to 2.8.1</li><li>Prometheus to 0.16.1</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="resources"></a>Resources<a class="hash-link" href="#resources" title="Direct link to heading">#</a></h2><p>If you want to try out Apache Pinot, the following resources will help you get started:</p><ul><li>Download page: <a href="https://pinot.apache.org/download/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/download/</a></li><li>Getting started: <a href="https://docs.pinot.apache.org/getting-started" target="_blank" rel="noopener noreferrer">https://docs.pinot.apache.org/getting-started</a></li><li>Apache Pinot Recipes: <a href="https://dev.startree.ai/docs/pinot/recipes/" target="_blank" rel="noopener noreferrer">https://dev.startree.ai/docs/pinot/recipes/</a></li><li>Join our Slack channel: <a href="https://communityinviter.com/apps/apache-pinot/apache-pinot" target="_blank" rel="noopener noreferrer">https://communityinviter.com/apps/apache-pinot/apache-pinot</a></li><li>See our upcoming events: <a href="https://www.meetup.com/apache-pinot" target="_blank" rel="noopener noreferrer">https://www.meetup.com/apache-pinot</a></li><li>Follow us on Twitter: <a href="https://twitter.com/startreedata" target="_blank" rel="noopener noreferrer">https://twitter.com/startreedata</a></li><li>Subscribe to our YouTube channel: <a 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You can find a full list of everything included in the <a href="https://docs.pinot.apache.org/basics/releases/0.10.0" target="_blank" rel="noopener noreferrer">release notes</a>.</p><p>Let’s have a look at some of the changes, with the help of the batch <a href="https://docs.pinot.apache.org/basics/getting-started/running-pinot-in-docker" target="_blank" rel="noopener noreferrer">QuickStart configuration</a>.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="query-plans"></a>Query Plans<a class="hash-link" href="#query-plans" title="Direct link to heading">#</a></h2><p>Amrish Lal implemented the <code>EXPLAIN PLAN</code> clause, which returns the execution plan that will be chosen by the Pinot Query Engine.
This lets us see what the query is likely to do without actually having to run it.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">EXPLAIN</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">PLAN</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FOR</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token operator">*</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following results:</p><table><thead><tr><th>Operator</th><th>Operator_Id</th><th>Parent_Id</th></tr></thead><tbody><tr><td>BROKER_REDUCE(limit:10)</td><td>0</td><td>-1</td></tr><tr><td>COMBINE_SELECT</td><td>1</td><td>0</td></tr><tr><td>SELECT(selectList:AtBatting, G_old, baseOnBalls, caughtStealing, doules, groundedIntoDoublePlays, hits, hitsByPitch, homeRuns, intentionalWalks, league, numberOfGames, numberOfGamesAsBatter, playerID, playerName, playerStint, runs, runsBattedIn, sacrificeFlies, sacrificeHits, stolenBases, strikeouts, teamID, tripples, yearID)</td><td>2</td><td>1</td></tr><tr><td>TRANSFORM_PASSTHROUGH(AtBatting, G_old, baseOnBalls, caughtStealing, doules, groundedIntoDoublePlays, hits, hitsByPitch, homeRuns, intentionalWalks, league, numberOfGames, numberOfGamesAsBatter, playerID, playerName, playerStint, runs, runsBattedIn, sacrificeFlies, sacrificeHits, stolenBases, strikeouts, teamID, tripples, yearID)</td><td>3</td><td>2</td></tr><tr><td>PROJECT(homeRuns, playerStint, groundedIntoDoublePlays, numberOfGames, AtBatting, stolenBases, tripples, hitsByPitch, teamID, numberOfGamesAsBatter, strikeouts, sacrificeFlies, caughtStealing, baseOnBalls, playerName, doules, league, yearID, hits, runsBattedIn, G_old, sacrificeHits, intentionalWalks, runs, playerID)</td><td>4</td><td>3</td></tr><tr><td>FILTER_FULL_SCAN(operator:EQ,predicate:league = 'NL')</td><td>5</td><td>4</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="filter-clauses-for-aggregates"></a>FILTER Clauses for Aggregates<a class="hash-link" href="#filter-clauses-for-aggregates" title="Direct link to heading">#</a></h2><p>Atri Sharma added the filter clause for aggregates.
This feature makes it possible to write queries like this:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> nlHomeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'AL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> alHomeRuns</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>nlHomeRuns</th><th>alHomeRuns</th></tr></thead><tbody><tr><td>135486</td><td>135990</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="greatest-and-least"></a>greatest and least<a class="hash-link" href="#greatest-and-least" title="Direct link to heading">#</a></h2><p>Richard Startin added the <code>greatest</code> and <code>least</code> functions:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> playerID</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> least</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> greatest</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">hits</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> hits</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token plain"> </span><span class="token operator">AND</span><span class="token plain"> teamID </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'SFN'</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">GROUP</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">BY</span><span class="token plain"> playerID</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">LIMIT</span><span class="token plain"> </span><span class="token number">5</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>playerID</th><th>homeRuns</th><th>hits</th></tr></thead><tbody><tr><td>ramirju01</td><td>0</td><td>5</td></tr><tr><td>milneed01</td><td>4</td><td>54</td></tr><tr><td>testani01</td><td>0</td><td>5</td></tr><tr><td>shawbo01</td><td>0</td><td>8</td></tr><tr><td>vogelry01</td><td>0</td><td>12</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="distinctcountsmarthll"></a>DistinctCountSmartHLL<a class="hash-link" href="#distinctcountsmarthll" title="Direct link to heading">#</a></h2><p> Xiaotian (Jackie) Jiang added the <code>DistinctCountSmartHLL</code> aggregation function, which automatically converts the Set to HyperLogLog if the set size grows too big to protect the servers from running out of memory:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> DISTINCTCOUNTSMARTHLL</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'hllLog2m=8;hllConversionThreshold=10'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>distinctcountsmarthll(homeRuns)</th></tr></thead><tbody><tr><td>66</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="ui-updates"></a>UI updates<a class="hash-link" href="#ui-updates" title="Direct link to heading">#</a></h2><p>There were also a bunch of updates to the Pinot Data Explorer, by Sanket Shah and Johan Adami.</p><p>The display of reported size and estimated size is now in a human readable format:</p><p><img alt="Human readable sizes" src="/assets/images/human-readable-sizes-b8c4009dd53d23da3b8637963827a8de.png"></p><p>Fixes for the following issues:</p><ul><li>Error messages weren't showing on the UI when an invalid operation is attempted:</li></ul><p><img alt="A backwards incompatible attempted schema change" src="/assets/images/backwards-incompatible-99886dcd0be55a8100a7d6c5f3da3bda.png"></p><ul><li>Query console goes blank on syntax error.</li><li>Query console cannot show query result when multiple columns have the same name.</li><li>Adding extra fields after <code>SELECT *</code> would throw a NullPointerException.</li><li>Some queries were returning <code>--</code> instead of <code>0</code>.</li><li>Query console couldn't show the query result if multiple columns had the same name.</li><li>Pinot Dashboard tenant view showing the incorrect amount of servers and brokers.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="realtimetooffline-task"></a>RealTimeToOffline Task<a class="hash-link" href="#realtimetooffline-task" title="Direct link to heading">#</a></h2><p>Xiaotian (Jackie) Jiang made some fixes to the <a href="https://dev.startree.ai/docs/pinot/recipes/real-time-offline-job" target="_blank" rel="noopener noreferrer">RealTimeToOffline job</a> to handle time gaps and proceed to the next time window when no segment matches the current one.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="empty-quickstart"></a>Empty QuickStart<a class="hash-link" href="#empty-quickstart" title="Direct link to heading">#</a></h2><p>Kenny Bastani added an empty QuickStart command, which lets you quickly spin up an empty Pinot cluster:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">docker</span><span class="token plain"> run </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">8000</span><span class="token plain">:8000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">9000</span><span class="token plain">:9000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> apachepinot/pinot:0.10.0 QuickStart </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -type empty</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>You can then ingest your own dataset without needing to worry about spinning up each of the Pinot components individually.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="data-ingestion"></a>Data Ingestion<a class="hash-link" href="#data-ingestion" title="Direct link to heading">#</a></h2><ul><li><p>Richard Startin fixed some issues with real-time ingestion where consumption of messages would stop if a bad batch of messages was consumed from Kafka.</p></li><li><p>Mohemmad Zaid Khan added the BoundedColumnValue partition function, which partitions segments based on column values.</p></li><li><p>Xiaobing Li added the fixed name segment generator, which can be used when you want to replace a specific existing segment.</p></li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="other-changes"></a>Other changes<a class="hash-link" href="#other-changes" title="Direct link to heading">#</a></h2><ul><li>Richard Startin set LZ4 compression as the default for all metrics fields.</li><li>Mark Needham added the <code>ST_Within</code> geospatial function.</li><li>Rong Rong fixed a bug where query stats wouldn't show if there was an error processing the query (e.g. if the query timed out).</li><li>Prashant Pandey fixed the query engine to handle extra columns added to a <code>SELECT *</code> statement.</li><li>Richard Startin added support for forward indexes on JSON columns.</li><li>Rong Rong added the GRPC broker request handler so that data can be streamed back from the server to the broker when processing queries.</li><li>deemoliu made it possible to add a default strategy when using the <a href="https://dev.startree.ai/docs/pinot/recipes/upserts-partial" target="_blank" rel="noopener noreferrer">partial upsert feature</a>.</li><li>Jeff Moszuti added support for the <code>TIMESTAMP</code> data type in the <a href="https://docs.pinot.apache.org/operators/configuration-recommendation-engine" target="_blank" rel="noopener noreferrer">configuration recommendation engine</a>.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="dependency-updates"></a>Dependency updates<a class="hash-link" href="#dependency-updates" title="Direct link to heading">#</a></h2><p>The following dependencies were updated:</p><ul><li>async-http-client because the library moved to a different organization.</li><li>RoaringBitmap to 0.9.25</li><li>JsonPath to 2.7.0</li><li>Kafka to 2.8.1</li><li>Prometheus to 0.16.1</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="resources"></a>Resources<a class="hash-link" href="#resources" title="Direct link to heading">#</a></h2><p>If you want to try out Apache Pinot, the following resources will help you get started:</p><ul><li>Download page: <a href="https://pinot.apache.org/download/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/download/</a></li><li>Getting started: <a href="https://docs.pinot.apache.org/getting-started" target="_blank" rel="noopener noreferrer">https://docs.pinot.apache.org/getting-started</a></li><li>Apache Pinot Recipes: <a href="https://dev.startree.ai/docs/pinot/recipes/" target="_blank" rel="noopener noreferrer">https://dev.startree.ai/docs/pinot/recipes/</a></li><li>Join our Slack channel: <a href="https://communityinviter.com/apps/apache-pinot/apache-pinot" target="_blank" rel="noopener noreferrer">https://communityinviter.com/apps/apache-pinot/apache-pinot</a></li><li>See our upcoming events: <a href="https://www.meetup.com/apache-pinot" target="_blank" rel="noopener noreferrer">https://www.meetup.com/apache-pinot</a></li><li>Follow us on Twitter: <a href="https://twitter.com/startreedata" target="_blank" rel="noopener noreferrer">https://twitter.com/startreedata</a></li><li>Subscribe to our YouTube channel: <a href="https://www.youtube.com/c/StarTree" target="_blank" rel="noopener noreferrer">https://www.youtube.com/c/StarTree</a></li></ul></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/releases">Releases</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/16/LinkedIn-TextAnalytics">Text analytics on LinkedIn Talent Insights using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-16T00:00:00.000Z">June 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn Talent Insights (LTI) is a platform that helps organizations understand the external labor market and their internal workforce, and enables the long term success of their employees. Users of LTI have the flexibility to construct searches using the various facets of the LinkedIn Economic Graph (skills, titles, location, company, etc.).</p><p><a href="https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/06/ltipinot6.png" alt="Text analytics on LinkedIn Talent Insights using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/text-analytics">Text analytics</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/13/DevBlog-Geospatial">Introduction to Geospatial Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-13T00:00:00.000Z">June 13, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Geospatial data has been widely used across the industry, spanning multiple verticals, such as ride-sharing and delivery, transportation infrastructure, defense and intel, public health. Deriving insights from timely and accurate geospatial data could enable mission-critical use cases in the organizations and fuel a vibrant marketplace across the industry. In the design document for this new Pinot feature, we discuss the challenges of analyzing geospatial at scale and propose the geospatial support in Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9</a></p><p><img src="https://miro.medium.com/max/1400/0*1xrDSs9lLZ5dD3zK" alt="Introduction to Geospatial Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/h-3">H3</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/29/Uber-Charon">Automating Merchant Live Monitoring with Real-Time Analytics - Charon</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-29T00:00:00.000Z">April 29, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, live monitoring and automation of Ops is critical to preserve marketplace health, maintain reliability, and gain efficiency in markets. By the virtue of the word “live”, this monitoring needs to show what is happening now, with prompt access to fresh data, and the ability to recommend appropriate actions based on that data. Uber’s data platform provides the self-serve tools which empower the Ops teams to build their own live monitoring tools, and support their regional teams by building rich solutions.</p><p>For this project, the requirement was to provide merchant level monitoring and handle the edge cases which remain unaddressed by the sophisticated internal marketplace management tools. We used a variety of Uber’s real-time data platform components to build a tool called Charon to reduce impact of poor marketplace reliability on the merchants.</p><p>Read More at <a href="https://eng.uber.com/charon/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/charon/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2021/04/deliverDoor@3x-768x263.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-27T00:00:00.000Z">April 27, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain</p><p><a href="https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38</a></p><p><img src="https://miro.medium.com/max/1400/1*EtqD0vTPEe569jybXCt69w.png" alt="Deploying Apache Pinot at a Large Retail Chain"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-16T00:00:00.000Z">April 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/04/thetasketches2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-08T00:00:00.000Z">April 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Since the 0.6.0 release of Apache Pinot, a new feature was made available for stream ingestion that allows you to upsert events from an immutable log. Typically, upsert is a term used to describe inserting a record into a database if it does not already exist or update it if it does exist. In Apache Pinot’s case, upsert isn’t precisely the same concept, and I wanted to write this blog post to explain why it’s exciting and how you can start using it.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93</a></p><p><img src="https://miro.medium.com/max/1400/0*So3GjHjLY7DJAiaP" alt="Introduction to Upserts in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/upsert">Upsert</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-02-02T00:00:00.000Z">February 2, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://www.startree.ai/wp-content/uploads/2021/05/team-xiang-150x150.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/trino">Trino</a><a class="margin-horiz--sm" href="/blog/tags/presto-sql">PrestoSQL</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-01-08T00:00:00.000Z">January 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’re going to explore an exciting new world of real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Change Data Analysis with Debezium and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/debezium">Debezium</a><a class="margin-horiz--sm" href="/blog/tags/cdc">CDC</a><a class="margin-horiz--sm" href="/blog/tags/change-data-analysis">Change Data Analysis</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-12-01T00:00:00.000Z">December 1, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a 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Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to enable our community operations team to take corrective action. Having a real-time dashboard of different events such as consumer demand, driver availability, or trips happening in a city is crucial for day-to-day operation, incident triaging, and financial intelligence.</p><p>Read More at <a href="https://eng.uber.com/operating-apache-pinot/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/operating-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/10/1224-5.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis">Deep Analysis of Russian Twitter Trolls</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-16T00:00:00.000Z">October 16, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>The history behind Russian disinformation is a dense and continuously evolving subject. The world’s best research hasn’t seemed to hit the mainstream yet, which made this an excellent opportunity to see if I could use some open source tooling to surface new analytical evidence.</p><p>In this blog post, I’ll show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.</p><p><a href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843" target="_blank" rel="noopener noreferrer">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png" alt="Deep Analysis of Russian Twitter Trolls"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-IngestPlugins">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-SLAApps">Achieving 99th percentile latency SLA using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-ScalarUDFs">Utilize UDFs to Supercharge Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-PinotMonitoring">Monitoring Apache Pinot with JMX, Prometheus and Grafana</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-AnomalyDetection">Building a culture around metrics and anomaly detection</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-DevUpStack">Moving developers up the stack with Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Once upon a time, an internet company named LinkedIn faced the challenge of having petabytes of connected data with no way to analyze it in real-time. As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Moving developers up the stack with Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/14/LinkedIn-BatchRealtime">Bridging batch and stream processing for the Recruiter usage statistics dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-14T00:00:00.000Z">July 14, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Batch and streaming computations are often combined together in the Lambda architecture,
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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/20/Uber-Operating">Operating Apache Pinot at Uber Scale</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-20T00:00:00.000Z">October 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to enable our community operations team to take corrective action. Having a real-time dashboard of different events such as consumer demand, driver availability, or trips happening in a city is crucial for day-to-day operation, incident triaging, and financial intelligence.</p><p>Read More at <a href="https://eng.uber.com/operating-apache-pinot/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/operating-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/10/1224-5.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis">Deep Analysis of Russian Twitter Trolls</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-16T00:00:00.000Z">October 16, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>The history behind Russian disinformation is a dense and continuously evolving subject. The world’s best research hasn’t seemed to hit the mainstream yet, which made this an excellent opportunity to see if I could use some open source tooling to surface new analytical evidence.</p><p>In this blog post, I’ll show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.</p><p><a href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843" target="_blank" rel="noopener noreferrer">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png" alt="Deep Analysis of Russian Twitter Trolls"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-IngestPlugins">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-PinotMonitoring">Monitoring Apache Pinot with JMX, Prometheus and Grafana</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-SLAApps">Achieving 99th percentile latency SLA using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-ScalarUDFs">Utilize UDFs to Supercharge Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-AnomalyDetection">Building a culture around metrics and anomaly detection</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-DevUpStack">Moving developers up the stack with Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Once upon a time, an internet company named LinkedIn faced the challenge of having petabytes of connected data with no way to analyze it in real-time. As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Moving developers up the stack with Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/14/LinkedIn-BatchRealtime">Bridging batch and stream processing for the Recruiter usage statistics dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-14T00:00:00.000Z">July 14, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Batch and streaming computations are often combined together in the Lambda architecture,
but carry the cost of maintaining two different code bases for the same logic.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/07/bridging-1.png" alt="Bridging batch and stream processing for the Recruiter usage statistics dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/06/29/LinkedIn-TalentInsight">Building LinkedIn Talent Insights to democratize data-driven decision making</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-06-29T00:00:00.000Z">June 29, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn is a mission-driven organization, and we take our mission of “connecting the world's professionals to make them more productive and successful” very seriously.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/building-linkedin-talent-insights" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/building-linkedin-talent-insights</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/06/lti-1.png" alt="Building LinkedIn Talent Insights to democratize data-driven decision making"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><nav class="pagination-nav" aria-label="Blog list page navigation"><div class="pagination-nav__item"><a class="pagination-nav__link" href="/blog"><div class="pagination-nav__label">« Newer Entries</div></a></div><div class="pagination-nav__item pagination-nav__item--next"><a class="pagination-nav__link" href="/blog/page/3"><div class="pagination-nav__label">Older Entries »</div></a></div></nav></main></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" href="/who_uses">Who uses Apache Pinot?</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" class="footer__link-item">Components</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/basics/architecture" target="_blank" rel="noopener noreferrer" class="footer__link-item">Architecture</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/plugins/plugin-architecture" target="_blank" rel="noopener noreferrer" class="footer__link-item">Plugins Architecture</a></li></ul></div><div class="col footer__col"><h4 class="footer__title">Integrations</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/integrations/trino" target="_blank" rel="noopener noreferrer" class="footer__link-item">Trino</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/integrations/presto" 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while using the storage space efficiently.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2019/06/star-tree-index--powering-fast-aggregations-on-pinot" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2019/06/star-tree-index--powering-fast-aggregations-on-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png" alt="Star-tree index - Powering fast aggregations on Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/01/09/LinkedIn-IntroThirdEye">Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-01-09T00:00:00.000Z">January 9, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>ThirdEye is a comprehensive platform for real-time monitoring of metrics that covers a wide variety of use-cases.
LinkedIn relies on ThirdEye to monitor site performance, track member growth, understand adoption of new features,
flag sustained attempts to circumvent system security, and many other areas</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png" alt="Star-tree index - Powering fast aggregations on Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2017/09/17/Restaurant-Manager">Engineering Restaurant Manager - UberEATS Analytics Dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2017-09-17T00:00:00.000Z">September 17, 2017</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, we use data analytics to architect more magical user experiences across our products. Whenever possible, we harness these data engineering capabilities to empower our partners to better serve their customers. For instance, in late 2016, the UberEATS engineering team built a comprehensive analytics dashboard that provides restaurant partners with additional insights about the health of their business.</p><p>Read More at <a href="https://eng.uber.com/restaurant-manager/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/restaurant-manager/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2017/09/image4-2.png" alt="Engineering Restaurant Manager - UberEATS Analytics Dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber-data">Uber Data</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2015/06/10/Open-Sourcing-Pinot">Open Sourcing Pinot - Scaling the Wall of Real-Time Analytics</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2015-06-10T00:00:00.000Z">June 10, 2015</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://github.com/kishoreg" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://avatars.githubusercontent.com/kishoreg" alt="Kishore Gopalakrishna"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://github.com/kishoreg" target="_blank" rel="noopener noreferrer">Kishore Gopalakrishna</a></div><small class="avatar__subtitle">Apache Pinot Team</small></div></div></header><div class="markdown"><p>LinkedIn’s real-time analytics infrastructure, that we built to allow us to slice and dice across billions of rows in real-time across a wide variety of products. Read More at (<a href="https://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics</a>)</p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/a-b-testing">A/B Testing</a><a class="margin-horiz--sm" href="/blog/tags/infrastructure">Infrastructure</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2015/05/16/LinkedIn-Scaling">A Brief History of Scaling LinkedIn</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2015-05-16T00:00:00.000Z">May 16, 2015</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn started in 2003 with the goal of connecting to your network for better job opportunities. It had only 2,700 members the first week. Fast forward many years, and LinkedIn’s product portfolio, member base, and server load has grown tremendously.</p><p>Today, LinkedIn operates globally with more than 350 million members. We serve tens of thousands of web pages every second of every day. We've hit our mobile moment where mobile accounts for more than 50 percent of all global traffic. All those requests are fetching data from our backend systems, which in turn handle millions of queries per second.</p><p>Read More at <a href="https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/en-us/blog/migrated/data_centers_pops_0.png" alt="A Brief History of Scaling LinkedIn"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" 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Scaling the Wall of Real-Time Analytics</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2015-06-10T00:00:00.000Z">June 10, 2015</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://github.com/kishoreg" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://avatars.githubusercontent.com/kishoreg" alt="Kishore Gopalakrishna"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://github.com/kishoreg" target="_blank" rel="noopener noreferrer">Kishore Gopalakrishna</a></div><small class="avatar__subtitle">Apache Pinot Team</small></div></div></header><div class="markdown"><p>LinkedIn’s real-time analytics infrastructure, that we built to allow us to slice and dice across billions of rows in real-time across a wide variety of products. Read More at (<a href="https://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics</a>)</p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/a-b-testing">A/B Testing</a><a class="margin-horiz--sm" href="/blog/tags/infrastructure">Infrastructure</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Open Sourcing Pinot - Scaling the Wall of Real-Time Analytics" href="/blog/2015/06/10/Open-Sourcing-Pinot"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div 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<link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">30 posts tagged with "analytics" | Apache Pinot™</title><meta data-react-helmet="true" property="og:title" content="30 posts tagged with "analytics" | Apache Pinot™"><meta data-react-helmet="true" property="og:url" content="https://pinot.apache.org/blog/tags/analytics"><meta data-react-helmet="true" name="docsearch:language" content="en"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="blog_tags_posts"><meta data-react-helmet="true" name="keywords" content="Pinot,LinkedIn,Data Scaling,User-Facing Analytics,Real-time data platform"><link data-react-helmet="true" rel="shortcut icon" href="/img/favicon.ico"><link data-react-helmet="true" rel="canonical" href="https://pinot.apache.org/blog/tags/analytics"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/blog/tags/analytics" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/blog/tags/analytics" hreflang="x-default"><link data-react-helmet="true" rel="preconnect" href="https://BH4D9OD16A-dsn.algolia.net" crossorigin="anonymous"><link rel="stylesheet" href="/assets/css/styles.f0793de6.css">
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Apache Pinot is a real-time distributed datastore designed to answer OLAP queries with high throughput and low latency.</p><p>This release is cut from commit <a href="https://github.com/apache/pinot/commit/fd9c58a11ed16d27109baefcee138eea30132ad3" target="_blank" rel="noopener noreferrer">fd9c58a11ed16d27109baefcee138eea30132ad3</a>.
You can find a full list of everything included in the <a href="https://docs.pinot.apache.org/basics/releases/0.10.0" target="_blank" rel="noopener noreferrer">release notes</a>.</p><p>Let’s have a look at some of the changes, with the help of the batch <a href="https://docs.pinot.apache.org/basics/getting-started/running-pinot-in-docker" target="_blank" rel="noopener noreferrer">QuickStart configuration</a>.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="query-plans"></a>Query Plans<a class="hash-link" href="#query-plans" title="Direct link to heading">#</a></h2><p>Amrish Lal implemented the <code>EXPLAIN PLAN</code> clause, which returns the execution plan that will be chosen by the Pinot Query Engine.
This lets us see what the query is likely to do without actually having to run it.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">EXPLAIN</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">PLAN</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FOR</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token operator">*</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following results:</p><table><thead><tr><th>Operator</th><th>Operator_Id</th><th>Parent_Id</th></tr></thead><tbody><tr><td>BROKER_REDUCE(limit:10)</td><td>0</td><td>-1</td></tr><tr><td>COMBINE_SELECT</td><td>1</td><td>0</td></tr><tr><td>SELECT(selectList:AtBatting, G_old, baseOnBalls, caughtStealing, doules, groundedIntoDoublePlays, hits, hitsByPitch, homeRuns, intentionalWalks, league, numberOfGames, numberOfGamesAsBatter, playerID, playerName, playerStint, runs, runsBattedIn, sacrificeFlies, sacrificeHits, stolenBases, strikeouts, teamID, tripples, yearID)</td><td>2</td><td>1</td></tr><tr><td>TRANSFORM_PASSTHROUGH(AtBatting, G_old, baseOnBalls, caughtStealing, doules, groundedIntoDoublePlays, hits, hitsByPitch, homeRuns, intentionalWalks, league, numberOfGames, numberOfGamesAsBatter, playerID, playerName, playerStint, runs, runsBattedIn, sacrificeFlies, sacrificeHits, stolenBases, strikeouts, teamID, tripples, yearID)</td><td>3</td><td>2</td></tr><tr><td>PROJECT(homeRuns, playerStint, groundedIntoDoublePlays, numberOfGames, AtBatting, stolenBases, tripples, hitsByPitch, teamID, numberOfGamesAsBatter, strikeouts, sacrificeFlies, caughtStealing, baseOnBalls, playerName, doules, league, yearID, hits, runsBattedIn, G_old, sacrificeHits, intentionalWalks, runs, playerID)</td><td>4</td><td>3</td></tr><tr><td>FILTER_FULL_SCAN(operator:EQ,predicate:league = 'NL')</td><td>5</td><td>4</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="filter-clauses-for-aggregates"></a>FILTER Clauses for Aggregates<a class="hash-link" href="#filter-clauses-for-aggregates" title="Direct link to heading">#</a></h2><p>Atri Sharma added the filter clause for aggregates.
-This feature makes it possible to write queries like this:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> nlHomeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'AL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> alHomeRuns</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>nlHomeRuns</th><th>alHomeRuns</th></tr></thead><tbody><tr><td>135486</td><td>135990</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="greatest-and-least"></a>greatest and least<a class="hash-link" href="#greatest-and-least" title="Direct link to heading">#</a></h2><p>Richard Startin added the <code>greatest</code> and <code>least</code> functions:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> playerID</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> least</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> greatest</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">hits</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> hits</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token plain"> </span><span class="token operator">AND</span><span class="token plain"> teamID </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'SFN'</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">GROUP</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">BY</span><span class="token plain"> playerID</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">LIMIT</span><span class="token plain"> </span><span class="token number">5</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>playerID</th><th>homeRuns</th><th>hits</th></tr></thead><tbody><tr><td>ramirju01</td><td>0</td><td>5</td></tr><tr><td>milneed01</td><td>4</td><td>54</td></tr><tr><td>testani01</td><td>0</td><td>5</td></tr><tr><td>shawbo01</td><td>0</td><td>8</td></tr><tr><td>vogelry01</td><td>0</td><td>12</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="distinctcountsmarthll"></a>DistinctCountSmartHLL<a class="hash-link" href="#distinctcountsmarthll" title="Direct link to heading">#</a></h2><p> Xiaotian (Jackie) Jiang added the <code>DistinctCountSmartHLL</code> aggregation function, which automatically converts the Set to HyperLogLog if the set size grows too big to protect the servers from running out of memory:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> DISTINCTCOUNTSMARTHLL</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'hllLog2m=8;hllConversionThreshold=10'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>distinctcountsmarthll(homeRuns)</th></tr></thead><tbody><tr><td>66</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="ui-updates"></a>UI updates<a class="hash-link" href="#ui-updates" title="Direct link to heading">#</a></h2><p>There were also a bunch of updates to the Pinot Data Explorer, by Sanket Shah and Johan Adami.</p><p>The display of reported size and estimated size is now in a human readable format:</p><p><img alt="Human readable sizes" src="/assets/images/human-readable-sizes-b8c4009dd53d23da3b8637963827a8de.png"></p><p>Fixes for the following issues:</p><ul><li>Error messages weren't showing on the UI when an invalid operation is attempted:</li></ul><p><img alt="A backwards incompatible attempted schema change" src="/assets/images/backwards-incompatible-99886dcd0be55a8100a7d6c5f3da3bda.png"></p><ul><li>Query console goes blank on syntax error.</li><li>Query console cannot show query result when multiple columns have the same name.</li><li>Adding extra fields after <code>SELECT *</code> would throw a NullPointerException.</li><li>Some queries were returning <code>--</code> instead of <code>0</code>.</li><li>Query console couldn't show the query result if multiple columns had the same name.</li><li>Pinot Dashboard tenant view showing the incorrect amount of servers and brokers.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="realtimetooffline-task"></a>RealTimeToOffline Task<a class="hash-link" href="#realtimetooffline-task" title="Direct link to heading">#</a></h2><p>Xiaotian (Jackie) Jiang made some fixes to the <a href="https://dev.startree.ai/docs/pinot/recipes/real-time-offline-job" target="_blank" rel="noopener noreferrer">RealTimeToOffline job</a> to handle time gaps and proceed to the next time window when no segment matches the current one.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="empty-quickstart"></a>Empty QuickStart<a class="hash-link" href="#empty-quickstart" title="Direct link to heading">#</a></h2><p>Kenny Bastani added an empty QuickStart command, which lets you quickly spin up an empty Pinot cluster:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">docker</span><span class="token plain"> run </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">8000</span><span class="token plain">:8000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">9000</span><span class="token plain">:9000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> apachepinot/pinot:0.10.0 QuickStart </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -type empty</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>You can then ingest your own dataset without needing to worry about spinning up each of the Pinot components individually.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="data-ingestion"></a>Data Ingestion<a class="hash-link" href="#data-ingestion" title="Direct link to heading">#</a></h2><ul><li><p>Richard Startin fixed some issues with real-time ingestion where consumption of messages would stop if a bad batch of messages was consumed from Kafka.</p></li><li><p>Mohemmad Zaid Khan added the BoundedColumnValue partition function, which partitions segments based on column values.</p></li><li><p>Xiaobing Li added the fixed name segment generator, which can be used when you want to replace a specific existing segment.</p></li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="other-changes"></a>Other changes<a class="hash-link" href="#other-changes" title="Direct link to heading">#</a></h2><ul><li>Richard Startin set LZ4 compression as the default for all metrics fields.</li><li>Mark Needham added the <code>ST_Within</code> geospatial function.</li><li>Rong Rong fixed a bug where query stats wouldn't show if there was an error processing the query (e.g. if the query timed out).</li><li>Prashant Pandey fixed the query engine to handle extra columns added to a <code>SELECT *</code> statement.</li><li>Richard Startin added support for forward indexes on JSON columns.</li><li>Rong Rong added the GRPC broker request handler so that data can be streamed back from the server to the broker when processing queries.</li><li>deemoliu made it possible to add a default strategy when using the <a href="https://dev.startree.ai/docs/pinot/recipes/upserts-partial" target="_blank" rel="noopener noreferrer">partial upsert feature</a>.</li><li>Jeff Moszuti added support for the <code>TIMESTAMP</code> data type in the <a href="https://docs.pinot.apache.org/operators/configuration-recommendation-engine" target="_blank" rel="noopener noreferrer">configuration recommendation engine</a>.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="dependency-updates"></a>Dependency updates<a class="hash-link" href="#dependency-updates" title="Direct link to heading">#</a></h2><p>The following dependencies were updated:</p><ul><li>async-http-client because the library moved to a different organization.</li><li>RoaringBitmap to 0.9.25</li><li>JsonPath to 2.7.0</li><li>Kafka to 2.8.1</li><li>Prometheus to 0.16.1</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="resources"></a>Resources<a class="hash-link" href="#resources" title="Direct link to heading">#</a></h2><p>If you want to try out Apache Pinot, the following resources will help you get started:</p><ul><li>Download page: <a href="https://pinot.apache.org/download/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/download/</a></li><li>Getting started: <a href="https://docs.pinot.apache.org/getting-started" target="_blank" rel="noopener noreferrer">https://docs.pinot.apache.org/getting-started</a></li><li>Apache Pinot Recipes: <a href="https://dev.startree.ai/docs/pinot/recipes/" target="_blank" rel="noopener noreferrer">https://dev.startree.ai/docs/pinot/recipes/</a></li><li>Join our Slack channel: <a href="https://communityinviter.com/apps/apache-pinot/apache-pinot" target="_blank" rel="noopener noreferrer">https://communityinviter.com/apps/apache-pinot/apache-pinot</a></li><li>See our upcoming events: <a href="https://www.meetup.com/apache-pinot" target="_blank" rel="noopener noreferrer">https://www.meetup.com/apache-pinot</a></li><li>Follow us on Twitter: <a href="https://twitter.com/startreedata" target="_blank" rel="noopener noreferrer">https://twitter.com/startreedata</a></li><li>Subscribe to our YouTube channel: <a href="https://www.youtube.com/c/StarTree" target="_blank" rel="noopener noreferrer">https://www.youtube.com/c/StarTree</a></li></ul></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/releases">Releases</a></div><div class="col text--right"><a aria-label="Read more about Announcing Apache Pinot 0.10" href="/blog/2022/04/04/Announcing-Apache-Pinot-0-10"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/16/LinkedIn-TextAnalytics">Text analytics on LinkedIn Talent Insights using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-16T00:00:00.000Z">June 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn Talent Insights (LTI) is a platform that helps organizations understand the external labor market and their internal workforce, and enables the long term success of their employees. Users of LTI have the flexibility to construct searches using the various facets of the LinkedIn Economic Graph (skills, titles, location, company, etc.).</p><p><a href="https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/06/ltipinot6.png" alt="Text analytics on LinkedIn Talent Insights using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/text-analytics">Text analytics</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Text analytics on LinkedIn Talent Insights using Apache Pinot" href="/blog/2021/06/16/LinkedIn-TextAnalytics"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/13/DevBlog-Geospatial">Introduction to Geospatial Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-13T00:00:00.000Z">June 13, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Geospatial data has been widely used across the industry, spanning multiple verticals, such as ride-sharing and delivery, transportation infrastructure, defense and intel, public health. Deriving insights from timely and accurate geospatial data could enable mission-critical use cases in the organizations and fuel a vibrant marketplace across the industry. In the design document for this new Pinot feature, we discuss the challenges of analyzing geospatial at scale and propose the geospatial support in Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9</a></p><p><img src="https://miro.medium.com/max/1400/0*1xrDSs9lLZ5dD3zK" alt="Introduction to Geospatial Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/h-3">H3</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Geospatial Queries in Apache Pinot" href="/blog/2021/06/13/DevBlog-Geospatial"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/29/Uber-Charon">Automating Merchant Live Monitoring with Real-Time Analytics - Charon</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-29T00:00:00.000Z">April 29, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, live monitoring and automation of Ops is critical to preserve marketplace health, maintain reliability, and gain efficiency in markets. By the virtue of the word “live”, this monitoring needs to show what is happening now, with prompt access to fresh data, and the ability to recommend appropriate actions based on that data. Uber’s data platform provides the self-serve tools which empower the Ops teams to build their own live monitoring tools, and support their regional teams by building rich solutions.</p><p>For this project, the requirement was to provide merchant level monitoring and handle the edge cases which remain unaddressed by the sophisticated internal marketplace management tools. We used a variety of Uber’s real-time data platform components to build a tool called Charon to reduce impact of poor marketplace reliability on the merchants.</p><p>Read More at <a href="https://eng.uber.com/charon/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/charon/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2021/04/deliverDoor@3x-768x263.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Automating Merchant Live Monitoring with Real-Time Analytics - Charon" href="/blog/2021/04/29/Uber-Charon"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-27T00:00:00.000Z">April 27, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain</p><p><a href="https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38</a></p><p><img src="https://miro.medium.com/max/1400/1*EtqD0vTPEe569jybXCt69w.png" alt="Deploying Apache Pinot at a Large Retail Chain"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deploying Apache Pinot at a Large Retail Chain" href="/blog/2021/04/27/DevBlog-PinotInRetailChain"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-16T00:00:00.000Z">April 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/04/thetasketches2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches" href="/blog/2021/04/16/LinkedIn-Theta"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-08T00:00:00.000Z">April 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Since the 0.6.0 release of Apache Pinot, a new feature was made available for stream ingestion that allows you to upsert events from an immutable log. Typically, upsert is a term used to describe inserting a record into a database if it does not already exist or update it if it does exist. In Apache Pinot’s case, upsert isn’t precisely the same concept, and I wanted to write this blog post to explain why it’s exciting and how you can start using it.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93</a></p><p><img src="https://miro.medium.com/max/1400/0*So3GjHjLY7DJAiaP" alt="Introduction to Upserts in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/upsert">Upsert</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Upserts in Apache Pinot" href="/blog/2021/04/08/DevBlog-UpsertsIntro"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-02-02T00:00:00.000Z">February 2, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://www.startree.ai/wp-content/uploads/2021/05/team-xiang-150x150.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/trino">Trino</a><a class="margin-horiz--sm" href="/blog/tags/presto-sql">PrestoSQL</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Real-time Analytics with Presto and Apache Pinot" href="/blog/2021/02/02/DevBlog-PrestoPinot"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-01-08T00:00:00.000Z">January 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’re going to explore an exciting new world of real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Change Data Analysis with Debezium and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/debezium">Debezium</a><a class="margin-horiz--sm" href="/blog/tags/cdc">CDC</a><a class="margin-horiz--sm" href="/blog/tags/change-data-analysis">Change Data Analysis</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Change Data Analysis with Debezium and Apache Pinot" href="/blog/2021/01/08/DevBlog-DebeziumCDC"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-12-01T00:00:00.000Z">December 1, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/03/pinotincubator2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about From Lambda to Lambda-less Lessons learned" href="/blog/2020/12/01/LinkedIn-Lamda"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/20/Uber-Operating">Operating Apache Pinot at Uber Scale</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-20T00:00:00.000Z">October 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to enable our community operations team to take corrective action. Having a real-time dashboard of different events such as consumer demand, driver availability, or trips happening in a city is crucial for day-to-day operation, incident triaging, and financial intelligence.</p><p>Read More at <a href="https://eng.uber.com/operating-apache-pinot/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/operating-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/10/1224-5.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Operating Apache Pinot at Uber Scale" href="/blog/2020/10/20/Uber-Operating"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis">Deep Analysis of Russian Twitter Trolls</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-16T00:00:00.000Z">October 16, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>The history behind Russian disinformation is a dense and continuously evolving subject. The world’s best research hasn’t seemed to hit the mainstream yet, which made this an excellent opportunity to see if I could use some open source tooling to surface new analytical evidence.</p><p>In this blog post, I’ll show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.</p><p><a href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843" target="_blank" rel="noopener noreferrer">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png" alt="Deep Analysis of Russian Twitter Trolls"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deep Analysis of Russian Twitter Trolls" href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-IngestPlugins">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Leverage Plugins to Ingest Parquet Files from S3 in Pinot" href="/blog/2020/08/08/DevBlog-IngestPlugins"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-SLAApps">Achieving 99th percentile latency SLA using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Achieving 99th percentile latency SLA using Apache Pinot" href="/blog/2020/08/08/DevBlog-SLAApps"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-ScalarUDFs">Utilize UDFs to Supercharge Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Utilize UDFs to Supercharge Queries in Apache Pinot" href="/blog/2020/08/08/DevBlog-ScalarUDFs"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-PinotMonitoring">Monitoring Apache Pinot with JMX, Prometheus and Grafana</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring Apache Pinot with JMX, Prometheus and Grafana" href="/blog/2020/08/08/DevBlog-PinotMonitoring"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-AnomalyDetection">Building a culture around metrics and anomaly detection</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building a culture around metrics and anomaly detection" href="/blog/2020/07/28/DevBlog-AnomalyDetection"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-DevUpStack">Moving developers up the stack with Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Once upon a time, an internet company named LinkedIn faced the challenge of having petabytes of connected data with no way to analyze it in real-time. As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Moving developers up the stack with Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Moving developers up the stack with Apache Pinot" href="/blog/2020/07/28/DevBlog-DevUpStack"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/14/LinkedIn-BatchRealtime">Bridging batch and stream processing for the Recruiter usage statistics dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-14T00:00:00.000Z">July 14, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Batch and streaming computations are often combined together in the Lambda architecture,
+This feature makes it possible to write queries like this:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> nlHomeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'AL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> alHomeRuns</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>nlHomeRuns</th><th>alHomeRuns</th></tr></thead><tbody><tr><td>135486</td><td>135990</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="greatest-and-least"></a>greatest and least<a class="hash-link" href="#greatest-and-least" title="Direct link to heading">#</a></h2><p>Richard Startin added the <code>greatest</code> and <code>least</code> functions:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> playerID</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> least</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> greatest</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">hits</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> hits</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token plain"> </span><span class="token operator">AND</span><span class="token plain"> teamID </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'SFN'</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">GROUP</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">BY</span><span class="token plain"> playerID</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">LIMIT</span><span class="token plain"> </span><span class="token number">5</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>playerID</th><th>homeRuns</th><th>hits</th></tr></thead><tbody><tr><td>ramirju01</td><td>0</td><td>5</td></tr><tr><td>milneed01</td><td>4</td><td>54</td></tr><tr><td>testani01</td><td>0</td><td>5</td></tr><tr><td>shawbo01</td><td>0</td><td>8</td></tr><tr><td>vogelry01</td><td>0</td><td>12</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="distinctcountsmarthll"></a>DistinctCountSmartHLL<a class="hash-link" href="#distinctcountsmarthll" title="Direct link to heading">#</a></h2><p> Xiaotian (Jackie) Jiang added the <code>DistinctCountSmartHLL</code> aggregation function, which automatically converts the Set to HyperLogLog if the set size grows too big to protect the servers from running out of memory:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> DISTINCTCOUNTSMARTHLL</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'hllLog2m=8;hllConversionThreshold=10'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>distinctcountsmarthll(homeRuns)</th></tr></thead><tbody><tr><td>66</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="ui-updates"></a>UI updates<a class="hash-link" href="#ui-updates" title="Direct link to heading">#</a></h2><p>There were also a bunch of updates to the Pinot Data Explorer, by Sanket Shah and Johan Adami.</p><p>The display of reported size and estimated size is now in a human readable format:</p><p><img alt="Human readable sizes" src="/assets/images/human-readable-sizes-b8c4009dd53d23da3b8637963827a8de.png"></p><p>Fixes for the following issues:</p><ul><li>Error messages weren't showing on the UI when an invalid operation is attempted:</li></ul><p><img alt="A backwards incompatible attempted schema change" src="/assets/images/backwards-incompatible-99886dcd0be55a8100a7d6c5f3da3bda.png"></p><ul><li>Query console goes blank on syntax error.</li><li>Query console cannot show query result when multiple columns have the same name.</li><li>Adding extra fields after <code>SELECT *</code> would throw a NullPointerException.</li><li>Some queries were returning <code>--</code> instead of <code>0</code>.</li><li>Query console couldn't show the query result if multiple columns had the same name.</li><li>Pinot Dashboard tenant view showing the incorrect amount of servers and brokers.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="realtimetooffline-task"></a>RealTimeToOffline Task<a class="hash-link" href="#realtimetooffline-task" title="Direct link to heading">#</a></h2><p>Xiaotian (Jackie) Jiang made some fixes to the <a href="https://dev.startree.ai/docs/pinot/recipes/real-time-offline-job" target="_blank" rel="noopener noreferrer">RealTimeToOffline job</a> to handle time gaps and proceed to the next time window when no segment matches the current one.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="empty-quickstart"></a>Empty QuickStart<a class="hash-link" href="#empty-quickstart" title="Direct link to heading">#</a></h2><p>Kenny Bastani added an empty QuickStart command, which lets you quickly spin up an empty Pinot cluster:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">docker</span><span class="token plain"> run </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">8000</span><span class="token plain">:8000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">9000</span><span class="token plain">:9000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> apachepinot/pinot:0.10.0 QuickStart </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -type empty</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>You can then ingest your own dataset without needing to worry about spinning up each of the Pinot components individually.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="data-ingestion"></a>Data Ingestion<a class="hash-link" href="#data-ingestion" title="Direct link to heading">#</a></h2><ul><li><p>Richard Startin fixed some issues with real-time ingestion where consumption of messages would stop if a bad batch of messages was consumed from Kafka.</p></li><li><p>Mohemmad Zaid Khan added the BoundedColumnValue partition function, which partitions segments based on column values.</p></li><li><p>Xiaobing Li added the fixed name segment generator, which can be used when you want to replace a specific existing segment.</p></li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="other-changes"></a>Other changes<a class="hash-link" href="#other-changes" title="Direct link to heading">#</a></h2><ul><li>Richard Startin set LZ4 compression as the default for all metrics fields.</li><li>Mark Needham added the <code>ST_Within</code> geospatial function.</li><li>Rong Rong fixed a bug where query stats wouldn't show if there was an error processing the query (e.g. if the query timed out).</li><li>Prashant Pandey fixed the query engine to handle extra columns added to a <code>SELECT *</code> statement.</li><li>Richard Startin added support for forward indexes on JSON columns.</li><li>Rong Rong added the GRPC broker request handler so that data can be streamed back from the server to the broker when processing queries.</li><li>deemoliu made it possible to add a default strategy when using the <a href="https://dev.startree.ai/docs/pinot/recipes/upserts-partial" target="_blank" rel="noopener noreferrer">partial upsert feature</a>.</li><li>Jeff Moszuti added support for the <code>TIMESTAMP</code> data type in the <a href="https://docs.pinot.apache.org/operators/configuration-recommendation-engine" target="_blank" rel="noopener noreferrer">configuration recommendation engine</a>.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="dependency-updates"></a>Dependency updates<a class="hash-link" href="#dependency-updates" title="Direct link to heading">#</a></h2><p>The following dependencies were updated:</p><ul><li>async-http-client because the library moved to a different organization.</li><li>RoaringBitmap to 0.9.25</li><li>JsonPath to 2.7.0</li><li>Kafka to 2.8.1</li><li>Prometheus to 0.16.1</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="resources"></a>Resources<a class="hash-link" href="#resources" title="Direct link to heading">#</a></h2><p>If you want to try out Apache Pinot, the following resources will help you get started:</p><ul><li>Download page: <a href="https://pinot.apache.org/download/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/download/</a></li><li>Getting started: <a href="https://docs.pinot.apache.org/getting-started" target="_blank" rel="noopener noreferrer">https://docs.pinot.apache.org/getting-started</a></li><li>Apache Pinot Recipes: <a href="https://dev.startree.ai/docs/pinot/recipes/" target="_blank" rel="noopener noreferrer">https://dev.startree.ai/docs/pinot/recipes/</a></li><li>Join our Slack channel: <a href="https://communityinviter.com/apps/apache-pinot/apache-pinot" target="_blank" rel="noopener noreferrer">https://communityinviter.com/apps/apache-pinot/apache-pinot</a></li><li>See our upcoming events: <a href="https://www.meetup.com/apache-pinot" target="_blank" rel="noopener noreferrer">https://www.meetup.com/apache-pinot</a></li><li>Follow us on Twitter: <a href="https://twitter.com/startreedata" target="_blank" rel="noopener noreferrer">https://twitter.com/startreedata</a></li><li>Subscribe to our YouTube channel: <a href="https://www.youtube.com/c/StarTree" target="_blank" rel="noopener noreferrer">https://www.youtube.com/c/StarTree</a></li></ul></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/releases">Releases</a></div><div class="col text--right"><a aria-label="Read more about Announcing Apache Pinot 0.10" href="/blog/2022/04/04/Announcing-Apache-Pinot-0-10"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/16/LinkedIn-TextAnalytics">Text analytics on LinkedIn Talent Insights using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-16T00:00:00.000Z">June 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn Talent Insights (LTI) is a platform that helps organizations understand the external labor market and their internal workforce, and enables the long term success of their employees. Users of LTI have the flexibility to construct searches using the various facets of the LinkedIn Economic Graph (skills, titles, location, company, etc.).</p><p><a href="https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/06/ltipinot6.png" alt="Text analytics on LinkedIn Talent Insights using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/text-analytics">Text analytics</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Text analytics on LinkedIn Talent Insights using Apache Pinot" href="/blog/2021/06/16/LinkedIn-TextAnalytics"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/13/DevBlog-Geospatial">Introduction to Geospatial Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-13T00:00:00.000Z">June 13, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Geospatial data has been widely used across the industry, spanning multiple verticals, such as ride-sharing and delivery, transportation infrastructure, defense and intel, public health. Deriving insights from timely and accurate geospatial data could enable mission-critical use cases in the organizations and fuel a vibrant marketplace across the industry. In the design document for this new Pinot feature, we discuss the challenges of analyzing geospatial at scale and propose the geospatial support in Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9</a></p><p><img src="https://miro.medium.com/max/1400/0*1xrDSs9lLZ5dD3zK" alt="Introduction to Geospatial Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/h-3">H3</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Geospatial Queries in Apache Pinot" href="/blog/2021/06/13/DevBlog-Geospatial"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/29/Uber-Charon">Automating Merchant Live Monitoring with Real-Time Analytics - Charon</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-29T00:00:00.000Z">April 29, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, live monitoring and automation of Ops is critical to preserve marketplace health, maintain reliability, and gain efficiency in markets. By the virtue of the word “live”, this monitoring needs to show what is happening now, with prompt access to fresh data, and the ability to recommend appropriate actions based on that data. Uber’s data platform provides the self-serve tools which empower the Ops teams to build their own live monitoring tools, and support their regional teams by building rich solutions.</p><p>For this project, the requirement was to provide merchant level monitoring and handle the edge cases which remain unaddressed by the sophisticated internal marketplace management tools. We used a variety of Uber’s real-time data platform components to build a tool called Charon to reduce impact of poor marketplace reliability on the merchants.</p><p>Read More at <a href="https://eng.uber.com/charon/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/charon/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2021/04/deliverDoor@3x-768x263.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Automating Merchant Live Monitoring with Real-Time Analytics - Charon" href="/blog/2021/04/29/Uber-Charon"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-27T00:00:00.000Z">April 27, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain</p><p><a href="https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38</a></p><p><img src="https://miro.medium.com/max/1400/1*EtqD0vTPEe569jybXCt69w.png" alt="Deploying Apache Pinot at a Large Retail Chain"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deploying Apache Pinot at a Large Retail Chain" href="/blog/2021/04/27/DevBlog-PinotInRetailChain"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-16T00:00:00.000Z">April 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/04/thetasketches2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches" href="/blog/2021/04/16/LinkedIn-Theta"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-08T00:00:00.000Z">April 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Since the 0.6.0 release of Apache Pinot, a new feature was made available for stream ingestion that allows you to upsert events from an immutable log. Typically, upsert is a term used to describe inserting a record into a database if it does not already exist or update it if it does exist. In Apache Pinot’s case, upsert isn’t precisely the same concept, and I wanted to write this blog post to explain why it’s exciting and how you can start using it.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93</a></p><p><img src="https://miro.medium.com/max/1400/0*So3GjHjLY7DJAiaP" alt="Introduction to Upserts in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/upsert">Upsert</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Upserts in Apache Pinot" href="/blog/2021/04/08/DevBlog-UpsertsIntro"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-02-02T00:00:00.000Z">February 2, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://www.startree.ai/wp-content/uploads/2021/05/team-xiang-150x150.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/trino">Trino</a><a class="margin-horiz--sm" href="/blog/tags/presto-sql">PrestoSQL</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Real-time Analytics with Presto and Apache Pinot" href="/blog/2021/02/02/DevBlog-PrestoPinot"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-01-08T00:00:00.000Z">January 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’re going to explore an exciting new world of real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Change Data Analysis with Debezium and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/debezium">Debezium</a><a class="margin-horiz--sm" href="/blog/tags/cdc">CDC</a><a class="margin-horiz--sm" href="/blog/tags/change-data-analysis">Change Data Analysis</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Change Data Analysis with Debezium and Apache Pinot" href="/blog/2021/01/08/DevBlog-DebeziumCDC"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-12-01T00:00:00.000Z">December 1, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/03/pinotincubator2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about From Lambda to Lambda-less Lessons learned" href="/blog/2020/12/01/LinkedIn-Lamda"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/20/Uber-Operating">Operating Apache Pinot at Uber Scale</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-20T00:00:00.000Z">October 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to enable our community operations team to take corrective action. Having a real-time dashboard of different events such as consumer demand, driver availability, or trips happening in a city is crucial for day-to-day operation, incident triaging, and financial intelligence.</p><p>Read More at <a href="https://eng.uber.com/operating-apache-pinot/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/operating-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/10/1224-5.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Operating Apache Pinot at Uber Scale" href="/blog/2020/10/20/Uber-Operating"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis">Deep Analysis of Russian Twitter Trolls</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-16T00:00:00.000Z">October 16, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>The history behind Russian disinformation is a dense and continuously evolving subject. The world’s best research hasn’t seemed to hit the mainstream yet, which made this an excellent opportunity to see if I could use some open source tooling to surface new analytical evidence.</p><p>In this blog post, I’ll show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.</p><p><a href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843" target="_blank" rel="noopener noreferrer">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png" alt="Deep Analysis of Russian Twitter Trolls"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deep Analysis of Russian Twitter Trolls" href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-IngestPlugins">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Leverage Plugins to Ingest Parquet Files from S3 in Pinot" href="/blog/2020/08/08/DevBlog-IngestPlugins"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-PinotMonitoring">Monitoring Apache Pinot with JMX, Prometheus and Grafana</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring Apache Pinot with JMX, Prometheus and Grafana" href="/blog/2020/08/08/DevBlog-PinotMonitoring"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-SLAApps">Achieving 99th percentile latency SLA using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Achieving 99th percentile latency SLA using Apache Pinot" href="/blog/2020/08/08/DevBlog-SLAApps"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-ScalarUDFs">Utilize UDFs to Supercharge Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Utilize UDFs to Supercharge Queries in Apache Pinot" href="/blog/2020/08/08/DevBlog-ScalarUDFs"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-AnomalyDetection">Building a culture around metrics and anomaly detection</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building a culture around metrics and anomaly detection" href="/blog/2020/07/28/DevBlog-AnomalyDetection"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-DevUpStack">Moving developers up the stack with Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Once upon a time, an internet company named LinkedIn faced the challenge of having petabytes of connected data with no way to analyze it in real-time. As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Moving developers up the stack with Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Moving developers up the stack with Apache Pinot" href="/blog/2020/07/28/DevBlog-DevUpStack"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/14/LinkedIn-BatchRealtime">Bridging batch and stream processing for the Recruiter usage statistics dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-14T00:00:00.000Z">July 14, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Batch and streaming computations are often combined together in the Lambda architecture,
but carry the cost of maintaining two different code bases for the same logic.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/07/bridging-1.png" alt="Bridging batch and stream processing for the Recruiter usage statistics dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Bridging batch and stream processing for the Recruiter usage statistics dashboard" href="/blog/2020/07/14/LinkedIn-BatchRealtime"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/06/29/LinkedIn-TalentInsight">Building LinkedIn Talent Insights to democratize data-driven decision making</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-06-29T00:00:00.000Z">June 29, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn is a mission-driven organization, and we take our mission of “connecting the world's professionals to make them more productive and successful” very seriously.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/building-linkedin-talent-insights" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/building-linkedin-talent-insights</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/06/lti-1.png" alt="Building LinkedIn Talent Insights to democratize data-driven decision making"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building LinkedIn Talent Insights to democratize data-driven decision making" href="/blog/2020/06/29/LinkedIn-TalentInsight"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/06/25/LinkedIn-SmartAlerts">Monitoring business performance data with ThirdEye smart alerts</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-06-25T00:00:00.000Z">June 25, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Explain how ThirdEye smart alerts and automated dashboards helped the LinkedIn Premium business operations team monitor key metrics—such as new free trial signups—for the timely detection of outliers in business performance data.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/06/thirdeye_business_performance-3.png" alt="Monitoring business performance data with ThirdEye smart alerts"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring business performance data with ThirdEye smart alerts" href="/blog/2020/06/25/LinkedIn-SmartAlerts"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents">Using Apache Pinot and Kafka to Analyze GitHub Events</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-04-10T00:00:00.000Z">April 10, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’ll show you how Pinot and Kafka can be used together to ingest, query, and visualize event streams sourced from the public GitHub API. For the step-by-step instructions, please visit our documentation, which will guide you through the specifics of running this example in your development environment.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7</a></p><p><img src="https://miro.medium.com/max/4728/1*eR64jBH1ZvC3uNfPP56p5g.png" alt="Using Apache Pinot and Kafka to Analyze GitHub Events"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Using Apache Pinot and Kafka to Analyze GitHub Events" href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/02/20/LinkedIn-Thirdeye">Analyzing anomalies with ThirdEye</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-02-20T00:00:00.000Z">February 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Focus on the behind-the-scenes functionalities of ThirdEye that analyze the multi-dimensional time series data
and help our engineers understand why these anomalies happened through a dimension heatmap</p><p><a href="https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/02/datacube-1.png" alt="Analyzing anomalies with ThirdEye"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Analyzing anomalies with ThirdEye" href="/blog/2020/02/20/LinkedIn-Thirdeye"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/01/15/Pinot-Presto-SQL">Engineering SQL Support on Apache Pinot at Uber</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-01-15T00:00:00.000Z">January 15, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber leverages real-time analytics on aggregate data to improve the user experience across our products, from fighting fraudulent behavior on Uber Eats to forecasting demand on our platform.</p><p>To resolve these issues, we built a solution that linked Presto, a query engine that supports full ANSI SQL, and Pinot, a real-time OLAP (online analytical processing) datastore. This married solution allows users to write ad-hoc SQL queries, empowering teams to unlock significant analysis capabilities.</p><p><a href="https://eng.uber.com/engineering-sql-support-on-apache-pinot/" target="_blank" rel="noopener noreferrer">Read More at https://eng.uber.com/engineering-sql-support-on-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/01/Header-SQL-768x329.png" alt="SQL Support on Apache Pinot at Uber"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/sql">SQL</a></div><div class="col text--right"><a aria-label="Read more about Engineering SQL Support on Apache Pinot at Uber" href="/blog/2020/01/15/Pinot-Presto-SQL"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/07/11/LinkedIn-AutoTune">Auto-tuning Pinot real-time consumption</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-07-11T00:00:00.000Z">July 11, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Focus on Auto tuning Pinot, a scalable distributed columnar OLAP data store developed at LinkedIn, delivers real-time analytics for site-facing use cases such as LinkedIn's Who viewed my profile, Talent insights, and more.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/07/bridging-1.png" alt="Bridging batch and stream processing for the Recruiter usage statistics dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Auto-tuning Pinot real-time consumption" href="/blog/2019/07/11/LinkedIn-AutoTune"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/06/14/LinkedIn-StarTree">Star-tree index - Powering fast aggregations on Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-06-14T00:00:00.000Z">June 14, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Introduced Star-Tree index to utilize the pre-aggregated documents in a smart way that achieves low query latencies,
while using the storage space efficiently.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2019/06/star-tree-index--powering-fast-aggregations-on-pinot" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2019/06/star-tree-index--powering-fast-aggregations-on-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png" alt="Star-tree index - Powering fast aggregations on Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Star-tree index - Powering fast aggregations on Pinot" href="/blog/2019/06/14/LinkedIn-StarTree"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/01/09/LinkedIn-IntroThirdEye">Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-01-09T00:00:00.000Z">January 9, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>ThirdEye is a comprehensive platform for real-time monitoring of metrics that covers a wide variety of use-cases.
LinkedIn relies on ThirdEye to monitor site performance, track member growth, understand adoption of new features,
flag sustained attempts to circumvent system security, and many other areas</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png" alt="Star-tree index - Powering fast aggregations on Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform" href="/blog/2019/01/09/LinkedIn-IntroThirdEye"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2017/09/17/Restaurant-Manager">Engineering Restaurant Manager - UberEATS Analytics Dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2017-09-17T00:00:00.000Z">September 17, 2017</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, we use data analytics to architect more magical user experiences across our products. Whenever possible, we harness these data engineering capabilities to empower our partners to better serve their customers. For instance, in late 2016, the UberEATS engineering team built a comprehensive analytics dashboard that provides restaurant partners with additional insights about the health of their business.</p><p>Read More at <a href="https://eng.uber.com/restaurant-manager/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/restaurant-manager/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2017/09/image4-2.png" alt="Engineering Restaurant Manager - UberEATS Analytics Dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber-data">Uber Data</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Engineering Restaurant Manager - UberEATS Analytics Dashboard" href="/blog/2017/09/17/Restaurant-Manager"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2015/06/10/Open-Sourcing-Pinot">Open Sourcing Pinot - Scaling the Wall of Real-Time Analytics</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2015-06-10T00:00:00.000Z">June 10, 2015</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://github.com/kishoreg" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://avatars.githubusercontent.com/kishoreg" alt="Kishore Gopalakrishna"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://github.com/kishoreg" target="_blank" rel="noopener noreferrer">Kishore Gopalakrishna</a></div><small class="avatar__subtitle">Apache Pinot Team</small></div></div></header><div class="markdown"><p>LinkedIn’s real-time analytics infrastructure, that we built to allow us to slice and dice across billions of rows in real-time across a wide variety of products. Read More at (<a href="https://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics</a>)</p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/a-b-testing">A/B Testing</a><a class="margin-horiz--sm" href="/blog/tags/infrastructure">Infrastructure</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Open Sourcing Pinot - Scaling the Wall of Real-Time Analytics" href="/blog/2015/06/10/Open-Sourcing-Pinot"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2015/05/16/LinkedIn-Scaling">A Brief History of Scaling LinkedIn</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2015-05-16T00:00:00.000Z">May 16, 2015</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn started in 2003 with the goal of connecting to your network for better job opportunities. It had only 2,700 members the first week. Fast forward many years, and LinkedIn’s product portfolio, member base, and server load has grown tremendously.</p><p>Today, LinkedIn operates globally with more than 350 million members. We serve tens of thousands of web pages every second of every day. We've hit our mobile moment where mobile accounts for more than 50 percent of all global traffic. All those requests are fetching data from our backend systems, which in turn handle millions of queries per second.</p><p>Read More at <a href="https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/en-us/blog/migrated/data_centers_pops_0.png" alt="A Brief History of Scaling LinkedIn"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" 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href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><header class="margin-bottom--xl"><h1>One post tagged with "cdc"</h1><a href="/blog/tags">View All Tags</a></header><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-01-08T00:00:00.000Z">January 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’re going to explore an exciting new world of real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Change Data Analysis with Debezium and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/debezium">Debezium</a><a 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href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><header class="margin-bottom--xl"><h1>One post tagged with "change data analysis"</h1><a href="/blog/tags">View All Tags</a></header><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-01-08T00:00:00.000Z">January 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’re going to explore an exciting new world of real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Change Data Analysis with Debezium and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" 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You can find a full list of everything included in the <a href="https://docs.pinot.apache.org/basics/releases/0.10.0" target="_blank" rel="noopener noreferrer">release notes</a>.</p><p>Let’s have a look at some of the changes, with the help of the batch <a href="https://docs.pinot.apache.org/basics/getting-started/running-pinot-in-docker" target="_blank" rel="noopener noreferrer">QuickStart configuration</a>.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="query-plans"></a>Query Plans<a class="hash-link" href="#query-plans" title="Direct link to heading">#</a></h2><p>Amrish Lal implemented the <code>EXPLAIN PLAN</code> clause, which returns the execution plan that will be chosen by the Pinot Query Engine.
This lets us see what the query is likely to do without actually having to run it.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">EXPLAIN</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">PLAN</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FOR</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token operator">*</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following results:</p><table><thead><tr><th>Operator</th><th>Operator_Id</th><th>Parent_Id</th></tr></thead><tbody><tr><td>BROKER_REDUCE(limit:10)</td><td>0</td><td>-1</td></tr><tr><td>COMBINE_SELECT</td><td>1</td><td>0</td></tr><tr><td>SELECT(selectList:AtBatting, G_old, baseOnBalls, caughtStealing, doules, groundedIntoDoublePlays, hits, hitsByPitch, homeRuns, intentionalWalks, league, numberOfGames, numberOfGamesAsBatter, playerID, playerName, playerStint, runs, runsBattedIn, sacrificeFlies, sacrificeHits, stolenBases, strikeouts, teamID, tripples, yearID)</td><td>2</td><td>1</td></tr><tr><td>TRANSFORM_PASSTHROUGH(AtBatting, G_old, baseOnBalls, caughtStealing, doules, groundedIntoDoublePlays, hits, hitsByPitch, homeRuns, intentionalWalks, league, numberOfGames, numberOfGamesAsBatter, playerID, playerName, playerStint, runs, runsBattedIn, sacrificeFlies, sacrificeHits, stolenBases, strikeouts, teamID, tripples, yearID)</td><td>3</td><td>2</td></tr><tr><td>PROJECT(homeRuns, playerStint, groundedIntoDoublePlays, numberOfGames, AtBatting, stolenBases, tripples, hitsByPitch, teamID, numberOfGamesAsBatter, strikeouts, sacrificeFlies, caughtStealing, baseOnBalls, playerName, doules, league, yearID, hits, runsBattedIn, G_old, sacrificeHits, intentionalWalks, runs, playerID)</td><td>4</td><td>3</td></tr><tr><td>FILTER_FULL_SCAN(operator:EQ,predicate:league = 'NL')</td><td>5</td><td>4</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="filter-clauses-for-aggregates"></a>FILTER Clauses for Aggregates<a class="hash-link" href="#filter-clauses-for-aggregates" title="Direct link to heading">#</a></h2><p>Atri Sharma added the filter clause for aggregates.
This feature makes it possible to write queries like this:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> nlHomeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'AL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> alHomeRuns</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>nlHomeRuns</th><th>alHomeRuns</th></tr></thead><tbody><tr><td>135486</td><td>135990</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="greatest-and-least"></a>greatest and least<a class="hash-link" href="#greatest-and-least" title="Direct link to heading">#</a></h2><p>Richard Startin added the <code>greatest</code> and <code>least</code> functions:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> playerID</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> least</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> greatest</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">hits</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> hits</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token plain"> </span><span class="token operator">AND</span><span class="token plain"> teamID </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'SFN'</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">GROUP</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">BY</span><span class="token plain"> playerID</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">LIMIT</span><span class="token plain"> </span><span class="token number">5</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>playerID</th><th>homeRuns</th><th>hits</th></tr></thead><tbody><tr><td>ramirju01</td><td>0</td><td>5</td></tr><tr><td>milneed01</td><td>4</td><td>54</td></tr><tr><td>testani01</td><td>0</td><td>5</td></tr><tr><td>shawbo01</td><td>0</td><td>8</td></tr><tr><td>vogelry01</td><td>0</td><td>12</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="distinctcountsmarthll"></a>DistinctCountSmartHLL<a class="hash-link" href="#distinctcountsmarthll" title="Direct link to heading">#</a></h2><p> Xiaotian (Jackie) Jiang added the <code>DistinctCountSmartHLL</code> aggregation function, which automatically converts the Set to HyperLogLog if the set size grows too big to protect the servers from running out of memory:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> DISTINCTCOUNTSMARTHLL</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'hllLog2m=8;hllConversionThreshold=10'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>distinctcountsmarthll(homeRuns)</th></tr></thead><tbody><tr><td>66</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="ui-updates"></a>UI updates<a class="hash-link" href="#ui-updates" title="Direct link to heading">#</a></h2><p>There were also a bunch of updates to the Pinot Data Explorer, by Sanket Shah and Johan Adami.</p><p>The display of reported size and estimated size is now in a human readable format:</p><p><img alt="Human readable sizes" src="/assets/images/human-readable-sizes-b8c4009dd53d23da3b8637963827a8de.png"></p><p>Fixes for the following issues:</p><ul><li>Error messages weren't showing on the UI when an invalid operation is attempted:</li></ul><p><img alt="A backwards incompatible attempted schema change" src="/assets/images/backwards-incompatible-99886dcd0be55a8100a7d6c5f3da3bda.png"></p><ul><li>Query console goes blank on syntax error.</li><li>Query console cannot show query result when multiple columns have the same name.</li><li>Adding extra fields after <code>SELECT *</code> would throw a NullPointerException.</li><li>Some queries were returning <code>--</code> instead of <code>0</code>.</li><li>Query console couldn't show the query result if multiple columns had the same name.</li><li>Pinot Dashboard tenant view showing the incorrect amount of servers and brokers.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="realtimetooffline-task"></a>RealTimeToOffline Task<a class="hash-link" href="#realtimetooffline-task" title="Direct link to heading">#</a></h2><p>Xiaotian (Jackie) Jiang made some fixes to the <a href="https://dev.startree.ai/docs/pinot/recipes/real-time-offline-job" target="_blank" rel="noopener noreferrer">RealTimeToOffline job</a> to handle time gaps and proceed to the next time window when no segment matches the current one.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="empty-quickstart"></a>Empty QuickStart<a class="hash-link" href="#empty-quickstart" title="Direct link to heading">#</a></h2><p>Kenny Bastani added an empty QuickStart command, which lets you quickly spin up an empty Pinot cluster:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">docker</span><span class="token plain"> run </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">8000</span><span class="token plain">:8000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">9000</span><span class="token plain">:9000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> apachepinot/pinot:0.10.0 QuickStart </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -type empty</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>You can then ingest your own dataset without needing to worry about spinning up each of the Pinot components individually.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="data-ingestion"></a>Data Ingestion<a class="hash-link" href="#data-ingestion" title="Direct link to heading">#</a></h2><ul><li><p>Richard Startin fixed some issues with real-time ingestion where consumption of messages would stop if a bad batch of messages was consumed from Kafka.</p></li><li><p>Mohemmad Zaid Khan added the BoundedColumnValue partition function, which partitions segments based on column values.</p></li><li><p>Xiaobing Li added the fixed name segment generator, which can be used when you want to replace a specific existing segment.</p></li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="other-changes"></a>Other changes<a class="hash-link" href="#other-changes" title="Direct link to heading">#</a></h2><ul><li>Richard Startin set LZ4 compression as the default for all metrics fields.</li><li>Mark Needham added the <code>ST_Within</code> geospatial function.</li><li>Rong Rong fixed a bug where query stats wouldn't show if there was an error processing the query (e.g. if the query timed out).</li><li>Prashant Pandey fixed the query engine to handle extra columns added to a <code>SELECT *</code> statement.</li><li>Richard Startin added support for forward indexes on JSON columns.</li><li>Rong Rong added the GRPC broker request handler so that data can be streamed back from the server to the broker when processing queries.</li><li>deemoliu made it possible to add a default strategy when using the <a href="https://dev.startree.ai/docs/pinot/recipes/upserts-partial" target="_blank" rel="noopener noreferrer">partial upsert feature</a>.</li><li>Jeff Moszuti added support for the <code>TIMESTAMP</code> data type in the <a href="https://docs.pinot.apache.org/operators/configuration-recommendation-engine" target="_blank" rel="noopener noreferrer">configuration recommendation engine</a>.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="dependency-updates"></a>Dependency updates<a class="hash-link" href="#dependency-updates" title="Direct link to heading">#</a></h2><p>The following dependencies were updated:</p><ul><li>async-http-client because the library moved to a different organization.</li><li>RoaringBitmap to 0.9.25</li><li>JsonPath to 2.7.0</li><li>Kafka to 2.8.1</li><li>Prometheus to 0.16.1</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="resources"></a>Resources<a class="hash-link" href="#resources" title="Direct link to heading">#</a></h2><p>If you want to try out Apache Pinot, the following resources will help you get started:</p><ul><li>Download page: <a href="https://pinot.apache.org/download/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/download/</a></li><li>Getting started: <a href="https://docs.pinot.apache.org/getting-started" target="_blank" rel="noopener noreferrer">https://docs.pinot.apache.org/getting-started</a></li><li>Apache Pinot Recipes: <a href="https://dev.startree.ai/docs/pinot/recipes/" target="_blank" rel="noopener noreferrer">https://dev.startree.ai/docs/pinot/recipes/</a></li><li>Join our Slack channel: <a href="https://communityinviter.com/apps/apache-pinot/apache-pinot" target="_blank" rel="noopener noreferrer">https://communityinviter.com/apps/apache-pinot/apache-pinot</a></li><li>See our upcoming events: <a href="https://www.meetup.com/apache-pinot" target="_blank" rel="noopener noreferrer">https://www.meetup.com/apache-pinot</a></li><li>Follow us on Twitter: <a href="https://twitter.com/startreedata" target="_blank" rel="noopener noreferrer">https://twitter.com/startreedata</a></li><li>Subscribe to our YouTube channel: <a href="https://www.youtube.com/c/StarTree" target="_blank" rel="noopener noreferrer">https://www.youtube.com/c/StarTree</a></li></ul></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/releases">Releases</a></div><div class="col text--right"><a aria-label="Read more about Announcing Apache Pinot 0.10" href="/blog/2022/04/04/Announcing-Apache-Pinot-0-10"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/16/LinkedIn-TextAnalytics">Text analytics on LinkedIn Talent Insights using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-16T00:00:00.000Z">June 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn Talent Insights (LTI) is a platform that helps organizations understand the external labor market and their internal workforce, and enables the long term success of their employees. Users of LTI have the flexibility to construct searches using the various facets of the LinkedIn Economic Graph (skills, titles, location, company, etc.).</p><p><a href="https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/06/ltipinot6.png" alt="Text analytics on LinkedIn Talent Insights using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/text-analytics">Text analytics</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Text analytics on LinkedIn Talent Insights using Apache Pinot" href="/blog/2021/06/16/LinkedIn-TextAnalytics"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" 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href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><header class="margin-bottom--xl"><h1>One post tagged with "debezium"</h1><a href="/blog/tags">View All Tags</a></header><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-01-08T00:00:00.000Z">January 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’re going to explore an exciting new world of real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Change Data Analysis with Debezium and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" 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<link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">13 posts tagged with "devblog" | Apache Pinot™</title><meta data-react-helmet="true" property="og:title" content="13 posts tagged with "devblog" | Apache Pinot™"><meta data-react-helmet="true" property="og:url" content="https://pinot.apache.org/blog/tags/dev-blog"><meta data-react-helmet="true" name="docsearch:language" content="en"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="blog_tags_posts"><meta data-react-helmet="true" name="keywords" content="Pinot,LinkedIn,User Analytics,User-Facing Analytics,Real-time data platform"><link data-react-helmet="true" rel="shortcut icon" href="/img/favicon.ico"><link data-react-helmet="true" rel="canonical" href="https://pinot.apache.org/blog/tags/dev-blog"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/blog/tags/dev-blog" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/blog/tags/dev-blog" hreflang="x-default"><link data-react-helmet="true" rel="preconnect" href="https://BH4D9OD16A-dsn.algolia.net" crossorigin="anonymous"><link rel="stylesheet" href="/assets/css/styles.f0793de6.css">
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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><header class="margin-bottom--xl"><h1>13 posts tagged with "devblog"</h1><a href="/blog/tags">View All Tags</a></header><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/13/DevBlog-Geospatial">Introduction to Geospatial Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-13T00:00:00.000Z">June 13, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Geospatial data has been widely used across the industry, spanning multiple verticals, such as ride-sharing and delivery, transportation infrastructure, defense and intel, public health. Deriving insights from timely and accurate geospatial data could enable mission-critical use cases in the organizations and fuel a vibrant marketplace across the industry. In the design document for this new Pinot feature, we discuss the challenges of analyzing geospatial at scale and propose the geospatial support in Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9</a></p><p><img src="https://miro.medium.com/max/1400/0*1xrDSs9lLZ5dD3zK" alt="Introduction to Geospatial Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/h-3">H3</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Geospatial Queries in Apache Pinot" href="/blog/2021/06/13/DevBlog-Geospatial"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-27T00:00:00.000Z">April 27, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain</p><p><a href="https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38</a></p><p><img src="https://miro.medium.com/max/1400/1*EtqD0vTPEe569jybXCt69w.png" alt="Deploying Apache Pinot at a Large Retail Chain"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deploying Apache Pinot at a Large Retail Chain" href="/blog/2021/04/27/DevBlog-PinotInRetailChain"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-08T00:00:00.000Z">April 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Since the 0.6.0 release of Apache Pinot, a new feature was made available for stream ingestion that allows you to upsert events from an immutable log. Typically, upsert is a term used to describe inserting a record into a database if it does not already exist or update it if it does exist. In Apache Pinot’s case, upsert isn’t precisely the same concept, and I wanted to write this blog post to explain why it’s exciting and how you can start using it.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93</a></p><p><img src="https://miro.medium.com/max/1400/0*So3GjHjLY7DJAiaP" alt="Introduction to Upserts in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/upsert">Upsert</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Upserts in Apache Pinot" href="/blog/2021/04/08/DevBlog-UpsertsIntro"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-02-02T00:00:00.000Z">February 2, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://www.startree.ai/wp-content/uploads/2021/05/team-xiang-150x150.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/trino">Trino</a><a class="margin-horiz--sm" href="/blog/tags/presto-sql">PrestoSQL</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Real-time Analytics with Presto and Apache Pinot" href="/blog/2021/02/02/DevBlog-PrestoPinot"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-01-08T00:00:00.000Z">January 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’re going to explore an exciting new world of real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Change Data Analysis with Debezium and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/debezium">Debezium</a><a class="margin-horiz--sm" href="/blog/tags/cdc">CDC</a><a class="margin-horiz--sm" href="/blog/tags/change-data-analysis">Change Data Analysis</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Change Data Analysis with Debezium and Apache Pinot" href="/blog/2021/01/08/DevBlog-DebeziumCDC"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis">Deep Analysis of Russian Twitter Trolls</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-16T00:00:00.000Z">October 16, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>The history behind Russian disinformation is a dense and continuously evolving subject. The world’s best research hasn’t seemed to hit the mainstream yet, which made this an excellent opportunity to see if I could use some open source tooling to surface new analytical evidence.</p><p>In this blog post, I’ll show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.</p><p><a href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843" target="_blank" rel="noopener noreferrer">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png" alt="Deep Analysis of Russian Twitter Trolls"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deep Analysis of Russian Twitter Trolls" href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-IngestPlugins">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Leverage Plugins to Ingest Parquet Files from S3 in Pinot" href="/blog/2020/08/08/DevBlog-IngestPlugins"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-SLAApps">Achieving 99th percentile latency SLA using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Achieving 99th percentile latency SLA using Apache Pinot" href="/blog/2020/08/08/DevBlog-SLAApps"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-ScalarUDFs">Utilize UDFs to Supercharge Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Utilize UDFs to Supercharge Queries in Apache Pinot" href="/blog/2020/08/08/DevBlog-ScalarUDFs"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-PinotMonitoring">Monitoring Apache Pinot with JMX, Prometheus and Grafana</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring Apache Pinot with JMX, Prometheus and Grafana" href="/blog/2020/08/08/DevBlog-PinotMonitoring"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-AnomalyDetection">Building a culture around metrics and anomaly detection</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building a culture around metrics and anomaly detection" href="/blog/2020/07/28/DevBlog-AnomalyDetection"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-DevUpStack">Moving developers up the stack with Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Once upon a time, an internet company named LinkedIn faced the challenge of having petabytes of connected data with no way to analyze it in real-time. As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Moving developers up the stack with Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Moving developers up the stack with Apache Pinot" href="/blog/2020/07/28/DevBlog-DevUpStack"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents">Using Apache Pinot and Kafka to Analyze GitHub Events</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-04-10T00:00:00.000Z">April 10, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’ll show you how Pinot and Kafka can be used together to ingest, query, and visualize event streams sourced from the public GitHub API. For the step-by-step instructions, please visit our documentation, which will guide you through the specifics of running this example in your development environment.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7</a></p><p><img src="https://miro.medium.com/max/4728/1*eR64jBH1ZvC3uNfPP56p5g.png" alt="Using Apache Pinot and Kafka to Analyze GitHub Events"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Using Apache Pinot and Kafka to Analyze GitHub Events" href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" href="/who_uses">Who uses Apache Pinot?</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" 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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><header class="margin-bottom--xl"><h1>13 posts tagged with "devblog"</h1><a href="/blog/tags">View All Tags</a></header><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/13/DevBlog-Geospatial">Introduction to Geospatial Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-13T00:00:00.000Z">June 13, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Geospatial data has been widely used across the industry, spanning multiple verticals, such as ride-sharing and delivery, transportation infrastructure, defense and intel, public health. Deriving insights from timely and accurate geospatial data could enable mission-critical use cases in the organizations and fuel a vibrant marketplace across the industry. In the design document for this new Pinot feature, we discuss the challenges of analyzing geospatial at scale and propose the geospatial support in Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9</a></p><p><img src="https://miro.medium.com/max/1400/0*1xrDSs9lLZ5dD3zK" alt="Introduction to Geospatial Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/h-3">H3</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Geospatial Queries in Apache Pinot" href="/blog/2021/06/13/DevBlog-Geospatial"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-27T00:00:00.000Z">April 27, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain</p><p><a href="https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38</a></p><p><img src="https://miro.medium.com/max/1400/1*EtqD0vTPEe569jybXCt69w.png" alt="Deploying Apache Pinot at a Large Retail Chain"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deploying Apache Pinot at a Large Retail Chain" href="/blog/2021/04/27/DevBlog-PinotInRetailChain"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-08T00:00:00.000Z">April 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Since the 0.6.0 release of Apache Pinot, a new feature was made available for stream ingestion that allows you to upsert events from an immutable log. Typically, upsert is a term used to describe inserting a record into a database if it does not already exist or update it if it does exist. In Apache Pinot’s case, upsert isn’t precisely the same concept, and I wanted to write this blog post to explain why it’s exciting and how you can start using it.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93</a></p><p><img src="https://miro.medium.com/max/1400/0*So3GjHjLY7DJAiaP" alt="Introduction to Upserts in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/upsert">Upsert</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Upserts in Apache Pinot" href="/blog/2021/04/08/DevBlog-UpsertsIntro"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-02-02T00:00:00.000Z">February 2, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://www.startree.ai/wp-content/uploads/2021/05/team-xiang-150x150.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/trino">Trino</a><a class="margin-horiz--sm" href="/blog/tags/presto-sql">PrestoSQL</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Real-time Analytics with Presto and Apache Pinot" href="/blog/2021/02/02/DevBlog-PrestoPinot"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-01-08T00:00:00.000Z">January 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’re going to explore an exciting new world of real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Change Data Analysis with Debezium and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/debezium">Debezium</a><a class="margin-horiz--sm" href="/blog/tags/cdc">CDC</a><a class="margin-horiz--sm" href="/blog/tags/change-data-analysis">Change Data Analysis</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Change Data Analysis with Debezium and Apache Pinot" href="/blog/2021/01/08/DevBlog-DebeziumCDC"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis">Deep Analysis of Russian Twitter Trolls</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-16T00:00:00.000Z">October 16, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>The history behind Russian disinformation is a dense and continuously evolving subject. The world’s best research hasn’t seemed to hit the mainstream yet, which made this an excellent opportunity to see if I could use some open source tooling to surface new analytical evidence.</p><p>In this blog post, I’ll show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.</p><p><a href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843" target="_blank" rel="noopener noreferrer">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png" alt="Deep Analysis of Russian Twitter Trolls"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deep Analysis of Russian Twitter Trolls" href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-IngestPlugins">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Leverage Plugins to Ingest Parquet Files from S3 in Pinot" href="/blog/2020/08/08/DevBlog-IngestPlugins"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-PinotMonitoring">Monitoring Apache Pinot with JMX, Prometheus and Grafana</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring Apache Pinot with JMX, Prometheus and Grafana" href="/blog/2020/08/08/DevBlog-PinotMonitoring"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-SLAApps">Achieving 99th percentile latency SLA using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Achieving 99th percentile latency SLA using Apache Pinot" href="/blog/2020/08/08/DevBlog-SLAApps"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-ScalarUDFs">Utilize UDFs to Supercharge Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Utilize UDFs to Supercharge Queries in Apache Pinot" href="/blog/2020/08/08/DevBlog-ScalarUDFs"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-AnomalyDetection">Building a culture around metrics and anomaly detection</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building a culture around metrics and anomaly detection" href="/blog/2020/07/28/DevBlog-AnomalyDetection"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-DevUpStack">Moving developers up the stack with Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Once upon a time, an internet company named LinkedIn faced the challenge of having petabytes of connected data with no way to analyze it in real-time. As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Moving developers up the stack with Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Moving developers up the stack with Apache Pinot" href="/blog/2020/07/28/DevBlog-DevUpStack"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents">Using Apache Pinot and Kafka to Analyze GitHub Events</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-04-10T00:00:00.000Z">April 10, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’ll show you how Pinot and Kafka can be used together to ingest, query, and visualize event streams sourced from the public GitHub API. For the step-by-step instructions, please visit our documentation, which will guide you through the specifics of running this example in your development environment.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7</a></p><p><img src="https://miro.medium.com/max/4728/1*eR64jBH1ZvC3uNfPP56p5g.png" alt="Using Apache Pinot and Kafka to Analyze GitHub Events"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Using Apache Pinot and Kafka to Analyze GitHub Events" href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" href="/who_uses">Who uses Apache Pinot?</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" class="footer__link-item">Components</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/basics/architecture" target="_blank" rel="noopener noreferrer" class="footer__link-item">Architecture</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/plugins/plugin-architecture" target="_blank" rel="noopener noreferrer" class="footer__link-item">Plugins Architecture</a></li></ul></div><div class="col footer__col"><h4 class="footer__title">Integrations</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/integrations/trino" target="_blank" rel="noopener noreferrer" class="footer__link-item">Trino</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/integrations/presto" target="_blank" rel="noopener noreferrer" class="footer__link-item">Presto</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/integrations/superset" target="_blank" rel="noopener 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<link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">2 posts tagged with "financial intelligence" | Apache Pinot™</title><meta data-react-helmet="true" property="og:title" content="2 posts tagged with "financial intelligence" | Apache Pinot™"><meta data-react-helmet="true" property="og:url" content="https://pinot.apache.org/blog/tags/financial-intelligence"><meta data-react-helmet="true" name="docsearch:language" content="en"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="blog_tags_posts"><meta data-react-helmet="true" name="keywords" content="Pinot,Uber Data,User Analytics Dashboard,User-Facing Analytics,Real-time data platform"><link data-react-helmet="true" rel="shortcut icon" href="/img/favicon.ico"><link data-react-helmet="true" rel="canonical" href="https://pinot.apache.org/blog/tags/financial-intelligence"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/blog/tags/financial-intelligence" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/blog/tags/financial-intelligence" hreflang="x-default"><link data-react-helmet="true" rel="preconnect" href="https://BH4D9OD16A-dsn.algolia.net" crossorigin="anonymous"><link rel="stylesheet" href="/assets/css/styles.f0793de6.css">
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Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to enable our community operations team to take corrective action. Having a real-time dashboard of different events such as consumer demand, driver availability, or trips happening in a city is crucial for day-to-day operation, incident triaging, and financial intelligence.</p><p>Read More at <a href="https://eng.uber.com/operating-apache-pinot/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/operating-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/10/1224-5.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Operating Apache Pinot at Uber Scale" href="/blog/2020/10/20/Uber-Operating"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2017/09/17/Restaurant-Manager">Engineering Restaurant Manager - UberEATS Analytics Dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2017-09-17T00:00:00.000Z">September 17, 2017</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, we use data analytics to architect more magical user experiences across our products. Whenever possible, we harness these data engineering capabilities to empower our partners to better serve their customers. For instance, in late 2016, the UberEATS engineering team built a comprehensive analytics dashboard that provides restaurant partners with additional insights about the health of their business.</p><p>Read More at <a href="https://eng.uber.com/restaurant-manager/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/restaurant-manager/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2017/09/image4-2.png" alt="Engineering Restaurant Manager - UberEATS Analytics Dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber-data">Uber Data</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Engineering Restaurant Manager - UberEATS Analytics Dashboard" href="/blog/2017/09/17/Restaurant-Manager"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" href="/who_uses">Who uses Apache Pinot?</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" 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Deriving insights from timely and accurate geospatial data could enable mission-critical use cases in the organizations and fuel a vibrant marketplace across the industry. In the design document for this new Pinot feature, we discuss the challenges of analyzing geospatial at scale and propose the geospatial support in Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9</a></p><p><img src="https://miro.medium.com/max/1400/0*1xrDSs9lLZ5dD3zK" alt="Introduction to Geospatial Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/h-3">H3</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" 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Scaling the Wall of Real-Time Analytics</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2015-06-10T00:00:00.000Z">June 10, 2015</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://github.com/kishoreg" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://avatars.githubusercontent.com/kishoreg" alt="Kishore Gopalakrishna"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://github.com/kishoreg" target="_blank" rel="noopener noreferrer">Kishore Gopalakrishna</a></div><small class="avatar__subtitle">Apache Pinot Team</small></div></div></header><div class="markdown"><p>LinkedIn’s real-time analytics infrastructure, that we built to allow us to slice and dice across billions of rows in real-time across a wide variety of products. Read More at (<a href="https://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics</a>)</p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/a-b-testing">A/B Testing</a><a class="margin-horiz--sm" href="/blog/tags/infrastructure">Infrastructure</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Open Sourcing Pinot - Scaling the Wall of Real-Time Analytics" href="/blog/2015/06/10/Open-Sourcing-Pinot"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div 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When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a 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while using the storage space efficiently.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2019/06/star-tree-index--powering-fast-aggregations-on-pinot" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2019/06/star-tree-index--powering-fast-aggregations-on-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png" alt="Star-tree index - Powering fast aggregations on Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Star-tree index - Powering fast aggregations on Pinot" href="/blog/2019/06/14/LinkedIn-StarTree"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/01/09/LinkedIn-IntroThirdEye">Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-01-09T00:00:00.000Z">January 9, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>ThirdEye is a comprehensive platform for real-time monitoring of metrics that covers a wide variety of use-cases.
LinkedIn relies on ThirdEye to monitor site performance, track member growth, understand adoption of new features,
flag sustained attempts to circumvent system security, and many other areas</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png" alt="Star-tree index - Powering fast aggregations on Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform" href="/blog/2019/01/09/LinkedIn-IntroThirdEye"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2015/05/16/LinkedIn-Scaling">A Brief History of Scaling LinkedIn</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2015-05-16T00:00:00.000Z">May 16, 2015</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn started in 2003 with the goal of connecting to your network for better job opportunities. It had only 2,700 members the first week. Fast forward many years, and LinkedIn’s product portfolio, member base, and server load has grown tremendously.</p><p>Today, LinkedIn operates globally with more than 350 million members. We serve tens of thousands of web pages every second of every day. We've hit our mobile moment where mobile accounts for more than 50 percent of all global traffic. All those requests are fetching data from our backend systems, which in turn handle millions of queries per second.</p><p>Read More at <a href="https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/en-us/blog/migrated/data_centers_pops_0.png" alt="A Brief History of Scaling LinkedIn"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about A Brief History of Scaling LinkedIn" href="/blog/2015/05/16/LinkedIn-Scaling"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" href="/who_uses">Who uses Apache Pinot?</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" class="footer__link-item">Components</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/basics/architecture" target="_blank" rel="noopener 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When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring Apache Pinot with JMX, Prometheus and Grafana" href="/blog/2020/08/08/DevBlog-PinotMonitoring"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a 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<link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">30 posts tagged with "pinot" | Apache Pinot™</title><meta data-react-helmet="true" property="og:title" content="30 posts tagged with "pinot" | Apache Pinot™"><meta data-react-helmet="true" property="og:url" content="https://pinot.apache.org/blog/tags/pinot"><meta data-react-helmet="true" name="docsearch:language" content="en"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="blog_tags_posts"><meta data-react-helmet="true" name="keywords" content="Pinot,LinkedIn,Data Scaling,User-Facing Analytics,Real-time data platform"><link data-react-helmet="true" rel="shortcut icon" href="/img/favicon.ico"><link data-react-helmet="true" rel="canonical" href="https://pinot.apache.org/blog/tags/pinot"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/blog/tags/pinot" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/blog/tags/pinot" hreflang="x-default"><link data-react-helmet="true" rel="preconnect" href="https://BH4D9OD16A-dsn.algolia.net" crossorigin="anonymous"><link rel="stylesheet" href="/assets/css/styles.f0793de6.css">
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Apache Pinot is a real-time distributed datastore designed to answer OLAP queries with high throughput and low latency.</p><p>This release is cut from commit <a href="https://github.com/apache/pinot/commit/fd9c58a11ed16d27109baefcee138eea30132ad3" target="_blank" rel="noopener noreferrer">fd9c58a11ed16d27109baefcee138eea30132ad3</a>.
You can find a full list of everything included in the <a href="https://docs.pinot.apache.org/basics/releases/0.10.0" target="_blank" rel="noopener noreferrer">release notes</a>.</p><p>Let’s have a look at some of the changes, with the help of the batch <a href="https://docs.pinot.apache.org/basics/getting-started/running-pinot-in-docker" target="_blank" rel="noopener noreferrer">QuickStart configuration</a>.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="query-plans"></a>Query Plans<a class="hash-link" href="#query-plans" title="Direct link to heading">#</a></h2><p>Amrish Lal implemented the <code>EXPLAIN PLAN</code> clause, which returns the execution plan that will be chosen by the Pinot Query Engine.
This lets us see what the query is likely to do without actually having to run it.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">EXPLAIN</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">PLAN</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FOR</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token operator">*</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following results:</p><table><thead><tr><th>Operator</th><th>Operator_Id</th><th>Parent_Id</th></tr></thead><tbody><tr><td>BROKER_REDUCE(limit:10)</td><td>0</td><td>-1</td></tr><tr><td>COMBINE_SELECT</td><td>1</td><td>0</td></tr><tr><td>SELECT(selectList:AtBatting, G_old, baseOnBalls, caughtStealing, doules, groundedIntoDoublePlays, hits, hitsByPitch, homeRuns, intentionalWalks, league, numberOfGames, numberOfGamesAsBatter, playerID, playerName, playerStint, runs, runsBattedIn, sacrificeFlies, sacrificeHits, stolenBases, strikeouts, teamID, tripples, yearID)</td><td>2</td><td>1</td></tr><tr><td>TRANSFORM_PASSTHROUGH(AtBatting, G_old, baseOnBalls, caughtStealing, doules, groundedIntoDoublePlays, hits, hitsByPitch, homeRuns, intentionalWalks, league, numberOfGames, numberOfGamesAsBatter, playerID, playerName, playerStint, runs, runsBattedIn, sacrificeFlies, sacrificeHits, stolenBases, strikeouts, teamID, tripples, yearID)</td><td>3</td><td>2</td></tr><tr><td>PROJECT(homeRuns, playerStint, groundedIntoDoublePlays, numberOfGames, AtBatting, stolenBases, tripples, hitsByPitch, teamID, numberOfGamesAsBatter, strikeouts, sacrificeFlies, caughtStealing, baseOnBalls, playerName, doules, league, yearID, hits, runsBattedIn, G_old, sacrificeHits, intentionalWalks, runs, playerID)</td><td>4</td><td>3</td></tr><tr><td>FILTER_FULL_SCAN(operator:EQ,predicate:league = 'NL')</td><td>5</td><td>4</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="filter-clauses-for-aggregates"></a>FILTER Clauses for Aggregates<a class="hash-link" href="#filter-clauses-for-aggregates" title="Direct link to heading">#</a></h2><p>Atri Sharma added the filter clause for aggregates.
-This feature makes it possible to write queries like this:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> nlHomeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'AL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> alHomeRuns</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>nlHomeRuns</th><th>alHomeRuns</th></tr></thead><tbody><tr><td>135486</td><td>135990</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="greatest-and-least"></a>greatest and least<a class="hash-link" href="#greatest-and-least" title="Direct link to heading">#</a></h2><p>Richard Startin added the <code>greatest</code> and <code>least</code> functions:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> playerID</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> least</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> greatest</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">hits</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> hits</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token plain"> </span><span class="token operator">AND</span><span class="token plain"> teamID </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'SFN'</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">GROUP</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">BY</span><span class="token plain"> playerID</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">LIMIT</span><span class="token plain"> </span><span class="token number">5</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>playerID</th><th>homeRuns</th><th>hits</th></tr></thead><tbody><tr><td>ramirju01</td><td>0</td><td>5</td></tr><tr><td>milneed01</td><td>4</td><td>54</td></tr><tr><td>testani01</td><td>0</td><td>5</td></tr><tr><td>shawbo01</td><td>0</td><td>8</td></tr><tr><td>vogelry01</td><td>0</td><td>12</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="distinctcountsmarthll"></a>DistinctCountSmartHLL<a class="hash-link" href="#distinctcountsmarthll" title="Direct link to heading">#</a></h2><p> Xiaotian (Jackie) Jiang added the <code>DistinctCountSmartHLL</code> aggregation function, which automatically converts the Set to HyperLogLog if the set size grows too big to protect the servers from running out of memory:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> DISTINCTCOUNTSMARTHLL</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'hllLog2m=8;hllConversionThreshold=10'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>distinctcountsmarthll(homeRuns)</th></tr></thead><tbody><tr><td>66</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="ui-updates"></a>UI updates<a class="hash-link" href="#ui-updates" title="Direct link to heading">#</a></h2><p>There were also a bunch of updates to the Pinot Data Explorer, by Sanket Shah and Johan Adami.</p><p>The display of reported size and estimated size is now in a human readable format:</p><p><img alt="Human readable sizes" src="/assets/images/human-readable-sizes-b8c4009dd53d23da3b8637963827a8de.png"></p><p>Fixes for the following issues:</p><ul><li>Error messages weren't showing on the UI when an invalid operation is attempted:</li></ul><p><img alt="A backwards incompatible attempted schema change" src="/assets/images/backwards-incompatible-99886dcd0be55a8100a7d6c5f3da3bda.png"></p><ul><li>Query console goes blank on syntax error.</li><li>Query console cannot show query result when multiple columns have the same name.</li><li>Adding extra fields after <code>SELECT *</code> would throw a NullPointerException.</li><li>Some queries were returning <code>--</code> instead of <code>0</code>.</li><li>Query console couldn't show the query result if multiple columns had the same name.</li><li>Pinot Dashboard tenant view showing the incorrect amount of servers and brokers.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="realtimetooffline-task"></a>RealTimeToOffline Task<a class="hash-link" href="#realtimetooffline-task" title="Direct link to heading">#</a></h2><p>Xiaotian (Jackie) Jiang made some fixes to the <a href="https://dev.startree.ai/docs/pinot/recipes/real-time-offline-job" target="_blank" rel="noopener noreferrer">RealTimeToOffline job</a> to handle time gaps and proceed to the next time window when no segment matches the current one.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="empty-quickstart"></a>Empty QuickStart<a class="hash-link" href="#empty-quickstart" title="Direct link to heading">#</a></h2><p>Kenny Bastani added an empty QuickStart command, which lets you quickly spin up an empty Pinot cluster:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">docker</span><span class="token plain"> run </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">8000</span><span class="token plain">:8000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">9000</span><span class="token plain">:9000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> apachepinot/pinot:0.10.0 QuickStart </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -type empty</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>You can then ingest your own dataset without needing to worry about spinning up each of the Pinot components individually.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="data-ingestion"></a>Data Ingestion<a class="hash-link" href="#data-ingestion" title="Direct link to heading">#</a></h2><ul><li><p>Richard Startin fixed some issues with real-time ingestion where consumption of messages would stop if a bad batch of messages was consumed from Kafka.</p></li><li><p>Mohemmad Zaid Khan added the BoundedColumnValue partition function, which partitions segments based on column values.</p></li><li><p>Xiaobing Li added the fixed name segment generator, which can be used when you want to replace a specific existing segment.</p></li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="other-changes"></a>Other changes<a class="hash-link" href="#other-changes" title="Direct link to heading">#</a></h2><ul><li>Richard Startin set LZ4 compression as the default for all metrics fields.</li><li>Mark Needham added the <code>ST_Within</code> geospatial function.</li><li>Rong Rong fixed a bug where query stats wouldn't show if there was an error processing the query (e.g. if the query timed out).</li><li>Prashant Pandey fixed the query engine to handle extra columns added to a <code>SELECT *</code> statement.</li><li>Richard Startin added support for forward indexes on JSON columns.</li><li>Rong Rong added the GRPC broker request handler so that data can be streamed back from the server to the broker when processing queries.</li><li>deemoliu made it possible to add a default strategy when using the <a href="https://dev.startree.ai/docs/pinot/recipes/upserts-partial" target="_blank" rel="noopener noreferrer">partial upsert feature</a>.</li><li>Jeff Moszuti added support for the <code>TIMESTAMP</code> data type in the <a href="https://docs.pinot.apache.org/operators/configuration-recommendation-engine" target="_blank" rel="noopener noreferrer">configuration recommendation engine</a>.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="dependency-updates"></a>Dependency updates<a class="hash-link" href="#dependency-updates" title="Direct link to heading">#</a></h2><p>The following dependencies were updated:</p><ul><li>async-http-client because the library moved to a different organization.</li><li>RoaringBitmap to 0.9.25</li><li>JsonPath to 2.7.0</li><li>Kafka to 2.8.1</li><li>Prometheus to 0.16.1</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="resources"></a>Resources<a class="hash-link" href="#resources" title="Direct link to heading">#</a></h2><p>If you want to try out Apache Pinot, the following resources will help you get started:</p><ul><li>Download page: <a href="https://pinot.apache.org/download/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/download/</a></li><li>Getting started: <a href="https://docs.pinot.apache.org/getting-started" target="_blank" rel="noopener noreferrer">https://docs.pinot.apache.org/getting-started</a></li><li>Apache Pinot Recipes: <a href="https://dev.startree.ai/docs/pinot/recipes/" target="_blank" rel="noopener noreferrer">https://dev.startree.ai/docs/pinot/recipes/</a></li><li>Join our Slack channel: <a href="https://communityinviter.com/apps/apache-pinot/apache-pinot" target="_blank" rel="noopener noreferrer">https://communityinviter.com/apps/apache-pinot/apache-pinot</a></li><li>See our upcoming events: <a href="https://www.meetup.com/apache-pinot" target="_blank" rel="noopener noreferrer">https://www.meetup.com/apache-pinot</a></li><li>Follow us on Twitter: <a href="https://twitter.com/startreedata" target="_blank" rel="noopener noreferrer">https://twitter.com/startreedata</a></li><li>Subscribe to our YouTube channel: <a href="https://www.youtube.com/c/StarTree" target="_blank" rel="noopener noreferrer">https://www.youtube.com/c/StarTree</a></li></ul></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/releases">Releases</a></div><div class="col text--right"><a aria-label="Read more about Announcing Apache Pinot 0.10" href="/blog/2022/04/04/Announcing-Apache-Pinot-0-10"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/16/LinkedIn-TextAnalytics">Text analytics on LinkedIn Talent Insights using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-16T00:00:00.000Z">June 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn Talent Insights (LTI) is a platform that helps organizations understand the external labor market and their internal workforce, and enables the long term success of their employees. Users of LTI have the flexibility to construct searches using the various facets of the LinkedIn Economic Graph (skills, titles, location, company, etc.).</p><p><a href="https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/06/ltipinot6.png" alt="Text analytics on LinkedIn Talent Insights using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/text-analytics">Text analytics</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Text analytics on LinkedIn Talent Insights using Apache Pinot" href="/blog/2021/06/16/LinkedIn-TextAnalytics"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/13/DevBlog-Geospatial">Introduction to Geospatial Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-13T00:00:00.000Z">June 13, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Geospatial data has been widely used across the industry, spanning multiple verticals, such as ride-sharing and delivery, transportation infrastructure, defense and intel, public health. Deriving insights from timely and accurate geospatial data could enable mission-critical use cases in the organizations and fuel a vibrant marketplace across the industry. In the design document for this new Pinot feature, we discuss the challenges of analyzing geospatial at scale and propose the geospatial support in Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9</a></p><p><img src="https://miro.medium.com/max/1400/0*1xrDSs9lLZ5dD3zK" alt="Introduction to Geospatial Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/h-3">H3</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Geospatial Queries in Apache Pinot" href="/blog/2021/06/13/DevBlog-Geospatial"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/29/Uber-Charon">Automating Merchant Live Monitoring with Real-Time Analytics - Charon</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-29T00:00:00.000Z">April 29, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, live monitoring and automation of Ops is critical to preserve marketplace health, maintain reliability, and gain efficiency in markets. By the virtue of the word “live”, this monitoring needs to show what is happening now, with prompt access to fresh data, and the ability to recommend appropriate actions based on that data. Uber’s data platform provides the self-serve tools which empower the Ops teams to build their own live monitoring tools, and support their regional teams by building rich solutions.</p><p>For this project, the requirement was to provide merchant level monitoring and handle the edge cases which remain unaddressed by the sophisticated internal marketplace management tools. We used a variety of Uber’s real-time data platform components to build a tool called Charon to reduce impact of poor marketplace reliability on the merchants.</p><p>Read More at <a href="https://eng.uber.com/charon/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/charon/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2021/04/deliverDoor@3x-768x263.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Automating Merchant Live Monitoring with Real-Time Analytics - Charon" href="/blog/2021/04/29/Uber-Charon"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-27T00:00:00.000Z">April 27, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain</p><p><a href="https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38</a></p><p><img src="https://miro.medium.com/max/1400/1*EtqD0vTPEe569jybXCt69w.png" alt="Deploying Apache Pinot at a Large Retail Chain"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deploying Apache Pinot at a Large Retail Chain" href="/blog/2021/04/27/DevBlog-PinotInRetailChain"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-16T00:00:00.000Z">April 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/04/thetasketches2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches" href="/blog/2021/04/16/LinkedIn-Theta"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-08T00:00:00.000Z">April 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Since the 0.6.0 release of Apache Pinot, a new feature was made available for stream ingestion that allows you to upsert events from an immutable log. Typically, upsert is a term used to describe inserting a record into a database if it does not already exist or update it if it does exist. In Apache Pinot’s case, upsert isn’t precisely the same concept, and I wanted to write this blog post to explain why it’s exciting and how you can start using it.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93</a></p><p><img src="https://miro.medium.com/max/1400/0*So3GjHjLY7DJAiaP" alt="Introduction to Upserts in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/upsert">Upsert</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Upserts in Apache Pinot" href="/blog/2021/04/08/DevBlog-UpsertsIntro"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-02-02T00:00:00.000Z">February 2, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://www.startree.ai/wp-content/uploads/2021/05/team-xiang-150x150.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/trino">Trino</a><a class="margin-horiz--sm" href="/blog/tags/presto-sql">PrestoSQL</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Real-time Analytics with Presto and Apache Pinot" href="/blog/2021/02/02/DevBlog-PrestoPinot"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-01-08T00:00:00.000Z">January 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’re going to explore an exciting new world of real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Change Data Analysis with Debezium and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/debezium">Debezium</a><a class="margin-horiz--sm" href="/blog/tags/cdc">CDC</a><a class="margin-horiz--sm" href="/blog/tags/change-data-analysis">Change Data Analysis</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Change Data Analysis with Debezium and Apache Pinot" href="/blog/2021/01/08/DevBlog-DebeziumCDC"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-12-01T00:00:00.000Z">December 1, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/03/pinotincubator2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about From Lambda to Lambda-less Lessons learned" href="/blog/2020/12/01/LinkedIn-Lamda"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/20/Uber-Operating">Operating Apache Pinot at Uber Scale</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-20T00:00:00.000Z">October 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to enable our community operations team to take corrective action. Having a real-time dashboard of different events such as consumer demand, driver availability, or trips happening in a city is crucial for day-to-day operation, incident triaging, and financial intelligence.</p><p>Read More at <a href="https://eng.uber.com/operating-apache-pinot/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/operating-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/10/1224-5.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Operating Apache Pinot at Uber Scale" href="/blog/2020/10/20/Uber-Operating"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis">Deep Analysis of Russian Twitter Trolls</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-16T00:00:00.000Z">October 16, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>The history behind Russian disinformation is a dense and continuously evolving subject. The world’s best research hasn’t seemed to hit the mainstream yet, which made this an excellent opportunity to see if I could use some open source tooling to surface new analytical evidence.</p><p>In this blog post, I’ll show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.</p><p><a href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843" target="_blank" rel="noopener noreferrer">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png" alt="Deep Analysis of Russian Twitter Trolls"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deep Analysis of Russian Twitter Trolls" href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-IngestPlugins">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Leverage Plugins to Ingest Parquet Files from S3 in Pinot" href="/blog/2020/08/08/DevBlog-IngestPlugins"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-SLAApps">Achieving 99th percentile latency SLA using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Achieving 99th percentile latency SLA using Apache Pinot" href="/blog/2020/08/08/DevBlog-SLAApps"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-ScalarUDFs">Utilize UDFs to Supercharge Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Utilize UDFs to Supercharge Queries in Apache Pinot" href="/blog/2020/08/08/DevBlog-ScalarUDFs"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-PinotMonitoring">Monitoring Apache Pinot with JMX, Prometheus and Grafana</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring Apache Pinot with JMX, Prometheus and Grafana" href="/blog/2020/08/08/DevBlog-PinotMonitoring"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-AnomalyDetection">Building a culture around metrics and anomaly detection</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building a culture around metrics and anomaly detection" href="/blog/2020/07/28/DevBlog-AnomalyDetection"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-DevUpStack">Moving developers up the stack with Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Once upon a time, an internet company named LinkedIn faced the challenge of having petabytes of connected data with no way to analyze it in real-time. As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Moving developers up the stack with Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Moving developers up the stack with Apache Pinot" href="/blog/2020/07/28/DevBlog-DevUpStack"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/14/LinkedIn-BatchRealtime">Bridging batch and stream processing for the Recruiter usage statistics dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-14T00:00:00.000Z">July 14, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Batch and streaming computations are often combined together in the Lambda architecture,
+This feature makes it possible to write queries like this:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> nlHomeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'AL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> alHomeRuns</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>nlHomeRuns</th><th>alHomeRuns</th></tr></thead><tbody><tr><td>135486</td><td>135990</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="greatest-and-least"></a>greatest and least<a class="hash-link" href="#greatest-and-least" title="Direct link to heading">#</a></h2><p>Richard Startin added the <code>greatest</code> and <code>least</code> functions:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> playerID</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> least</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> greatest</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">hits</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> hits</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token plain"> </span><span class="token operator">AND</span><span class="token plain"> teamID </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'SFN'</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">GROUP</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">BY</span><span class="token plain"> playerID</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">LIMIT</span><span class="token plain"> </span><span class="token number">5</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>playerID</th><th>homeRuns</th><th>hits</th></tr></thead><tbody><tr><td>ramirju01</td><td>0</td><td>5</td></tr><tr><td>milneed01</td><td>4</td><td>54</td></tr><tr><td>testani01</td><td>0</td><td>5</td></tr><tr><td>shawbo01</td><td>0</td><td>8</td></tr><tr><td>vogelry01</td><td>0</td><td>12</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="distinctcountsmarthll"></a>DistinctCountSmartHLL<a class="hash-link" href="#distinctcountsmarthll" title="Direct link to heading">#</a></h2><p> Xiaotian (Jackie) Jiang added the <code>DistinctCountSmartHLL</code> aggregation function, which automatically converts the Set to HyperLogLog if the set size grows too big to protect the servers from running out of memory:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> DISTINCTCOUNTSMARTHLL</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'hllLog2m=8;hllConversionThreshold=10'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>distinctcountsmarthll(homeRuns)</th></tr></thead><tbody><tr><td>66</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="ui-updates"></a>UI updates<a class="hash-link" href="#ui-updates" title="Direct link to heading">#</a></h2><p>There were also a bunch of updates to the Pinot Data Explorer, by Sanket Shah and Johan Adami.</p><p>The display of reported size and estimated size is now in a human readable format:</p><p><img alt="Human readable sizes" src="/assets/images/human-readable-sizes-b8c4009dd53d23da3b8637963827a8de.png"></p><p>Fixes for the following issues:</p><ul><li>Error messages weren't showing on the UI when an invalid operation is attempted:</li></ul><p><img alt="A backwards incompatible attempted schema change" src="/assets/images/backwards-incompatible-99886dcd0be55a8100a7d6c5f3da3bda.png"></p><ul><li>Query console goes blank on syntax error.</li><li>Query console cannot show query result when multiple columns have the same name.</li><li>Adding extra fields after <code>SELECT *</code> would throw a NullPointerException.</li><li>Some queries were returning <code>--</code> instead of <code>0</code>.</li><li>Query console couldn't show the query result if multiple columns had the same name.</li><li>Pinot Dashboard tenant view showing the incorrect amount of servers and brokers.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="realtimetooffline-task"></a>RealTimeToOffline Task<a class="hash-link" href="#realtimetooffline-task" title="Direct link to heading">#</a></h2><p>Xiaotian (Jackie) Jiang made some fixes to the <a href="https://dev.startree.ai/docs/pinot/recipes/real-time-offline-job" target="_blank" rel="noopener noreferrer">RealTimeToOffline job</a> to handle time gaps and proceed to the next time window when no segment matches the current one.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="empty-quickstart"></a>Empty QuickStart<a class="hash-link" href="#empty-quickstart" title="Direct link to heading">#</a></h2><p>Kenny Bastani added an empty QuickStart command, which lets you quickly spin up an empty Pinot cluster:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">docker</span><span class="token plain"> run </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">8000</span><span class="token plain">:8000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">9000</span><span class="token plain">:9000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> apachepinot/pinot:0.10.0 QuickStart </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -type empty</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>You can then ingest your own dataset without needing to worry about spinning up each of the Pinot components individually.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="data-ingestion"></a>Data Ingestion<a class="hash-link" href="#data-ingestion" title="Direct link to heading">#</a></h2><ul><li><p>Richard Startin fixed some issues with real-time ingestion where consumption of messages would stop if a bad batch of messages was consumed from Kafka.</p></li><li><p>Mohemmad Zaid Khan added the BoundedColumnValue partition function, which partitions segments based on column values.</p></li><li><p>Xiaobing Li added the fixed name segment generator, which can be used when you want to replace a specific existing segment.</p></li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="other-changes"></a>Other changes<a class="hash-link" href="#other-changes" title="Direct link to heading">#</a></h2><ul><li>Richard Startin set LZ4 compression as the default for all metrics fields.</li><li>Mark Needham added the <code>ST_Within</code> geospatial function.</li><li>Rong Rong fixed a bug where query stats wouldn't show if there was an error processing the query (e.g. if the query timed out).</li><li>Prashant Pandey fixed the query engine to handle extra columns added to a <code>SELECT *</code> statement.</li><li>Richard Startin added support for forward indexes on JSON columns.</li><li>Rong Rong added the GRPC broker request handler so that data can be streamed back from the server to the broker when processing queries.</li><li>deemoliu made it possible to add a default strategy when using the <a href="https://dev.startree.ai/docs/pinot/recipes/upserts-partial" target="_blank" rel="noopener noreferrer">partial upsert feature</a>.</li><li>Jeff Moszuti added support for the <code>TIMESTAMP</code> data type in the <a href="https://docs.pinot.apache.org/operators/configuration-recommendation-engine" target="_blank" rel="noopener noreferrer">configuration recommendation engine</a>.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="dependency-updates"></a>Dependency updates<a class="hash-link" href="#dependency-updates" title="Direct link to heading">#</a></h2><p>The following dependencies were updated:</p><ul><li>async-http-client because the library moved to a different organization.</li><li>RoaringBitmap to 0.9.25</li><li>JsonPath to 2.7.0</li><li>Kafka to 2.8.1</li><li>Prometheus to 0.16.1</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="resources"></a>Resources<a class="hash-link" href="#resources" title="Direct link to heading">#</a></h2><p>If you want to try out Apache Pinot, the following resources will help you get started:</p><ul><li>Download page: <a href="https://pinot.apache.org/download/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/download/</a></li><li>Getting started: <a href="https://docs.pinot.apache.org/getting-started" target="_blank" rel="noopener noreferrer">https://docs.pinot.apache.org/getting-started</a></li><li>Apache Pinot Recipes: <a href="https://dev.startree.ai/docs/pinot/recipes/" target="_blank" rel="noopener noreferrer">https://dev.startree.ai/docs/pinot/recipes/</a></li><li>Join our Slack channel: <a href="https://communityinviter.com/apps/apache-pinot/apache-pinot" target="_blank" rel="noopener noreferrer">https://communityinviter.com/apps/apache-pinot/apache-pinot</a></li><li>See our upcoming events: <a href="https://www.meetup.com/apache-pinot" target="_blank" rel="noopener noreferrer">https://www.meetup.com/apache-pinot</a></li><li>Follow us on Twitter: <a href="https://twitter.com/startreedata" target="_blank" rel="noopener noreferrer">https://twitter.com/startreedata</a></li><li>Subscribe to our YouTube channel: <a href="https://www.youtube.com/c/StarTree" target="_blank" rel="noopener noreferrer">https://www.youtube.com/c/StarTree</a></li></ul></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/releases">Releases</a></div><div class="col text--right"><a aria-label="Read more about Announcing Apache Pinot 0.10" href="/blog/2022/04/04/Announcing-Apache-Pinot-0-10"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/16/LinkedIn-TextAnalytics">Text analytics on LinkedIn Talent Insights using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-16T00:00:00.000Z">June 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn Talent Insights (LTI) is a platform that helps organizations understand the external labor market and their internal workforce, and enables the long term success of their employees. Users of LTI have the flexibility to construct searches using the various facets of the LinkedIn Economic Graph (skills, titles, location, company, etc.).</p><p><a href="https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/06/ltipinot6.png" alt="Text analytics on LinkedIn Talent Insights using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/text-analytics">Text analytics</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Text analytics on LinkedIn Talent Insights using Apache Pinot" href="/blog/2021/06/16/LinkedIn-TextAnalytics"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/13/DevBlog-Geospatial">Introduction to Geospatial Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-13T00:00:00.000Z">June 13, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Geospatial data has been widely used across the industry, spanning multiple verticals, such as ride-sharing and delivery, transportation infrastructure, defense and intel, public health. Deriving insights from timely and accurate geospatial data could enable mission-critical use cases in the organizations and fuel a vibrant marketplace across the industry. In the design document for this new Pinot feature, we discuss the challenges of analyzing geospatial at scale and propose the geospatial support in Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9</a></p><p><img src="https://miro.medium.com/max/1400/0*1xrDSs9lLZ5dD3zK" alt="Introduction to Geospatial Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/h-3">H3</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Geospatial Queries in Apache Pinot" href="/blog/2021/06/13/DevBlog-Geospatial"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/29/Uber-Charon">Automating Merchant Live Monitoring with Real-Time Analytics - Charon</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-29T00:00:00.000Z">April 29, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, live monitoring and automation of Ops is critical to preserve marketplace health, maintain reliability, and gain efficiency in markets. By the virtue of the word “live”, this monitoring needs to show what is happening now, with prompt access to fresh data, and the ability to recommend appropriate actions based on that data. Uber’s data platform provides the self-serve tools which empower the Ops teams to build their own live monitoring tools, and support their regional teams by building rich solutions.</p><p>For this project, the requirement was to provide merchant level monitoring and handle the edge cases which remain unaddressed by the sophisticated internal marketplace management tools. We used a variety of Uber’s real-time data platform components to build a tool called Charon to reduce impact of poor marketplace reliability on the merchants.</p><p>Read More at <a href="https://eng.uber.com/charon/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/charon/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2021/04/deliverDoor@3x-768x263.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Automating Merchant Live Monitoring with Real-Time Analytics - Charon" href="/blog/2021/04/29/Uber-Charon"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-27T00:00:00.000Z">April 27, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain</p><p><a href="https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38</a></p><p><img src="https://miro.medium.com/max/1400/1*EtqD0vTPEe569jybXCt69w.png" alt="Deploying Apache Pinot at a Large Retail Chain"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deploying Apache Pinot at a Large Retail Chain" href="/blog/2021/04/27/DevBlog-PinotInRetailChain"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-16T00:00:00.000Z">April 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/04/thetasketches2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches" href="/blog/2021/04/16/LinkedIn-Theta"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-08T00:00:00.000Z">April 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Since the 0.6.0 release of Apache Pinot, a new feature was made available for stream ingestion that allows you to upsert events from an immutable log. Typically, upsert is a term used to describe inserting a record into a database if it does not already exist or update it if it does exist. In Apache Pinot’s case, upsert isn’t precisely the same concept, and I wanted to write this blog post to explain why it’s exciting and how you can start using it.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93</a></p><p><img src="https://miro.medium.com/max/1400/0*So3GjHjLY7DJAiaP" alt="Introduction to Upserts in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/upsert">Upsert</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Upserts in Apache Pinot" href="/blog/2021/04/08/DevBlog-UpsertsIntro"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-02-02T00:00:00.000Z">February 2, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://www.startree.ai/wp-content/uploads/2021/05/team-xiang-150x150.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/trino">Trino</a><a class="margin-horiz--sm" href="/blog/tags/presto-sql">PrestoSQL</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Real-time Analytics with Presto and Apache Pinot" href="/blog/2021/02/02/DevBlog-PrestoPinot"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-01-08T00:00:00.000Z">January 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’re going to explore an exciting new world of real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Change Data Analysis with Debezium and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/debezium">Debezium</a><a class="margin-horiz--sm" href="/blog/tags/cdc">CDC</a><a class="margin-horiz--sm" href="/blog/tags/change-data-analysis">Change Data Analysis</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Change Data Analysis with Debezium and Apache Pinot" href="/blog/2021/01/08/DevBlog-DebeziumCDC"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-12-01T00:00:00.000Z">December 1, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/03/pinotincubator2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about From Lambda to Lambda-less Lessons learned" href="/blog/2020/12/01/LinkedIn-Lamda"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/20/Uber-Operating">Operating Apache Pinot at Uber Scale</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-20T00:00:00.000Z">October 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to enable our community operations team to take corrective action. Having a real-time dashboard of different events such as consumer demand, driver availability, or trips happening in a city is crucial for day-to-day operation, incident triaging, and financial intelligence.</p><p>Read More at <a href="https://eng.uber.com/operating-apache-pinot/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/operating-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/10/1224-5.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Operating Apache Pinot at Uber Scale" href="/blog/2020/10/20/Uber-Operating"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis">Deep Analysis of Russian Twitter Trolls</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-16T00:00:00.000Z">October 16, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>The history behind Russian disinformation is a dense and continuously evolving subject. The world’s best research hasn’t seemed to hit the mainstream yet, which made this an excellent opportunity to see if I could use some open source tooling to surface new analytical evidence.</p><p>In this blog post, I’ll show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.</p><p><a href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843" target="_blank" rel="noopener noreferrer">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png" alt="Deep Analysis of Russian Twitter Trolls"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deep Analysis of Russian Twitter Trolls" href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-IngestPlugins">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Leverage Plugins to Ingest Parquet Files from S3 in Pinot" href="/blog/2020/08/08/DevBlog-IngestPlugins"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-PinotMonitoring">Monitoring Apache Pinot with JMX, Prometheus and Grafana</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring Apache Pinot with JMX, Prometheus and Grafana" href="/blog/2020/08/08/DevBlog-PinotMonitoring"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-SLAApps">Achieving 99th percentile latency SLA using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Achieving 99th percentile latency SLA using Apache Pinot" href="/blog/2020/08/08/DevBlog-SLAApps"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-ScalarUDFs">Utilize UDFs to Supercharge Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Utilize UDFs to Supercharge Queries in Apache Pinot" href="/blog/2020/08/08/DevBlog-ScalarUDFs"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-AnomalyDetection">Building a culture around metrics and anomaly detection</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building a culture around metrics and anomaly detection" href="/blog/2020/07/28/DevBlog-AnomalyDetection"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-DevUpStack">Moving developers up the stack with Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Once upon a time, an internet company named LinkedIn faced the challenge of having petabytes of connected data with no way to analyze it in real-time. As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Moving developers up the stack with Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Moving developers up the stack with Apache Pinot" href="/blog/2020/07/28/DevBlog-DevUpStack"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/14/LinkedIn-BatchRealtime">Bridging batch and stream processing for the Recruiter usage statistics dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-14T00:00:00.000Z">July 14, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Batch and streaming computations are often combined together in the Lambda architecture,
but carry the cost of maintaining two different code bases for the same logic.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/07/bridging-1.png" alt="Bridging batch and stream processing for the Recruiter usage statistics dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Bridging batch and stream processing for the Recruiter usage statistics dashboard" href="/blog/2020/07/14/LinkedIn-BatchRealtime"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/06/29/LinkedIn-TalentInsight">Building LinkedIn Talent Insights to democratize data-driven decision making</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-06-29T00:00:00.000Z">June 29, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn is a mission-driven organization, and we take our mission of “connecting the world's professionals to make them more productive and successful” very seriously.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/building-linkedin-talent-insights" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/building-linkedin-talent-insights</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/06/lti-1.png" alt="Building LinkedIn Talent Insights to democratize data-driven decision making"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building LinkedIn Talent Insights to democratize data-driven decision making" href="/blog/2020/06/29/LinkedIn-TalentInsight"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/06/25/LinkedIn-SmartAlerts">Monitoring business performance data with ThirdEye smart alerts</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-06-25T00:00:00.000Z">June 25, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Explain how ThirdEye smart alerts and automated dashboards helped the LinkedIn Premium business operations team monitor key metrics—such as new free trial signups—for the timely detection of outliers in business performance data.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/06/thirdeye_business_performance-3.png" alt="Monitoring business performance data with ThirdEye smart alerts"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring business performance data with ThirdEye smart alerts" href="/blog/2020/06/25/LinkedIn-SmartAlerts"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents">Using Apache Pinot and Kafka to Analyze GitHub Events</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-04-10T00:00:00.000Z">April 10, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’ll show you how Pinot and Kafka can be used together to ingest, query, and visualize event streams sourced from the public GitHub API. For the step-by-step instructions, please visit our documentation, which will guide you through the specifics of running this example in your development environment.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7</a></p><p><img src="https://miro.medium.com/max/4728/1*eR64jBH1ZvC3uNfPP56p5g.png" alt="Using Apache Pinot and Kafka to Analyze GitHub Events"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Using Apache Pinot and Kafka to Analyze GitHub Events" href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/02/20/LinkedIn-Thirdeye">Analyzing anomalies with ThirdEye</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-02-20T00:00:00.000Z">February 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Focus on the behind-the-scenes functionalities of ThirdEye that analyze the multi-dimensional time series data
and help our engineers understand why these anomalies happened through a dimension heatmap</p><p><a href="https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/02/datacube-1.png" alt="Analyzing anomalies with ThirdEye"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Analyzing anomalies with ThirdEye" href="/blog/2020/02/20/LinkedIn-Thirdeye"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/01/15/Pinot-Presto-SQL">Engineering SQL Support on Apache Pinot at Uber</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-01-15T00:00:00.000Z">January 15, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber leverages real-time analytics on aggregate data to improve the user experience across our products, from fighting fraudulent behavior on Uber Eats to forecasting demand on our platform.</p><p>To resolve these issues, we built a solution that linked Presto, a query engine that supports full ANSI SQL, and Pinot, a real-time OLAP (online analytical processing) datastore. This married solution allows users to write ad-hoc SQL queries, empowering teams to unlock significant analysis capabilities.</p><p><a href="https://eng.uber.com/engineering-sql-support-on-apache-pinot/" target="_blank" rel="noopener noreferrer">Read More at https://eng.uber.com/engineering-sql-support-on-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/01/Header-SQL-768x329.png" alt="SQL Support on Apache Pinot at Uber"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/sql">SQL</a></div><div class="col text--right"><a aria-label="Read more about Engineering SQL Support on Apache Pinot at Uber" href="/blog/2020/01/15/Pinot-Presto-SQL"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/07/11/LinkedIn-AutoTune">Auto-tuning Pinot real-time consumption</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-07-11T00:00:00.000Z">July 11, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Focus on Auto tuning Pinot, a scalable distributed columnar OLAP data store developed at LinkedIn, delivers real-time analytics for site-facing use cases such as LinkedIn's Who viewed my profile, Talent insights, and more.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/07/bridging-1.png" alt="Bridging batch and stream processing for the Recruiter usage statistics dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Auto-tuning Pinot real-time consumption" href="/blog/2019/07/11/LinkedIn-AutoTune"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/06/14/LinkedIn-StarTree">Star-tree index - Powering fast aggregations on Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-06-14T00:00:00.000Z">June 14, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Introduced Star-Tree index to utilize the pre-aggregated documents in a smart way that achieves low query latencies,
while using the storage space efficiently.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2019/06/star-tree-index--powering-fast-aggregations-on-pinot" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2019/06/star-tree-index--powering-fast-aggregations-on-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png" alt="Star-tree index - Powering fast aggregations on Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Star-tree index - Powering fast aggregations on Pinot" href="/blog/2019/06/14/LinkedIn-StarTree"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/01/09/LinkedIn-IntroThirdEye">Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-01-09T00:00:00.000Z">January 9, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>ThirdEye is a comprehensive platform for real-time monitoring of metrics that covers a wide variety of use-cases.
LinkedIn relies on ThirdEye to monitor site performance, track member growth, understand adoption of new features,
flag sustained attempts to circumvent system security, and many other areas</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png" alt="Star-tree index - Powering fast aggregations on Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform" href="/blog/2019/01/09/LinkedIn-IntroThirdEye"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2017/09/17/Restaurant-Manager">Engineering Restaurant Manager - UberEATS Analytics Dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2017-09-17T00:00:00.000Z">September 17, 2017</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, we use data analytics to architect more magical user experiences across our products. Whenever possible, we harness these data engineering capabilities to empower our partners to better serve their customers. For instance, in late 2016, the UberEATS engineering team built a comprehensive analytics dashboard that provides restaurant partners with additional insights about the health of their business.</p><p>Read More at <a href="https://eng.uber.com/restaurant-manager/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/restaurant-manager/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2017/09/image4-2.png" alt="Engineering Restaurant Manager - UberEATS Analytics Dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber-data">Uber Data</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Engineering Restaurant Manager - UberEATS Analytics Dashboard" href="/blog/2017/09/17/Restaurant-Manager"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2015/06/10/Open-Sourcing-Pinot">Open Sourcing Pinot - Scaling the Wall of Real-Time Analytics</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2015-06-10T00:00:00.000Z">June 10, 2015</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://github.com/kishoreg" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://avatars.githubusercontent.com/kishoreg" alt="Kishore Gopalakrishna"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://github.com/kishoreg" target="_blank" rel="noopener noreferrer">Kishore Gopalakrishna</a></div><small class="avatar__subtitle">Apache Pinot Team</small></div></div></header><div class="markdown"><p>LinkedIn’s real-time analytics infrastructure, that we built to allow us to slice and dice across billions of rows in real-time across a wide variety of products. Read More at (<a href="https://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics</a>)</p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/a-b-testing">A/B Testing</a><a class="margin-horiz--sm" href="/blog/tags/infrastructure">Infrastructure</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Open Sourcing Pinot - Scaling the Wall of Real-Time Analytics" href="/blog/2015/06/10/Open-Sourcing-Pinot"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2015/05/16/LinkedIn-Scaling">A Brief History of Scaling LinkedIn</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2015-05-16T00:00:00.000Z">May 16, 2015</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn started in 2003 with the goal of connecting to your network for better job opportunities. It had only 2,700 members the first week. Fast forward many years, and LinkedIn’s product portfolio, member base, and server load has grown tremendously.</p><p>Today, LinkedIn operates globally with more than 350 million members. We serve tens of thousands of web pages every second of every day. We've hit our mobile moment where mobile accounts for more than 50 percent of all global traffic. All those requests are fetching data from our backend systems, which in turn handle millions of queries per second.</p><p>Read More at <a href="https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/en-us/blog/migrated/data_centers_pops_0.png" alt="A Brief History of Scaling LinkedIn"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about A Brief History of Scaling LinkedIn" href="/blog/2015/05/16/LinkedIn-Scaling"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" href="/who_uses">Who uses Apache Pinot?</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" class="footer__link-item">Components</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/basics/architecture" target="_blank" rel="noopener 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In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/trino">Trino</a><a 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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><header class="margin-bottom--xl"><h1>2 posts tagged with "presto"</h1><a href="/blog/tags">View All Tags</a></header><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-02-02T00:00:00.000Z">February 2, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://www.startree.ai/wp-content/uploads/2021/05/team-xiang-150x150.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/trino">Trino</a><a class="margin-horiz--sm" href="/blog/tags/presto-sql">PrestoSQL</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Real-time Analytics with Presto and Apache Pinot" href="/blog/2021/02/02/DevBlog-PrestoPinot"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/01/15/Pinot-Presto-SQL">Engineering SQL Support on Apache Pinot at Uber</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-01-15T00:00:00.000Z">January 15, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber leverages real-time analytics on aggregate data to improve the user experience across our products, from fighting fraudulent behavior on Uber Eats to forecasting demand on our platform.</p><p>To resolve these issues, we built a solution that linked Presto, a query engine that supports full ANSI SQL, and Pinot, a real-time OLAP (online analytical processing) datastore. This married solution allows users to write ad-hoc SQL queries, empowering teams to unlock significant analysis capabilities.</p><p><a href="https://eng.uber.com/engineering-sql-support-on-apache-pinot/" target="_blank" rel="noopener noreferrer">Read More at https://eng.uber.com/engineering-sql-support-on-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/01/Header-SQL-768x329.png" alt="SQL Support on Apache Pinot at Uber"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/sql">SQL</a></div><div class="col text--right"><a aria-label="Read more about Engineering SQL Support on Apache Pinot at Uber" href="/blog/2020/01/15/Pinot-Presto-SQL"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" href="/who_uses">Who uses Apache Pinot?</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" class="footer__link-item">Components</a></li><li class="footer__item"><a 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class="footerLogoLink_94kH"><img src="/img/logo.svg" alt="Apache Pinot™" class="themedImage_TMUO themedImage--light_4Vu1 footer__logo"><img src="/img/logo.svg" alt="Apache Pinot™" class="themedImage_TMUO themedImage--dark_uzRr footer__logo"></a></div><div class="footerCopyright_-piB">Copyright © 2022 The Apache Software Foundation.<br>Apache Pinot, Pinot, Apache, the Apache feather logo, and the Apache Pinot project logo are registered trademarks of The Apache Software Foundation.<br><br>This page has references to third party software - Presto, PrestoDB, ThirdEye, Trino, TrinoDB, that are not part of the Apache Software Foundation and are not covered under the Apache License.</div></div></div></footer></div>
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When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a 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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><header class="margin-bottom--xl"><h1>28 posts tagged with "real-time data platform"</h1><a href="/blog/tags">View All Tags</a></header><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/16/LinkedIn-TextAnalytics">Text analytics on LinkedIn Talent Insights using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-16T00:00:00.000Z">June 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn Talent Insights (LTI) is a platform that helps organizations understand the external labor market and their internal workforce, and enables the long term success of their employees. Users of LTI have the flexibility to construct searches using the various facets of the LinkedIn Economic Graph (skills, titles, location, company, etc.).</p><p><a href="https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/06/ltipinot6.png" alt="Text analytics on LinkedIn Talent Insights using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/text-analytics">Text analytics</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Text analytics on LinkedIn Talent Insights using Apache Pinot" href="/blog/2021/06/16/LinkedIn-TextAnalytics"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/13/DevBlog-Geospatial">Introduction to Geospatial Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-13T00:00:00.000Z">June 13, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Geospatial data has been widely used across the industry, spanning multiple verticals, such as ride-sharing and delivery, transportation infrastructure, defense and intel, public health. Deriving insights from timely and accurate geospatial data could enable mission-critical use cases in the organizations and fuel a vibrant marketplace across the industry. In the design document for this new Pinot feature, we discuss the challenges of analyzing geospatial at scale and propose the geospatial support in Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9</a></p><p><img src="https://miro.medium.com/max/1400/0*1xrDSs9lLZ5dD3zK" alt="Introduction to Geospatial Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/h-3">H3</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Geospatial Queries in Apache Pinot" href="/blog/2021/06/13/DevBlog-Geospatial"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/29/Uber-Charon">Automating Merchant Live Monitoring with Real-Time Analytics - Charon</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-29T00:00:00.000Z">April 29, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, live monitoring and automation of Ops is critical to preserve marketplace health, maintain reliability, and gain efficiency in markets. By the virtue of the word “live”, this monitoring needs to show what is happening now, with prompt access to fresh data, and the ability to recommend appropriate actions based on that data. Uber’s data platform provides the self-serve tools which empower the Ops teams to build their own live monitoring tools, and support their regional teams by building rich solutions.</p><p>For this project, the requirement was to provide merchant level monitoring and handle the edge cases which remain unaddressed by the sophisticated internal marketplace management tools. We used a variety of Uber’s real-time data platform components to build a tool called Charon to reduce impact of poor marketplace reliability on the merchants.</p><p>Read More at <a href="https://eng.uber.com/charon/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/charon/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2021/04/deliverDoor@3x-768x263.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Automating Merchant Live Monitoring with Real-Time Analytics - Charon" href="/blog/2021/04/29/Uber-Charon"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-27T00:00:00.000Z">April 27, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain</p><p><a href="https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38</a></p><p><img src="https://miro.medium.com/max/1400/1*EtqD0vTPEe569jybXCt69w.png" alt="Deploying Apache Pinot at a Large Retail Chain"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deploying Apache Pinot at a Large Retail Chain" href="/blog/2021/04/27/DevBlog-PinotInRetailChain"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-16T00:00:00.000Z">April 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/04/thetasketches2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches" href="/blog/2021/04/16/LinkedIn-Theta"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-08T00:00:00.000Z">April 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Since the 0.6.0 release of Apache Pinot, a new feature was made available for stream ingestion that allows you to upsert events from an immutable log. Typically, upsert is a term used to describe inserting a record into a database if it does not already exist or update it if it does exist. In Apache Pinot’s case, upsert isn’t precisely the same concept, and I wanted to write this blog post to explain why it’s exciting and how you can start using it.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93</a></p><p><img src="https://miro.medium.com/max/1400/0*So3GjHjLY7DJAiaP" alt="Introduction to Upserts in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/upsert">Upsert</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Upserts in Apache Pinot" href="/blog/2021/04/08/DevBlog-UpsertsIntro"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-02-02T00:00:00.000Z">February 2, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://www.startree.ai/wp-content/uploads/2021/05/team-xiang-150x150.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/trino">Trino</a><a class="margin-horiz--sm" href="/blog/tags/presto-sql">PrestoSQL</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Real-time Analytics with Presto and Apache Pinot" href="/blog/2021/02/02/DevBlog-PrestoPinot"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-01-08T00:00:00.000Z">January 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’re going to explore an exciting new world of real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Change Data Analysis with Debezium and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/debezium">Debezium</a><a class="margin-horiz--sm" href="/blog/tags/cdc">CDC</a><a class="margin-horiz--sm" href="/blog/tags/change-data-analysis">Change Data Analysis</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Change Data Analysis with Debezium and Apache Pinot" href="/blog/2021/01/08/DevBlog-DebeziumCDC"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-12-01T00:00:00.000Z">December 1, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/03/pinotincubator2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about From Lambda to Lambda-less Lessons learned" href="/blog/2020/12/01/LinkedIn-Lamda"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/20/Uber-Operating">Operating Apache Pinot at Uber Scale</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-20T00:00:00.000Z">October 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to enable our community operations team to take corrective action. Having a real-time dashboard of different events such as consumer demand, driver availability, or trips happening in a city is crucial for day-to-day operation, incident triaging, and financial intelligence.</p><p>Read More at <a href="https://eng.uber.com/operating-apache-pinot/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/operating-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/10/1224-5.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Operating Apache Pinot at Uber Scale" href="/blog/2020/10/20/Uber-Operating"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis">Deep Analysis of Russian Twitter Trolls</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-16T00:00:00.000Z">October 16, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>The history behind Russian disinformation is a dense and continuously evolving subject. The world’s best research hasn’t seemed to hit the mainstream yet, which made this an excellent opportunity to see if I could use some open source tooling to surface new analytical evidence.</p><p>In this blog post, I’ll show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.</p><p><a href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843" target="_blank" rel="noopener noreferrer">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png" alt="Deep Analysis of Russian Twitter Trolls"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deep Analysis of Russian Twitter Trolls" href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-IngestPlugins">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Leverage Plugins to Ingest Parquet Files from S3 in Pinot" href="/blog/2020/08/08/DevBlog-IngestPlugins"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-SLAApps">Achieving 99th percentile latency SLA using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Achieving 99th percentile latency SLA using Apache Pinot" href="/blog/2020/08/08/DevBlog-SLAApps"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-ScalarUDFs">Utilize UDFs to Supercharge Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Utilize UDFs to Supercharge Queries in Apache Pinot" href="/blog/2020/08/08/DevBlog-ScalarUDFs"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-PinotMonitoring">Monitoring Apache Pinot with JMX, Prometheus and Grafana</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring Apache Pinot with JMX, Prometheus and Grafana" href="/blog/2020/08/08/DevBlog-PinotMonitoring"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-AnomalyDetection">Building a culture around metrics and anomaly detection</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building a culture around metrics and anomaly detection" href="/blog/2020/07/28/DevBlog-AnomalyDetection"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-DevUpStack">Moving developers up the stack with Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Once upon a time, an internet company named LinkedIn faced the challenge of having petabytes of connected data with no way to analyze it in real-time. As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Moving developers up the stack with Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Moving developers up the stack with Apache Pinot" href="/blog/2020/07/28/DevBlog-DevUpStack"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/14/LinkedIn-BatchRealtime">Bridging batch and stream processing for the Recruiter usage statistics dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-14T00:00:00.000Z">July 14, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Batch and streaming computations are often combined together in the Lambda architecture,
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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><header class="margin-bottom--xl"><h1>28 posts tagged with "real-time data platform"</h1><a href="/blog/tags">View All Tags</a></header><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/16/LinkedIn-TextAnalytics">Text analytics on LinkedIn Talent Insights using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-16T00:00:00.000Z">June 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn Talent Insights (LTI) is a platform that helps organizations understand the external labor market and their internal workforce, and enables the long term success of their employees. Users of LTI have the flexibility to construct searches using the various facets of the LinkedIn Economic Graph (skills, titles, location, company, etc.).</p><p><a href="https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/06/ltipinot6.png" alt="Text analytics on LinkedIn Talent Insights using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/text-analytics">Text analytics</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Text analytics on LinkedIn Talent Insights using Apache Pinot" href="/blog/2021/06/16/LinkedIn-TextAnalytics"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/13/DevBlog-Geospatial">Introduction to Geospatial Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-13T00:00:00.000Z">June 13, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Geospatial data has been widely used across the industry, spanning multiple verticals, such as ride-sharing and delivery, transportation infrastructure, defense and intel, public health. Deriving insights from timely and accurate geospatial data could enable mission-critical use cases in the organizations and fuel a vibrant marketplace across the industry. In the design document for this new Pinot feature, we discuss the challenges of analyzing geospatial at scale and propose the geospatial support in Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9</a></p><p><img src="https://miro.medium.com/max/1400/0*1xrDSs9lLZ5dD3zK" alt="Introduction to Geospatial Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/h-3">H3</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Geospatial Queries in Apache Pinot" href="/blog/2021/06/13/DevBlog-Geospatial"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/29/Uber-Charon">Automating Merchant Live Monitoring with Real-Time Analytics - Charon</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-29T00:00:00.000Z">April 29, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, live monitoring and automation of Ops is critical to preserve marketplace health, maintain reliability, and gain efficiency in markets. By the virtue of the word “live”, this monitoring needs to show what is happening now, with prompt access to fresh data, and the ability to recommend appropriate actions based on that data. Uber’s data platform provides the self-serve tools which empower the Ops teams to build their own live monitoring tools, and support their regional teams by building rich solutions.</p><p>For this project, the requirement was to provide merchant level monitoring and handle the edge cases which remain unaddressed by the sophisticated internal marketplace management tools. We used a variety of Uber’s real-time data platform components to build a tool called Charon to reduce impact of poor marketplace reliability on the merchants.</p><p>Read More at <a href="https://eng.uber.com/charon/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/charon/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2021/04/deliverDoor@3x-768x263.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Automating Merchant Live Monitoring with Real-Time Analytics - Charon" href="/blog/2021/04/29/Uber-Charon"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-27T00:00:00.000Z">April 27, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain</p><p><a href="https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38</a></p><p><img src="https://miro.medium.com/max/1400/1*EtqD0vTPEe569jybXCt69w.png" alt="Deploying Apache Pinot at a Large Retail Chain"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deploying Apache Pinot at a Large Retail Chain" href="/blog/2021/04/27/DevBlog-PinotInRetailChain"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-16T00:00:00.000Z">April 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/04/thetasketches2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches" href="/blog/2021/04/16/LinkedIn-Theta"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-08T00:00:00.000Z">April 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Since the 0.6.0 release of Apache Pinot, a new feature was made available for stream ingestion that allows you to upsert events from an immutable log. Typically, upsert is a term used to describe inserting a record into a database if it does not already exist or update it if it does exist. In Apache Pinot’s case, upsert isn’t precisely the same concept, and I wanted to write this blog post to explain why it’s exciting and how you can start using it.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93</a></p><p><img src="https://miro.medium.com/max/1400/0*So3GjHjLY7DJAiaP" alt="Introduction to Upserts in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/upsert">Upsert</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Upserts in Apache Pinot" href="/blog/2021/04/08/DevBlog-UpsertsIntro"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-02-02T00:00:00.000Z">February 2, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://www.startree.ai/wp-content/uploads/2021/05/team-xiang-150x150.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/trino">Trino</a><a class="margin-horiz--sm" href="/blog/tags/presto-sql">PrestoSQL</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Real-time Analytics with Presto and Apache Pinot" href="/blog/2021/02/02/DevBlog-PrestoPinot"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-01-08T00:00:00.000Z">January 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’re going to explore an exciting new world of real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Change Data Analysis with Debezium and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/debezium">Debezium</a><a class="margin-horiz--sm" href="/blog/tags/cdc">CDC</a><a class="margin-horiz--sm" href="/blog/tags/change-data-analysis">Change Data Analysis</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Change Data Analysis with Debezium and Apache Pinot" href="/blog/2021/01/08/DevBlog-DebeziumCDC"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-12-01T00:00:00.000Z">December 1, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/03/pinotincubator2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about From Lambda to Lambda-less Lessons learned" href="/blog/2020/12/01/LinkedIn-Lamda"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/20/Uber-Operating">Operating Apache Pinot at Uber Scale</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-20T00:00:00.000Z">October 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to enable our community operations team to take corrective action. Having a real-time dashboard of different events such as consumer demand, driver availability, or trips happening in a city is crucial for day-to-day operation, incident triaging, and financial intelligence.</p><p>Read More at <a href="https://eng.uber.com/operating-apache-pinot/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/operating-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/10/1224-5.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Operating Apache Pinot at Uber Scale" href="/blog/2020/10/20/Uber-Operating"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis">Deep Analysis of Russian Twitter Trolls</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-16T00:00:00.000Z">October 16, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>The history behind Russian disinformation is a dense and continuously evolving subject. The world’s best research hasn’t seemed to hit the mainstream yet, which made this an excellent opportunity to see if I could use some open source tooling to surface new analytical evidence.</p><p>In this blog post, I’ll show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.</p><p><a href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843" target="_blank" rel="noopener noreferrer">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png" alt="Deep Analysis of Russian Twitter Trolls"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deep Analysis of Russian Twitter Trolls" href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-IngestPlugins">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Leverage Plugins to Ingest Parquet Files from S3 in Pinot" href="/blog/2020/08/08/DevBlog-IngestPlugins"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-PinotMonitoring">Monitoring Apache Pinot with JMX, Prometheus and Grafana</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring Apache Pinot with JMX, Prometheus and Grafana" href="/blog/2020/08/08/DevBlog-PinotMonitoring"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-SLAApps">Achieving 99th percentile latency SLA using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Achieving 99th percentile latency SLA using Apache Pinot" href="/blog/2020/08/08/DevBlog-SLAApps"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-ScalarUDFs">Utilize UDFs to Supercharge Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Utilize UDFs to Supercharge Queries in Apache Pinot" href="/blog/2020/08/08/DevBlog-ScalarUDFs"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-AnomalyDetection">Building a culture around metrics and anomaly detection</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building a culture around metrics and anomaly detection" href="/blog/2020/07/28/DevBlog-AnomalyDetection"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-DevUpStack">Moving developers up the stack with Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Once upon a time, an internet company named LinkedIn faced the challenge of having petabytes of connected data with no way to analyze it in real-time. As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Moving developers up the stack with Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Moving developers up the stack with Apache Pinot" href="/blog/2020/07/28/DevBlog-DevUpStack"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/14/LinkedIn-BatchRealtime">Bridging batch and stream processing for the Recruiter usage statistics dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-14T00:00:00.000Z">July 14, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Batch and streaming computations are often combined together in the Lambda architecture,
but carry the cost of maintaining two different code bases for the same logic.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/07/bridging-1.png" alt="Bridging batch and stream processing for the Recruiter usage statistics dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Bridging batch and stream processing for the Recruiter usage statistics dashboard" href="/blog/2020/07/14/LinkedIn-BatchRealtime"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/06/29/LinkedIn-TalentInsight">Building LinkedIn Talent Insights to democratize data-driven decision making</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-06-29T00:00:00.000Z">June 29, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn is a mission-driven organization, and we take our mission of “connecting the world's professionals to make them more productive and successful” very seriously.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/building-linkedin-talent-insights" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/building-linkedin-talent-insights</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/06/lti-1.png" alt="Building LinkedIn Talent Insights to democratize data-driven decision making"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building LinkedIn Talent Insights to democratize data-driven decision making" href="/blog/2020/06/29/LinkedIn-TalentInsight"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/06/25/LinkedIn-SmartAlerts">Monitoring business performance data with ThirdEye smart alerts</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-06-25T00:00:00.000Z">June 25, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Explain how ThirdEye smart alerts and automated dashboards helped the LinkedIn Premium business operations team monitor key metrics—such as new free trial signups—for the timely detection of outliers in business performance data.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/06/thirdeye_business_performance-3.png" alt="Monitoring business performance data with ThirdEye smart alerts"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring business performance data with ThirdEye smart alerts" href="/blog/2020/06/25/LinkedIn-SmartAlerts"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents">Using Apache Pinot and Kafka to Analyze GitHub Events</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-04-10T00:00:00.000Z">April 10, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’ll show you how Pinot and Kafka can be used together to ingest, query, and visualize event streams sourced from the public GitHub API. For the step-by-step instructions, please visit our documentation, which will guide you through the specifics of running this example in your development environment.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7</a></p><p><img src="https://miro.medium.com/max/4728/1*eR64jBH1ZvC3uNfPP56p5g.png" alt="Using Apache Pinot and Kafka to Analyze GitHub Events"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Using Apache Pinot and Kafka to Analyze GitHub Events" href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/02/20/LinkedIn-Thirdeye">Analyzing anomalies with ThirdEye</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-02-20T00:00:00.000Z">February 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Focus on the behind-the-scenes functionalities of ThirdEye that analyze the multi-dimensional time series data
and help our engineers understand why these anomalies happened through a dimension heatmap</p><p><a href="https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/02/datacube-1.png" alt="Analyzing anomalies with ThirdEye"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Analyzing anomalies with ThirdEye" href="/blog/2020/02/20/LinkedIn-Thirdeye"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/01/15/Pinot-Presto-SQL">Engineering SQL Support on Apache Pinot at Uber</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-01-15T00:00:00.000Z">January 15, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber leverages real-time analytics on aggregate data to improve the user experience across our products, from fighting fraudulent behavior on Uber Eats to forecasting demand on our platform.</p><p>To resolve these issues, we built a solution that linked Presto, a query engine that supports full ANSI SQL, and Pinot, a real-time OLAP (online analytical processing) datastore. This married solution allows users to write ad-hoc SQL queries, empowering teams to unlock significant analysis capabilities.</p><p><a href="https://eng.uber.com/engineering-sql-support-on-apache-pinot/" target="_blank" rel="noopener noreferrer">Read More at https://eng.uber.com/engineering-sql-support-on-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/01/Header-SQL-768x329.png" alt="SQL Support on Apache Pinot at Uber"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/sql">SQL</a></div><div class="col text--right"><a aria-label="Read more about Engineering SQL Support on Apache Pinot at Uber" href="/blog/2020/01/15/Pinot-Presto-SQL"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/07/11/LinkedIn-AutoTune">Auto-tuning Pinot real-time consumption</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-07-11T00:00:00.000Z">July 11, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Focus on Auto tuning Pinot, a scalable distributed columnar OLAP data store developed at LinkedIn, delivers real-time analytics for site-facing use cases such as LinkedIn's Who viewed my profile, Talent insights, and more.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/07/bridging-1.png" alt="Bridging batch and stream processing for the Recruiter usage statistics dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Auto-tuning Pinot real-time consumption" href="/blog/2019/07/11/LinkedIn-AutoTune"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/06/14/LinkedIn-StarTree">Star-tree index - Powering fast aggregations on Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-06-14T00:00:00.000Z">June 14, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Introduced Star-Tree index to utilize the pre-aggregated documents in a smart way that achieves low query latencies,
while using the storage space efficiently.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2019/06/star-tree-index--powering-fast-aggregations-on-pinot" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2019/06/star-tree-index--powering-fast-aggregations-on-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png" alt="Star-tree index - Powering fast aggregations on Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Star-tree index - Powering fast aggregations on Pinot" href="/blog/2019/06/14/LinkedIn-StarTree"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/01/09/LinkedIn-IntroThirdEye">Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-01-09T00:00:00.000Z">January 9, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>ThirdEye is a comprehensive platform for real-time monitoring of metrics that covers a wide variety of use-cases.
LinkedIn relies on ThirdEye to monitor site performance, track member growth, understand adoption of new features,
flag sustained attempts to circumvent system security, and many other areas</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png" alt="Star-tree index - Powering fast aggregations on Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform" href="/blog/2019/01/09/LinkedIn-IntroThirdEye"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2017/09/17/Restaurant-Manager">Engineering Restaurant Manager - UberEATS Analytics Dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2017-09-17T00:00:00.000Z">September 17, 2017</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, we use data analytics to architect more magical user experiences across our products. Whenever possible, we harness these data engineering capabilities to empower our partners to better serve their customers. For instance, in late 2016, the UberEATS engineering team built a comprehensive analytics dashboard that provides restaurant partners with additional insights about the health of their business.</p><p>Read More at <a href="https://eng.uber.com/restaurant-manager/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/restaurant-manager/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2017/09/image4-2.png" alt="Engineering Restaurant Manager - UberEATS Analytics Dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber-data">Uber Data</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Engineering Restaurant Manager - UberEATS Analytics Dashboard" href="/blog/2017/09/17/Restaurant-Manager"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2015/05/16/LinkedIn-Scaling">A Brief History of Scaling LinkedIn</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2015-05-16T00:00:00.000Z">May 16, 2015</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn started in 2003 with the goal of connecting to your network for better job opportunities. It had only 2,700 members the first week. Fast forward many years, and LinkedIn’s product portfolio, member base, and server load has grown tremendously.</p><p>Today, LinkedIn operates globally with more than 350 million members. We serve tens of thousands of web pages every second of every day. We've hit our mobile moment where mobile accounts for more than 50 percent of all global traffic. All those requests are fetching data from our backend systems, which in turn handle millions of queries per second.</p><p>Read More at <a href="https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/en-us/blog/migrated/data_centers_pops_0.png" alt="A Brief History of Scaling LinkedIn"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about A Brief History of Scaling LinkedIn" href="/blog/2015/05/16/LinkedIn-Scaling"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" href="/who_uses">Who uses Apache Pinot?</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" class="footer__link-item">Components</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/basics/architecture" target="_blank" rel="noopener 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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><header class="margin-bottom--xl"><h1>28 posts tagged with "realtime"</h1><a href="/blog/tags">View All Tags</a></header><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/16/LinkedIn-TextAnalytics">Text analytics on LinkedIn Talent Insights using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-16T00:00:00.000Z">June 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn Talent Insights (LTI) is a platform that helps organizations understand the external labor market and their internal workforce, and enables the long term success of their employees. Users of LTI have the flexibility to construct searches using the various facets of the LinkedIn Economic Graph (skills, titles, location, company, etc.).</p><p><a href="https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/06/ltipinot6.png" alt="Text analytics on LinkedIn Talent Insights using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/text-analytics">Text analytics</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Text analytics on LinkedIn Talent Insights using Apache Pinot" href="/blog/2021/06/16/LinkedIn-TextAnalytics"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/13/DevBlog-Geospatial">Introduction to Geospatial Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-13T00:00:00.000Z">June 13, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Geospatial data has been widely used across the industry, spanning multiple verticals, such as ride-sharing and delivery, transportation infrastructure, defense and intel, public health. Deriving insights from timely and accurate geospatial data could enable mission-critical use cases in the organizations and fuel a vibrant marketplace across the industry. In the design document for this new Pinot feature, we discuss the challenges of analyzing geospatial at scale and propose the geospatial support in Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9</a></p><p><img src="https://miro.medium.com/max/1400/0*1xrDSs9lLZ5dD3zK" alt="Introduction to Geospatial Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/h-3">H3</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Geospatial Queries in Apache Pinot" href="/blog/2021/06/13/DevBlog-Geospatial"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/29/Uber-Charon">Automating Merchant Live Monitoring with Real-Time Analytics - Charon</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-29T00:00:00.000Z">April 29, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, live monitoring and automation of Ops is critical to preserve marketplace health, maintain reliability, and gain efficiency in markets. By the virtue of the word “live”, this monitoring needs to show what is happening now, with prompt access to fresh data, and the ability to recommend appropriate actions based on that data. Uber’s data platform provides the self-serve tools which empower the Ops teams to build their own live monitoring tools, and support their regional teams by building rich solutions.</p><p>For this project, the requirement was to provide merchant level monitoring and handle the edge cases which remain unaddressed by the sophisticated internal marketplace management tools. We used a variety of Uber’s real-time data platform components to build a tool called Charon to reduce impact of poor marketplace reliability on the merchants.</p><p>Read More at <a href="https://eng.uber.com/charon/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/charon/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2021/04/deliverDoor@3x-768x263.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Automating Merchant Live Monitoring with Real-Time Analytics - Charon" href="/blog/2021/04/29/Uber-Charon"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-27T00:00:00.000Z">April 27, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain</p><p><a href="https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38</a></p><p><img src="https://miro.medium.com/max/1400/1*EtqD0vTPEe569jybXCt69w.png" alt="Deploying Apache Pinot at a Large Retail Chain"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deploying Apache Pinot at a Large Retail Chain" href="/blog/2021/04/27/DevBlog-PinotInRetailChain"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-16T00:00:00.000Z">April 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/04/thetasketches2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches" href="/blog/2021/04/16/LinkedIn-Theta"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-08T00:00:00.000Z">April 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Since the 0.6.0 release of Apache Pinot, a new feature was made available for stream ingestion that allows you to upsert events from an immutable log. Typically, upsert is a term used to describe inserting a record into a database if it does not already exist or update it if it does exist. In Apache Pinot’s case, upsert isn’t precisely the same concept, and I wanted to write this blog post to explain why it’s exciting and how you can start using it.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93</a></p><p><img src="https://miro.medium.com/max/1400/0*So3GjHjLY7DJAiaP" alt="Introduction to Upserts in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/upsert">Upsert</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Upserts in Apache Pinot" href="/blog/2021/04/08/DevBlog-UpsertsIntro"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-02-02T00:00:00.000Z">February 2, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://www.startree.ai/wp-content/uploads/2021/05/team-xiang-150x150.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/trino">Trino</a><a class="margin-horiz--sm" href="/blog/tags/presto-sql">PrestoSQL</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Real-time Analytics with Presto and Apache Pinot" href="/blog/2021/02/02/DevBlog-PrestoPinot"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-01-08T00:00:00.000Z">January 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’re going to explore an exciting new world of real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Change Data Analysis with Debezium and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/debezium">Debezium</a><a class="margin-horiz--sm" href="/blog/tags/cdc">CDC</a><a class="margin-horiz--sm" href="/blog/tags/change-data-analysis">Change Data Analysis</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Change Data Analysis with Debezium and Apache Pinot" href="/blog/2021/01/08/DevBlog-DebeziumCDC"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-12-01T00:00:00.000Z">December 1, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/03/pinotincubator2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about From Lambda to Lambda-less Lessons learned" href="/blog/2020/12/01/LinkedIn-Lamda"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/20/Uber-Operating">Operating Apache Pinot at Uber Scale</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-20T00:00:00.000Z">October 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to enable our community operations team to take corrective action. Having a real-time dashboard of different events such as consumer demand, driver availability, or trips happening in a city is crucial for day-to-day operation, incident triaging, and financial intelligence.</p><p>Read More at <a href="https://eng.uber.com/operating-apache-pinot/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/operating-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/10/1224-5.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Operating Apache Pinot at Uber Scale" href="/blog/2020/10/20/Uber-Operating"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis">Deep Analysis of Russian Twitter Trolls</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-16T00:00:00.000Z">October 16, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>The history behind Russian disinformation is a dense and continuously evolving subject. The world’s best research hasn’t seemed to hit the mainstream yet, which made this an excellent opportunity to see if I could use some open source tooling to surface new analytical evidence.</p><p>In this blog post, I’ll show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.</p><p><a href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843" target="_blank" rel="noopener noreferrer">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png" alt="Deep Analysis of Russian Twitter Trolls"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deep Analysis of Russian Twitter Trolls" href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-IngestPlugins">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Leverage Plugins to Ingest Parquet Files from S3 in Pinot" href="/blog/2020/08/08/DevBlog-IngestPlugins"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-SLAApps">Achieving 99th percentile latency SLA using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Achieving 99th percentile latency SLA using Apache Pinot" href="/blog/2020/08/08/DevBlog-SLAApps"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-ScalarUDFs">Utilize UDFs to Supercharge Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Utilize UDFs to Supercharge Queries in Apache Pinot" href="/blog/2020/08/08/DevBlog-ScalarUDFs"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-PinotMonitoring">Monitoring Apache Pinot with JMX, Prometheus and Grafana</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring Apache Pinot with JMX, Prometheus and Grafana" href="/blog/2020/08/08/DevBlog-PinotMonitoring"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-AnomalyDetection">Building a culture around metrics and anomaly detection</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building a culture around metrics and anomaly detection" href="/blog/2020/07/28/DevBlog-AnomalyDetection"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-DevUpStack">Moving developers up the stack with Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Once upon a time, an internet company named LinkedIn faced the challenge of having petabytes of connected data with no way to analyze it in real-time. As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Moving developers up the stack with Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Moving developers up the stack with Apache Pinot" href="/blog/2020/07/28/DevBlog-DevUpStack"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/14/LinkedIn-BatchRealtime">Bridging batch and stream processing for the Recruiter usage statistics dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-14T00:00:00.000Z">July 14, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Batch and streaming computations are often combined together in the Lambda architecture,
+<div><a href="#main" class="skipToContent_OuoZ shadow--md">Skip to main content</a></div><nav class="navbar navbar--fixed-top navbarHideable_RReh"><div class="navbar__inner"><div class="navbar__items"><button aria-label="Navigation bar toggle" class="navbar__toggle clean-btn" type="button" tabindex="0"><svg width="30" height="30" viewBox="0 0 30 30" aria-hidden="true"><path stroke="currentColor" stroke-linecap="round" stroke-miterlimit="10" stroke-width="2" d="M4 7h22M4 15h22M4 23h22"></path></svg></button><a class="navbar__brand" href="/"><img src="/img/pinot-navbar-logo-722f37.svg" alt="Pinot" class="themedImage_TMUO themedImage--light_4Vu1 navbar__logo"><img src="/img/pinot-navbar-logo-722f37.svg" alt="Pinot" class="themedImage_TMUO themedImage--dark_uzRr navbar__logo"></a></div><div class="navbar__items navbar__items--right"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="navbar__item navbar__link">Docs</a><a class="navbar__item navbar__link" href="/download">Download</a><a aria-current="page" class="navbar__item navbar__link navbar__link--active" href="/blog">Blog</a><a href="https://github.com/apache/pinot" target="_blank" rel="noopener noreferrer" class="navbar__item navbar__link"><span>GitHub<svg width="13.5" height="13.5" aria-hidden="true" viewBox="0 0 24 24" class="iconExternalLink_wgqa"><path fill="currentColor" d="M21 13v10h-21v-19h12v2h-10v15h17v-8h2zm3-12h-10.988l4.035 4-6.977 7.07 2.828 2.828 6.977-7.07 4.125 4.172v-11z"></path></svg></span></a><div class="react-toggle displayOnlyInLargeViewport_cxYs react-toggle--checked react-toggle--disabled"><div class="react-toggle-track" role="button" tabindex="-1"><div class="react-toggle-track-check"><span class="toggle_iYfV" style="margin-left:2px">🌙</span></div><div class="react-toggle-track-x"><span class="toggle_iYfV" style="margin-left:2px">☀️</span></div><div class="react-toggle-thumb"></div></div><input type="checkbox" checked="" class="react-toggle-screenreader-only" aria-label="Switch between dark and light mode"></div><div class="searchBox_NKBi"><button type="button" class="DocSearch DocSearch-Button" aria-label="Search"><span class="DocSearch-Button-Container"><svg width="20" height="20" class="DocSearch-Search-Icon" viewBox="0 0 20 20"><path d="M14.386 14.386l4.0877 4.0877-4.0877-4.0877c-2.9418 2.9419-7.7115 2.9419-10.6533 0-2.9419-2.9418-2.9419-7.7115 0-10.6533 2.9418-2.9419 7.7115-2.9419 10.6533 0 2.9419 2.9418 2.9419 7.7115 0 10.6533z" stroke="currentColor" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg><span class="DocSearch-Button-Placeholder">Search</span></span><span class="DocSearch-Button-Keys"></span></button></div></div></div><div role="presentation" class="navbar-sidebar__backdrop"></div><div class="navbar-sidebar"><div class="navbar-sidebar__brand"><a class="navbar__brand" href="/"><img src="/img/pinot-navbar-logo-722f37.svg" alt="Pinot" class="themedImage_TMUO themedImage--light_4Vu1 navbar__logo"><img src="/img/pinot-navbar-logo-722f37.svg" alt="Pinot" class="themedImage_TMUO themedImage--dark_uzRr navbar__logo"></a></div><div class="navbar-sidebar__items"><div class="menu"><ul class="menu__list"><li class="menu__list-item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="menu__link">Docs</a></li><li class="menu__list-item"><a class="menu__link" href="/download">Download</a></li><li class="menu__list-item"><a aria-current="page" class="menu__link navbar__link--active" href="/blog">Blog</a></li><li class="menu__list-item"><a href="https://github.com/apache/pinot" target="_blank" rel="noopener noreferrer" class="menu__link"><span>GitHub<svg width="13.5" height="13.5" aria-hidden="true" viewBox="0 0 24 24" class="iconExternalLink_wgqa"><path fill="currentColor" d="M21 13v10h-21v-19h12v2h-10v15h17v-8h2zm3-12h-10.988l4.035 4-6.977 7.07 2.828 2.828 6.977-7.07 4.125 4.172v-11z"></path></svg></span></a></li></ul></div></div></div></nav><div class="main-wrapper blog-wrapper blog-tags-post-page"><div class="container margin-vert--lg"><div class="row"><aside class="col col--3"><nav class="sidebar_q+wC thin-scrollbar" aria-label="Blog recent posts navigation"><div class="sidebarItemTitle_9G5K margin-bottom--md">All our posts</div><ul class="sidebarItemList_6T4b"><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2022/04/04/Announcing-Apache-Pinot-0-10">Announcing Apache Pinot 0.10</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/06/16/LinkedIn-TextAnalytics">Text analytics on LinkedIn Talent Insights using Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/06/13/DevBlog-Geospatial">Introduction to Geospatial Queries in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/29/Uber-Charon">Automating Merchant Live Monitoring with Real-Time Analytics - Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><header class="margin-bottom--xl"><h1>28 posts tagged with "realtime"</h1><a href="/blog/tags">View All Tags</a></header><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/16/LinkedIn-TextAnalytics">Text analytics on LinkedIn Talent Insights using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-16T00:00:00.000Z">June 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn Talent Insights (LTI) is a platform that helps organizations understand the external labor market and their internal workforce, and enables the long term success of their employees. Users of LTI have the flexibility to construct searches using the various facets of the LinkedIn Economic Graph (skills, titles, location, company, etc.).</p><p><a href="https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/06/ltipinot6.png" alt="Text analytics on LinkedIn Talent Insights using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/text-analytics">Text analytics</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Text analytics on LinkedIn Talent Insights using Apache Pinot" href="/blog/2021/06/16/LinkedIn-TextAnalytics"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/13/DevBlog-Geospatial">Introduction to Geospatial Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-13T00:00:00.000Z">June 13, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Geospatial data has been widely used across the industry, spanning multiple verticals, such as ride-sharing and delivery, transportation infrastructure, defense and intel, public health. Deriving insights from timely and accurate geospatial data could enable mission-critical use cases in the organizations and fuel a vibrant marketplace across the industry. In the design document for this new Pinot feature, we discuss the challenges of analyzing geospatial at scale and propose the geospatial support in Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9</a></p><p><img src="https://miro.medium.com/max/1400/0*1xrDSs9lLZ5dD3zK" alt="Introduction to Geospatial Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/h-3">H3</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Geospatial Queries in Apache Pinot" href="/blog/2021/06/13/DevBlog-Geospatial"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/29/Uber-Charon">Automating Merchant Live Monitoring with Real-Time Analytics - Charon</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-29T00:00:00.000Z">April 29, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, live monitoring and automation of Ops is critical to preserve marketplace health, maintain reliability, and gain efficiency in markets. By the virtue of the word “live”, this monitoring needs to show what is happening now, with prompt access to fresh data, and the ability to recommend appropriate actions based on that data. Uber’s data platform provides the self-serve tools which empower the Ops teams to build their own live monitoring tools, and support their regional teams by building rich solutions.</p><p>For this project, the requirement was to provide merchant level monitoring and handle the edge cases which remain unaddressed by the sophisticated internal marketplace management tools. We used a variety of Uber’s real-time data platform components to build a tool called Charon to reduce impact of poor marketplace reliability on the merchants.</p><p>Read More at <a href="https://eng.uber.com/charon/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/charon/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2021/04/deliverDoor@3x-768x263.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Automating Merchant Live Monitoring with Real-Time Analytics - Charon" href="/blog/2021/04/29/Uber-Charon"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-27T00:00:00.000Z">April 27, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain</p><p><a href="https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38</a></p><p><img src="https://miro.medium.com/max/1400/1*EtqD0vTPEe569jybXCt69w.png" alt="Deploying Apache Pinot at a Large Retail Chain"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deploying Apache Pinot at a Large Retail Chain" href="/blog/2021/04/27/DevBlog-PinotInRetailChain"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-16T00:00:00.000Z">April 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/04/thetasketches2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches" href="/blog/2021/04/16/LinkedIn-Theta"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-08T00:00:00.000Z">April 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Since the 0.6.0 release of Apache Pinot, a new feature was made available for stream ingestion that allows you to upsert events from an immutable log. Typically, upsert is a term used to describe inserting a record into a database if it does not already exist or update it if it does exist. In Apache Pinot’s case, upsert isn’t precisely the same concept, and I wanted to write this blog post to explain why it’s exciting and how you can start using it.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93</a></p><p><img src="https://miro.medium.com/max/1400/0*So3GjHjLY7DJAiaP" alt="Introduction to Upserts in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/upsert">Upsert</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Upserts in Apache Pinot" href="/blog/2021/04/08/DevBlog-UpsertsIntro"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-02-02T00:00:00.000Z">February 2, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://www.startree.ai/wp-content/uploads/2021/05/team-xiang-150x150.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/trino">Trino</a><a class="margin-horiz--sm" href="/blog/tags/presto-sql">PrestoSQL</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Real-time Analytics with Presto and Apache Pinot" href="/blog/2021/02/02/DevBlog-PrestoPinot"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-01-08T00:00:00.000Z">January 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’re going to explore an exciting new world of real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Change Data Analysis with Debezium and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/debezium">Debezium</a><a class="margin-horiz--sm" href="/blog/tags/cdc">CDC</a><a class="margin-horiz--sm" href="/blog/tags/change-data-analysis">Change Data Analysis</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Change Data Analysis with Debezium and Apache Pinot" href="/blog/2021/01/08/DevBlog-DebeziumCDC"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-12-01T00:00:00.000Z">December 1, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/03/pinotincubator2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about From Lambda to Lambda-less Lessons learned" href="/blog/2020/12/01/LinkedIn-Lamda"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/20/Uber-Operating">Operating Apache Pinot at Uber Scale</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-20T00:00:00.000Z">October 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to enable our community operations team to take corrective action. Having a real-time dashboard of different events such as consumer demand, driver availability, or trips happening in a city is crucial for day-to-day operation, incident triaging, and financial intelligence.</p><p>Read More at <a href="https://eng.uber.com/operating-apache-pinot/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/operating-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/10/1224-5.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Operating Apache Pinot at Uber Scale" href="/blog/2020/10/20/Uber-Operating"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis">Deep Analysis of Russian Twitter Trolls</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-16T00:00:00.000Z">October 16, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>The history behind Russian disinformation is a dense and continuously evolving subject. The world’s best research hasn’t seemed to hit the mainstream yet, which made this an excellent opportunity to see if I could use some open source tooling to surface new analytical evidence.</p><p>In this blog post, I’ll show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.</p><p><a href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843" target="_blank" rel="noopener noreferrer">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png" alt="Deep Analysis of Russian Twitter Trolls"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deep Analysis of Russian Twitter Trolls" href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-IngestPlugins">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Leverage Plugins to Ingest Parquet Files from S3 in Pinot" href="/blog/2020/08/08/DevBlog-IngestPlugins"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-PinotMonitoring">Monitoring Apache Pinot with JMX, Prometheus and Grafana</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring Apache Pinot with JMX, Prometheus and Grafana" href="/blog/2020/08/08/DevBlog-PinotMonitoring"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-SLAApps">Achieving 99th percentile latency SLA using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Achieving 99th percentile latency SLA using Apache Pinot" href="/blog/2020/08/08/DevBlog-SLAApps"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-ScalarUDFs">Utilize UDFs to Supercharge Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Utilize UDFs to Supercharge Queries in Apache Pinot" href="/blog/2020/08/08/DevBlog-ScalarUDFs"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-AnomalyDetection">Building a culture around metrics and anomaly detection</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building a culture around metrics and anomaly detection" href="/blog/2020/07/28/DevBlog-AnomalyDetection"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-DevUpStack">Moving developers up the stack with Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Once upon a time, an internet company named LinkedIn faced the challenge of having petabytes of connected data with no way to analyze it in real-time. As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Moving developers up the stack with Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Moving developers up the stack with Apache Pinot" href="/blog/2020/07/28/DevBlog-DevUpStack"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/14/LinkedIn-BatchRealtime">Bridging batch and stream processing for the Recruiter usage statistics dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-14T00:00:00.000Z">July 14, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Batch and streaming computations are often combined together in the Lambda architecture,
but carry the cost of maintaining two different code bases for the same logic.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/07/bridging-1.png" alt="Bridging batch and stream processing for the Recruiter usage statistics dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Bridging batch and stream processing for the Recruiter usage statistics dashboard" href="/blog/2020/07/14/LinkedIn-BatchRealtime"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/06/29/LinkedIn-TalentInsight">Building LinkedIn Talent Insights to democratize data-driven decision making</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-06-29T00:00:00.000Z">June 29, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn is a mission-driven organization, and we take our mission of “connecting the world's professionals to make them more productive and successful” very seriously.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/building-linkedin-talent-insights" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/building-linkedin-talent-insights</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/06/lti-1.png" alt="Building LinkedIn Talent Insights to democratize data-driven decision making"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building LinkedIn Talent Insights to democratize data-driven decision making" href="/blog/2020/06/29/LinkedIn-TalentInsight"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/06/25/LinkedIn-SmartAlerts">Monitoring business performance data with ThirdEye smart alerts</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-06-25T00:00:00.000Z">June 25, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Explain how ThirdEye smart alerts and automated dashboards helped the LinkedIn Premium business operations team monitor key metrics—such as new free trial signups—for the timely detection of outliers in business performance data.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/06/thirdeye_business_performance-3.png" alt="Monitoring business performance data with ThirdEye smart alerts"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring business performance data with ThirdEye smart alerts" href="/blog/2020/06/25/LinkedIn-SmartAlerts"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents">Using Apache Pinot and Kafka to Analyze GitHub Events</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-04-10T00:00:00.000Z">April 10, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’ll show you how Pinot and Kafka can be used together to ingest, query, and visualize event streams sourced from the public GitHub API. For the step-by-step instructions, please visit our documentation, which will guide you through the specifics of running this example in your development environment.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7</a></p><p><img src="https://miro.medium.com/max/4728/1*eR64jBH1ZvC3uNfPP56p5g.png" alt="Using Apache Pinot and Kafka to Analyze GitHub Events"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Using Apache Pinot and Kafka to Analyze GitHub Events" href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/02/20/LinkedIn-Thirdeye">Analyzing anomalies with ThirdEye</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-02-20T00:00:00.000Z">February 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Focus on the behind-the-scenes functionalities of ThirdEye that analyze the multi-dimensional time series data
and help our engineers understand why these anomalies happened through a dimension heatmap</p><p><a href="https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/02/datacube-1.png" alt="Analyzing anomalies with ThirdEye"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Analyzing anomalies with ThirdEye" href="/blog/2020/02/20/LinkedIn-Thirdeye"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/01/15/Pinot-Presto-SQL">Engineering SQL Support on Apache Pinot at Uber</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-01-15T00:00:00.000Z">January 15, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber leverages real-time analytics on aggregate data to improve the user experience across our products, from fighting fraudulent behavior on Uber Eats to forecasting demand on our platform.</p><p>To resolve these issues, we built a solution that linked Presto, a query engine that supports full ANSI SQL, and Pinot, a real-time OLAP (online analytical processing) datastore. This married solution allows users to write ad-hoc SQL queries, empowering teams to unlock significant analysis capabilities.</p><p><a href="https://eng.uber.com/engineering-sql-support-on-apache-pinot/" target="_blank" rel="noopener noreferrer">Read More at https://eng.uber.com/engineering-sql-support-on-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/01/Header-SQL-768x329.png" alt="SQL Support on Apache Pinot at Uber"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/sql">SQL</a></div><div class="col text--right"><a aria-label="Read more about Engineering SQL Support on Apache Pinot at Uber" href="/blog/2020/01/15/Pinot-Presto-SQL"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/07/11/LinkedIn-AutoTune">Auto-tuning Pinot real-time consumption</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-07-11T00:00:00.000Z">July 11, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Focus on Auto tuning Pinot, a scalable distributed columnar OLAP data store developed at LinkedIn, delivers real-time analytics for site-facing use cases such as LinkedIn's Who viewed my profile, Talent insights, and more.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/07/bridging-1.png" alt="Bridging batch and stream processing for the Recruiter usage statistics dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Auto-tuning Pinot real-time consumption" href="/blog/2019/07/11/LinkedIn-AutoTune"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/06/14/LinkedIn-StarTree">Star-tree index - Powering fast aggregations on Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-06-14T00:00:00.000Z">June 14, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Introduced Star-Tree index to utilize the pre-aggregated documents in a smart way that achieves low query latencies,
while using the storage space efficiently.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2019/06/star-tree-index--powering-fast-aggregations-on-pinot" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2019/06/star-tree-index--powering-fast-aggregations-on-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png" alt="Star-tree index - Powering fast aggregations on Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Star-tree index - Powering fast aggregations on Pinot" href="/blog/2019/06/14/LinkedIn-StarTree"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/01/09/LinkedIn-IntroThirdEye">Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-01-09T00:00:00.000Z">January 9, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>ThirdEye is a comprehensive platform for real-time monitoring of metrics that covers a wide variety of use-cases.
LinkedIn relies on ThirdEye to monitor site performance, track member growth, understand adoption of new features,
flag sustained attempts to circumvent system security, and many other areas</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png" alt="Star-tree index - Powering fast aggregations on Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform" href="/blog/2019/01/09/LinkedIn-IntroThirdEye"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2017/09/17/Restaurant-Manager">Engineering Restaurant Manager - UberEATS Analytics Dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2017-09-17T00:00:00.000Z">September 17, 2017</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, we use data analytics to architect more magical user experiences across our products. Whenever possible, we harness these data engineering capabilities to empower our partners to better serve their customers. For instance, in late 2016, the UberEATS engineering team built a comprehensive analytics dashboard that provides restaurant partners with additional insights about the health of their business.</p><p>Read More at <a href="https://eng.uber.com/restaurant-manager/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/restaurant-manager/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2017/09/image4-2.png" alt="Engineering Restaurant Manager - UberEATS Analytics Dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber-data">Uber Data</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Engineering Restaurant Manager - UberEATS Analytics Dashboard" href="/blog/2017/09/17/Restaurant-Manager"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2015/05/16/LinkedIn-Scaling">A Brief History of Scaling LinkedIn</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2015-05-16T00:00:00.000Z">May 16, 2015</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn started in 2003 with the goal of connecting to your network for better job opportunities. It had only 2,700 members the first week. Fast forward many years, and LinkedIn’s product portfolio, member base, and server load has grown tremendously.</p><p>Today, LinkedIn operates globally with more than 350 million members. We serve tens of thousands of web pages every second of every day. We've hit our mobile moment where mobile accounts for more than 50 percent of all global traffic. All those requests are fetching data from our backend systems, which in turn handle millions of queries per second.</p><p>Read More at <a href="https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/en-us/blog/migrated/data_centers_pops_0.png" alt="A Brief History of Scaling LinkedIn"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about A Brief History of Scaling LinkedIn" href="/blog/2015/05/16/LinkedIn-Scaling"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" href="/who_uses">Who uses Apache Pinot?</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" class="footer__link-item">Components</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/basics/architecture" target="_blank" rel="noopener 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You can find a full list of everything included in the <a href="https://docs.pinot.apache.org/basics/releases/0.10.0" target="_blank" rel="noopener noreferrer">release notes</a>.</p><p>Let’s have a look at some of the changes, with the help of the batch <a href="https://docs.pinot.apache.org/basics/getting-started/running-pinot-in-docker" target="_blank" rel="noopener noreferrer">QuickStart configuration</a>.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="query-plans"></a>Query Plans<a class="hash-link" href="#query-plans" title="Direct link to heading">#</a></h2><p>Amrish Lal implemented the <code>EXPLAIN PLAN</code> clause, which returns the execution plan that will be chosen by the Pinot Query Engine.
This lets us see what the query is likely to do without actually having to run it.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">EXPLAIN</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">PLAN</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FOR</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token operator">*</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following results:</p><table><thead><tr><th>Operator</th><th>Operator_Id</th><th>Parent_Id</th></tr></thead><tbody><tr><td>BROKER_REDUCE(limit:10)</td><td>0</td><td>-1</td></tr><tr><td>COMBINE_SELECT</td><td>1</td><td>0</td></tr><tr><td>SELECT(selectList:AtBatting, G_old, baseOnBalls, caughtStealing, doules, groundedIntoDoublePlays, hits, hitsByPitch, homeRuns, intentionalWalks, league, numberOfGames, numberOfGamesAsBatter, playerID, playerName, playerStint, runs, runsBattedIn, sacrificeFlies, sacrificeHits, stolenBases, strikeouts, teamID, tripples, yearID)</td><td>2</td><td>1</td></tr><tr><td>TRANSFORM_PASSTHROUGH(AtBatting, G_old, baseOnBalls, caughtStealing, doules, groundedIntoDoublePlays, hits, hitsByPitch, homeRuns, intentionalWalks, league, numberOfGames, numberOfGamesAsBatter, playerID, playerName, playerStint, runs, runsBattedIn, sacrificeFlies, sacrificeHits, stolenBases, strikeouts, teamID, tripples, yearID)</td><td>3</td><td>2</td></tr><tr><td>PROJECT(homeRuns, playerStint, groundedIntoDoublePlays, numberOfGames, AtBatting, stolenBases, tripples, hitsByPitch, teamID, numberOfGamesAsBatter, strikeouts, sacrificeFlies, caughtStealing, baseOnBalls, playerName, doules, league, yearID, hits, runsBattedIn, G_old, sacrificeHits, intentionalWalks, runs, playerID)</td><td>4</td><td>3</td></tr><tr><td>FILTER_FULL_SCAN(operator:EQ,predicate:league = 'NL')</td><td>5</td><td>4</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="filter-clauses-for-aggregates"></a>FILTER Clauses for Aggregates<a class="hash-link" href="#filter-clauses-for-aggregates" title="Direct link to heading">#</a></h2><p>Atri Sharma added the filter clause for aggregates.
This feature makes it possible to write queries like this:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> nlHomeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'AL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> alHomeRuns</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>nlHomeRuns</th><th>alHomeRuns</th></tr></thead><tbody><tr><td>135486</td><td>135990</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="greatest-and-least"></a>greatest and least<a class="hash-link" href="#greatest-and-least" title="Direct link to heading">#</a></h2><p>Richard Startin added the <code>greatest</code> and <code>least</code> functions:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> playerID</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> least</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> greatest</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">hits</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> hits</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token plain"> </span><span class="token operator">AND</span><span class="token plain"> teamID </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'SFN'</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">GROUP</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">BY</span><span class="token plain"> playerID</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">LIMIT</span><span class="token plain"> </span><span class="token number">5</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>playerID</th><th>homeRuns</th><th>hits</th></tr></thead><tbody><tr><td>ramirju01</td><td>0</td><td>5</td></tr><tr><td>milneed01</td><td>4</td><td>54</td></tr><tr><td>testani01</td><td>0</td><td>5</td></tr><tr><td>shawbo01</td><td>0</td><td>8</td></tr><tr><td>vogelry01</td><td>0</td><td>12</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="distinctcountsmarthll"></a>DistinctCountSmartHLL<a class="hash-link" href="#distinctcountsmarthll" title="Direct link to heading">#</a></h2><p> Xiaotian (Jackie) Jiang added the <code>DistinctCountSmartHLL</code> aggregation function, which automatically converts the Set to HyperLogLog if the set size grows too big to protect the servers from running out of memory:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> DISTINCTCOUNTSMARTHLL</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'hllLog2m=8;hllConversionThreshold=10'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>distinctcountsmarthll(homeRuns)</th></tr></thead><tbody><tr><td>66</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="ui-updates"></a>UI updates<a class="hash-link" href="#ui-updates" title="Direct link to heading">#</a></h2><p>There were also a bunch of updates to the Pinot Data Explorer, by Sanket Shah and Johan Adami.</p><p>The display of reported size and estimated size is now in a human readable format:</p><p><img alt="Human readable sizes" src="/assets/images/human-readable-sizes-b8c4009dd53d23da3b8637963827a8de.png"></p><p>Fixes for the following issues:</p><ul><li>Error messages weren't showing on the UI when an invalid operation is attempted:</li></ul><p><img alt="A backwards incompatible attempted schema change" src="/assets/images/backwards-incompatible-99886dcd0be55a8100a7d6c5f3da3bda.png"></p><ul><li>Query console goes blank on syntax error.</li><li>Query console cannot show query result when multiple columns have the same name.</li><li>Adding extra fields after <code>SELECT *</code> would throw a NullPointerException.</li><li>Some queries were returning <code>--</code> instead of <code>0</code>.</li><li>Query console couldn't show the query result if multiple columns had the same name.</li><li>Pinot Dashboard tenant view showing the incorrect amount of servers and brokers.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="realtimetooffline-task"></a>RealTimeToOffline Task<a class="hash-link" href="#realtimetooffline-task" title="Direct link to heading">#</a></h2><p>Xiaotian (Jackie) Jiang made some fixes to the <a href="https://dev.startree.ai/docs/pinot/recipes/real-time-offline-job" target="_blank" rel="noopener noreferrer">RealTimeToOffline job</a> to handle time gaps and proceed to the next time window when no segment matches the current one.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="empty-quickstart"></a>Empty QuickStart<a class="hash-link" href="#empty-quickstart" title="Direct link to heading">#</a></h2><p>Kenny Bastani added an empty QuickStart command, which lets you quickly spin up an empty Pinot cluster:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">docker</span><span class="token plain"> run </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">8000</span><span class="token plain">:8000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">9000</span><span class="token plain">:9000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> apachepinot/pinot:0.10.0 QuickStart </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -type empty</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>You can then ingest your own dataset without needing to worry about spinning up each of the Pinot components individually.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="data-ingestion"></a>Data Ingestion<a class="hash-link" href="#data-ingestion" title="Direct link to heading">#</a></h2><ul><li><p>Richard Startin fixed some issues with real-time ingestion where consumption of messages would stop if a bad batch of messages was consumed from Kafka.</p></li><li><p>Mohemmad Zaid Khan added the BoundedColumnValue partition function, which partitions segments based on column values.</p></li><li><p>Xiaobing Li added the fixed name segment generator, which can be used when you want to replace a specific existing segment.</p></li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="other-changes"></a>Other changes<a class="hash-link" href="#other-changes" title="Direct link to heading">#</a></h2><ul><li>Richard Startin set LZ4 compression as the default for all metrics fields.</li><li>Mark Needham added the <code>ST_Within</code> geospatial function.</li><li>Rong Rong fixed a bug where query stats wouldn't show if there was an error processing the query (e.g. if the query timed out).</li><li>Prashant Pandey fixed the query engine to handle extra columns added to a <code>SELECT *</code> statement.</li><li>Richard Startin added support for forward indexes on JSON columns.</li><li>Rong Rong added the GRPC broker request handler so that data can be streamed back from the server to the broker when processing queries.</li><li>deemoliu made it possible to add a default strategy when using the <a href="https://dev.startree.ai/docs/pinot/recipes/upserts-partial" target="_blank" rel="noopener noreferrer">partial upsert feature</a>.</li><li>Jeff Moszuti added support for the <code>TIMESTAMP</code> data type in the <a href="https://docs.pinot.apache.org/operators/configuration-recommendation-engine" target="_blank" rel="noopener noreferrer">configuration recommendation engine</a>.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="dependency-updates"></a>Dependency updates<a class="hash-link" href="#dependency-updates" title="Direct link to heading">#</a></h2><p>The following dependencies were updated:</p><ul><li>async-http-client because the library moved to a different organization.</li><li>RoaringBitmap to 0.9.25</li><li>JsonPath to 2.7.0</li><li>Kafka to 2.8.1</li><li>Prometheus to 0.16.1</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="resources"></a>Resources<a class="hash-link" href="#resources" title="Direct link to heading">#</a></h2><p>If you want to try out Apache Pinot, the following resources will help you get started:</p><ul><li>Download page: <a href="https://pinot.apache.org/download/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/download/</a></li><li>Getting started: <a href="https://docs.pinot.apache.org/getting-started" target="_blank" rel="noopener noreferrer">https://docs.pinot.apache.org/getting-started</a></li><li>Apache Pinot Recipes: <a href="https://dev.startree.ai/docs/pinot/recipes/" target="_blank" rel="noopener noreferrer">https://dev.startree.ai/docs/pinot/recipes/</a></li><li>Join our Slack channel: <a href="https://communityinviter.com/apps/apache-pinot/apache-pinot" target="_blank" rel="noopener noreferrer">https://communityinviter.com/apps/apache-pinot/apache-pinot</a></li><li>See our upcoming events: <a href="https://www.meetup.com/apache-pinot" target="_blank" rel="noopener noreferrer">https://www.meetup.com/apache-pinot</a></li><li>Follow us on Twitter: <a href="https://twitter.com/startreedata" target="_blank" rel="noopener noreferrer">https://twitter.com/startreedata</a></li><li>Subscribe to our YouTube channel: <a 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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><header class="margin-bottom--xl"><h1>3 posts tagged with "sla"</h1><a href="/blog/tags">View All Tags</a></header><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-IngestPlugins">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Leverage Plugins to Ingest Parquet Files from S3 in Pinot" href="/blog/2020/08/08/DevBlog-IngestPlugins"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-SLAApps">Achieving 99th percentile latency SLA using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Achieving 99th percentile latency SLA using Apache Pinot" href="/blog/2020/08/08/DevBlog-SLAApps"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-ScalarUDFs">Utilize UDFs to Supercharge Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Utilize UDFs to Supercharge Queries in Apache Pinot" href="/blog/2020/08/08/DevBlog-ScalarUDFs"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" href="/who_uses">Who uses Apache Pinot?</a></li><li class="footer__item"><a 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This married solution allows users to write ad-hoc SQL queries, empowering teams to unlock significant analysis capabilities.</p><p><a href="https://eng.uber.com/engineering-sql-support-on-apache-pinot/" target="_blank" rel="noopener noreferrer">Read More at https://eng.uber.com/engineering-sql-support-on-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/01/Header-SQL-768x329.png" alt="SQL Support on Apache Pinot at Uber"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing 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Users of LTI have the flexibility to construct searches using the various facets of the LinkedIn Economic Graph (skills, titles, location, company, etc.).</p><p><a href="https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/06/ltipinot6.png" alt="Text analytics on LinkedIn Talent Insights using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/text-analytics">Text analytics</a><a class="margin-horiz--sm" 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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><header class="margin-bottom--xl"><h1>12 posts tagged with "thirdeye"</h1><a href="/blog/tags">View All Tags</a></header><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/16/LinkedIn-TextAnalytics">Text analytics on LinkedIn Talent Insights using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-16T00:00:00.000Z">June 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn Talent Insights (LTI) is a platform that helps organizations understand the external labor market and their internal workforce, and enables the long term success of their employees. Users of LTI have the flexibility to construct searches using the various facets of the LinkedIn Economic Graph (skills, titles, location, company, etc.).</p><p><a href="https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/06/ltipinot6.png" alt="Text analytics on LinkedIn Talent Insights using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/text-analytics">Text analytics</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Text analytics on LinkedIn Talent Insights using Apache Pinot" href="/blog/2021/06/16/LinkedIn-TextAnalytics"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-27T00:00:00.000Z">April 27, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain</p><p><a href="https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38</a></p><p><img src="https://miro.medium.com/max/1400/1*EtqD0vTPEe569jybXCt69w.png" alt="Deploying Apache Pinot at a Large Retail Chain"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deploying Apache Pinot at a Large Retail Chain" href="/blog/2021/04/27/DevBlog-PinotInRetailChain"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-02-02T00:00:00.000Z">February 2, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://www.startree.ai/wp-content/uploads/2021/05/team-xiang-150x150.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/trino">Trino</a><a class="margin-horiz--sm" href="/blog/tags/presto-sql">PrestoSQL</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Real-time Analytics with Presto and Apache Pinot" href="/blog/2021/02/02/DevBlog-PrestoPinot"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis">Deep Analysis of Russian Twitter Trolls</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-16T00:00:00.000Z">October 16, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>The history behind Russian disinformation is a dense and continuously evolving subject. The world’s best research hasn’t seemed to hit the mainstream yet, which made this an excellent opportunity to see if I could use some open source tooling to surface new analytical evidence.</p><p>In this blog post, I’ll show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.</p><p><a href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843" target="_blank" rel="noopener noreferrer">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png" alt="Deep Analysis of Russian Twitter Trolls"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deep Analysis of Russian Twitter Trolls" href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-IngestPlugins">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Leverage Plugins to Ingest Parquet Files from S3 in Pinot" href="/blog/2020/08/08/DevBlog-IngestPlugins"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-SLAApps">Achieving 99th percentile latency SLA using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Achieving 99th percentile latency SLA using Apache Pinot" href="/blog/2020/08/08/DevBlog-SLAApps"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-ScalarUDFs">Utilize UDFs to Supercharge Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Utilize UDFs to Supercharge Queries in Apache Pinot" href="/blog/2020/08/08/DevBlog-ScalarUDFs"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-PinotMonitoring">Monitoring Apache Pinot with JMX, Prometheus and Grafana</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring Apache Pinot with JMX, Prometheus and Grafana" href="/blog/2020/08/08/DevBlog-PinotMonitoring"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-AnomalyDetection">Building a culture around metrics and anomaly detection</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building a culture around metrics and anomaly detection" href="/blog/2020/07/28/DevBlog-AnomalyDetection"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-DevUpStack">Moving developers up the stack with Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Once upon a time, an internet company named LinkedIn faced the challenge of having petabytes of connected data with no way to analyze it in real-time. As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Moving developers up the stack with Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Moving developers up the stack with Apache Pinot" href="/blog/2020/07/28/DevBlog-DevUpStack"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents">Using Apache Pinot and Kafka to Analyze GitHub Events</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-04-10T00:00:00.000Z">April 10, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’ll show you how Pinot and Kafka can be used together to ingest, query, and visualize event streams sourced from the public GitHub API. For the step-by-step instructions, please visit our documentation, which will guide you through the specifics of running this example in your development environment.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7</a></p><p><img src="https://miro.medium.com/max/4728/1*eR64jBH1ZvC3uNfPP56p5g.png" alt="Using Apache Pinot and Kafka to Analyze GitHub Events"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Using Apache Pinot and Kafka to Analyze GitHub Events" href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/01/09/LinkedIn-IntroThirdEye">Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-01-09T00:00:00.000Z">January 9, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>ThirdEye is a comprehensive platform for real-time monitoring of metrics that covers a wide variety of use-cases.
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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><header class="margin-bottom--xl"><h1>12 posts tagged with "thirdeye"</h1><a href="/blog/tags">View All Tags</a></header><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/16/LinkedIn-TextAnalytics">Text analytics on LinkedIn Talent Insights using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-16T00:00:00.000Z">June 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn Talent Insights (LTI) is a platform that helps organizations understand the external labor market and their internal workforce, and enables the long term success of their employees. Users of LTI have the flexibility to construct searches using the various facets of the LinkedIn Economic Graph (skills, titles, location, company, etc.).</p><p><a href="https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/06/ltipinot6.png" alt="Text analytics on LinkedIn Talent Insights using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/text-analytics">Text analytics</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Text analytics on LinkedIn Talent Insights using Apache Pinot" href="/blog/2021/06/16/LinkedIn-TextAnalytics"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-27T00:00:00.000Z">April 27, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain</p><p><a href="https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38</a></p><p><img src="https://miro.medium.com/max/1400/1*EtqD0vTPEe569jybXCt69w.png" alt="Deploying Apache Pinot at a Large Retail Chain"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deploying Apache Pinot at a Large Retail Chain" href="/blog/2021/04/27/DevBlog-PinotInRetailChain"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-02-02T00:00:00.000Z">February 2, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://www.startree.ai/wp-content/uploads/2021/05/team-xiang-150x150.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/trino">Trino</a><a class="margin-horiz--sm" href="/blog/tags/presto-sql">PrestoSQL</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Real-time Analytics with Presto and Apache Pinot" href="/blog/2021/02/02/DevBlog-PrestoPinot"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis">Deep Analysis of Russian Twitter Trolls</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-16T00:00:00.000Z">October 16, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>The history behind Russian disinformation is a dense and continuously evolving subject. The world’s best research hasn’t seemed to hit the mainstream yet, which made this an excellent opportunity to see if I could use some open source tooling to surface new analytical evidence.</p><p>In this blog post, I’ll show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.</p><p><a href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843" target="_blank" rel="noopener noreferrer">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png" alt="Deep Analysis of Russian Twitter Trolls"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deep Analysis of Russian Twitter Trolls" href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-IngestPlugins">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Leverage Plugins to Ingest Parquet Files from S3 in Pinot" href="/blog/2020/08/08/DevBlog-IngestPlugins"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-PinotMonitoring">Monitoring Apache Pinot with JMX, Prometheus and Grafana</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring Apache Pinot with JMX, Prometheus and Grafana" href="/blog/2020/08/08/DevBlog-PinotMonitoring"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-SLAApps">Achieving 99th percentile latency SLA using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Achieving 99th percentile latency SLA using Apache Pinot" href="/blog/2020/08/08/DevBlog-SLAApps"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-ScalarUDFs">Utilize UDFs to Supercharge Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Utilize UDFs to Supercharge Queries in Apache Pinot" href="/blog/2020/08/08/DevBlog-ScalarUDFs"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-AnomalyDetection">Building a culture around metrics and anomaly detection</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building a culture around metrics and anomaly detection" href="/blog/2020/07/28/DevBlog-AnomalyDetection"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-DevUpStack">Moving developers up the stack with Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Once upon a time, an internet company named LinkedIn faced the challenge of having petabytes of connected data with no way to analyze it in real-time. As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Moving developers up the stack with Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Moving developers up the stack with Apache Pinot" href="/blog/2020/07/28/DevBlog-DevUpStack"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents">Using Apache Pinot and Kafka to Analyze GitHub Events</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-04-10T00:00:00.000Z">April 10, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’ll show you how Pinot and Kafka can be used together to ingest, query, and visualize event streams sourced from the public GitHub API. For the step-by-step instructions, please visit our documentation, which will guide you through the specifics of running this example in your development environment.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7</a></p><p><img src="https://miro.medium.com/max/4728/1*eR64jBH1ZvC3uNfPP56p5g.png" alt="Using Apache Pinot and Kafka to Analyze GitHub Events"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Using Apache Pinot and Kafka to Analyze GitHub Events" href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/01/09/LinkedIn-IntroThirdEye">Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-01-09T00:00:00.000Z">January 9, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>ThirdEye is a comprehensive platform for real-time monitoring of metrics that covers a wide variety of use-cases.
LinkedIn relies on ThirdEye to monitor site performance, track member growth, understand adoption of new features,
flag sustained attempts to circumvent system security, and many other areas</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png" alt="Star-tree index - Powering fast aggregations on Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform" href="/blog/2019/01/09/LinkedIn-IntroThirdEye"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" href="/who_uses">Who uses Apache Pinot?</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" class="footer__link-item">Components</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/basics/architecture" target="_blank" rel="noopener noreferrer" class="footer__link-item">Architecture</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/plugins/plugin-architecture" target="_blank" rel="noopener noreferrer" class="footer__link-item">Plugins Architecture</a></li></ul></div><div class="col footer__col"><h4 class="footer__title">Integrations</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/integrations/trino" target="_blank" rel="noopener noreferrer" class="footer__link-item">Trino</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/integrations/presto" target="_blank" rel="noopener noreferrer" class="footer__link-item">Presto</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/integrations/superset" target="_blank" rel="noopener noreferrer" class="footer__link-item">Superset</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/integrations/thirdeye" target="_blank" rel="noopener noreferrer" class="footer__link-item">ThirdEye</a></li></ul></div><div class="col footer__col"><h4 class="footer__title">Docs</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/getting-started" target="_blank" rel="noopener noreferrer" class="footer__link-item">Getting Started</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" class="footer__link-item">Pinot Components</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/users" target="_blank" rel="noopener noreferrer" class="footer__link-item">User Guide</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/operating-pinot" target="_blank" rel="noopener noreferrer" class="footer__link-item">Administration</a></li></ul></div><div class="col footer__col"><h4 class="footer__title">Community</h4><ul class="footer__items"><li class="footer__item"><a href="https://join.slack.com/t/apache-pinot/shared_invite/zt-5z7pav2f-yYtjZdVA~EDmrGkho87Vzw" target="_blank" rel="noopener noreferrer" class="footer__link-item">Slack</a></li><li class="footer__item"><a href="https://github.com/apache/pinot" target="_blank" rel="noopener noreferrer" class="footer__link-item">Github</a></li><li class="footer__item"><a href="https://twitter.com/ApachePinot" target="_blank" rel="noopener noreferrer" class="footer__link-item">Twitter</a></li><li class="footer__item"><a href="mailto:dev-subscribe@pinot.apache.org?Subject=SubscribeToPinot" target="_blank" rel="noopener noreferrer" class="footer__link-item">Mailing List</a></li></ul></div><div class="col footer__col"><h4 class="footer__title">Apache</h4><ul class="footer__items"><li class="footer__item"><a href="https://www.apache.org/events/current-event" target="_blank" rel="noopener noreferrer" class="footer__link-item">Events</a></li><li class="footer__item"><a href="https://www.apache.org/foundation/thanks.html" target="_blank" rel="noopener noreferrer" class="footer__link-item">Thanks</a></li><li class="footer__item"><a href="https://www.apache.org/licenses" target="_blank" rel="noopener noreferrer" class="footer__link-item">License</a></li><li class="footer__item"><a href="https://www.apache.org/security" target="_blank" rel="noopener noreferrer" class="footer__link-item">Security</a></li><li class="footer__item"><a href="https://www.apache.org/foundation/sponsorship.html" target="_blank" rel="noopener noreferrer" class="footer__link-item">Sponsorship</a></li><li class="footer__item"><a href="https://www.apache.org" target="_blank" rel="noopener noreferrer" class="footer__link-item">Foundation</a></li></ul></div></div><div class="footer__bottom text--center"><div class="margin-bottom--sm"><a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footerLogoLink_94kH"><img src="/img/logo.svg" alt="Apache Pinot™" class="themedImage_TMUO themedImage--light_4Vu1 footer__logo"><img src="/img/logo.svg" alt="Apache Pinot™" class="themedImage_TMUO themedImage--dark_uzRr footer__logo"></a></div><div class="footerCopyright_-piB">Copyright © 2022 The Apache Software Foundation.<br>Apache Pinot, Pinot, Apache, the Apache feather logo, and the Apache Pinot project logo are registered trademarks of The Apache Software Foundation.<br><br>This page has references to third party software - Presto, PrestoDB, ThirdEye, Trino, TrinoDB, that are not part of the Apache Software Foundation and are not covered under the Apache License.</div></div></div></footer></div>
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In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/trino">Trino</a><a 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UberEATS Analytics Dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2017-09-17T00:00:00.000Z">September 17, 2017</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, we use data analytics to architect more magical user experiences across our products. Whenever possible, we harness these data engineering capabilities to empower our partners to better serve their customers. For instance, in late 2016, the UberEATS engineering team built a comprehensive analytics dashboard that provides restaurant partners with additional insights about the health of their business.</p><p>Read More at <a href="https://eng.uber.com/restaurant-manager/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/restaurant-manager/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2017/09/image4-2.png" alt="Engineering Restaurant Manager - UberEATS Analytics Dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber-data">Uber Data</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Engineering Restaurant Manager - UberEATS Analytics Dashboard" href="/blog/2017/09/17/Restaurant-Manager"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" href="/who_uses">Who uses Apache Pinot?</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" 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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><header class="margin-bottom--xl"><h1>3 posts tagged with "uber"</h1><a href="/blog/tags">View All Tags</a></header><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/29/Uber-Charon">Automating Merchant Live Monitoring with Real-Time Analytics - Charon</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-29T00:00:00.000Z">April 29, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, live monitoring and automation of Ops is critical to preserve marketplace health, maintain reliability, and gain efficiency in markets. By the virtue of the word “live”, this monitoring needs to show what is happening now, with prompt access to fresh data, and the ability to recommend appropriate actions based on that data. Uber’s data platform provides the self-serve tools which empower the Ops teams to build their own live monitoring tools, and support their regional teams by building rich solutions.</p><p>For this project, the requirement was to provide merchant level monitoring and handle the edge cases which remain unaddressed by the sophisticated internal marketplace management tools. We used a variety of Uber’s real-time data platform components to build a tool called Charon to reduce impact of poor marketplace reliability on the merchants.</p><p>Read More at <a href="https://eng.uber.com/charon/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/charon/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2021/04/deliverDoor@3x-768x263.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Automating Merchant Live Monitoring with Real-Time Analytics - Charon" href="/blog/2021/04/29/Uber-Charon"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/20/Uber-Operating">Operating Apache Pinot at Uber Scale</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-20T00:00:00.000Z">October 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to enable our community operations team to take corrective action. Having a real-time dashboard of different events such as consumer demand, driver availability, or trips happening in a city is crucial for day-to-day operation, incident triaging, and financial intelligence.</p><p>Read More at <a href="https://eng.uber.com/operating-apache-pinot/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/operating-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/10/1224-5.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Operating Apache Pinot at Uber Scale" href="/blog/2020/10/20/Uber-Operating"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/01/15/Pinot-Presto-SQL">Engineering SQL Support on Apache Pinot at Uber</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-01-15T00:00:00.000Z">January 15, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber leverages real-time analytics on aggregate data to improve the user experience across our products, from fighting fraudulent behavior on Uber Eats to forecasting demand on our platform.</p><p>To resolve these issues, we built a solution that linked Presto, a query engine that supports full ANSI SQL, and Pinot, a real-time OLAP (online analytical processing) datastore. This married solution allows users to write ad-hoc SQL queries, empowering teams to unlock significant analysis capabilities.</p><p><a href="https://eng.uber.com/engineering-sql-support-on-apache-pinot/" target="_blank" rel="noopener noreferrer">Read More at https://eng.uber.com/engineering-sql-support-on-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/01/Header-SQL-768x329.png" alt="SQL Support on Apache Pinot at Uber"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/sql">SQL</a></div><div class="col text--right"><a aria-label="Read more about Engineering SQL Support on Apache Pinot at Uber" href="/blog/2020/01/15/Pinot-Presto-SQL"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" href="/who_uses">Who uses Apache Pinot?</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" class="footer__link-item">Components</a></li><li class="footer__item"><a 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Charon</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></li><li class="sidebarItem_cjdF"><a class="sidebarItemLink_zyXk" href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></li></ul></nav></aside><main class="col col--7"><header class="margin-bottom--xl"><h1>One post tagged with "upsert"</h1><a href="/blog/tags">View All Tags</a></header><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-08T00:00:00.000Z">April 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Since the 0.6.0 release of Apache Pinot, a new feature was made available for stream ingestion that allows you to upsert events from an immutable log. Typically, upsert is a term used to describe inserting a record into a database if it does not already exist or update it if it does exist. In Apache Pinot’s case, upsert isn’t precisely the same concept, and I wanted to write this blog post to explain why it’s exciting and how you can start using it.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93</a></p><p><img src="https://miro.medium.com/max/1400/0*So3GjHjLY7DJAiaP" alt="Introduction to Upserts in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/upsert">Upsert</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Upserts in Apache Pinot" href="/blog/2021/04/08/DevBlog-UpsertsIntro"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" href="/who_uses">Who uses Apache Pinot?</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" class="footer__link-item">Components</a></li><li 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<link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">30 posts tagged with "user-facing analytics" | Apache Pinot™</title><meta data-react-helmet="true" property="og:title" content="30 posts tagged with "user-facing analytics" | Apache Pinot™"><meta data-react-helmet="true" property="og:url" content="https://pinot.apache.org/blog/tags/user-facing-analytics"><meta data-react-helmet="true" name="docsearch:language" content="en"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="blog_tags_posts"><meta data-react-helmet="true" name="keywords" content="Pinot,LinkedIn,Data Scaling,User-Facing Analytics,Real-time data platform"><link data-react-helmet="true" rel="shortcut icon" href="/img/favicon.ico"><link data-react-helmet="true" rel="canonical" href="https://pinot.apache.org/blog/tags/user-facing-analytics"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/blog/tags/user-facing-analytics" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/blog/tags/user-facing-analytics" hreflang="x-default"><link data-react-helmet="true" rel="preconnect" href="https://BH4D9OD16A-dsn.algolia.net" crossorigin="anonymous"><link rel="stylesheet" href="/assets/css/styles.f0793de6.css">
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Apache Pinot is a real-time distributed datastore designed to answer OLAP queries with high throughput and low latency.</p><p>This release is cut from commit <a href="https://github.com/apache/pinot/commit/fd9c58a11ed16d27109baefcee138eea30132ad3" target="_blank" rel="noopener noreferrer">fd9c58a11ed16d27109baefcee138eea30132ad3</a>.
You can find a full list of everything included in the <a href="https://docs.pinot.apache.org/basics/releases/0.10.0" target="_blank" rel="noopener noreferrer">release notes</a>.</p><p>Let’s have a look at some of the changes, with the help of the batch <a href="https://docs.pinot.apache.org/basics/getting-started/running-pinot-in-docker" target="_blank" rel="noopener noreferrer">QuickStart configuration</a>.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="query-plans"></a>Query Plans<a class="hash-link" href="#query-plans" title="Direct link to heading">#</a></h2><p>Amrish Lal implemented the <code>EXPLAIN PLAN</code> clause, which returns the execution plan that will be chosen by the Pinot Query Engine.
This lets us see what the query is likely to do without actually having to run it.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">EXPLAIN</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">PLAN</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FOR</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token operator">*</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following results:</p><table><thead><tr><th>Operator</th><th>Operator_Id</th><th>Parent_Id</th></tr></thead><tbody><tr><td>BROKER_REDUCE(limit:10)</td><td>0</td><td>-1</td></tr><tr><td>COMBINE_SELECT</td><td>1</td><td>0</td></tr><tr><td>SELECT(selectList:AtBatting, G_old, baseOnBalls, caughtStealing, doules, groundedIntoDoublePlays, hits, hitsByPitch, homeRuns, intentionalWalks, league, numberOfGames, numberOfGamesAsBatter, playerID, playerName, playerStint, runs, runsBattedIn, sacrificeFlies, sacrificeHits, stolenBases, strikeouts, teamID, tripples, yearID)</td><td>2</td><td>1</td></tr><tr><td>TRANSFORM_PASSTHROUGH(AtBatting, G_old, baseOnBalls, caughtStealing, doules, groundedIntoDoublePlays, hits, hitsByPitch, homeRuns, intentionalWalks, league, numberOfGames, numberOfGamesAsBatter, playerID, playerName, playerStint, runs, runsBattedIn, sacrificeFlies, sacrificeHits, stolenBases, strikeouts, teamID, tripples, yearID)</td><td>3</td><td>2</td></tr><tr><td>PROJECT(homeRuns, playerStint, groundedIntoDoublePlays, numberOfGames, AtBatting, stolenBases, tripples, hitsByPitch, teamID, numberOfGamesAsBatter, strikeouts, sacrificeFlies, caughtStealing, baseOnBalls, playerName, doules, league, yearID, hits, runsBattedIn, G_old, sacrificeHits, intentionalWalks, runs, playerID)</td><td>4</td><td>3</td></tr><tr><td>FILTER_FULL_SCAN(operator:EQ,predicate:league = 'NL')</td><td>5</td><td>4</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="filter-clauses-for-aggregates"></a>FILTER Clauses for Aggregates<a class="hash-link" href="#filter-clauses-for-aggregates" title="Direct link to heading">#</a></h2><p>Atri Sharma added the filter clause for aggregates.
-This feature makes it possible to write queries like this:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> nlHomeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'AL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> alHomeRuns</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>nlHomeRuns</th><th>alHomeRuns</th></tr></thead><tbody><tr><td>135486</td><td>135990</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="greatest-and-least"></a>greatest and least<a class="hash-link" href="#greatest-and-least" title="Direct link to heading">#</a></h2><p>Richard Startin added the <code>greatest</code> and <code>least</code> functions:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> playerID</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> least</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> greatest</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">hits</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> hits</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token plain"> </span><span class="token operator">AND</span><span class="token plain"> teamID </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'SFN'</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">GROUP</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">BY</span><span class="token plain"> playerID</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">LIMIT</span><span class="token plain"> </span><span class="token number">5</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>playerID</th><th>homeRuns</th><th>hits</th></tr></thead><tbody><tr><td>ramirju01</td><td>0</td><td>5</td></tr><tr><td>milneed01</td><td>4</td><td>54</td></tr><tr><td>testani01</td><td>0</td><td>5</td></tr><tr><td>shawbo01</td><td>0</td><td>8</td></tr><tr><td>vogelry01</td><td>0</td><td>12</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="distinctcountsmarthll"></a>DistinctCountSmartHLL<a class="hash-link" href="#distinctcountsmarthll" title="Direct link to heading">#</a></h2><p> Xiaotian (Jackie) Jiang added the <code>DistinctCountSmartHLL</code> aggregation function, which automatically converts the Set to HyperLogLog if the set size grows too big to protect the servers from running out of memory:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> DISTINCTCOUNTSMARTHLL</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'hllLog2m=8;hllConversionThreshold=10'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>distinctcountsmarthll(homeRuns)</th></tr></thead><tbody><tr><td>66</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="ui-updates"></a>UI updates<a class="hash-link" href="#ui-updates" title="Direct link to heading">#</a></h2><p>There were also a bunch of updates to the Pinot Data Explorer, by Sanket Shah and Johan Adami.</p><p>The display of reported size and estimated size is now in a human readable format:</p><p><img alt="Human readable sizes" src="/assets/images/human-readable-sizes-b8c4009dd53d23da3b8637963827a8de.png"></p><p>Fixes for the following issues:</p><ul><li>Error messages weren't showing on the UI when an invalid operation is attempted:</li></ul><p><img alt="A backwards incompatible attempted schema change" src="/assets/images/backwards-incompatible-99886dcd0be55a8100a7d6c5f3da3bda.png"></p><ul><li>Query console goes blank on syntax error.</li><li>Query console cannot show query result when multiple columns have the same name.</li><li>Adding extra fields after <code>SELECT *</code> would throw a NullPointerException.</li><li>Some queries were returning <code>--</code> instead of <code>0</code>.</li><li>Query console couldn't show the query result if multiple columns had the same name.</li><li>Pinot Dashboard tenant view showing the incorrect amount of servers and brokers.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="realtimetooffline-task"></a>RealTimeToOffline Task<a class="hash-link" href="#realtimetooffline-task" title="Direct link to heading">#</a></h2><p>Xiaotian (Jackie) Jiang made some fixes to the <a href="https://dev.startree.ai/docs/pinot/recipes/real-time-offline-job" target="_blank" rel="noopener noreferrer">RealTimeToOffline job</a> to handle time gaps and proceed to the next time window when no segment matches the current one.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="empty-quickstart"></a>Empty QuickStart<a class="hash-link" href="#empty-quickstart" title="Direct link to heading">#</a></h2><p>Kenny Bastani added an empty QuickStart command, which lets you quickly spin up an empty Pinot cluster:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">docker</span><span class="token plain"> run </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">8000</span><span class="token plain">:8000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">9000</span><span class="token plain">:9000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> apachepinot/pinot:0.10.0 QuickStart </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -type empty</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>You can then ingest your own dataset without needing to worry about spinning up each of the Pinot components individually.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="data-ingestion"></a>Data Ingestion<a class="hash-link" href="#data-ingestion" title="Direct link to heading">#</a></h2><ul><li><p>Richard Startin fixed some issues with real-time ingestion where consumption of messages would stop if a bad batch of messages was consumed from Kafka.</p></li><li><p>Mohemmad Zaid Khan added the BoundedColumnValue partition function, which partitions segments based on column values.</p></li><li><p>Xiaobing Li added the fixed name segment generator, which can be used when you want to replace a specific existing segment.</p></li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="other-changes"></a>Other changes<a class="hash-link" href="#other-changes" title="Direct link to heading">#</a></h2><ul><li>Richard Startin set LZ4 compression as the default for all metrics fields.</li><li>Mark Needham added the <code>ST_Within</code> geospatial function.</li><li>Rong Rong fixed a bug where query stats wouldn't show if there was an error processing the query (e.g. if the query timed out).</li><li>Prashant Pandey fixed the query engine to handle extra columns added to a <code>SELECT *</code> statement.</li><li>Richard Startin added support for forward indexes on JSON columns.</li><li>Rong Rong added the GRPC broker request handler so that data can be streamed back from the server to the broker when processing queries.</li><li>deemoliu made it possible to add a default strategy when using the <a href="https://dev.startree.ai/docs/pinot/recipes/upserts-partial" target="_blank" rel="noopener noreferrer">partial upsert feature</a>.</li><li>Jeff Moszuti added support for the <code>TIMESTAMP</code> data type in the <a href="https://docs.pinot.apache.org/operators/configuration-recommendation-engine" target="_blank" rel="noopener noreferrer">configuration recommendation engine</a>.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="dependency-updates"></a>Dependency updates<a class="hash-link" href="#dependency-updates" title="Direct link to heading">#</a></h2><p>The following dependencies were updated:</p><ul><li>async-http-client because the library moved to a different organization.</li><li>RoaringBitmap to 0.9.25</li><li>JsonPath to 2.7.0</li><li>Kafka to 2.8.1</li><li>Prometheus to 0.16.1</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="resources"></a>Resources<a class="hash-link" href="#resources" title="Direct link to heading">#</a></h2><p>If you want to try out Apache Pinot, the following resources will help you get started:</p><ul><li>Download page: <a href="https://pinot.apache.org/download/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/download/</a></li><li>Getting started: <a href="https://docs.pinot.apache.org/getting-started" target="_blank" rel="noopener noreferrer">https://docs.pinot.apache.org/getting-started</a></li><li>Apache Pinot Recipes: <a href="https://dev.startree.ai/docs/pinot/recipes/" target="_blank" rel="noopener noreferrer">https://dev.startree.ai/docs/pinot/recipes/</a></li><li>Join our Slack channel: <a href="https://communityinviter.com/apps/apache-pinot/apache-pinot" target="_blank" rel="noopener noreferrer">https://communityinviter.com/apps/apache-pinot/apache-pinot</a></li><li>See our upcoming events: <a href="https://www.meetup.com/apache-pinot" target="_blank" rel="noopener noreferrer">https://www.meetup.com/apache-pinot</a></li><li>Follow us on Twitter: <a href="https://twitter.com/startreedata" target="_blank" rel="noopener noreferrer">https://twitter.com/startreedata</a></li><li>Subscribe to our YouTube channel: <a href="https://www.youtube.com/c/StarTree" target="_blank" rel="noopener noreferrer">https://www.youtube.com/c/StarTree</a></li></ul></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/releases">Releases</a></div><div class="col text--right"><a aria-label="Read more about Announcing Apache Pinot 0.10" href="/blog/2022/04/04/Announcing-Apache-Pinot-0-10"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/16/LinkedIn-TextAnalytics">Text analytics on LinkedIn Talent Insights using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-16T00:00:00.000Z">June 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn Talent Insights (LTI) is a platform that helps organizations understand the external labor market and their internal workforce, and enables the long term success of their employees. Users of LTI have the flexibility to construct searches using the various facets of the LinkedIn Economic Graph (skills, titles, location, company, etc.).</p><p><a href="https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/06/ltipinot6.png" alt="Text analytics on LinkedIn Talent Insights using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/text-analytics">Text analytics</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Text analytics on LinkedIn Talent Insights using Apache Pinot" href="/blog/2021/06/16/LinkedIn-TextAnalytics"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/13/DevBlog-Geospatial">Introduction to Geospatial Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-13T00:00:00.000Z">June 13, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Geospatial data has been widely used across the industry, spanning multiple verticals, such as ride-sharing and delivery, transportation infrastructure, defense and intel, public health. Deriving insights from timely and accurate geospatial data could enable mission-critical use cases in the organizations and fuel a vibrant marketplace across the industry. In the design document for this new Pinot feature, we discuss the challenges of analyzing geospatial at scale and propose the geospatial support in Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9</a></p><p><img src="https://miro.medium.com/max/1400/0*1xrDSs9lLZ5dD3zK" alt="Introduction to Geospatial Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/h-3">H3</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Geospatial Queries in Apache Pinot" href="/blog/2021/06/13/DevBlog-Geospatial"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/29/Uber-Charon">Automating Merchant Live Monitoring with Real-Time Analytics - Charon</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-29T00:00:00.000Z">April 29, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, live monitoring and automation of Ops is critical to preserve marketplace health, maintain reliability, and gain efficiency in markets. By the virtue of the word “live”, this monitoring needs to show what is happening now, with prompt access to fresh data, and the ability to recommend appropriate actions based on that data. Uber’s data platform provides the self-serve tools which empower the Ops teams to build their own live monitoring tools, and support their regional teams by building rich solutions.</p><p>For this project, the requirement was to provide merchant level monitoring and handle the edge cases which remain unaddressed by the sophisticated internal marketplace management tools. We used a variety of Uber’s real-time data platform components to build a tool called Charon to reduce impact of poor marketplace reliability on the merchants.</p><p>Read More at <a href="https://eng.uber.com/charon/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/charon/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2021/04/deliverDoor@3x-768x263.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Automating Merchant Live Monitoring with Real-Time Analytics - Charon" href="/blog/2021/04/29/Uber-Charon"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-27T00:00:00.000Z">April 27, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain</p><p><a href="https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38</a></p><p><img src="https://miro.medium.com/max/1400/1*EtqD0vTPEe569jybXCt69w.png" alt="Deploying Apache Pinot at a Large Retail Chain"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deploying Apache Pinot at a Large Retail Chain" href="/blog/2021/04/27/DevBlog-PinotInRetailChain"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-16T00:00:00.000Z">April 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/04/thetasketches2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches" href="/blog/2021/04/16/LinkedIn-Theta"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-08T00:00:00.000Z">April 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Since the 0.6.0 release of Apache Pinot, a new feature was made available for stream ingestion that allows you to upsert events from an immutable log. Typically, upsert is a term used to describe inserting a record into a database if it does not already exist or update it if it does exist. In Apache Pinot’s case, upsert isn’t precisely the same concept, and I wanted to write this blog post to explain why it’s exciting and how you can start using it.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93</a></p><p><img src="https://miro.medium.com/max/1400/0*So3GjHjLY7DJAiaP" alt="Introduction to Upserts in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/upsert">Upsert</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Upserts in Apache Pinot" href="/blog/2021/04/08/DevBlog-UpsertsIntro"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-02-02T00:00:00.000Z">February 2, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://www.startree.ai/wp-content/uploads/2021/05/team-xiang-150x150.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/trino">Trino</a><a class="margin-horiz--sm" href="/blog/tags/presto-sql">PrestoSQL</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Real-time Analytics with Presto and Apache Pinot" href="/blog/2021/02/02/DevBlog-PrestoPinot"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-01-08T00:00:00.000Z">January 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’re going to explore an exciting new world of real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Change Data Analysis with Debezium and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/debezium">Debezium</a><a class="margin-horiz--sm" href="/blog/tags/cdc">CDC</a><a class="margin-horiz--sm" href="/blog/tags/change-data-analysis">Change Data Analysis</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Change Data Analysis with Debezium and Apache Pinot" href="/blog/2021/01/08/DevBlog-DebeziumCDC"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-12-01T00:00:00.000Z">December 1, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/03/pinotincubator2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about From Lambda to Lambda-less Lessons learned" href="/blog/2020/12/01/LinkedIn-Lamda"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/20/Uber-Operating">Operating Apache Pinot at Uber Scale</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-20T00:00:00.000Z">October 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to enable our community operations team to take corrective action. Having a real-time dashboard of different events such as consumer demand, driver availability, or trips happening in a city is crucial for day-to-day operation, incident triaging, and financial intelligence.</p><p>Read More at <a href="https://eng.uber.com/operating-apache-pinot/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/operating-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/10/1224-5.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Operating Apache Pinot at Uber Scale" href="/blog/2020/10/20/Uber-Operating"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis">Deep Analysis of Russian Twitter Trolls</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-16T00:00:00.000Z">October 16, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>The history behind Russian disinformation is a dense and continuously evolving subject. The world’s best research hasn’t seemed to hit the mainstream yet, which made this an excellent opportunity to see if I could use some open source tooling to surface new analytical evidence.</p><p>In this blog post, I’ll show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.</p><p><a href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843" target="_blank" rel="noopener noreferrer">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png" alt="Deep Analysis of Russian Twitter Trolls"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deep Analysis of Russian Twitter Trolls" href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-IngestPlugins">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Leverage Plugins to Ingest Parquet Files from S3 in Pinot" href="/blog/2020/08/08/DevBlog-IngestPlugins"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-SLAApps">Achieving 99th percentile latency SLA using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Achieving 99th percentile latency SLA using Apache Pinot" href="/blog/2020/08/08/DevBlog-SLAApps"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-ScalarUDFs">Utilize UDFs to Supercharge Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Utilize UDFs to Supercharge Queries in Apache Pinot" href="/blog/2020/08/08/DevBlog-ScalarUDFs"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-PinotMonitoring">Monitoring Apache Pinot with JMX, Prometheus and Grafana</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring Apache Pinot with JMX, Prometheus and Grafana" href="/blog/2020/08/08/DevBlog-PinotMonitoring"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-AnomalyDetection">Building a culture around metrics and anomaly detection</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building a culture around metrics and anomaly detection" href="/blog/2020/07/28/DevBlog-AnomalyDetection"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-DevUpStack">Moving developers up the stack with Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Once upon a time, an internet company named LinkedIn faced the challenge of having petabytes of connected data with no way to analyze it in real-time. As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Moving developers up the stack with Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Moving developers up the stack with Apache Pinot" href="/blog/2020/07/28/DevBlog-DevUpStack"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/14/LinkedIn-BatchRealtime">Bridging batch and stream processing for the Recruiter usage statistics dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-14T00:00:00.000Z">July 14, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Batch and streaming computations are often combined together in the Lambda architecture,
+This feature makes it possible to write queries like this:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> nlHomeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> FILTER</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'AL'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> alHomeRuns</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>nlHomeRuns</th><th>alHomeRuns</th></tr></thead><tbody><tr><td>135486</td><td>135990</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="greatest-and-least"></a>greatest and least<a class="hash-link" href="#greatest-and-least" title="Direct link to heading">#</a></h2><p>Richard Startin added the <code>greatest</code> and <code>least</code> functions:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> playerID</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> least</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> greatest</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">5.0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">max</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">hits</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">AS</span><span class="token plain"> hits</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">WHERE</span><span class="token plain"> league </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'NL'</span><span class="token plain"> </span><span class="token operator">AND</span><span class="token plain"> teamID </span><span class="token operator">=</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'SFN'</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">GROUP</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">BY</span><span class="token plain"> playerID</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">LIMIT</span><span class="token plain"> </span><span class="token number">5</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>playerID</th><th>homeRuns</th><th>hits</th></tr></thead><tbody><tr><td>ramirju01</td><td>0</td><td>5</td></tr><tr><td>milneed01</td><td>4</td><td>54</td></tr><tr><td>testani01</td><td>0</td><td>5</td></tr><tr><td>shawbo01</td><td>0</td><td>8</td></tr><tr><td>vogelry01</td><td>0</td><td>12</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="distinctcountsmarthll"></a>DistinctCountSmartHLL<a class="hash-link" href="#distinctcountsmarthll" title="Direct link to heading">#</a></h2><p> Xiaotian (Jackie) Jiang added the <code>DistinctCountSmartHLL</code> aggregation function, which automatically converts the Set to HyperLogLog if the set size grows too big to protect the servers from running out of memory:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> DISTINCTCOUNTSMARTHLL</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">homeRuns</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">'hllLog2m=8;hllConversionThreshold=10'</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> baseballStats</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If we run this query, we'll see the following output:</p><table><thead><tr><th>distinctcountsmarthll(homeRuns)</th></tr></thead><tbody><tr><td>66</td></tr></tbody></table><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="ui-updates"></a>UI updates<a class="hash-link" href="#ui-updates" title="Direct link to heading">#</a></h2><p>There were also a bunch of updates to the Pinot Data Explorer, by Sanket Shah and Johan Adami.</p><p>The display of reported size and estimated size is now in a human readable format:</p><p><img alt="Human readable sizes" src="/assets/images/human-readable-sizes-b8c4009dd53d23da3b8637963827a8de.png"></p><p>Fixes for the following issues:</p><ul><li>Error messages weren't showing on the UI when an invalid operation is attempted:</li></ul><p><img alt="A backwards incompatible attempted schema change" src="/assets/images/backwards-incompatible-99886dcd0be55a8100a7d6c5f3da3bda.png"></p><ul><li>Query console goes blank on syntax error.</li><li>Query console cannot show query result when multiple columns have the same name.</li><li>Adding extra fields after <code>SELECT *</code> would throw a NullPointerException.</li><li>Some queries were returning <code>--</code> instead of <code>0</code>.</li><li>Query console couldn't show the query result if multiple columns had the same name.</li><li>Pinot Dashboard tenant view showing the incorrect amount of servers and brokers.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="realtimetooffline-task"></a>RealTimeToOffline Task<a class="hash-link" href="#realtimetooffline-task" title="Direct link to heading">#</a></h2><p>Xiaotian (Jackie) Jiang made some fixes to the <a href="https://dev.startree.ai/docs/pinot/recipes/real-time-offline-job" target="_blank" rel="noopener noreferrer">RealTimeToOffline job</a> to handle time gaps and proceed to the next time window when no segment matches the current one.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="empty-quickstart"></a>Empty QuickStart<a class="hash-link" href="#empty-quickstart" title="Direct link to heading">#</a></h2><p>Kenny Bastani added an empty QuickStart command, which lets you quickly spin up an empty Pinot cluster:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">docker</span><span class="token plain"> run </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">8000</span><span class="token plain">:8000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">9000</span><span class="token plain">:9000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> apachepinot/pinot:0.10.0 QuickStart </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -type empty</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>You can then ingest your own dataset without needing to worry about spinning up each of the Pinot components individually.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="data-ingestion"></a>Data Ingestion<a class="hash-link" href="#data-ingestion" title="Direct link to heading">#</a></h2><ul><li><p>Richard Startin fixed some issues with real-time ingestion where consumption of messages would stop if a bad batch of messages was consumed from Kafka.</p></li><li><p>Mohemmad Zaid Khan added the BoundedColumnValue partition function, which partitions segments based on column values.</p></li><li><p>Xiaobing Li added the fixed name segment generator, which can be used when you want to replace a specific existing segment.</p></li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="other-changes"></a>Other changes<a class="hash-link" href="#other-changes" title="Direct link to heading">#</a></h2><ul><li>Richard Startin set LZ4 compression as the default for all metrics fields.</li><li>Mark Needham added the <code>ST_Within</code> geospatial function.</li><li>Rong Rong fixed a bug where query stats wouldn't show if there was an error processing the query (e.g. if the query timed out).</li><li>Prashant Pandey fixed the query engine to handle extra columns added to a <code>SELECT *</code> statement.</li><li>Richard Startin added support for forward indexes on JSON columns.</li><li>Rong Rong added the GRPC broker request handler so that data can be streamed back from the server to the broker when processing queries.</li><li>deemoliu made it possible to add a default strategy when using the <a href="https://dev.startree.ai/docs/pinot/recipes/upserts-partial" target="_blank" rel="noopener noreferrer">partial upsert feature</a>.</li><li>Jeff Moszuti added support for the <code>TIMESTAMP</code> data type in the <a href="https://docs.pinot.apache.org/operators/configuration-recommendation-engine" target="_blank" rel="noopener noreferrer">configuration recommendation engine</a>.</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="dependency-updates"></a>Dependency updates<a class="hash-link" href="#dependency-updates" title="Direct link to heading">#</a></h2><p>The following dependencies were updated:</p><ul><li>async-http-client because the library moved to a different organization.</li><li>RoaringBitmap to 0.9.25</li><li>JsonPath to 2.7.0</li><li>Kafka to 2.8.1</li><li>Prometheus to 0.16.1</li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="resources"></a>Resources<a class="hash-link" href="#resources" title="Direct link to heading">#</a></h2><p>If you want to try out Apache Pinot, the following resources will help you get started:</p><ul><li>Download page: <a href="https://pinot.apache.org/download/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/download/</a></li><li>Getting started: <a href="https://docs.pinot.apache.org/getting-started" target="_blank" rel="noopener noreferrer">https://docs.pinot.apache.org/getting-started</a></li><li>Apache Pinot Recipes: <a href="https://dev.startree.ai/docs/pinot/recipes/" target="_blank" rel="noopener noreferrer">https://dev.startree.ai/docs/pinot/recipes/</a></li><li>Join our Slack channel: <a href="https://communityinviter.com/apps/apache-pinot/apache-pinot" target="_blank" rel="noopener noreferrer">https://communityinviter.com/apps/apache-pinot/apache-pinot</a></li><li>See our upcoming events: <a href="https://www.meetup.com/apache-pinot" target="_blank" rel="noopener noreferrer">https://www.meetup.com/apache-pinot</a></li><li>Follow us on Twitter: <a href="https://twitter.com/startreedata" target="_blank" rel="noopener noreferrer">https://twitter.com/startreedata</a></li><li>Subscribe to our YouTube channel: <a href="https://www.youtube.com/c/StarTree" target="_blank" rel="noopener noreferrer">https://www.youtube.com/c/StarTree</a></li></ul></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/releases">Releases</a></div><div class="col text--right"><a aria-label="Read more about Announcing Apache Pinot 0.10" href="/blog/2022/04/04/Announcing-Apache-Pinot-0-10"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/16/LinkedIn-TextAnalytics">Text analytics on LinkedIn Talent Insights using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-16T00:00:00.000Z">June 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn Talent Insights (LTI) is a platform that helps organizations understand the external labor market and their internal workforce, and enables the long term success of their employees. Users of LTI have the flexibility to construct searches using the various facets of the LinkedIn Economic Graph (skills, titles, location, company, etc.).</p><p><a href="https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/06/ltipinot6.png" alt="Text analytics on LinkedIn Talent Insights using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/data">Data</a><a class="margin-horiz--sm" href="/blog/tags/text-analytics">Text analytics</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Text analytics on LinkedIn Talent Insights using Apache Pinot" href="/blog/2021/06/16/LinkedIn-TextAnalytics"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/06/13/DevBlog-Geospatial">Introduction to Geospatial Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-06-13T00:00:00.000Z">June 13, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Geospatial data has been widely used across the industry, spanning multiple verticals, such as ride-sharing and delivery, transportation infrastructure, defense and intel, public health. Deriving insights from timely and accurate geospatial data could enable mission-critical use cases in the organizations and fuel a vibrant marketplace across the industry. In the design document for this new Pinot feature, we discuss the challenges of analyzing geospatial at scale and propose the geospatial support in Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9</a></p><p><img src="https://miro.medium.com/max/1400/0*1xrDSs9lLZ5dD3zK" alt="Introduction to Geospatial Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/h-3">H3</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Geospatial Queries in Apache Pinot" href="/blog/2021/06/13/DevBlog-Geospatial"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/29/Uber-Charon">Automating Merchant Live Monitoring with Real-Time Analytics - Charon</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-29T00:00:00.000Z">April 29, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, live monitoring and automation of Ops is critical to preserve marketplace health, maintain reliability, and gain efficiency in markets. By the virtue of the word “live”, this monitoring needs to show what is happening now, with prompt access to fresh data, and the ability to recommend appropriate actions based on that data. Uber’s data platform provides the self-serve tools which empower the Ops teams to build their own live monitoring tools, and support their regional teams by building rich solutions.</p><p>For this project, the requirement was to provide merchant level monitoring and handle the edge cases which remain unaddressed by the sophisticated internal marketplace management tools. We used a variety of Uber’s real-time data platform components to build a tool called Charon to reduce impact of poor marketplace reliability on the merchants.</p><p>Read More at <a href="https://eng.uber.com/charon/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/charon/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2021/04/deliverDoor@3x-768x263.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Automating Merchant Live Monitoring with Real-Time Analytics - Charon" href="/blog/2021/04/29/Uber-Charon"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/27/DevBlog-PinotInRetailChain">Deploying Apache Pinot at a Large Retail Chain</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-27T00:00:00.000Z">April 27, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain</p><p><a href="https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38</a></p><p><img src="https://miro.medium.com/max/1400/1*EtqD0vTPEe569jybXCt69w.png" alt="Deploying Apache Pinot at a Large Retail Chain"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deploying Apache Pinot at a Large Retail Chain" href="/blog/2021/04/27/DevBlog-PinotInRetailChain"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/16/LinkedIn-Theta">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-16T00:00:00.000Z">April 16, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/04/thetasketches2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches" href="/blog/2021/04/16/LinkedIn-Theta"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/04/08/DevBlog-UpsertsIntro">Introduction to Upserts in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-04-08T00:00:00.000Z">April 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Since the 0.6.0 release of Apache Pinot, a new feature was made available for stream ingestion that allows you to upsert events from an immutable log. Typically, upsert is a term used to describe inserting a record into a database if it does not already exist or update it if it does exist. In Apache Pinot’s case, upsert isn’t precisely the same concept, and I wanted to write this blog post to explain why it’s exciting and how you can start using it.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93</a></p><p><img src="https://miro.medium.com/max/1400/0*So3GjHjLY7DJAiaP" alt="Introduction to Upserts in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/upsert">Upsert</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introduction to Upserts in Apache Pinot" href="/blog/2021/04/08/DevBlog-UpsertsIntro"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/02/02/DevBlog-PrestoPinot">Real-time Analytics with Presto and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-02-02T00:00:00.000Z">February 2, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://www.startree.ai/wp-content/uploads/2021/05/team-xiang-150x150.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.</p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/" target="_blank" rel="noopener noreferrer">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/" target="_blank" rel="noopener noreferrer">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx" alt="Real-time Analytics with Presto and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/trino">Trino</a><a class="margin-horiz--sm" href="/blog/tags/presto-sql">PrestoSQL</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Real-time Analytics with Presto and Apache Pinot" href="/blog/2021/02/02/DevBlog-PrestoPinot"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2021/01/08/DevBlog-DebeziumCDC">Change Data Analysis with Debezium and Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2021-01-08T00:00:00.000Z">January 8, 2021</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’re going to explore an exciting new world of real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7" target="_blank" rel="noopener noreferrer">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Change Data Analysis with Debezium and Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/debezium">Debezium</a><a class="margin-horiz--sm" href="/blog/tags/cdc">CDC</a><a class="margin-horiz--sm" href="/blog/tags/change-data-analysis">Change Data Analysis</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Change Data Analysis with Debezium and Apache Pinot" href="/blog/2021/01/08/DevBlog-DebeziumCDC"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/12/01/LinkedIn-Lamda">From Lambda to Lambda-less Lessons learned</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-12-01T00:00:00.000Z">December 1, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/03/pinotincubator2.png" alt="From Lambda to Lambda-less Lessons learned"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about From Lambda to Lambda-less Lessons learned" href="/blog/2020/12/01/LinkedIn-Lamda"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/20/Uber-Operating">Operating Apache Pinot at Uber Scale</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-20T00:00:00.000Z">October 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to enable our community operations team to take corrective action. Having a real-time dashboard of different events such as consumer demand, driver availability, or trips happening in a city is crucial for day-to-day operation, incident triaging, and financial intelligence.</p><p>Read More at <a href="https://eng.uber.com/operating-apache-pinot/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/operating-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/10/1224-5.png" alt="Operating Apache Pinot at Uber Scale"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Operating Apache Pinot at Uber Scale" href="/blog/2020/10/20/Uber-Operating"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis">Deep Analysis of Russian Twitter Trolls</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-10-16T00:00:00.000Z">October 16, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/kenny.jpeg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>The history behind Russian disinformation is a dense and continuously evolving subject. The world’s best research hasn’t seemed to hit the mainstream yet, which made this an excellent opportunity to see if I could use some open source tooling to surface new analytical evidence.</p><p>In this blog post, I’ll show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.</p><p><a href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843" target="_blank" rel="noopener noreferrer">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png" alt="Deep Analysis of Russian Twitter Trolls"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Deep Analysis of Russian Twitter Trolls" href="/blog/2020/10/16/DevBlog-TwitterTrollAnalysis"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-IngestPlugins">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.</p><p>In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -</p><ul><li>pinot-batch-ingestion-spark</li><li>pinot-s3</li><li>pinot-parquet</li></ul><p><a href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d</a></p><p><img src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP" alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Leverage Plugins to Ingest Parquet Files from S3 in Pinot" href="/blog/2020/08/08/DevBlog-IngestPlugins"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-PinotMonitoring">Monitoring Apache Pinot with JMX, Prometheus and Grafana</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?</p><p><a href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png" alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/monitoring">Monitoring</a><a class="margin-horiz--sm" href="/blog/tags/jmx">JMX</a><a class="margin-horiz--sm" href="/blog/tags/prometheus">Prometheus</a><a class="margin-horiz--sm" href="/blog/tags/grafana">Grafana</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring Apache Pinot with JMX, Prometheus and Grafana" href="/blog/2020/08/08/DevBlog-PinotMonitoring"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-SLAApps">Achieving 99th percentile latency SLA using Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.</p><p><a href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC" alt="Achieving 99th percentile latency SLA using Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Achieving 99th percentile latency SLA using Apache Pinot" href="/blog/2020/08/08/DevBlog-SLAApps"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/08/08/DevBlog-ScalarUDFs">Utilize UDFs to Supercharge Queries in Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-08-08T00:00:00.000Z">August 8, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="PinotDev"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">PinotDev</a></div><small class="avatar__subtitle">Pinot Editorial Team</small></div></div></header><div class="markdown"><p>Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to <a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer">https://pinot.apache.org/</a> to get started with Apache Pinot.</p><p>While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.</p><p>Scalar Functions that allow users to write and add their functions as a plugin.</p><p><a href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1" target="_blank" rel="noopener noreferrer">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK" alt="Utilize UDFs to Supercharge Queries in Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/sla">SLA</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Utilize UDFs to Supercharge Queries in Apache Pinot" href="/blog/2020/08/08/DevBlog-ScalarUDFs"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-AnomalyDetection">Building a culture around metrics and anomaly detection</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5" alt="Building a culture around metrics and anomaly detection"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building a culture around metrics and anomaly detection" href="/blog/2020/07/28/DevBlog-AnomalyDetection"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/28/DevBlog-DevUpStack">Moving developers up the stack with Apache Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-28T00:00:00.000Z">July 28, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>Once upon a time, an internet company named LinkedIn faced the challenge of having petabytes of connected data with no way to analyze it in real-time. As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png" alt="Moving developers up the stack with Apache Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Moving developers up the stack with Apache Pinot" href="/blog/2020/07/28/DevBlog-DevUpStack"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/07/14/LinkedIn-BatchRealtime">Bridging batch and stream processing for the Recruiter usage statistics dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-07-14T00:00:00.000Z">July 14, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Batch and streaming computations are often combined together in the Lambda architecture,
but carry the cost of maintaining two different code bases for the same logic.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/07/bridging-1.png" alt="Bridging batch and stream processing for the Recruiter usage statistics dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Bridging batch and stream processing for the Recruiter usage statistics dashboard" href="/blog/2020/07/14/LinkedIn-BatchRealtime"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/06/29/LinkedIn-TalentInsight">Building LinkedIn Talent Insights to democratize data-driven decision making</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-06-29T00:00:00.000Z">June 29, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn is a mission-driven organization, and we take our mission of “connecting the world's professionals to make them more productive and successful” very seriously.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/building-linkedin-talent-insights" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/building-linkedin-talent-insights</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/06/lti-1.png" alt="Building LinkedIn Talent Insights to democratize data-driven decision making"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Building LinkedIn Talent Insights to democratize data-driven decision making" href="/blog/2020/06/29/LinkedIn-TalentInsight"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/06/25/LinkedIn-SmartAlerts">Monitoring business performance data with ThirdEye smart alerts</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-06-25T00:00:00.000Z">June 25, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Explain how ThirdEye smart alerts and automated dashboards helped the LinkedIn Premium business operations team monitor key metrics—such as new free trial signups—for the timely detection of outliers in business performance data.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/06/thirdeye_business_performance-3.png" alt="Monitoring business performance data with ThirdEye smart alerts"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Monitoring business performance data with ThirdEye smart alerts" href="/blog/2020/06/25/LinkedIn-SmartAlerts"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents">Using Apache Pinot and Kafka to Analyze GitHub Events</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-04-10T00:00:00.000Z">April 10, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pinot.apache.org/authors/pinot_team.jpg" alt="Kenny Bastani"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://medium.com/apache-pinot-developer-blog" target="_blank" rel="noopener noreferrer">Kenny Bastani</a></div><small class="avatar__subtitle">Kenny Bastani</small></div></div></header><div class="markdown"><p>In this blog post, we’ll show you how Pinot and Kafka can be used together to ingest, query, and visualize event streams sourced from the public GitHub API. For the step-by-step instructions, please visit our documentation, which will guide you through the specifics of running this example in your development environment.</p><p>Read More at <a href="https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7" target="_blank" rel="noopener noreferrer">https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7</a></p><p><img src="https://miro.medium.com/max/4728/1*eR64jBH1ZvC3uNfPP56p5g.png" alt="Using Apache Pinot and Kafka to Analyze GitHub Events"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/dev-blog">DevBlog</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Using Apache Pinot and Kafka to Analyze GitHub Events" href="/blog/2020/04/10/DevBlog-AnalyzeGitEvents"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/02/20/LinkedIn-Thirdeye">Analyzing anomalies with ThirdEye</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-02-20T00:00:00.000Z">February 20, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Focus on the behind-the-scenes functionalities of ThirdEye that analyze the multi-dimensional time series data
and help our engineers understand why these anomalies happened through a dimension heatmap</p><p><a href="https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye" target="_blank" rel="noopener noreferrer">Read More at https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/02/datacube-1.png" alt="Analyzing anomalies with ThirdEye"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Analyzing anomalies with ThirdEye" href="/blog/2020/02/20/LinkedIn-Thirdeye"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2020/01/15/Pinot-Presto-SQL">Engineering SQL Support on Apache Pinot at Uber</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2020-01-15T00:00:00.000Z">January 15, 2020</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>Uber leverages real-time analytics on aggregate data to improve the user experience across our products, from fighting fraudulent behavior on Uber Eats to forecasting demand on our platform.</p><p>To resolve these issues, we built a solution that linked Presto, a query engine that supports full ANSI SQL, and Pinot, a real-time OLAP (online analytical processing) datastore. This married solution allows users to write ad-hoc SQL queries, empowering teams to unlock significant analysis capabilities.</p><p><a href="https://eng.uber.com/engineering-sql-support-on-apache-pinot/" target="_blank" rel="noopener noreferrer">Read More at https://eng.uber.com/engineering-sql-support-on-apache-pinot/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/01/Header-SQL-768x329.png" alt="SQL Support on Apache Pinot at Uber"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber">Uber</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/presto">Presto</a><a class="margin-horiz--sm" href="/blog/tags/sql">SQL</a></div><div class="col text--right"><a aria-label="Read more about Engineering SQL Support on Apache Pinot at Uber" href="/blog/2020/01/15/Pinot-Presto-SQL"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/07/11/LinkedIn-AutoTune">Auto-tuning Pinot real-time consumption</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-07-11T00:00:00.000Z">July 11, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Focus on Auto tuning Pinot, a scalable distributed columnar OLAP data store developed at LinkedIn, delivers real-time analytics for site-facing use cases such as LinkedIn's Who viewed my profile, Talent insights, and more.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/07/bridging-1.png" alt="Bridging batch and stream processing for the Recruiter usage statistics dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Auto-tuning Pinot real-time consumption" href="/blog/2019/07/11/LinkedIn-AutoTune"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/06/14/LinkedIn-StarTree">Star-tree index - Powering fast aggregations on Pinot</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-06-14T00:00:00.000Z">June 14, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>Introduced Star-Tree index to utilize the pre-aggregated documents in a smart way that achieves low query latencies,
while using the storage space efficiently.</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2019/06/star-tree-index--powering-fast-aggregations-on-pinot" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2019/06/star-tree-index--powering-fast-aggregations-on-pinot</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png" alt="Star-tree index - Powering fast aggregations on Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Star-tree index - Powering fast aggregations on Pinot" href="/blog/2019/06/14/LinkedIn-StarTree"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2019/01/09/LinkedIn-IntroThirdEye">Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2019-01-09T00:00:00.000Z">January 9, 2019</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>ThirdEye is a comprehensive platform for real-time monitoring of metrics that covers a wide variety of use-cases.
LinkedIn relies on ThirdEye to monitor site performance, track member growth, understand adoption of new features,
flag sustained attempts to circumvent system security, and many other areas</p><p>Read More at <a href="https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png" alt="Star-tree index - Powering fast aggregations on Pinot"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/third-eye">ThirdEye</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform" href="/blog/2019/01/09/LinkedIn-IntroThirdEye"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2017/09/17/Restaurant-Manager">Engineering Restaurant Manager - UberEATS Analytics Dashboard</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2017-09-17T00:00:00.000Z">September 17, 2017</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://pbs.twimg.com/profile_images/1192909783856103427/6A4s8gW2_400x400.png" alt="Uber"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://eng.uber.com/category/articles/uberdata/" target="_blank" rel="noopener noreferrer">Uber</a></div><small class="avatar__subtitle">Uber Data Team</small></div></div></header><div class="markdown"><p>At Uber, we use data analytics to architect more magical user experiences across our products. Whenever possible, we harness these data engineering capabilities to empower our partners to better serve their customers. For instance, in late 2016, the UberEATS engineering team built a comprehensive analytics dashboard that provides restaurant partners with additional insights about the health of their business.</p><p>Read More at <a href="https://eng.uber.com/restaurant-manager/" target="_blank" rel="noopener noreferrer">https://eng.uber.com/restaurant-manager/</a></p><p><img src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2017/09/image4-2.png" alt="Engineering Restaurant Manager - UberEATS Analytics Dashboard"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/uber-data">Uber Data</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a><a class="margin-horiz--sm" href="/blog/tags/financial-intelligence">financial intelligence</a></div><div class="col text--right"><a aria-label="Read more about Engineering Restaurant Manager - UberEATS Analytics Dashboard" href="/blog/2017/09/17/Restaurant-Manager"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2015/06/10/Open-Sourcing-Pinot">Open Sourcing Pinot - Scaling the Wall of Real-Time Analytics</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2015-06-10T00:00:00.000Z">June 10, 2015</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://github.com/kishoreg" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://avatars.githubusercontent.com/kishoreg" alt="Kishore Gopalakrishna"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://github.com/kishoreg" target="_blank" rel="noopener noreferrer">Kishore Gopalakrishna</a></div><small class="avatar__subtitle">Apache Pinot Team</small></div></div></header><div class="markdown"><p>LinkedIn’s real-time analytics infrastructure, that we built to allow us to slice and dice across billions of rows in real-time across a wide variety of products. Read More at (<a href="https://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics</a>)</p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/a-b-testing">A/B Testing</a><a class="margin-horiz--sm" href="/blog/tags/infrastructure">Infrastructure</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about Open Sourcing Pinot - Scaling the Wall of Real-Time Analytics" href="/blog/2015/06/10/Open-Sourcing-Pinot"><b>Read More</b></a></div></footer></article><article class="margin-bottom--xl"><header><h2 class="blogPostTitle_d4p0"><a href="/blog/2015/05/16/LinkedIn-Scaling">A Brief History of Scaling LinkedIn</a></h2><div class="blogPostData_-Im+ margin-vert--md"><time datetime="2015-05-16T00:00:00.000Z">May 16, 2015</time> · One min read</div><div class="avatar margin-vert--md"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer" class="avatar__photo-link avatar__photo"><img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/e9/Linkedin_icon.svg/512px-Linkedin_icon.svg.png" alt="LinkedIn"></a><div class="avatar__intro"><div class="avatar__name"><a href="https://engineering.linkedin.com/blog/topic/pinot" target="_blank" rel="noopener noreferrer">LinkedIn</a></div><small class="avatar__subtitle">LinkedIn Engineering Team</small></div></div></header><div class="markdown"><p>LinkedIn started in 2003 with the goal of connecting to your network for better job opportunities. It had only 2,700 members the first week. Fast forward many years, and LinkedIn’s product portfolio, member base, and server load has grown tremendously.</p><p>Today, LinkedIn operates globally with more than 350 million members. We serve tens of thousands of web pages every second of every day. We've hit our mobile moment where mobile accounts for more than 50 percent of all global traffic. All those requests are fetching data from our backend systems, which in turn handle millions of queries per second.</p><p>Read More at <a href="https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin" target="_blank" rel="noopener noreferrer">https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin</a></p><p><img src="https://content.linkedin.com/content/dam/engineering/en-us/blog/migrated/data_centers_pops_0.png" alt="A Brief History of Scaling LinkedIn"></p></div><footer class="row docusaurus-mt-lg"><div class="col"><b>Tags:</b><a class="margin-horiz--sm" href="/blog/tags/pinot">Pinot</a><a class="margin-horiz--sm" href="/blog/tags/linked-in">LinkedIn</a><a class="margin-horiz--sm" href="/blog/tags/real-time-data-platform">real-time data platform</a><a class="margin-horiz--sm" href="/blog/tags/realtime">Realtime</a><a class="margin-horiz--sm" href="/blog/tags/analytics">Analytics</a><a class="margin-horiz--sm" href="/blog/tags/user-facing-analytics">User-Facing Analytics</a></div><div class="col text--right"><a aria-label="Read more about A Brief History of Scaling LinkedIn" href="/blog/2015/05/16/LinkedIn-Scaling"><b>Read More</b></a></div></footer></article></main></div></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" href="/who_uses">Who uses Apache Pinot?</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" class="footer__link-item">Components</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/basics/architecture" target="_blank" rel="noopener 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class="footer__link-item">ThirdEye</a></li></ul></div><div class="col footer__col"><h4 class="footer__title">Docs</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/getting-started" target="_blank" rel="noopener noreferrer" class="footer__link-item">Getting Started</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" class="footer__link-item">Pinot Components</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/users" target="_blank" rel="noopener noreferrer" class="footer__link-item">User Guide</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/operating-pinot" target="_blank" rel="noopener noreferrer" class="footer__link-item">Administration</a></li></ul></div><div class="col footer__col"><h4 class="footer__title">Community</h4><ul class="footer__items"><li class="footer__item"><a href="https://join.slack.com/t/apache-pinot/shared_invite/zt-5z7pav2f-yYtjZdVA~EDmrGkho87Vzw" target="_blank" rel="noopener noreferrer" class="footer__link-item">Slack</a></li><li class="footer__item"><a href="https://github.com/apache/pinot" target="_blank" rel="noopener noreferrer" class="footer__link-item">Github</a></li><li class="footer__item"><a href="https://twitter.com/ApachePinot" target="_blank" rel="noopener noreferrer" class="footer__link-item">Twitter</a></li><li class="footer__item"><a href="mailto:dev-subscribe@pinot.apache.org?Subject=SubscribeToPinot" target="_blank" rel="noopener noreferrer" class="footer__link-item">Mailing List</a></li></ul></div><div class="col footer__col"><h4 class="footer__title">Apache</h4><ul class="footer__items"><li class="footer__item"><a href="https://www.apache.org/events/current-event" target="_blank" rel="noopener noreferrer" class="footer__link-item">Events</a></li><li class="footer__item"><a href="https://www.apache.org/foundation/thanks.html" target="_blank" rel="noopener noreferrer" class="footer__link-item">Thanks</a></li><li class="footer__item"><a href="https://www.apache.org/licenses" target="_blank" rel="noopener noreferrer" class="footer__link-item">License</a></li><li class="footer__item"><a href="https://www.apache.org/security" target="_blank" rel="noopener noreferrer" class="footer__link-item">Security</a></li><li class="footer__item"><a href="https://www.apache.org/foundation/sponsorship.html" target="_blank" rel="noopener noreferrer" class="footer__link-item">Sponsorship</a></li><li class="footer__item"><a href="https://www.apache.org" target="_blank" rel="noopener noreferrer" class="footer__link-item">Foundation</a></li></ul></div></div><div class="footer__bottom text--center"><div class="margin-bottom--sm"><a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footerLogoLink_94kH"><img src="/img/logo.svg" alt="Apache Pinot™" class="themedImage_TMUO themedImage--light_4Vu1 footer__logo"><img src="/img/logo.svg" alt="Apache Pinot™" class="themedImage_TMUO themedImage--dark_uzRr footer__logo"></a></div><div class="footerCopyright_-piB">Copyright © 2022 The Apache Software Foundation.<br>Apache Pinot, Pinot, Apache, the Apache feather logo, and the Apache Pinot project logo are registered trademarks of The Apache Software Foundation.<br><br>This page has references to third party software - Presto, PrestoDB, ThirdEye, Trino, TrinoDB, that are not part of the Apache Software Foundation and are not covered under the Apache License.</div></div></div></footer></div>
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@@ -12,8 +12,8 @@
<link rel="search" type="application/opensearchdescription+xml" title="Apache Pinot™" href="/opensearch.xml">
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Ubuntu|Roboto|Source+Code+Pro">
<link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">Features of Pinot | Apache Pinot™</title><meta data-react-helmet="true" property="og:url" content="https://pinot.apache.org/docs/about/features_of_pinot"><meta data-react-helmet="true" name="docsearch:language" content="en"><meta data-react-helmet="true" name="docsearch:version" content="current"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="docs-default-current"><meta data-react-helmet="true" property="og:title" content="Features of Pinot | Apache Pinot™"><meta data-react-helmet="true" name="description" content="- A column-oriented database with various compression schemes such as Run Length, Fixed Bit Length"><meta data-react-helmet="true" property="og:description" content="- A column-oriented database with various compression schemes such as Run Length, Fixed Bit Length"><link data-react-helmet="true" rel="shortcut icon" href="/img/favicon.ico"><link data-react-helmet="true" rel="canonical" href="https://pinot.apache.org/docs/about/features_of_pinot"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/docs/about/features_of_pinot" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/docs/about/features_of_pinot" hreflang="x-default"><link data-react-helmet="true" rel="preconnect" href="https://BH4D9OD16A-dsn.algolia.net" crossorigin="anonymous"><link rel="stylesheet" href="/assets/css/styles.f0793de6.css">
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@@ -21,7 +21,7 @@
Not a replacement for search engine i.e Full text search, relevance not supported
Query cannot span across multiple tables.
Pinot works very well for querying time series data with lots of Dimensions and Metrics. <br></p><p>For example:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI SQL"><pre tabindex="0" class="prism-code language-SQL codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">SELECT distinctCount(advertiserId), daysSinceEpoch FROM AdAnalyticsTable</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> WHERE (daysSinceEpoch >= 17849 AND daysSinceEpoch <= 17856)</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> GROUP BY daysSinceEpoch</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> ORDER BY daysSinceEpoch</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> LIMIT 100</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI SQL"><pre tabindex="0" class="prism-code language-SQL codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">SELECT sum(impressions), daysSinceEpoch, advertiserId FROM AdAnalyticsTable</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> WHERE (daysSinceEpoch >= 17824 and daysSinceEpoch <= 17854) AND adveriserId = '1234356789'</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> GROUP BY daysSinceEpoch, advertiserId</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> ORDER BY sum(impressions) DESC</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> LIMIT 100</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI SQL"><pre tabindex="0" class="prism-code language-SQL codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">SELECT sum(cost), advertiserId FROM AdAnalyticsTable GROUP BY advertiserId ORDER BY sum(cost) DESC LIMIT 50</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></div><footer class="row docusaurus-mt-lg"><div class="col"><a href="https://github.com/apache/pinot/edit/master/website/docs/about/features_of_pinot.md" target="_blank" rel="noreferrer noopener"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" 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class="markdown"><header><h1 class="h1Heading_dC7a">What is Pinot</h1></header><p>Apache Pinot is a realtime distributed OLAP datastore, which is used to deliver scalable real time analytics with low latency. It can ingest data from batch data sources (such as HDFS, S3, Azure Data Lake, Google Cloud Storage) as well as streaming sources (such as Kafka). Pinot is designed to scale horizontally, so that it can scale to larger data sets and higher query rates as needed.</p><p><img alt="Pinot" src="/assets/images/pinot-overview-f1b5a17c2e53228461f1510ea2fe0e9b.png"></p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="when-should-i-use-it"></a>When should I use it?<a class="hash-link" href="#when-should-i-use-it" title="Direct link to heading">#</a></h2><p>Pinot is designed to answer OLAP queries with low latency. It is suited in contexts where fast analytics, such as aggregations, are needed on immutable data, possibly, with real-time data ingestion.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="when-should-i-not-use-it"></a>When should I not use it?<a class="hash-link" href="#when-should-i-not-use-it" title="Direct link to heading">#</a></h2><p>Pinot is not a replacement for your database, nor a search engine. It addresses fast analytics on immutable data and it is not thought by design, to handle data updates or deletions. Joins are currently not supported, but this problem can be overcome by using <a href="/docs/about/(https://trino.io/)">Trino</a> or <a href="/docs/about/(https://prestodb.io/)">PrestoDB</a> for querying Pinot.</p><p>For more information about <a href="https://trino.io/docs/current/connector/pinot.html" target="_blank" rel="noopener noreferrer">Trino Pinot Connector</a>, see <a href="https://docs.pinot.apache.org/integrations/trino" target="_blank" rel="noopener noreferrer">Trino Pinot Integration</a>.</p><p>For more information about <a href="https://prestodb.io/docs/current/connector/pinot.html" target="_blank" rel="noopener noreferrer">Presto Pinot Connector</a>, see <a href="https://docs.pinot.apache.org/integrations/presto" target="_blank" rel="noopener noreferrer">Presto Pinot Integration</a>.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="quick-example"></a>Quick example<a class="hash-link" href="#quick-example" title="Direct link to heading">#</a></h2><p>Pinot works very well for querying time series data with lots of Dimensions and Metrics.
Filters and aggregations are easy and fast.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI SQL"><pre tabindex="0" class="prism-code language-SQL codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">SELECT sum(clicks), sum(impressions) FROM AdAnalyticsTable</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> WHERE ((daysSinceEpoch >= 17849 AND daysSinceEpoch <= 17856)) AND accountId IN (123456789)</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> GROUP BY daysSinceEpoch TOP 100</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="who-uses-pinot"></a>Who uses Pinot?<a class="hash-link" href="#who-uses-pinot" title="Direct link to heading">#</a></h2><p>Pinot powers several big players, including LinkedIn, Uber, Factual, Weibo, Slack and more..</p></div><footer class="row docusaurus-mt-lg"><div class="col"><a href="https://github.com/apache/pinot/edit/master/website/docs/about/what_is_pinot.md" target="_blank" rel="noreferrer noopener"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" class="iconEdit_mS5F" aria-hidden="true"><g><path d="m34.5 11.7l-3 3.1-6.3-6.3 3.1-3q0.5-0.5 1.2-0.5t1.1 0.5l3.9 3.9q0.5 0.4 0.5 1.1t-0.5 1.2z m-29.5 17.1l18.4-18.5 6.3 6.3-18.4 18.4h-6.3v-6.2z"></path></g></svg>Edit this page</a></div><div class="col lastUpdated_wj+Z"></div></footer></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages navigation"><div class="pagination-nav__item"></div><div class="pagination-nav__item pagination-nav__item--next"><a class="pagination-nav__link" href="/docs/about/features_of_pinot"><div class="pagination-nav__sublabel">Next</div><div 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<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Ubuntu|Roboto|Source+Code+Pro">
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diff --git a/docs/administration/index.html b/docs/administration/index.html
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+<link rel="preload" href="/assets/js/runtime~main.82dd65bd.js" as="script">
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diff --git a/docs/administration/installation/cloud/aws/index.html b/docs/administration/installation/cloud/aws/index.html
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--- a/docs/administration/installation/cloud/aws/index.html
+++ b/docs/administration/installation/cloud/aws/index.html
@@ -12,14 +12,14 @@
<link rel="search" type="application/opensearchdescription+xml" title="Apache Pinot™" href="/opensearch.xml">
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class="docItemContainer_oiyr"><article><div class="markdown"><header><h1 class="h1Heading_dC7a">AWS Quickstart</h1></header><p>This document provides the basic instruction to set up a Kubernetes Cluster on <a href="https://aws.amazon.com/eks/" target="_blank" rel="noopener noreferrer">Amazon Elastic Kubernetes Service (Amazon EKS)</a></p><div class="alert alert--info alert--icon" role="alert"><i class="feather icon-info"></i> Because Pinot must be manually updated on Nix, new Pinot releases will be delayed. Generally new Pinot releases are made available within a few days.</div><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="tooling-installation"></a>Tooling Installation<a class="hash-link" href="#tooling-installation" title="Direct link to heading">#</a></h2><ol><li><p>Install Pinot</p><p>Please follow this link (<a href="https://kubernetes.io/docs/tasks/tools/install-kubectl" target="_blank" rel="noopener noreferrer">https://kubernetes.io/docs/tasks/tools/install-kubectl</a>) to install kubectl.</p><p>For Mac User</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">brew </span><span class="token function" style="color:rgb(80, 250, 123)">install</span><span class="token plain"> kubernetes-cli</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>Please check kubectl version after installation:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">kubectl version</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><div class="alert alert--info" role="alert">QuickStart scripts are tested under kubectl client version v1.16.3 and server version v1.13.12</div></li><li><p>Install Helm</p><p>Please follow this link (<a href="https://helm.sh/docs/using_helm/#installing-helm" target="_blank" rel="noopener noreferrer">https://helm.sh/docs/using_helm/#installing-helm</a>) to install helm.</p><p>For Mac User</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">brew </span><span class="token function" style="color:rgb(80, 250, 123)">install</span><span class="token plain"> kubernetes-helm</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>Please check helm version after installation.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">helm version</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><div class="alert alert--info" role="alert"><ul><li>This QuickStart provides helm supports for helm v3.0.0 and v2.12.1.</li><li>Please pick the script based on your helm version.</li></ul></div></li><li><p>Install AWS CLI</p><p><a href="/docs/administration/installation/cloud/(https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-install.html#install-tool-bundled)">Install AWS CLI</a></p><p>For Mac User</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">curl</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"https://d1vvhvl2y92vvt.cloudfront.net/awscli-exe-macos.zip"</span><span class="token plain"> -o </span><span class="token string" style="color:rgb(255, 121, 198)">"awscliv2.zip"</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token function" style="color:rgb(80, 250, 123)">unzip</span><span class="token plain"> awscliv2.zip</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token function" style="color:rgb(80, 250, 123)">sudo</span><span class="token plain"> ./aws/install</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></li><li><p>Install Eksctl</p><p>Install <a href="https://docs.aws.amazon.com/eks/latest/userguide/eksctl.html#installing-eksctl" target="_blank" rel="noopener noreferrer">AWS CLI</a></p><p>For Mac User</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">brew tap weaveworks/tap</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">brew </span><span class="token function" style="color:rgb(80, 250, 123)">install</span><span class="token plain"> weaveworks/tap/eksctl</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></li></ol><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="optional-login-to-your-aws-account"></a>(Optional) Login to your AWS account<a class="hash-link" href="#optional-login-to-your-aws-account" title="Direct link to heading">#</a></h2><p>For first time AWS user, please register your <a href="https://aws.amazon.com/" target="_blank" rel="noopener noreferrer">aws account</a></p><p>Once created the account, you can go to <a href="https://console.aws.amazon.com/iam/home#/home" target="_blank" rel="noopener noreferrer">AWS Identity and Access Management (IAM)</a> to create a user and create access keys under Security Credential tab.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">aws configure</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><div class="alert alert--info alert--icon" role="alert"><i class="feather icon-info"></i> Environment variables AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY will override AWS configuration stored in file ~/.aws/credentials</div><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="optional-create-a-kubernetes-clustereks-in-aws"></a>(Optional) Create a Kubernetes cluster(EKS) in AWS<a class="hash-link" href="#optional-create-a-kubernetes-clustereks-in-aws" title="Direct link to heading">#</a></h3><p>Below script will create a 3 nodes cluster named pinot-quickstart in us-west-2 with t3.small machines for demo purposes.
Please modify the parameters in the example command below:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">EKS_CLUSTER_NAME</span><span class="token operator">=</span><span class="token plain">pinot-quickstart</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">eksctl create cluster </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --name </span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${EKS_CLUSTER_NAME}</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --version </span><span class="token number">1.14</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --region us-west-2 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --nodegroup-name standard-workers </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --node-type t3.small </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --nodes </span><span class="token number">3</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --nodes-min </span><span class="token number">3</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --nodes-max </span><span class="token number">4</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --node-ami auto</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>You can monitor cluster status by command:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">EKS_CLUSTER_NAME</span><span class="token operator">=</span><span class="token plain">pinot-quickstart</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">aws eks describe-cluster --name </span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${EKS_CLUSTER_NAME}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>Once the cluster is in ACTIVE status, it's ready to be used.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="connect-to-an-existing-cluster"></a>Connect to an existing cluster<a class="hash-link" href="#connect-to-an-existing-cluster" title="Direct link to heading">#</a></h2><p>Simply run below command to get the credential for the cluster pinot-quickstart that you just created or your existing cluster.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">EKS_CLUSTER_NAME</span><span class="token operator">=</span><span class="token plain">pinot-quickstart</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">aws eks update-kubeconfig --name </span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${EKS_CLUSTER_NAME}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>To verify the connection, you can run:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">kubectl get nodes</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="pinot-quickstart"></a>Pinot Quickstart<a class="hash-link" href="#pinot-quickstart" title="Direct link to heading">#</a></h2><p>Please follow this <a href="/docs/administration/installation/cloud/on-premises">Kubernetes QuickStart</a> to deploy your Pinot Demo.</p><a class="jump-to jump-to--undefined" href="/docs/administration/installation/cloud/on-premises"><div class="jump-to--inner"><div class="jump-to--inner-2"><div class="jump-to--main">Kubernetes Quickstart</div><div class="jump-to--right"><i class="feather icon-chevron-right arrow"></i></div></div></div></a><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="delete-a-kubernetes-cluster"></a>Delete a Kubernetes Cluster<a class="hash-link" href="#delete-a-kubernetes-cluster" title="Direct link to heading">#</a></h2><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">EKS_CLUSTER_NAME</span><span class="token operator">=</span><span class="token plain">pinot-quickstart</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">aws eks delete-cluster --name </span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${EKS_CLUSTER_NAME}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></div><footer class="row docusaurus-mt-lg"><div class="col"><a href="https://github.com/apache/pinot/edit/master/website/docs/administration/installation/cloud/aws.md" target="_blank" rel="noreferrer noopener"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" class="iconEdit_mS5F" aria-hidden="true"><g><path d="m34.5 11.7l-3 3.1-6.3-6.3 3.1-3q0.5-0.5 1.2-0.5t1.1 0.5l3.9 3.9q0.5 0.4 0.5 1.1t-0.5 1.2z m-29.5 17.1l18.4-18.5 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class="docItemContainer_oiyr"><article><div class="markdown"><header><h1 class="h1Heading_dC7a">Azure Quickstart</h1></header><p>To set up a Kubernetes Cluster on <a href="https://azure.microsoft.com/en-us/services/kubernetes-service/" target="_blank" rel="noopener noreferrer">Azure Kubernetes Service (AKS)</a></p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="install-kubectl"></a>Install Kubectl<a class="hash-link" href="#install-kubectl" title="Direct link to heading">#</a></h2><p>Please follow this link (<a href="https://kubernetes.io/docs/tasks/tools/install-kubectl" target="_blank" rel="noopener noreferrer">https://kubernetes.io/docs/tasks/tools/install-kubectl</a>) to install kubectl.</p><ul><li>For Mac User</li></ul><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">brew </span><span class="token function" style="color:rgb(80, 250, 123)">install</span><span class="token plain"> kubernetes-cli</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>Please check kubectl version after installation.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">kubectl version</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><div class="alert alert--info" role="alert">QuickStart scripts are tested under kubectl client version v1.16.3 and server version v1.13.12</div><ul><li>Install Helm</li></ul><p>Please follow this link (<a href="https://helm.sh/docs/using_helm/#installing-helm" target="_blank" rel="noopener noreferrer">https://helm.sh/docs/using_helm/#installing-helm</a>) to install helm.</p><p>For Mac User</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">brew </span><span class="token function" style="color:rgb(80, 250, 123)">install</span><span class="token plain"> kubernetes-helm</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>Please check helm version after installation.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">helm version</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><div class="alert alert--info" role="alert">This QuickStart provides helm supports for helm v3.0.0 and v2.12.1. Please pick the script based on your helm version.</div><ul><li>Install Azure CLI</li></ul><p>Please follow this link to <a href="https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest" target="_blank" rel="noopener noreferrer">install Azure CLI</a></p><ul><li>For Mac User</li></ul><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">brew update </span><span class="token operator">&&</span><span class="token plain"> brew </span><span class="token function" style="color:rgb(80, 250, 123)">install</span><span class="token plain"> azure-cli</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="optional-login-to-your-azure-account"></a>(Optional) Login to your Azure account<a class="hash-link" href="#optional-login-to-your-azure-account" title="Direct link to heading">#</a></h2><p>Below script will open default browser to sign-in to <strong>your Azure Account</strong>.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">az login</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="optional-create-a-resource-group"></a>(Optional) Create a Resource Group<a class="hash-link" href="#optional-create-a-resource-group" title="Direct link to heading">#</a></h2><p>Below script will create a resource group in location <code>eastus</code>.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">AKS_RESOURCE_GROUP</span><span class="token operator">=</span><span class="token plain">pinot-demo</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">AKS_RESOURCE_GROUP_LOCATION</span><span class="token operator">=</span><span class="token plain">eastus</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">az group create --name </span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${AKS_RESOURCE_GROUP}</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --location </span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${AKS_RESOURCE_GROUP_LOCATION}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="optional-create-a-kubernetes-clusteraks-in-azure"></a>(Optional) Create a Kubernetes cluster(AKS) in Azure<a class="hash-link" href="#optional-create-a-kubernetes-clusteraks-in-azure" title="Direct link to heading">#</a></h2><p>Below script will create a <strong>3 nodes</strong> cluster named <strong>pinot-quickstart</strong> for demo purposes.
Please modify the parameters in the example command below:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">AKS_RESOURCE_GROUP</span><span class="token operator">=</span><span class="token plain">pinot-demo</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">AKS_CLUSTER_NAME</span><span class="token operator">=</span><span class="token plain">pinot-quickstart</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">az aks create --resource-group </span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${AKS_RESOURCE_GROUP}</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --name </span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${AKS_CLUSTER_NAME}</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --node-count </span><span class="token number">3</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>Once the command is succeed, it's ready to be used.</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="connect-to-an-existing-cluster"></a>Connect to an existing cluster<a class="hash-link" href="#connect-to-an-existing-cluster" title="Direct link to heading">#</a></h2><p>Simply run below command to get the credential for the cluster <strong>pinot-quickstart</strong> that you just created or your existing cluster.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">AKS_RESOURCE_GROUP</span><span class="token operator">=</span><span class="token plain">pinot-demo</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">AKS_CLUSTER_NAME</span><span class="token operator">=</span><span class="token plain">pinot-quickstart</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">az aks get-credentials --resource-group </span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${AKS_RESOURCE_GROUP}</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --name </span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${AKS_CLUSTER_NAME}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>To verify the connection, you can run:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">kubectl get nodes</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="pinot-quickstart"></a>Pinot Quickstart<a class="hash-link" href="#pinot-quickstart" title="Direct link to heading">#</a></h2><p>Please follow this <a href="/docs/administration/installation/cloud/on-premises">Kubernetes QuickStart</a> to deploy your Pinot Demo.</p><a class="jump-to jump-to--undefined" href="/docs/administration/installation/cloud/on-premises"><div class="jump-to--inner"><div class="jump-to--inner-2"><div class="jump-to--main">Kubernetes Quickstart</div><div class="jump-to--right"><i class="feather icon-chevron-right arrow"></i></div></div></div></a><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="delete-a-kubernetes-cluster"></a>Delete a Kubernetes Cluster<a class="hash-link" href="#delete-a-kubernetes-cluster" title="Direct link to heading">#</a></h2><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">AKS_RESOURCE_GROUP</span><span class="token operator">=</span><span class="token plain">pinot-demo</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">AKS_CLUSTER_NAME</span><span class="token operator">=</span><span class="token plain">pinot-quickstart</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">az aks delete --resource-group </span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${AKS_RESOURCE_GROUP}</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --name </span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${AKS_CLUSTER_NAME}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></div><footer class="row docusaurus-mt-lg"><div class="col"><a href="https://github.com/apache/pinot/edit/master/website/docs/administration/installation/cloud/azure.md" target="_blank" rel="noreferrer noopener"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" class="iconEdit_mS5F" aria-hidden="true"><g><path d="m34.5 11.7l-3 3.1-6.3-6.3 3.1-3q0.5-0.5 1.2-0.5t1.1 0.5l3.9 3.9q0.5 0.4 0.5 1.1t-0.5 1.2z m-29.5 17.1l18.4-18.5 6.3 6.3-18.4 18.4h-6.3v-6.2z"></path></g></svg>Edit this page</a></div><div class="col lastUpdated_wj+Z"></div></footer></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages navigation"><div class="pagination-nav__item"><a class="pagination-nav__link" href="/docs/administration/installation/cloud/gcp"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">« GCP</div></a></div><div class="pagination-nav__item pagination-nav__item--next"><a class="pagination-nav__link" href="/docs/administration/installation/operating-systems/macos"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">MacOS »</div></a></div></nav></div></div><div class="col col--3"><div class="tableOfContents_vrFS thin-scrollbar"><ul class="table-of-contents table-of-contents__left-border"><li><a href="#install-kubectl" class="table-of-contents__link">Install Kubectl</a></li><li><a href="#optional-login-to-your-azure-account" class="table-of-contents__link">(Optional) Login to your Azure account</a></li><li><a href="#optional-create-a-resource-group" class="table-of-contents__link">(Optional) Create a Resource Group</a></li><li><a href="#optional-create-a-kubernetes-clusteraks-in-azure" class="table-of-contents__link">(Optional) Create a Kubernetes cluster(AKS) in Azure</a></li><li><a href="#connect-to-an-existing-cluster" class="table-of-contents__link">Connect to an existing cluster</a></li><li><a href="#pinot-quickstart" class="table-of-contents__link">Pinot Quickstart</a></li><li><a href="#delete-a-kubernetes-cluster" class="table-of-contents__link">Delete a Kubernetes Cluster</a></li></ul></div></div></div></div></main></div></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul 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class="docItemContainer_oiyr"><article><div class="markdown"><header><h1 class="h1Heading_dC7a">GCP Quickstart</h1></header><p>To have basic set with <a href="https://cloud.google.com/kubernetes-engine" target="_blank" rel="noopener noreferrer">Google Kubernetes Engine(GKE)</a> on GCP:</p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="tooling-installation"></a>Tooling Installation<a class="hash-link" href="#tooling-installation" title="Direct link to heading">#</a></h2><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="install-kubectl"></a>Install Kubectl<a class="hash-link" href="#install-kubectl" title="Direct link to heading">#</a></h3><p>Please follow this link (<a href="https://kubernetes.io/docs/tasks/tools/install-kubectl" target="_blank" rel="noopener noreferrer">https://kubernetes.io/docs/tasks/tools/install-kubectl</a>) to install kubectl.</p><p>For Mac User</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">brew </span><span class="token function" style="color:rgb(80, 250, 123)">install</span><span class="token plain"> kubernetes-cli</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>Please check kubectl version after installation.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">kubectl version</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>QuickStart scripts are tested under kubectl client version v1.16.3 and server version v1.13.12</p><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="install-helm"></a>Install Helm<a class="hash-link" href="#install-helm" title="Direct link to heading">#</a></h3><p>Please follow this <a href="https://helm.sh/docs/using_helm/#installing-helm" target="_blank" rel="noopener noreferrer">link to install helm</a></p><p>For Mac User</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">brew </span><span class="token function" style="color:rgb(80, 250, 123)">install</span><span class="token plain"> kubernetes-helm</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>Please check helm version after installation.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">helm version</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><div class="alert alert--info" role="alert">This QuickStart provides helm supports for helm v3.0.0 and v2.12.1. Please pick the script based on your helm version.</div><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="install-google-cloud-sdk"></a>Install Google Cloud SDK<a class="hash-link" href="#install-google-cloud-sdk" title="Direct link to heading">#</a></h2><p>Please follow this link to <a href="https://cloud.google.com/sdk/install" target="_blank" rel="noopener noreferrer">install Google Cloud SDK</a></p><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="for-mac-user"></a>For Mac User<a class="hash-link" href="#for-mac-user" title="Direct link to heading">#</a></h3><ul><li>Install Google Cloud SDK</li></ul><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">curl</span><span class="token plain"> https://sdk.cloud.google.com </span><span class="token operator">|</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">bash</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><ul><li>Restart your shell</li></ul><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token builtin class-name" style="color:rgb(189, 147, 249)">exec</span><span class="token plain"> -l </span><span class="token environment constant" style="color:rgb(189, 147, 249)">$SHELL</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="optional-initialize-google-cloud-environment"></a>(Optional) Initialize Google Cloud Environment<a class="hash-link" href="#optional-initialize-google-cloud-environment" title="Direct link to heading">#</a></h3><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">gcloud init</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="optional-create-a-kubernetes-clustergke-in-google-cloud"></a>(Optional) Create a Kubernetes cluster(GKE) in Google Cloud<a class="hash-link" href="#optional-create-a-kubernetes-clustergke-in-google-cloud" title="Direct link to heading">#</a></h3><p>Below script will create a 3 nodes cluster named pinot-quickstart in <code>us-west1-b</code> with <code>n1-standard-2</code> machines for demo purposes.</p><p>Please modify the parameters in the example command below:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">GCLOUD_PROJECT</span><span class="token operator">=</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token plain">your gcloud project name</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">GCLOUD_ZONE</span><span class="token operator">=</span><span class="token plain">us-west1-b</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">GCLOUD_CLUSTER</span><span class="token operator">=</span><span class="token plain">pinot-quickstart</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">GCLOUD_MACHINE_TYPE</span><span class="token operator">=</span><span class="token plain">n1-standard-2</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">GCLOUD_NUM_NODES</span><span class="token operator">=</span><span class="token number">3</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">gcloud container clusters create </span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${GCLOUD_CLUSTER}</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --num-nodes</span><span class="token operator">=</span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${GCLOUD_NUM_NODES}</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --machine-type</span><span class="token operator">=</span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${GCLOUD_MACHINE_TYPE}</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --zone</span><span class="token operator">=</span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${GCLOUD_ZONE}</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --project</span><span class="token operator">=</span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${GCLOUD_PROJECT}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>You can monitor cluster status by command:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">gcloud compute instances list</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>Once the cluster is in RUNNING status, it's ready to be used.</p><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="connect-to-an-existing-cluster"></a>Connect to an existing cluster<a class="hash-link" href="#connect-to-an-existing-cluster" title="Direct link to heading">#</a></h3><p>Simply run below command to get the credential for the cluster pinot-quickstart that you just created or your existing cluster.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">GCLOUD_PROJECT</span><span class="token operator">=</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token plain">your gcloud project name</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">GCLOUD_ZONE</span><span class="token operator">=</span><span class="token plain">us-west1-b</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">GCLOUD_CLUSTER</span><span class="token operator">=</span><span class="token plain">pinot-quickstart</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">gcloud container clusters get-credentials </span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${GCLOUD_CLUSTER}</span><span class="token plain"> --zone </span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${GCLOUD_ZONE}</span><span class="token plain"> --project </span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${GCLOUD_PROJECT}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>To verify the connection, you can run:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">kubectl get nodes</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="pinot-quickstart"></a>Pinot Quickstart<a class="hash-link" href="#pinot-quickstart" title="Direct link to heading">#</a></h3><p>Please follow this <a href="/docs/administration/installation/cloud/on-premises">Kubernetes QuickStart</a> to deploy your Pinot Demo.</p><a class="jump-to jump-to--undefined" href="/docs/administration/installation/cloud/on-premises"><div class="jump-to--inner"><div class="jump-to--inner-2"><div class="jump-to--main">Kubernetes Quickstart</div><div class="jump-to--right"><i class="feather icon-chevron-right arrow"></i></div></div></div></a><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="delete-a-kubernetes-cluster"></a>Delete a Kubernetes Cluster<a class="hash-link" href="#delete-a-kubernetes-cluster" title="Direct link to heading">#</a></h3><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">GCLOUD_ZONE</span><span class="token operator">=</span><span class="token plain">us-west1-b</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">gcloud container clusters delete pinot-quickstart --zone</span><span class="token operator">=</span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${GCLOUD_ZONE}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></div><footer class="row docusaurus-mt-lg"><div class="col"><a href="https://github.com/apache/pinot/edit/master/website/docs/administration/installation/cloud/gcp.md" target="_blank" rel="noreferrer noopener"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" class="iconEdit_mS5F" aria-hidden="true"><g><path d="m34.5 11.7l-3 3.1-6.3-6.3 3.1-3q0.5-0.5 1.2-0.5t1.1 0.5l3.9 3.9q0.5 0.4 0.5 1.1t-0.5 1.2z m-29.5 17.1l18.4-18.5 6.3 6.3-18.4 18.4h-6.3v-6.2z"></path></g></svg>Edit this page</a></div><div class="col lastUpdated_wj+Z"></div></footer></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages navigation"><div class="pagination-nav__item"><a class="pagination-nav__link" 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<link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">Running Pinot in Kubernetes | Apache Pinot™</title><meta data-react-helmet="true" property="og:url" content="https://pinot.apache.org/docs/administration/installation/cloud/on-premise"><meta data-react-helmet="true" name="docsearch:language" content="en"><meta data-react-helmet="true" name="docsearch:version" content="current"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="docs-default-current"><meta data-react-helmet="true" property="og:title" content="Running Pinot in Kubernetes | Apache Pinot™"><meta data-react-helmet="true" name="description" content="Running Pinot in Kubernetes"><meta data-react-helmet="true" property="og:description" content="Running Pinot in Kubernetes"><link data-react-helmet="true" rel="shortcut icon" href="/img/favicon.ico"><link data-react-helmet="true" rel="canonical" href="https://pinot.apache.org/docs/administration/installation/cloud/on-premise"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/docs/administration/installation/cloud/on-premise" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/docs/administration/installation/cloud/on-premise" hreflang="x-default"><link data-react-helmet="true" rel="preconnect" href="https://BH4D9OD16A-dsn.algolia.net" crossorigin="anonymous"><link rel="stylesheet" href="/assets/css/styles.f0793de6.css">
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@@ -22,7 +22,7 @@
</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">Query 20191112_050907_00004_xkm4g, FINISHED, </span><span class="token number">1</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">node</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">Splits: </span><span class="token number">19</span><span class="token plain"> total, </span><span class="token number">19</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">done</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">100.00</span><span class="token plain">%</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token number">0</span><span class="token plain">:01 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token number">1</span><span class="token plain"> rows, 29B</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token number">1</span><span class="token plain"> rows/s, 41B/s</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><ul><li>Show schema</li></ul><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">presto:</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">default</span><span class="token operator">></span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">DESCRIBE</span><span class="token plain"> pinot</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token plain">dontcare</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token plain">airlinestats</span><span class="token punctuation" style="color:rgb(248, 248, 242)">;</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain"> Column </span><span class="token operator">|</span><span class="token plain"> Type </span><span class="token operator">|</span><span class="token plain"> Extra </span><span class="token operator">|</span><span class="token plain"> Comment</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">----------------------+---------+-------+---------</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> flightnum </span><span class="token operator">|</span><span class="token plain"> integer </span><span class="token operator">|</span><span class="token plain"> </span><span class="token operator">|</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> origin </span><span class="token operator">|</span><span class="token plain"> varchar </span><span class="token operator">|</span><span class="token plain"> </span><span class="token operator">|</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> quarter </span><span class="token operator">|</span><span class="token plain"> integer </span><span class="token operator">|</span><span class="token plain"> </span><span class="token operator">|</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> lateaircraftdelay </span><span class="token operator">|</span><span class="token plain"> integer </span><span class="token operator">|</span><span class="token plain"> </span><span class="token operator">|</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> divactualelapsedtime </span><span class="token operator">|</span><span class="token plain"> integer </span><span class="token operator">|</span><span class="token plain"> </span><span class="token operator">|</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token punctuation" style="color:rgb(248, 248, 242)">..</span><span class="token punctuation" style="color:rgb(248, 248, 242)">..</span><span class="token punctuation" style="color:rgb(248, 248, 242)">..</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain" style="display:inline-block">
</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">Query 20191112_051021_00005_xkm4g, FINISHED, </span><span class="token number">1</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">node</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">Splits: </span><span class="token number">19</span><span class="token plain"> total, </span><span class="token number">19</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">done</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">100.00</span><span class="token plain">%</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token number">0</span><span class="token plain">:02 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token number">80</span><span class="token plain"> rows, </span><span class="token number">6</span><span class="token plain">.06KB</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token number">35</span><span class="token plain"> rows/s, </span><span class="token number">2</span><span class="token plain">.66KB/s</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><ul><li>Count total documents</li></ul><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI SQL"><pre tabindex="0" class="prism-code language-SQL codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">presto:default> select count(*) as cnt from pinot.dontcare.airlinestats limit 10;</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain"> cnt</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">------</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token number">9745</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">1</span><span class="token plain"> row</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain" style="display:inline-block">
</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">Query 20191112_051114_00006_xkm4g, FINISHED, </span><span class="token number">1</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">node</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">Splits: </span><span class="token number">17</span><span class="token plain"> total, </span><span class="token number">17</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">done</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token number">100.00</span><span class="token plain">%</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token number">0</span><span class="token plain">:00 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token number">1</span><span class="token plain"> rows, 8B</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token number">2</span><span class="token plain"> rows/s, 19B/s</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="how-to-clean-up-pinot-deployment"></a>How to clean up Pinot deployment<a class="hash-link" href="#how-to-clean-up-pinot-deployment" title="Direct link to heading">#</a></h3><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">kubectl delete ns pinot-quickstart</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><a class="jump-to jump-to--undefined" href="/docs/administration/"><div class="jump-to--inner"><div class="jump-to--inner-2"><div class="jump-to--main">Administration</div><div class="jump-to--right"><i class="feather icon-chevron-right arrow"></i></div></div></div></a></div><footer class="row docusaurus-mt-lg"><div class="col"><a href="https://github.com/apache/pinot/edit/master/website/docs/administration/installation/cloud/on-premise.md" target="_blank" rel="noreferrer noopener"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 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class="markdown"><header><h1 class="h1Heading_dC7a">Running Pinot in Docker</h1></header><p>Apache Pinot started maintaing the <a href="https://hub.docker.com/r/apachepinot/pinot" target="_blank" rel="noopener noreferrer"><code>apachepinot/pinot</code></a> Docker images
available on <a href="https://hub.docker.com/r/apachepinot/pinot/tags" target="_blank" rel="noopener noreferrer">Docker Hub</a></p><blockquote><b>Prerequisites:</b> <br> <p> <a href="https://hub.docker.com/editions/community/docker-ce-desktop-mac" target="_blank" rel="noopener noreferrer">Install Docker</a><br> Try <a href="https://hub.docker.com/editions/community/docker-ce-desktop-mac" target="_blank" rel="noopener noreferrer">Kubernetes Quickstart</a> if you already have a minikube cluster or docker kubernetes setup.</p></blockquote><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="running"></a>Running<a class="hash-link" href="#running" title="Direct link to heading">#</a></h2><p>Create an isolated bridge network in docker</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">docker</span><span class="token plain"> network create -d bridge pinot-demo</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>We'll be running a docker image apachepinot/pinot:latest to run a quick start, which does the following:</p><ul><li>Sets up the Pinot cluster QuickStartCluster</li><li>Creates a sample table and loads sample data</li></ul><p>There's 3 types of quick start</p><div class="tabs-container"><ul role="tablist" aria-orientation="horizontal" class="tabs tabs--block"><li role="tab" tabindex="0" aria-selected="true" class="tabs__item tabItem_vU9c tabs__item--active">Batch</li><li role="tab" tabindex="-1" aria-selected="false" class="tabs__item tabItem_vU9c">Streaming</li><li role="tab" tabindex="-1" aria-selected="false" class="tabs__item tabItem_vU9c">Hybrid</li></ul><div class="margin-vert--md"><div role="tabpanel"><p>Batch</p><ol><li><p>Starts Pinot deployment by starting:</p><ul><li>Apache Zookeeper</li><li>Pinot Controller</li><li>Pinot Broker</li><li>Pinot Server</li></ul></li><li><p>Creates a demo table:</p><ul><li>baseballStats</li></ul></li><li><p>Launches a standalone data ingestion job:</p><ul><li>build one Pinot segment for a given csv data file for table baseballStats</li><li>push built segment to Pinot controller</li></ul></li><li><p>Issues sample queries to Pinot</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">docker</span><span class="token plain"> run </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --network</span><span class="token operator">=</span><span class="token plain">pinot-demo </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --name pinot-quickstart </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">9000</span><span class="token plain">:9000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -d apachepinot/pinot:latest QuickStart </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -type batch</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></li></ol></div><div role="tabpanel" hidden=""><p>Streaming:</p><ol><li><p>Starts Pinot deployment by starting:</p><ul><li>Apache Kafka</li><li>Apache Zookeeper</li><li>Pinot Controller</li><li>Pinot Broker</li><li>Pinot Server</li></ul></li><li><p>Creates a demo table: <strong>meetupRsvp</strong></p></li><li><p>Launches a <strong>meetup</strong> stream and publish data to a Kafka: <strong>meetupRSVPEvents</strong> to be subscribed by Pinot</p></li><li><p>Issues sample queries to Pinot</p></li></ol></div><div role="tabpanel" hidden=""><p>Hybrid:</p><ol><li><p>Starts Pinot deployment by starting:</p><ul><li>Apache Kafka</li><li>Apache Zookeeper</li><li>Pinot Controller</li><li>Pinot Broker</li><li>Pinot Server</li></ul></li><li><p>Creates a demo table: <strong>airlineStats</strong></p></li><li><p>Launches a standalone data ingestion job:</p><ul><li>build Pinot segments under a given directory of Avro files for table airlineStats</li><li>push built segments to Pinot controller</li></ul></li><li><p>Launches a stream of flights stats and publish data to a Kafka topic airlineStatsEvents to be subscribed by Pinot</p></li><li><p>Issues sample queries to Pinot</p></li></ol><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token comment" style="color:rgb(98, 114, 164)"># Stop previous container, if any, or use different network</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token function" style="color:rgb(80, 250, 123)">docker</span><span class="token plain"> run </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --network</span><span class="token operator">=</span><span class="token plain">pinot-demo </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --name pinot-quickstart </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">9000</span><span class="token plain">:9000 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -d apachepinot/pinot:latest QuickStart </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -type hybrid</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></div></div></div><p>That's it! We've spun up a Pinot cluster.</p><p>It may take a while for all Pinot components to start and for the sample data to be loaded. Use the below command to check the container logs</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">docker</span><span class="token plain"> logs pinot-quickstart -f</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>You can head over to Exploring Pinot to check out the data in the <code>baseballStats</code>, <code>meetupRSVP</code> or the <code>airlineStats</code> table.</p></div><footer class="row docusaurus-mt-lg"><div class="col"><a href="https://github.com/apache/pinot/edit/master/website/docs/administration/installation/containers/docker.md" target="_blank" rel="noreferrer noopener"><svg 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diff --git a/docs/administration/installation/operating-systems/macos/index.html b/docs/administration/installation/operating-systems/macos/index.html
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--- a/docs/administration/installation/operating-systems/macos/index.html
+++ b/docs/administration/installation/operating-systems/macos/index.html
@@ -12,13 +12,13 @@
<link rel="search" type="application/opensearchdescription+xml" title="Apache Pinot™" href="/opensearch.xml">
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Ubuntu|Roboto|Source+Code+Pro">
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diff --git a/docs/administration/installation/operating-systems/ubuntu/index.html b/docs/administration/installation/operating-systems/ubuntu/index.html
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--- a/docs/administration/installation/operating-systems/ubuntu/index.html
+++ b/docs/administration/installation/operating-systems/ubuntu/index.html
@@ -12,13 +12,13 @@
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class="docItemContainer_oiyr"><article><div class="markdown"><header><h1 class="h1Heading_dC7a">Install Pinot On Ubuntu</h1></header><p>Pinot can be installed on the Ubuntu through the following methods:</p></div><footer class="row docusaurus-mt-lg"><div class="col"><a href="https://github.com/apache/pinot/edit/master/website/docs/administration/installation/operating-systems/ubuntu.md" target="_blank" rel="noreferrer noopener"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" class="iconEdit_mS5F" aria-hidden="true"><g><path d="m34.5 11.7l-3 3.1-6.3-6.3 3.1-3q0.5-0.5 1.2-0.5t1.1 0.5l3.9 3.9q0.5 0.4 0.5 1.1t-0.5 1.2z m-29.5 17.1l18.4-18.5 6.3 6.3-18.4 18.4h-6.3v-6.2z"></path></g></svg>Edit this page</a></div><div class="col lastUpdated_wj+Z"></div></footer></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages navigation"><div class="pagination-nav__item"><a class="pagination-nav__link" 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href="https://docs.pinot.apache.org/integrations/superset" target="_blank" rel="noopener noreferrer" class="footer__link-item">Superset</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/integrations/thirdeye" target="_blank" rel="noopener noreferrer" class="footer__link-item">ThirdEye</a></li></ul></div><div class="col footer__col"><h4 class="footer__title">Docs</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/getting-started" target="_blank" rel="noopener noreferrer" class="footer__link-item">Getting Started</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" class="footer__link-item">Pinot Components</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/users" target="_blank" rel="noopener noreferrer" class="footer__link-item">User Guide</a></li><li class="footer__item"><a 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<link rel="search" type="application/opensearchdescription+xml" title="Apache Pinot™" href="/opensearch.xml">
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Ubuntu|Roboto|Source+Code+Pro">
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@@ -22,7 +22,7 @@
</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token comment" style="color:rgb(98, 114, 164)"># navigate to directory containing the setup scripts</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token builtin class-name" style="color:rgb(189, 147, 249)">cd</span><span class="token plain"> pinot-distribution/target/apache-pinot-incubating-</span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${pinot.version}</span><span class="token plain">-bin/apache-pinot-incubating-</span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${pinot.version}</span><span class="token plain">-bin</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="download"></a>Download<a class="hash-link" href="#download" title="Direct link to heading">#</a></h3><p>Download the latest binary release from Apache Pinot, or use this command</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">wget</span><span class="token plain"> https://downloads.apache.org/pinot/apache-pinot-incubating-</span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${pinot.version}</span><span class="token plain">/apache-pinot-incubating-</span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${pinot.version}</span><span class="token plain">-bin.tar.gz</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>Once you have the tar file,</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token comment" style="color:rgb(98, 114, 164)"># untar it</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token function" style="color:rgb(80, 250, 123)">tar</span><span class="token plain"> -zxvf apache-pinot-incubating-</span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${pinot.version}</span><span class="token plain">-bin.tar.gz</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain" style="display:inline-block">
</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token comment" style="color:rgb(98, 114, 164)"># navigate to directory containing the launcher scripts</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token builtin class-name" style="color:rgb(189, 147, 249)">cd</span><span class="token plain"> apache-pinot-incubating-</span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">${pinot.version}</span><span class="token plain">-bin</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>If you want to run Pinot using a Docker image instead, head over to Running Pinot in Docker</p><div type="info"><p>Pro-tip: These field names can be controlled via the
[global <code>log_schema</code> options][docs.reference.global-options#log_schema].</p></div></div><footer class="row docusaurus-mt-lg"><div class="col"><a href="https://github.com/apache/pinot/edit/master/website/docs/administration/running_locally.md" target="_blank" rel="noreferrer noopener"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" class="iconEdit_mS5F" aria-hidden="true"><g><path d="m34.5 11.7l-3 3.1-6.3-6.3 3.1-3q0.5-0.5 1.2-0.5t1.1 0.5l3.9 3.9q0.5 0.4 0.5 1.1t-0.5 1.2z m-29.5 17.1l18.4-18.5 6.3 6.3-18.4 18.4h-6.3v-6.2z"></path></g></svg>Edit this page</a></div><div class="col lastUpdated_wj+Z"></div></footer></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages navigation"><div class="pagination-nav__item"><a class="pagination-nav__link" href="/docs/about/features_of_pinot"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">« Features of Pinot</div></a></div><div class="pagination-nav__item 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<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Ubuntu|Roboto|Source+Code+Pro">
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tabindex="-1" href="/docs/administration/installation/operating-systems/macos">MacOS</a></li><li class="menu__list-item"><a class="menu__link" tabindex="-1" href="/docs/administration/installation/operating-systems/ubuntu">Ubuntu</a></li></ul></li></ul></li></ul></li><li class="menu__list-item"><a class="menu__link menu__link--sublist menu__link--active" href="#!">Components</a><ul class="menu__list"><li class="menu__list-item"><a aria-current="page" class="menu__link menu__link--active active" tabindex="0" href="/docs/components/broker">broker</a></li><li class="menu__list-item"><a class="menu__link" tabindex="0" href="/docs/components/cluster">cluster</a></li><li class="menu__list-item"><a class="menu__link" tabindex="0" href="/docs/components/controller">PQL</a></li></ul></li></ul></nav></div></aside><main class="docMainContainer_r8cw"><div class="container padding-top--md padding-bottom--lg docItemWrapper_NJLN"><div class="row"><div class="col docItemCol_zHA2"><div class="docItemContainer_oiyr"><article><div class="markdown"><header><h1 class="h1Heading_dC7a">Broker</h1></header><p>Brokers are the components that handle Pinot queries. They accept queries from clients and forward them to the right servers. 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class="docItemContainer_oiyr"><article><div class="markdown"><header><h1 class="h1Heading_dC7a">Cluster</h1></header><p>Cluster is a set a nodes comprising of servers, brokers, controllers and minions.</p><p>Pinot leverages <a href="http://helix.apache.org" target="_blank" rel="noopener noreferrer">Apache Helix</a> for cluster management. Helix is a cluster management framework to manage replicated, partitioned resources in a distributed system. Helix uses Zookeeper to store cluster state and metadata.</p><p><img alt="Cluster Components" 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"></p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="cluster-components"></a>Cluster components<a class="hash-link" href="#cluster-components" title="Direct link to heading">#</a></h2><p>Briefly, Helix divides nodes into three logical components based on their responsibilities</p><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="participant"></a>Participant<a class="hash-link" href="#participant" title="Direct link to heading">#</a></h3><p>The nodes that host distributed, partitioned resources</p><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="spectator"></a>Spectator<a class="hash-link" href="#spectator" title="Direct link to heading">#</a></h3><p>The nodes that observe the current state of each Participant and use that information to access the resources. Spectators are notified of state changes in the cluster (state of a participant, or that of a partition in a participant).</p><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="controller"></a>Controller<a class="hash-link" href="#controller" title="Direct link to heading">#</a></h3><p>The node that observes and controls the Participant nodes. It is responsible for coordinating all transitions in the cluster and ensuring that state constraints are satisfied while maintaining cluster stability.</p><ul><li>Pinot Servers are modeled as Participants, more details about server nodes can be found in <a href="/docs/components/server">Server</a></li><li>Pinot Brokers are modeled as Spectators, more details about broker nodes can be found in <a href="/docs/components/broker">Broker</a></li><li>Pinot Controllers are modeled as Controllers, more details about controller nodes can be found in <a href="/docs/components/controller">Controller</a></li></ul><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="logical-view"></a>Logical view<a class="hash-link" href="#logical-view" title="Direct link to heading">#</a></h2><p>Another way to visualize the cluster is a logical view, wherein a cluster contains <a href="/docs/components/segments">tenants</a>, tenants contain <a href="/docs/components/tables">tables</a>, and tables contain <a href="/docs/components/segments">segments</a>.</p><p><img alt="Logical View" src="/assets/images/ClusterLogical-a82f8d136804e81aa069c9cb7f1f53ec.png"></p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="setup-a-pinot-cluster"></a>Setup a Pinot Cluster<a class="hash-link" href="#setup-a-pinot-cluster" title="Direct link to heading">#</a></h2><p>Typically, there is only cluster per environment/data center. There is no needed to create multiple Pinot clusters since Pinot supports the concept of <a href="/docs/components/tenants">tenants</a>.</p><p>At LinkedIn, the largest Pinot cluster consists of 1000+ nodes.</p><p>To setup a Pinot cluster, we need to first start Zookeeper:</p><div class="tabs-container"><ul role="tablist" aria-orientation="horizontal" class="tabs Install"><li role="tab" tabindex="-1" aria-selected="false" class="tabs__item tabItem_vU9c"><i class="Using docker images"></i> Using docker images</li><li role="tab" tabindex="0" aria-selected="true" class="tabs__item tabItem_vU9c tabs__item--active"><i class="Using launcher scripts"></i> Using launcher scripts</li></ul><div class="margin-vert--md"><div role="tabpanel" hidden=""><ol><li>Create a Network
Create an isolated bridge network in docker</li></ol><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">docker</span><span class="token plain"> network create -d bridge pinot-demo</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><ol start="2"><li>Start Zookeeper</li></ol><p>Start Zookeeper in daemon.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">docker</span><span class="token plain"> run </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --network</span><span class="token operator">=</span><span class="token plain">pinot-demo </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --name pinot-zookeeper </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --restart always </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">2181</span><span class="token plain">:2181 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -d zookeeper:3.5.6</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><ol start="3"><li>Start Zookeeper UI
Start ZKUI to browse Zookeeper data at http://localhost:9090.</li></ol><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">docker</span><span class="token plain"> run </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --network pinot-demo --name</span><span class="token operator">=</span><span class="token plain">zkui </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -p </span><span class="token number">9090</span><span class="token plain">:9090 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -e </span><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">ZK_SERVER</span><span class="token operator">=</span><span class="token plain">pinot-zookeeper:2181 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -d qnib/plain-zkui:latest</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><ol start="4"><li></li></ol></div><div role="tabpanel" hidden=""><p>Download Pinot Distribution using instructions in Download</p><p>Start Zookeeper</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">bin/pinot-admin.sh StartZookeeper</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><ol start="2"><li>Start Zooinspector</li></ol><p>Install <a href="https://github.com/jfim/zooinspector" target="_blank" rel="noopener noreferrer">zooinspector</a> to view the data in Zookeeper, and connect to localhost:2181</p></div></div></div><p>Once we've started Zookeeper, we can start other components to join this cluster. If you're using docker, pull the latest <code>apachepinot/pinot</code> image.</p><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="using-docker-images"></a>Using docker images<a class="hash-link" href="#using-docker-images" title="Direct link to heading">#</a></h3><h4><a aria-hidden="true" tabindex="-1" class="anchor" id="pull-pinot-docker-image"></a>Pull pinot docker image<a class="hash-link" href="#pull-pinot-docker-image" title="Direct link to heading">#</a></h4><p>(Optional) You can also follow the instructions here to build your own images.<br></p><p>You can try out pre-built Pinot all-in-one docker image.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">export PINOT_VERSION</span><span class="token operator">=</span><span class="token number">0.3</span><span class="token number">.0</span><span class="token operator">-</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SNAPSHOT</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">export PINOT_IMAGE</span><span class="token operator">=</span><span class="token plain">apachepinot</span><span class="token operator">/</span><span class="token plain">pinot:${PINOT_VERSION}</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">docker pull ${PINOT_IMAGE}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>To start other components to join the cluster</p><ul><li>Start Controller</li><li>Start Broker</li><li>Start Server</li></ul><p>Explore your cluster via Pinot Data Explorer</p></div><footer class="row 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<link rel="search" type="application/opensearchdescription+xml" title="Apache Pinot™" href="/opensearch.xml">
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Ubuntu|Roboto|Source+Code+Pro">
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<link rel="search" type="application/opensearchdescription+xml" title="Apache Pinot™" href="/opensearch.xml">
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Ubuntu|Roboto|Source+Code+Pro">
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+<link rel="preload" href="/assets/js/runtime~main.82dd65bd.js" as="script">
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</head>
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diff --git a/docs/concepts/index.html b/docs/concepts/index.html
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<link rel="search" type="application/opensearchdescription+xml" title="Apache Pinot™" href="/opensearch.xml">
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Ubuntu|Roboto|Source+Code+Pro">
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diff --git a/docs/concepts/pinot-architecture/index.html b/docs/concepts/pinot-architecture/index.html
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<link rel="search" type="application/opensearchdescription+xml" title="Apache Pinot™" href="/opensearch.xml">
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Ubuntu|Roboto|Source+Code+Pro">
<link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">Pinot Architecture | Apache Pinot™</title><meta data-react-helmet="true" property="og:url" content="https://pinot.apache.org/docs/concepts/pinot-architecture"><meta data-react-helmet="true" name="docsearch:language" content="en"><meta data-react-helmet="true" name="docsearch:version" content="current"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="docs-default-current"><meta data-react-helmet="true" property="og:title" content="Pinot Architecture | Apache Pinot™"><meta data-react-helmet="true" name="description" content="Arch"><meta data-react-helmet="true" property="og:description" content="Arch"><link data-react-helmet="true" rel="shortcut icon" href="/img/favicon.ico"><link data-react-helmet="true" rel="canonical" href="https://pinot.apache.org/docs/concepts/pinot-architecture"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/docs/concepts/pinot-architecture" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/docs/concepts/pinot-architecture" hreflang="x-default"><link data-react-helmet="true" rel="preconnect" href="https://BH4D9OD16A-dsn.algolia.net" crossorigin="anonymous"><link rel="stylesheet" href="/assets/css/styles.f0793de6.css">
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@@ -21,7 +21,7 @@
First, a bit of naming notions. Pinot has has different components, and different ways of representing the data. In particular, data is represented by:</p></li><li><p>Table:
A table is a logical abstraction to refer to a collection of related data. It consists of columns and rows (documents).</p></li><li><p>Segment:
Data in table is divided into (horizontal) shards referred to as segments.</p></li></ul></div><footer class="row docusaurus-mt-lg"><div class="col"><a href="https://github.com/apache/pinot/edit/master/website/docs/concepts/pinot-architecture.md" target="_blank" rel="noreferrer noopener"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" class="iconEdit_mS5F" aria-hidden="true"><g><path d="m34.5 11.7l-3 3.1-6.3-6.3 3.1-3q0.5-0.5 1.2-0.5t1.1 0.5l3.9 3.9q0.5 0.4 0.5 1.1t-0.5 1.2z m-29.5 17.1l18.4-18.5 6.3 6.3-18.4 18.4h-6.3v-6.2z"></path></g></svg>Edit this page</a></div><div class="col lastUpdated_wj+Z"></div></footer></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages navigation"><div class="pagination-nav__item"></div><div class="pagination-nav__item pagination-nav__item--next"></div></nav></div></div><div class="col col--3"><div class="tableOfContents_vrFS thin-scrollbar"></div></div></div></div></main></div></div><footer class="footer"><div 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Started</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" class="footer__link-item">Pinot Components</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/users" target="_blank" rel="noopener noreferrer" class="footer__link-item">User Guide</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/operating-pinot" target="_blank" rel="noopener noreferrer" class="footer__link-item">Administration</a></li></ul></div><div class="col footer__col"><h4 class="footer__title">Community</h4><ul class="footer__items"><li class="footer__item"><a href="https://join.slack.com/t/apache-pinot/shared_invite/zt-5z7pav2f-yYtjZdVA~EDmrGkho87Vzw" target="_blank" rel="noopener noreferrer" class="footer__link-item">Slack</a></li><li class="footer__item"><a href="https://github.com/apache/pinot" target="_blank" rel="noopener noreferrer" class="footer__link-item">Github</a></li><li 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trademarks of The Apache Software Foundation.<br><br>This page has references to third party software - Presto, PrestoDB, ThirdEye, Trino, TrinoDB, that are not part of the Apache Software Foundation and are not covered under the Apache License.</div></div></div></footer></div>
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diff --git a/docs/how-to/index.html b/docs/how-to/index.html
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@@ -12,13 +12,13 @@
<link rel="search" type="application/opensearchdescription+xml" title="Apache Pinot™" href="/opensearch.xml">
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Ubuntu|Roboto|Source+Code+Pro">
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<link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">Build Docker Images | Apache Pinot™</title><meta data-react-helmet="true" property="og:url" content="https://pinot.apache.org/docs/misc/build-docker"><meta data-react-helmet="true" name="docsearch:language" content="en"><meta data-react-helmet="true" name="docsearch:version" content="current"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="docs-default-current"><meta data-react-helmet="true" property="og:title" content="Build Docker Images | Apache Pinot™"><meta data-react-helmet="true" name="description" content="Build Docker Images"><meta data-react-helmet="true" property="og:description" content="Build Docker Images"><link data-react-helmet="true" rel="shortcut icon" href="/img/favicon.ico"><link data-react-helmet="true" rel="canonical" href="https://pinot.apache.org/docs/misc/build-docker"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/docs/misc/build-docker" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/docs/misc/build-docker" hreflang="x-default"><link data-react-helmet="true" rel="preconnect" href="https://BH4D9OD16A-dsn.algolia.net" crossorigin="anonymous"><link rel="stylesheet" href="/assets/css/styles.f0793de6.css">
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This docker build project is based on Project docker-superset and specialized for Pinot.</p><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="how-to-build-1"></a>How to build<a class="hash-link" href="#how-to-build-1" title="Direct link to heading">#</a></h3><p>Please modify file <code>Makefile</code> to change image and <code>superset_version</code> accordingly.
Below command will build docker image and tag it as <code>superset_version</code> and <code>latest</code>.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">make</span><span class="token plain"> latest</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>You can also build directly with <code>docker build</code> command by setting arguments:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">docker</span><span class="token plain"> build </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --build-arg </span><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">NODE_VERSION</span><span class="token operator">=</span><span class="token plain">latest </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --build-arg </span><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">PYTHON_VERSION</span><span class="token operator">=</span><span class="token number">3.6</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --build-arg </span><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">SUPERSET_VERSION</span><span class="token operator">=</span><span class="token number">0.34</span><span class="token plain">.1 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --tag apachepinot/pinot-superset:0.34.1 </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> --target build </span><span class="token builtin class-name" style="color:rgb(189, 147, 249)">.</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>How to push</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">make</span><span class="token plain"> push</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="configuration-1"></a>Configuration<a class="hash-link" href="#configuration-1" title="Direct link to heading">#</a></h3><p>Follow the <a href="https://superset.apache.org/installation.html#configuration" target="_blank" rel="noopener noreferrer">instructions</a> provided by Apache Superset for writing your own superset_config.py.</p><p>Place this file in a local directory and mount this directory to /etc/superset inside the container. This location
is included in the image's <code>PYTHONPATH</code>. Mounting this file to a different location is possible, but it will need to be in the PYTHONPATH.</p><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="volumes-1"></a>Volumes<a class="hash-link" href="#volumes-1" title="Direct link to heading">#</a></h3><p>The image defines two data volumes: one for mounting configuration into the container, and one for data (logs, SQLite DBs, &c).</p><p>The configuration volume is located alternatively at <code>/etc/superset</code> or <code>/home/superset</code>; either is acceptable. Both of these directories are included in the PYTHONPATH of the image. Mount any configuration (specifically the superset_config.py file) here to have it read by the app on startup.</p><p>The data volume is located at <code>/var/lib/superset</code> and it is where you would mount your SQLite file (if you are using that as your backend), or a volume to collect any logs that are routed there. This location is used as the value of the <code>SUPERSET_HOME</code> environmental variable.</p><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="kubernetes-examples-2"></a>Kubernetes Examples<a class="hash-link" href="#kubernetes-examples-2" title="Direct link to heading">#</a></h3><p>Please refer to superset.yaml as k8s deployment example.</p></div><footer class="row docusaurus-mt-lg"><div class="col"><a href="https://github.com/apache/pinot/edit/master/website/docs/misc/build-docker.md" target="_blank" rel="noreferrer noopener"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" class="iconEdit_mS5F" aria-hidden="true"><g><path d="m34.5 11.7l-3 3.1-6.3-6.3 3.1-3q0.5-0.5 1.2-0.5t1.1 0.5l3.9 3.9q0.5 0.4 0.5 1.1t-0.5 1.2z m-29.5 17.1l18.4-18.5 6.3 6.3-18.4 18.4h-6.3v-6.2z"></path></g></svg>Edit this page</a></div><div class="col lastUpdated_wj+Z"></div></footer></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages navigation"><div 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class="docPage_lDyR"><main class="docMainContainer_r8cw docMainContainerEnhanced_SOUu"><div class="container padding-top--md padding-bottom--lg docItemWrapper_NJLN"><div class="row"><div class="col docItemCol_zHA2"><div class="docItemContainer_oiyr"><article><div class="markdown"><header><h1 class="h1Heading_dC7a">GoLang</h1></header><p>Applications can use this golang client library to query Apache Pinot.</p><p>Source Code Repo: <a href="https://github.com/fx19880617/pinot-client-go" target="_blank" rel="noopener noreferrer">https://github.com/fx19880617/pinot-client-go</a></p><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="examples"></a>Examples<a class="hash-link" href="#examples" title="Direct link to heading">#</a></h2><p>Please follow this Pinot Quickstart link to install and start Pinot batch QuickStart locally.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">bin/quick-start-batch.sh</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>Check out Client library Github Repo</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token function" style="color:rgb(80, 250, 123)">git</span><span class="token plain"> clone git@github.com:fx19880617/pinot-client-go.git</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token builtin class-name" style="color:rgb(189, 147, 249)">cd</span><span class="token plain"> pinot-client-go</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>Build and run the example application to query from Pinot Batch Quickstart</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">go build ./examples/batch-quickstart</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">./batch-quickstart</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="usage"></a>Usage<a class="hash-link" href="#usage" title="Direct link to heading">#</a></h2><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="create-a-pinot-connection"></a>Create a Pinot Connection<a class="hash-link" href="#create-a-pinot-connection" title="Direct link to heading">#</a></h3><p>Pinot client could be initialized through:</p><ol><li>Zookeeper Path</li></ol><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI go"><pre tabindex="0" class="prism-code language-go codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">pinotClient </span><span class="token operator">:=</span><span class="token plain"> pinot</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">NewFromZookeeper</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token builtin" style="color:rgb(189, 147, 249)">string</span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token string" style="color:rgb(255, 121, 198)">"localhost:2123"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">""</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"QuickStartCluster"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><ol><li>A list of broker addresses.</li></ol><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI go"><pre tabindex="0" class="prism-code language-go codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">pinotClient </span><span class="token operator">:=</span><span class="token plain"> pinot</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">NewFromBrokerList</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token builtin" style="color:rgb(189, 147, 249)">string</span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token string" style="color:rgb(255, 121, 198)">"localhost:8000"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><ol><li>ClientConfig</li></ol><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI go"><pre tabindex="0" class="prism-code language-go codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">pinotClient </span><span class="token operator">:=</span><span class="token plain"> pinot</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">NewWithConfig</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token operator">&</span><span class="token plain">pinot</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token plain">ClientConfig</span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> ZkConfig</span><span class="token punctuation" style="color:rgb(248, 248, 242)">:</span><span class="token plain"> </span><span class="token operator">&</span><span class="token plain">pinot</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token plain">ZookeeperConfig</span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> ZookeeperPath</span><span class="token punctuation" style="color:rgb(248, 248, 242)">:</span><span class="token plain"> zkPath</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> PathPrefix</span><span class="token punctuation" style="color:rgb(248, 248, 242)">:</span><span class="token plain"> strings</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">Join</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token builtin" style="color:rgb(189, 147, 249)">string</span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token plain">zkPathPrefix</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> pinotCluster</span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"/"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> SessionTimeoutSec</span><span class="token punctuation" style="color:rgb(248, 248, 242)">:</span><span class="token plain"> defaultZkSessionTimeoutSec</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> ExtraHTTPHeader</span><span class="token punctuation" style="color:rgb(248, 248, 242)">:</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">map</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token builtin" style="color:rgb(189, 147, 249)">string</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token builtin" style="color:rgb(189, 147, 249)">string</span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"extra-header"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">:</span><span class="token string" style="color:rgb(255, 121, 198)">"value"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="query-pinot"></a>Query Pinot<a class="hash-link" href="#query-pinot" title="Direct link to heading">#</a></h2><p>Please see this <a href="https://github.com/fx19880617/pinot-client-go/blob/master/examples/batch-quickstart/main.go" target="_blank" rel="noopener noreferrer">example</a> for your reference.</p><p>Code snippet:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI go"><pre tabindex="0" class="prism-code language-go codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">pinotClient</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> err </span><span class="token operator">:=</span><span class="token plain"> pinot</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">NewFromZookeeper</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token builtin" style="color:rgb(189, 147, 249)">string</span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token string" style="color:rgb(255, 121, 198)">"localhost:2123"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">""</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"QuickStartCluster"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">if</span><span class="token plain"> err </span><span class="token operator">!=</span><span class="token plain"> </span><span class="token boolean">nil</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> log</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">Error</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">err</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">brokerResp</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> err </span><span class="token operator">:=</span><span class="token plain"> pinotClient</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">ExecuteSQL</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token string" style="color:rgb(255, 121, 198)">"baseballStats"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"select count(*) as cnt, sum(homeRuns) as sum_homeRuns from baseballStats group by teamID limit 10"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">if</span><span class="token plain"> err </span><span class="token operator">!=</span><span class="token plain"> </span><span class="token boolean">nil</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> log</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">Error</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">err</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">log</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">Infof</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token string" style="color:rgb(255, 121, 198)">"Query Stats: response time - %d ms, scanned docs - %d, total docs - %d"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> brokerResp</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token plain">TimeUsedMs</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> brokerResp</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token plain">NumDocsScanned</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> brokerResp</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token plain">TotalDocs</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>Response Format</p><p>Query Response is defined as the struct of following:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI go"><pre tabindex="0" class="prism-code language-go codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">type</span><span class="token plain"> BrokerResponse </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">struct</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> AggregationResults </span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token operator">*</span><span class="token plain">AggregationResult </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"aggregationResults,omitempty"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> SelectionResults </span><span class="token operator">*</span><span class="token plain">SelectionResults </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"SelectionResults,omitempty"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> ResultTable </span><span class="token operator">*</span><span class="token plain">ResultTable </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"resultTable,omitempty"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> Exceptions </span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token plain">Exception </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"exceptions"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> TraceInfo </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">map</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token builtin" style="color:rgb(189, 147, 249)">string</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token builtin" style="color:rgb(189, 147, 249)">string</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"traceInfo,omitempty"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> NumServersQueried </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"numServersQueried"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> NumServersResponded </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"numServersResponded"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> NumSegmentsQueried </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"numSegmentsQueried"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> NumSegmentsProcessed </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"numSegmentsProcessed"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> NumSegmentsMatched </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"numSegmentsMatched"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> NumConsumingSegmentsQueried </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"numConsumingSegmentsQueried"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> NumDocsScanned </span><span class="token builtin" style="color:rgb(189, 147, 249)">int64</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"numDocsScanned"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> NumEntriesScannedInFilter </span><span class="token builtin" style="color:rgb(189, 147, 249)">int64</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"numEntriesScannedInFilter"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> NumEntriesScannedPostFilter </span><span class="token builtin" style="color:rgb(189, 147, 249)">int64</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"numEntriesScannedPostFilter"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> NumGroupsLimitReached </span><span class="token builtin" style="color:rgb(189, 147, 249)">bool</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"numGroupsLimitReached"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> TotalDocs </span><span class="token builtin" style="color:rgb(189, 147, 249)">int64</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"totalDocs"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> TimeUsedMs </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"timeUsedMs"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> MinConsumingFreshnessTimeMs </span><span class="token builtin" style="color:rgb(189, 147, 249)">int64</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"minConsumingFreshnessTimeMs"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>Note that <code>AggregationResults</code> and <code>SelectionResults</code> are holders for PQL queries.</p><p>Meanwhile <code>ResultTable</code> is the holder for SQL queries. <code>ResultTable</code> is defined as:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI go"><pre tabindex="0" class="prism-code language-go codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token comment" style="color:rgb(98, 114, 164)">// ResultTable is a ResultTable</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">type</span><span class="token plain"> ResultTable </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">struct</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> DataSchema RespSchema </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"dataSchema"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> Rows </span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">interface</span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"rows"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p><code>RespSchema</code> is defined as:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI go"><pre tabindex="0" class="prism-code language-go codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token comment" style="color:rgb(98, 114, 164)">// RespSchema is response schema</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">type</span><span class="token plain"> RespSchema </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">struct</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> ColumnDataTypes </span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token builtin" style="color:rgb(189, 147, 249)">string</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"columnDataTypes"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> ColumnNames </span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token builtin" style="color:rgb(189, 147, 249)">string</span><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">`json:"columnNames"`</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>There are multiple functions defined for <code>ResultTable</code>, like:</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI go"><pre tabindex="0" class="prism-code language-go codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">func</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">r ResultTable</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">GetRowCount</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">func</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">r ResultTable</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">GetColumnCount</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">func</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">r ResultTable</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">GetColumnName</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">columnIndex </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token builtin" style="color:rgb(189, 147, 249)">string</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">func</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">r ResultTable</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">GetColumnDataType</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">columnIndex </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token builtin" style="color:rgb(189, 147, 249)">string</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">func</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">r ResultTable</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">Get</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">rowIndex </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> columnIndex </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">interface</span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">func</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">r ResultTable</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">GetString</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">rowIndex </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> columnIndex </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token builtin" style="color:rgb(189, 147, 249)">string</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">func</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">r ResultTable</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">GetInt</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">rowIndex </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> columnIndex </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">func</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">r ResultTable</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">GetLong</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">rowIndex </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> columnIndex </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token builtin" style="color:rgb(189, 147, 249)">int64</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">func</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">r ResultTable</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">GetFloat</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">rowIndex </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> columnIndex </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token builtin" style="color:rgb(189, 147, 249)">float32</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">func</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">r ResultTable</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">GetDouble</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">rowIndex </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> columnIndex </span><span class="token builtin" style="color:rgb(189, 147, 249)">int</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token builtin" style="color:rgb(189, 147, 249)">float64</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>Sample Usage is <a href="https://github.com/fx19880617/pinot-client-go/blob/master/examples/batch-quickstart/main.go#L58" target="_blank" rel="noopener noreferrer">here</a></p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI go"><pre tabindex="0" class="prism-code language-go codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token comment" style="color:rgb(98, 114, 164)">// Print Response Schema</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">for</span><span class="token plain"> c </span><span class="token operator">:=</span><span class="token plain"> </span><span class="token number">0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">;</span><span class="token plain"> c </span><span class="token operator"><</span><span class="token plain"> brokerResp</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token plain">ResultTable</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">GetColumnCount</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">;</span><span class="token plain"> c</span><span class="token operator">++</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> fmt</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">Printf</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token string" style="color:rgb(255, 121, 198)">"%s(%s)\t"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> brokerResp</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token plain">ResultTable</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">GetColumnName</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">c</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> brokerResp</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token plain">ResultTable</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">GetColumnDataType</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">c</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">fmt</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">Println</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain" style="display:inline-block">
</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token comment" style="color:rgb(98, 114, 164)">// Print Row Table</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">for</span><span class="token plain"> r </span><span class="token operator">:=</span><span class="token plain"> </span><span class="token number">0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">;</span><span class="token plain"> r </span><span class="token operator"><</span><span class="token plain"> brokerResp</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token plain">ResultTable</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">GetRowCount</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">;</span><span class="token plain"> r</span><span class="token operator">++</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">for</span><span class="token plain"> c </span><span class="token operator">:=</span><span class="token plain"> </span><span class="token number">0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">;</span><span class="token plain"> c </span><span class="token operator"><</span><span class="token plain"> brokerResp</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token plain">ResultTable</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">GetColumnCount</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">;</span><span class="token plain"> c</span><span class="token operator">++</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> fmt</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">Printf</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token string" style="color:rgb(255, 121, 198)">"%v\t"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> brokerResp</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token plain">ResultTable</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">Get</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">r</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> c</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> fmt</span><span class="token punctuation" style="color:rgb(248, 248, 242)">.</span><span class="token function" style="color:rgb(80, 250, 123)">Println</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></div><footer class="row docusaurus-mt-lg"><div class="col"><a href="https://github.com/apache/pinot/edit/master/website/docs/user-guide/clients/golang.md" target="_blank" rel="noreferrer noopener"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" class="iconEdit_mS5F" aria-hidden="true"><g><path d="m34.5 11.7l-3 3.1-6.3-6.3 3.1-3q0.5-0.5 1.2-0.5t1.1 0.5l3.9 3.9q0.5 0.4 0.5 1.1t-0.5 1.2z m-29.5 17.1l18.4-18.5 6.3 6.3-18.4 18.4h-6.3v-6.2z"></path></g></svg>Edit this page</a></div><div class="col lastUpdated_wj+Z"></div></footer></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages navigation"><div class="pagination-nav__item"></div><div class="pagination-nav__item pagination-nav__item--next"></div></nav></div></div><div class="col col--3"><div class="tableOfContents_vrFS thin-scrollbar"><ul class="table-of-contents table-of-contents__left-border"><li><a href="#examples" class="table-of-contents__link">Examples</a></li><li><a href="#usage" class="table-of-contents__link">Usage</a><ul><li><a href="#create-a-pinot-connection" class="table-of-contents__link">Create a Pinot Connection</a></li></ul></li><li><a href="#query-pinot" class="table-of-contents__link">Query 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diff --git a/docs/user-guide/clients/java/index.html b/docs/user-guide/clients/java/index.html
index 5d641e1..31dd6c1 100644
--- a/docs/user-guide/clients/java/index.html
+++ b/docs/user-guide/clients/java/index.html
@@ -12,8 +12,8 @@
<link rel="search" type="application/opensearchdescription+xml" title="Apache Pinot™" href="/opensearch.xml">
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Ubuntu|Roboto|Source+Code+Pro">
<link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">Java | Apache Pinot™</title><meta data-react-helmet="true" property="og:url" content="https://pinot.apache.org/docs/user-guide/clients/java"><meta data-react-helmet="true" name="docsearch:language" content="en"><meta data-react-helmet="true" name="docsearch:version" content="current"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="docs-default-current"><meta data-react-helmet="true" property="og:title" content="Java | Apache Pinot™"><meta data-react-helmet="true" name="description" content="Pinot Java Client"><meta data-react-helmet="true" property="og:description" content="Pinot Java Client"><link data-react-helmet="true" rel="shortcut icon" href="/img/favicon.ico"><link data-react-helmet="true" rel="canonical" href="https://pinot.apache.org/docs/user-guide/clients/java"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/docs/user-guide/clients/java" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/docs/user-guide/clients/java" hreflang="x-default"><link data-react-helmet="true" rel="preconnect" href="https://BH4D9OD16A-dsn.algolia.net" crossorigin="anonymous"><link rel="stylesheet" href="/assets/css/styles.f0793de6.css">
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+<link rel="preload" href="/assets/js/runtime~main.82dd65bd.js" as="script">
+<link rel="preload" href="/assets/js/main.9b2ca7ec.js" as="script">
</head>
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@@ -34,7 +34,7 @@
</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">System.out.println("Number of result groups:" +</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> resultSetGroup.getResultSetCount(); // 2, min(foo) and max(foo)</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain" style="display:inline-block">
</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">ResultSet minResultSet = resultSetGroup.getResultSet(0);</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">for(int i = 0; i < minResultSet.length(); ++i) {</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> System.out.println("Minimum foo for " + minResultSet.getGroupKeyString(i, 1) +</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> ": " + minResultSet.getInt(i));</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">}</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain" style="display:inline-block">
</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">ResultSet maxResultSet = resultSetGroup.getResultSet(1);</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">for(int i = 0; i < maxResultSet.length(); ++i) {</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> System.out.println("Maximum foo for " + maxResultSet.getGroupKeyString(i, 1) +</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> ": " + maxResultSet.getInt(i));</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></div><footer class="row docusaurus-mt-lg"><div class="col"><a href="https://github.com/apache/pinot/edit/master/website/docs/user-guide/clients/java.md" target="_blank" rel="noreferrer noopener"><svg 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diff --git a/docs/user-guide/index.html b/docs/user-guide/index.html
index 740c31e..d351758 100644
--- a/docs/user-guide/index.html
+++ b/docs/user-guide/index.html
@@ -12,13 +12,13 @@
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<link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">PQL | Apache Pinot™</title><meta data-react-helmet="true" property="og:url" content="https://pinot.apache.org/docs/user-guide/pql"><meta data-react-helmet="true" name="docsearch:language" content="en"><meta data-react-helmet="true" name="docsearch:version" content="current"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="docs-default-current"><meta data-react-helmet="true" property="og:title" content="PQL | Apache Pinot™"><meta data-react-helmet="true" name="description" content="Pinot Query Language"><meta data-react-helmet="true" property="og:description" content="Pinot Query Language"><link data-react-helmet="true" rel="shortcut icon" href="/img/favicon.ico"><link data-react-helmet="true" rel="canonical" href="https://pinot.apache.org/docs/user-guide/pql"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/docs/user-guide/pql" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/docs/user-guide/pql" hreflang="x-default"><link data-react-helmet="true" rel="preconnect" href="https://BH4D9OD16A-dsn.algolia.net" crossorigin="anonymous"><link rel="stylesheet" href="/assets/css/styles.f0793de6.css">
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@@ -23,7 +23,7 @@
The following two queries are both supported in PQL, where the non-aggregation columns are ignored.</li></ul><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">MIN</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">foo</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">MAX</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">foo</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">foo</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">AVG</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">foo</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> mytable</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">GROUP</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">BY</span><span class="token plain"> bar</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> baz</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">TOP</span><span class="token plain"> </span><span class="token number">50</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain" style="display:inline-block">
</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> bar</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> baz</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">MIN</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">foo</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">MAX</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">foo</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">SUM</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">foo</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">AVG</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">foo</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> mytable</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">GROUP</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">BY</span><span class="token plain"> bar</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> baz</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">TOP</span><span class="token plain"> </span><span class="token number">50</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><ul><li>The results will always order by the aggregated value (descending).
The results for query</li></ul><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">MIN</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">foo</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">MAX</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">foo</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> myTable</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">GROUP</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">BY</span><span class="token plain"> bar</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">TOP</span><span class="token plain"> </span><span class="token number">50</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>will be the same as the combining results from the following queries</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">MIN</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">foo</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> myTable</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">GROUP</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">BY</span><span class="token plain"> bar</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">TOP</span><span class="token plain"> </span><span class="token number">50</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">SELECT</span><span class="token plain"> </span><span class="token function" style="color:rgb(80, 250, 123)">MAX</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">foo</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">FROM</span><span class="token plain"> myTable</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">GROUP</span><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">BY</span><span class="token plain"> bar</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">TOP</span><span class="token plain"> </span><span class="token number">50</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>where we don't put the results for the same group together.</p><ul><li>No support for <strong>ORDER BY</strong> in aggregation group by. However, ORDER BY support was added recently and is available in the standard-SQL endpoint. It can be used in the PQL endpoint by passing <code>queryOptions</code> into the payload as follows</li></ul><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">{</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"pql"</span><span class="token plain"> : </span><span class="token string" style="color:rgb(255, 121, 198)">"SELECT SUM(foo), SUM(bar) from myTable GROUP BY moo ORDER BY SUM(bar) ASC, moo DESC TOP 10"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"queryOptions"</span><span class="token plain"> : </span><span class="token string" style="color:rgb(255, 121, 198)">"groupByMode=sql;responseFormat=sql"</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><p>where:</p><ul><li><code>groupByMode=sql</code> - standard sql way of execution group by, hence accepting order by</li><li><code>responseFormat=sql</code> - standard sql way of displaying results, in a tabular manner</li></ul></div><footer class="row docusaurus-mt-lg"><div class="col"><a href="https://github.com/apache/pinot/edit/master/website/docs/user-guide/pql.md" target="_blank" rel="noreferrer noopener"><svg fill="currentColor" height="20" width="20" viewBox="0 0 40 40" class="iconEdit_mS5F" aria-hidden="true"><g><path d="m34.5 11.7l-3 3.1-6.3-6.3 3.1-3q0.5-0.5 1.2-0.5t1.1 0.5l3.9 3.9q0.5 0.4 0.5 1.1t-0.5 1.2z m-29.5 17.1l18.4-18.5 6.3 6.3-18.4 18.4h-6.3v-6.2z"></path></g></svg>Edit this page</a></div><div class="col lastUpdated_wj+Z"></div></footer></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages navigation"><div class="pagination-nav__item"></div><div class="pagination-nav__item pagination-nav__item--next"></div></nav></div></div><div class="col col--3"><div class="tableOfContents_vrFS thin-scrollbar"><ul class="table-of-contents table-of-contents__left-border"><li><a href="#pql-limitations" class="table-of-contents__link">PQL Limitations</a></li><li><a href="#pql-examples" class="table-of-contents__link">PQL Examples</a><ul><li><a href="#aggregation" class="table-of-contents__link">Aggregation</a></li><li><a href="#grouping-on-aggregation" class="table-of-contents__link">Grouping on Aggregation</a></li><li><a href="#ordering-on-aggregation" 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<div><a href="#main" class="skipToContent_OuoZ shadow--md">Skip to main content</a></div><nav class="navbar navbar--fixed-top navbarHideable_RReh"><div class="navbar__inner"><div class="navbar__items"><button aria-label="Navigation bar toggle" class="navbar__toggle clean-btn" type="button" tabindex="0"><svg width="30" height="30" viewBox="0 0 30 30" aria-hidden="true"><path stroke="currentColor" stroke-linecap="round" stroke-miterlimit="10" stroke-width="2" d="M4 7h22M4 15h22M4 23h22"></path></svg></button><a class="navbar__brand" href="/"><img src="/img/pinot-navbar-logo-722f37.svg" alt="Pinot" class="themedImage_TMUO themedImage--light_4Vu1 navbar__logo"><img src="/img/pinot-navbar-logo-722f37.svg" alt="Pinot" class="themedImage_TMUO themedImage--dark_uzRr navbar__logo"></a></div><div class="navbar__items navbar__items--right"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="navbar__item navbar__link">Docs</a><a class="navbar__item navbar__link" href="/download">Download</a><a class="navbar__item navbar__link" href="/blog">Blog</a><a href="https://github.com/apache/pinot" target="_blank" rel="noopener noreferrer" class="navbar__item navbar__link"><span>GitHub<svg width="13.5" height="13.5" aria-hidden="true" viewBox="0 0 24 24" class="iconExternalLink_wgqa"><path fill="currentColor" d="M21 13v10h-21v-19h12v2h-10v15h17v-8h2zm3-12h-10.988l4.035 4-6.977 7.07 2.828 2.828 6.977-7.07 4.125 4.172v-11z"></path></svg></span></a><div class="react-toggle displayOnlyInLargeViewport_cxYs react-toggle--checked react-toggle--disabled"><div class="react-toggle-track" role="button" tabindex="-1"><div class="react-toggle-track-check"><span class="toggle_iYfV" style="margin-left:2px">🌙</span></div><div class="react-toggle-track-x"><span class="toggle_iYfV" style="margin-left:2px">☀️</span></div><div class="react-toggle-thumb"></div></div><input type="checkbox" checked="" class="react-toggle-screenreader-only" aria-label="Switch between dark and light mode"></div><div class="searchBox_NKBi"><button type="button" class="DocSearch DocSearch-Button" aria-label="Search"><span class="DocSearch-Button-Container"><svg width="20" height="20" class="DocSearch-Search-Icon" viewBox="0 0 20 20"><path d="M14.386 14.386l4.0877 4.0877-4.0877-4.0877c-2.9418 2.9419-7.7115 2.9419-10.6533 0-2.9419-2.9418-2.9419-7.7115 0-10.6533 2.9418-2.9419 7.7115-2.9419 10.6533 0 2.9419 2.9418 2.9419 7.7115 0 10.6533z" stroke="currentColor" fill="none" fill-rule="evenodd" stroke-linecap="round" stroke-linejoin="round"></path></svg><span class="DocSearch-Button-Placeholder">Search</span></span><span class="DocSearch-Button-Keys"></span></button></div></div></div><div role="presentation" class="navbar-sidebar__backdrop"></div><div class="navbar-sidebar"><div class="navbar-sidebar__brand"><a class="navbar__brand" href="/"><img src="/img/pinot-navbar-logo-722f37.svg" alt="Pinot" class="themedImage_TMUO themedImage--light_4Vu1 navbar__logo"><img src="/img/pinot-navbar-logo-722f37.svg" alt="Pinot" class="themedImage_TMUO themedImage--dark_uzRr navbar__logo"></a></div><div class="navbar-sidebar__items"><div class="menu"><ul class="menu__list"><li class="menu__list-item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="menu__link">Docs</a></li><li class="menu__list-item"><a class="menu__link" href="/download">Download</a></li><li class="menu__list-item"><a class="menu__link" href="/blog">Blog</a></li><li class="menu__list-item"><a href="https://github.com/apache/pinot" target="_blank" rel="noopener noreferrer" class="menu__link"><span>GitHub<svg width="13.5" height="13.5" aria-hidden="true" viewBox="0 0 24 24" class="iconExternalLink_wgqa"><path fill="currentColor" d="M21 13v10h-21v-19h12v2h-10v15h17v-8h2zm3-12h-10.988l4.035 4-6.977 7.07 2.828 2.828 6.977-7.07 4.125 4.172v-11z"></path></svg></span></a></li></ul></div></div></div></nav><div class="main-wrapper docs-wrapper doc-page"><div class="docPage_lDyR"><main class="docMainContainer_r8cw docMainContainerEnhanced_SOUu"><div class="container padding-top--md padding-bottom--lg docItemWrapper_NJLN"><div class="row"><div class="col docItemCol_zHA2"><div class="docItemContainer_oiyr"><article><div class="markdown"><header><h1 class="h1Heading_dC7a">Querying Pinot</h1></header><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="rest-api-on-the-broker"></a>REST API on the Broker<a class="hash-link" href="#rest-api-on-the-broker" title="Direct link to heading">#</a></h2><p>Pinot can be queried via a broker endpoint as follows. This example assumes broker is running on <code>localhost:8099</code></p><div class="tabs-container"><ul role="tablist" aria-orientation="horizontal" class="tabs tabs--block"><li role="tab" tabindex="0" aria-selected="true" class="tabs__item tabItem_vU9c tabs__item--active">Standard-SQL endpoint</li><li role="tab" tabindex="-1" aria-selected="false" class="tabs__item tabItem_vU9c">PQL endpoint</li></ul><div class="margin-vert--md"><div role="tabpanel"><p>The Pinot REST API can be accessed by invoking POST operation with a JSON body containing the parameter sql to the <code>/query/sql</code> endpoint on a broker.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">$ curl </span><span class="token operator">-</span><span class="token plain">H </span><span class="token string" style="color:rgb(255, 121, 198)">"Content-Type: application/json"</span><span class="token plain"> </span><span class="token operator">-</span><span class="token plain">X POST \</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token operator">-</span><span class="token plain">d </span><span class="token string" style="color:rgb(255, 121, 198)">'{"sql":"select foo, count(*) from myTable group by foo limit 100"}'</span><span class="token plain"> \</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> http:</span><span class="token comment" style="color:rgb(98, 114, 164)">//localhost:8099/query/sql</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></div><div role="tabpanel" hidden=""><div class="alert alert--info alert--icon" role="alert"><i class="feather icon-info"></i> This endpoint is deprecated, and will soon be removed. The standard-SQL endpoint is the recommended endpoint.</div><p>The PQL endpoint can be accessed by invoking POST operation with a JSON body containing the parameter pql to the <code>/query</code> endpoint on a broker.</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">$ curl </span><span class="token operator">-</span><span class="token plain">H </span><span class="token string" style="color:rgb(255, 121, 198)">"Content-Type: application/json"</span><span class="token plain"> </span><span class="token operator">-</span><span class="token plain">X POST \</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token operator">-</span><span class="token plain">d </span><span class="token string" style="color:rgb(255, 121, 198)">'{"pql":"select count(*) from myTable group by foo top 100"}'</span><span class="token plain"> \</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> http:</span><span class="token comment" style="color:rgb(98, 114, 164)">//localhost:8099/query</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></div></div></div><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="query-console"></a>Query Console<a class="hash-link" href="#query-console" title="Direct link to heading">#</a></h2><p>Query Console can be used for running ad-hoc queries (checkbox available to query the PQL endpoint). The Query Console can be accessed by entering the <code><controller host>:<controller port></code> in your browser</p><p><img alt="PQL-Query-Console" src="/assets/images/pql-query-console-9172787dd283a81158000e46310114f3.png"></p><h3><a aria-hidden="true" tabindex="-1" class="anchor" id="pinot-admin"></a>pinot-admin<a class="hash-link" href="#pinot-admin" title="Direct link to heading">#</a></h3><p>You can also query using the pinot-admin scripts. Make sure you follow instructions in Getting Pinot to get Pinot locally, and then</p><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token builtin class-name" style="color:rgb(189, 147, 249)">cd</span><span class="token plain"> pinot/pinot-tools/target/pinot-tools-pkg</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">bin/pinot-admin.sh PostQuery </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -queryType sql </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -brokerPort </span><span class="token number">8000</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">\</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> -query </span><span class="token string" style="color:rgb(255, 121, 198)">"select count(*) from baseballStats"</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain" style="display:inline-block">
</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token number">2020</span><span class="token plain">/03/04 </span><span class="token number">12</span><span class="token plain">:46:33.459 INFO </span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token plain">PostQueryCommand</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token plain">main</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token plain"> Executing command: PostQuery -brokerHost localhost -brokerPort </span><span class="token number">8000</span><span class="token plain"> -queryType sql -query </span><span class="token keyword" style="color:rgb(189, 147, 249);font-style:italic">select</span><span class="token plain"> count</span><span class="token punctuation" style="color:rgb(248, 248, 242)">(</span><span class="token plain">*</span><span class="token punctuation" style="color:rgb(248, 248, 242)">)</span><span class="token plain"> from baseballStats</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token number">2020</span><span class="token plain">/03/04 </span><span class="token number">12</span><span class="token plain">:46:33.854 INFO </span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token plain">PostQueryCommand</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token plain">main</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token plain"> Result: </span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token string" style="color:rgb(255, 121, 198)">"resultTable"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token string" style="color:rgb(255, 121, 198)">"dataSchema"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token string" style="color:rgb(255, 121, 198)">"columnDataTypes"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token string" style="color:rgb(255, 121, 198)">"LONG"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token plain">,</span><span class="token string" style="color:rgb(255, 121, 198)">"columnNames"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token string" style="color:rgb(255, 121, 198)">"count(*)"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span><span class="token plain">,</span><span class="token string" style="color:rgb(255, 121, 198)">"rows"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token number">97889</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span><span class="token plain">,</span><span class="token string" style="color:rgb(255, 121, 198)">"exceptions"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token plain">,</span><span class="token string" style="color:rgb(255, 121, 198)">"numServersQueried"</span><span class="token plain">:1,</span><span class="token string" style="color:rgb(255, 121, 198)">"numServersResponded"</span><span class="token plain">:1,</span><span class="token string" style="color:rgb(255, 121, 198)">"numSegmentsQueried"</span><span class="token plain">:1,</span><span class="token string" style="color:rgb(255, 121, 198)">"numSegmentsProcessed"</span><span class="token plain">:1,</span><span class="token string" style="color:rgb(255, 121, 198)">"numSegmentsMatched"</span><span class="token plain">:1,</span><span class="token string" style="color:rgb(255, 121, 198)">"numConsumingSegmentsQueried"</span><span class="token plain">:0,</span><span class="token string" style="color:rgb(255, 121, 198)">"numDocsScanned"</span><span class="token plain">:97889,</span><span class="token string" style="color:rgb(255, 121, 198)">"numEntriesScannedInFilter"</span><span class="token plain">:0,</span><span class="token string" style="color:rgb(255, 121, 198)">"numEntriesScannedPostFilter"</span><span class="token plain">:0,</span><span class="token string" style="color:rgb(255, 121, 198)">"numGroupsLimitReached"</span><span class="token plain">:false,</span><span class="token string" style="color:rgb(255, 121, 198)">"totalDocs"</span><span class="token plain">:97889,</span><span class="token string" style="color:rgb(255, 121, 198)">"timeUsedMs"</span><span class="token plain">:185,</span><span class="token string" style="color:rgb(255, 121, 198)">"segmentStatistics"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token plain">,</span><span class="token string" style="color:rgb(255, 121, 198)">"traceInfo"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">{</span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span><span class="token plain">,</span><span class="token string" style="color:rgb(255, 121, 198)">"minConsumingFreshnessTimeMs"</span><span class="token plain">:0</span><span class="token punctuation" style="color:rgb(248, 248, 242)">}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="pinot-clients"></a>Pinot Clients<a class="hash-link" href="#pinot-clients" title="Direct link to heading">#</a></h2><p>Here's a list of the clients available to query Pinot from your application</p><ul><li><a href="/docs/user-guide/clients/golang">Java client</a></li><li><a href="/docs/user-guide/clients/java">Go client</a></li><li>Coming soon - JDBC client</li></ul></div><footer class="row docusaurus-mt-lg"><div 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<link rel="stylesheet" href="https://at-ui.github.io/feather-font/css/iconfont.css"><title data-react-helmet="true">Response Format | Apache Pinot™</title><meta data-react-helmet="true" property="og:url" content="https://pinot.apache.org/docs/user-guide/response-format"><meta data-react-helmet="true" name="docsearch:language" content="en"><meta data-react-helmet="true" name="docsearch:version" content="current"><meta data-react-helmet="true" name="docsearch:docusaurus_tag" content="docs-default-current"><meta data-react-helmet="true" property="og:title" content="Response Format | Apache Pinot™"><meta data-react-helmet="true" name="description" content="REST Response Format"><meta data-react-helmet="true" property="og:description" content="REST Response Format"><link data-react-helmet="true" rel="shortcut icon" href="/img/favicon.ico"><link data-react-helmet="true" rel="canonical" href="https://pinot.apache.org/docs/user-guide/response-format"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/docs/user-guide/response-format" hreflang="en"><link data-react-helmet="true" rel="alternate" href="https://pinot.apache.org/docs/user-guide/response-format" hreflang="x-default"><link data-react-helmet="true" rel="preconnect" href="https://BH4D9OD16A-dsn.algolia.net" crossorigin="anonymous"><link rel="stylesheet" href="/assets/css/styles.f0793de6.css">
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@@ -24,7 +24,7 @@
</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">{</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"traceInfo"</span><span class="token plain">:{}</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"numDocsScanned"</span><span class="token plain">:</span><span class="token number">17</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"aggregationResults"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> {</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"function"</span><span class="token plain">:</span><span class="token string" style="color:rgb(255, 121, 198)">"count_star"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"value"</span><span class="token plain">:</span><span class="token string" style="color:rgb(255, 121, 198)">"17"</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> }</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"timeUsedMs"</span><span class="token plain">:</span><span class="token number">27</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"segmentStatistics"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"exceptions"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"totalDocs"</span><span class="token plain">:</span><span class="token number">17</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></div><div role="tabpanel" hidden=""><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI sql"><pre tabindex="0" class="prism-code language-sql codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">curl </span><span class="token operator">-</span><span class="token plain">X POST \</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token operator">-</span><span class="token plain">d </span><span class="token string" style="color:rgb(255, 121, 198)">'{"pql":"select count(*) from flights group by Carrier"}'</span><span class="token plain"> \</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> http:</span><span class="token comment" style="color:rgb(98, 114, 164)">//localhost:8099/query</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain" style="display:inline-block">
</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain" style="display:inline-block">
</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">{</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"traceInfo"</span><span class="token plain">:{}</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"numDocsScanned"</span><span class="token plain">:</span><span class="token number">23</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"aggregationResults"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> {</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"groupByResult"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> {</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"value"</span><span class="token plain">:</span><span class="token string" style="color:rgb(255, 121, 198)">"10"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"group"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token string" style="color:rgb(255, 121, 198)">"AA"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> }</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> {</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"value"</span><span class="token plain">:</span><span class="token string" style="color:rgb(255, 121, 198)">"9"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"group"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token string" style="color:rgb(255, 121, 198)">"VX"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> }</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> {</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"value"</span><span class="token plain">:</span><span class="token string" style="color:rgb(255, 121, 198)">"4"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"group"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token string" style="color:rgb(255, 121, 198)">"WN"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> }</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"function"</span><span class="token plain">:</span><span class="token string" style="color:rgb(255, 121, 198)">"count_star"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"groupByColumns"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token string" style="color:rgb(255, 121, 198)">"Carrier"</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> }</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"timeUsedMs"</span><span class="token plain">:</span><span class="token number">47</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"segmentStatistics"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"exceptions"</span><span class="token plain">:</span><span class="token punctuation" style="color:rgb(248, 248, 242)">[</span><span class="token punctuation" style="color:rgb(248, 248, 242)">]</span><span class="token punctuation" style="color:rgb(248, 248, 242)">,</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"> </span><span class="token string" style="color:rgb(255, 121, 198)">"totalDocs"</span><span class="token plain">:</span><span class="token number">23</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">}</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></div></div></div></div><footer class="row docusaurus-mt-lg"><div 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diff --git a/docs/user-guide/rest-admin-interface/index.html b/docs/user-guide/rest-admin-interface/index.html
index b903abe..27b6060 100644
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@@ -12,13 +12,13 @@
<link rel="search" type="application/opensearchdescription+xml" title="Apache Pinot™" href="/opensearch.xml">
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Ubuntu|Roboto|Source+Code+Pro">
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<link rel="search" type="application/opensearchdescription+xml" title="Apache Pinot™" href="/opensearch.xml">
<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Ubuntu|Roboto|Source+Code+Pro">
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icon-download"></i></div><div class="panel--title">0.9.2</div><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-src.tar.gz"><div class="panel--title">Official source release</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-src.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-src.tar.gz.asc"><div class="panel--subtitle">ASC</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-bin.tar.gz"><div class="panel--title">Binary download</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-bin.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-bin.tar.gz.asc"><div class="panel--subtitle">ASC </div></a></a></div><div class="col"><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-bin.tar.gz" class="panel panel--link text--center"><div class="panel--icon"><i class="feather icon-download"></i></div><div class="panel--title">0.9.1</div><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-src.tar.gz"><div class="panel--title">Official source release</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-src.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-src.tar.gz.asc"><div class="panel--subtitle">ASC</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-bin.tar.gz"><div class="panel--title">Binary download</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-bin.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-bin.tar.gz.asc"><div class="panel--subtitle">ASC </div></a></a></div><div class="col"><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-bin.tar.gz" class="panel panel--link text--center"><div class="panel--icon"><i class="feather icon-download"></i></div><div class="panel--title">0.9.0</div><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-src.tar.gz"><div class="panel--title">Official source release</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-src.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-src.tar.gz.asc"><div class="panel--subtitle">ASC</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-bin.tar.gz"><div class="panel--title">Binary download</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-bin.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-bin.tar.gz.asc"><div class="panel--subtitle">ASC </div></a></a></div><div class="col"><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-bin.tar.gz" class="panel panel--link text--center"><div class="panel--icon"><i class="feather icon-download"></i></div><div class="panel--title">0.8.0</div><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-src.tar.gz"><div class="panel--title">Official source release</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-src.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-src.tar.gz.asc"><div class="panel--subtitle">ASC</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-bin.tar.gz"><div class="panel--title">Binary download</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-bin.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-bin.tar.gz.asc"><div class="panel--subtitle">ASC </div></a></a></div><div class="col"><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-bin.tar.gz" class="panel panel--link text--center"><div class="panel--icon"><i class="feather icon-download"></i></div><div class="panel--title">0.7.1</div><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-src.tar.gz"><div class="panel--title">Official source release</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-src.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-src.tar.gz.asc"><div class="panel--subtitle">ASC</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-bin.tar.gz"><div class="panel--title">Binary download</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-bin.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-bin.tar.gz.asc"><div class="panel--subtitle">ASC </div></a></a></div><div class="col"><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-bin.tar.gz" class="panel panel--link text--center"><div class="panel--icon"><i class="feather icon-download"></i></div><div class="panel--title">0.6.0</div><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-src.tar.gz"><div class="panel--title">Official source release</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-src.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-src.tar.gz.asc"><div class="panel--subtitle">ASC</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-bin.tar.gz"><div class="panel--title">Binary download</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-bin.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-bin.tar.gz.asc"><div class="panel--subtitle">ASC </div></a></a></div><div class="col"><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-bin.tar.gz" class="panel panel--link text--center"><div class="panel--icon"><i class="feather icon-download"></i></div><div class="panel--title">0.5.0</div><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-src.tar.gz"><div class="panel--title">Official source release</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-src.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-src.tar.gz.asc"><div class="panel--subtitle">ASC</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-bin.tar.gz"><div class="panel--title">Binary download</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-bin.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-bin.tar.gz.asc"><div class="panel--subtitle">ASC </div></a></a></div><div class="col"><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-bin.tar.gz" class="panel panel--link text--center"><div class="panel--icon"><i class="feather icon-download"></i></div><div class="panel--title">0.4.0</div><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-src.tar.gz"><div class="panel--title">Official source release</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-src.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-src.tar.gz.asc"><div class="panel--subtitle">ASC</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-bin.tar.gz"><div class="panel--title">Binary download</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-bin.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-bin.tar.gz.asc"><div class="panel--subtitle">ASC </div></a></a></div><div class="col"><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-bin.tar.gz" class="panel panel--link text--center"><div class="panel--icon"><i class="feather icon-download"></i></div><div class="panel--title">0.3.0</div><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-src.tar.gz"><div class="panel--title">Official source release</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-src.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-src.tar.gz.asc"><div class="panel--subtitle">ASC</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-bin.tar.gz"><div class="panel--title">Binary download</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-bin.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-bin.tar.gz.asc"><div class="panel--subtitle">ASC </div></a></a></div><div class="col"><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-bin.tar.gz" class="panel panel--link text--center"><div class="panel--icon"><i class="feather icon-download"></i></div><div class="panel--title">0.2.0</div><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-src.tar.gz"><div class="panel--title">Official source release</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-src.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-src.tar.gz.asc"><div class="panel--subtitle">ASC</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-bin.tar.gz"><div class="panel--title">Binary download</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-bin.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-bin.tar.gz.asc"><div class="panel--subtitle">ASC </div></a></a></div><div class="col"><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-bin.tar.gz" class="panel panel--link text--center"><div class="panel--icon"><i class="feather icon-download"></i></div><div class="panel--title">0.1.0</div><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-src.tar.gz"><div class="panel--title">Official source release</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-src.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-src.tar.gz.asc"><div class="panel--subtitle">ASC</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-bin.tar.gz"><div class="panel--title">Binary download</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-bin.tar.gz.sha512"><div class="panel--subtitle">SHA512</div></a><a href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-bin.tar.gz.asc"><div class="panel--subtitle">ASC</div></a></a></div></div></div></section></main></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a 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font-weight="bold"><tspan x="0" y="19">EVENTS</tspan></text><g fill-rule="nonzero"><circle cx="32.5" cy="32.5" r="32.5" fill="#000" transform="translate(549 104)"></circle><circle cx="32.5" cy="32.5" r="32.5" fill="#FFF" transform="translate(549 104)"></circle></g><g transform="translate(549 104)"><mask id="pinot-overview-user_svg__h" fill="#fff"><use xlink:href="#pinot-overview-user_svg__g"></use></mask><image mask="url(#pinot-overview-user_svg__h)" x="10.432" y="12.84" width="43.333" height="43.333" 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jMP+7fePEre+Snk9hqEAAktukoOAII+CHgHvWvGNz1R3JV/z/LBX5ydT+39Pnh2iKNyySquenApoErW2zH6gAFCAACxCVpBBCIt8Cy259cWBmt3XjiqH9JvEvbc6U7XznOWoKA6NqVACA6e3JGAIGoBOQK/0vW7b7crtu3yon+66QYTOQTTVu4QcDX929cfh23CYbfAAQA4ZuTIwIIRCgwMLg1d8ng7j/WxnxXinFZhEUh6+MCC2Ua4S8+vWTZHxEEhPuTIAAI15vcEEAgQgF37v6MnvU3clHd56QY50RYFLI+WaDfGPUZgoCTUYL+iwAgaGHSRwCBWAisuH3XEpVVN8vjef9MCtQ7V9XHQteXQvTLRZifOLB42VW+pEYiLQUIAFoSsQECCCRdQG7xe6Wq63+Ui/1eLXVJ9KQ6SW+L6crvThgk915+6cD9yxmamQ7Kp3UEAD5BkgwCCMRTYOXgzivlFr+bpHR0KvFsolNLdb4y5jMyWdCSU1fwt78CBAD+epIaAgjESODSW/ZcaJT1BSnS+TEqFkVpLfBKozOf4tkBraG62YIAoBs99kUAgdgKuGP+jmXWSAFXxLaQFKy5gFHX1HP6hq1bB7hFs7lSV2sIALriY2cEEIijwPgFf3Xrq3Lq/8o4lo8yeRLIKK3fOWfEvNnT1mzUtgABQNtk7IAAAnEWoPOPc+u0XbY+mSPgYzw3oG03TzsQAHhiYiMEEEiCAJ1/Elqp7TLKREH6MzxGuG23ljsQALQkYgMEEEiCwEXfP9SvHP0pTvsnobXaLuNlVt18bN/9q5i/oW265jsQADS3YQ0CCCREwJ3eN18p3SATyVyTkCJTzLYF9JstNfLatndjh6YCBABNaViBAAJJEbB0/5tlhr93SnmZ5CcpjdZ+Od27AT74zM8vnNf+ruwxlQABwFQqfIcAAokRWHnb7su00R+TAnN6ODGt1mFBjV5Zz2bey0ODOvQ7ZTf7lL/5E4HYCKy8fc98p+Z8WKYGLcamUI2CaLN7aMT51M7rFpcaX/EevoD7cB/HMR+X38jC8HMnxwgEpKnVuw4suvhHSj1yVwT591SWBAA91Zy9VRnHmDlyC9Afyvzt/fGrmd6cnzPzs1IuAoCIGucNgybziNrzQa3M70RUBLKNQMB9XoBS1oefvWfpA2e8+IljERShZ7JkCKBnmpKKIJAugYfNU3LBn+M+2Y9x/3Q1vcwPZK6oZexrU1Zt36tLAOA7KQkigEDQApcO7p4n94Z/JJ5nh4KuPemLQM4o/V4uCOzut0AA0J0feyOAQNgCxmhHqXdLtivDzpr8YiWwvGbb7zYSCcSqVAkqDAFAghqLoiKAgFIDt+6Vjt+9NoR/+FP+e9DKMn944L7ly1Pu0HH1CQA6pmNHBBAIW8Cd8MeynA9KvtwLHjZ+HPMzSoaCzDs4C9BZ4xAAdObGXgggEIFAxsy6WrJlNrgI7GObpVarOQvQWesQAHTmxl4IIBCygHvPv9HGHftnwp+Q7WOdHWcBOm4eAoCO6dgRAQTCFNA59TbJ7+Vh5kleiRF4/aFfDJyfmNLGpKAEADFpCIqBAALNBdzb/uTif+b6b06U9jXzZUbIV6Qdod36EwC0K8b2CCAQukDdOO5T/rjaO3T5xGQotwKa/7p164D7wCAWjwIEAB6h2AwBBKIRWHrz47Nk6rc3SO7c7x1NEyQiV/lxvGL2mHNJIgobk0ISAMSkISgGAghMLVDI5X9f/nF/0dRr+RaB4wLuMwK0Ub+Nh3cBAgDvVmyJAAIhC7hH/9L5/4lky6ndkO2TmJ38Vl697/5V3CXisfEIADxCsRkCCIQvUMzm3CN/jv7Dp09qjiu0M7Y4qYUPu9wEAGGLkx8CCHgTkMv+ZXGf+MYRnTcxtlJKHiGuLgPCmwABgDcntkIAgZAFLrll33wZ131lyNmSXbIFtNLmNWb95XayqxFO6QkAwnEmFwQQaFfAqv+u7MLkLu26pX17o174TP/BM5PAYGrR3tlClJSEXwllRCBlAqvu2NdXGau9Saot13WxINCWwFl1rRbKHvvb2ivMja831qpzNxWq1eosk89H9hsnAAiz0ckLAQQ8CVRHKhcry1rlaWM2QuBkgRlyO+CvyVf3nPx19H9dfv16+/CSOfnKsY2FsmVsI68oT8MTAET/m6AECCBwioCxMjLxj5l9ytf8iYAnAXk88G942jCMjeRi1lVrN9nDdqmwZ6SvUCiPWXY+jIxb5xFl8NG6dGyBAAKpE3Cf+ied/6tSV3Eq7J+AUcsPPLhyhn8JdpCSdPxLv/R4fsnaTbOqTnl21rH7CsVKrPpczgB00K7sggACwQlYWesCRzkXMPwfnHHPp6zVC1SpfJbUc0fodZXx/fmzNuRnr7m7oLNW1rEyWl6hF8NLhgQAXpTYBgEEQhM4fuuf7g8tQzLqRYGz5PkR7nMBwgsABgczq44syY9UNxbtvGUbZSn5Lcd6IQCIdfNQOATSJXD5emM/c2j3yyO7LDpd3L1cW7cDnht4BSeN75sjhXzZqmXiMr7vpe4EAF6U2AYBBEIROHh415m2sniiWyjavZ2JtpyzA6uhdPwD67Zlx9ZuKh4tW7mCJRfzW7XAsgsqYQKAoGRJFwEE2hbIVu2FxnLcsVsWBLoTMOrC7hKYYm8Z31+08M5c/5q7i1pb2T47o1Vxiu0S8hUBQEIaimIikAqBTP1CZXS0V2+nAppKtiUweXy/VrRNVu5TaSuBeG5MABDPdqFUCKRSwBhrWW/805rK5otXpY1auGP9osLiK3aWOi2YO3HP/gXZYhLH973UmQDAixLbIIBAKALGmPPkaW4sCHQvoPXcmdm8HKur9gKAxvj+kbHiHnd835F79xM4vu8FkADAixLbIIBA4AIrv3VghtEV/8dtAy85GcRTQOfKWZ3zXLYeG9/3Um8CAC9KbIMAAoELVGbWCtmaPp0hgMCpU5KBOc+29LlS2WenrfCJ8f1SZkMhU8rZTjYjjxJIx0IAkI52ppYIxF4gV3HONZY6L/YFpYBJEag6ut703rxTx/e1/PiclPWIKatuUn63lBOBNApYM5Vy4jlnahqbI/l13p+vOftOqsaJ8f3ygaHCHpXL9/L4/kn1bvIHAUATGL5GAIGQBbgFMGTwns/OcWqZ42fzpeNf9M0786d/4ZcFpzCaKxTl/n1V6XmAVhUkAGglxHoEEAhFwCjN0X8o0unJZFdhgbXqK9/pK90k4/vVnF2bWZaOn59Z4xdAANCQ4B0BBKIVMFruAEjL5VfdU2ccc3fd0m/vPqXeS6FsbOvZ6unD/+fh9xRkfv5CGsf3vbQqAYAXJbZBAIHgBYw5WzEHgGfns1788OOysfticQUmje8rd3y/KPfvs0wrQADQhOeSW596u/xb9LImqyP92tLm6w+uXnh3pIUgcwQQQCAOAtLxL/3yEzln7aZCxilLx8/4vtdmIQBoIjXe+Wt1XZPVkX7taP2fUgACgEhbgcwRCE2gph3zTGi5JSUjmbhn/qwN+dlr7i7orJV1rIyWV1JKH4tyEgDEohkoBAIIINBMwIw5GWtXs7Vp+969f//wkjn5yrGNBTtv2UZZXDnS4Y+AAKBDOHZDAAEEwhHQWe2o165Y99SqcPKLXy7GUdoyjozpm+wzpp5VchrUni3TRpxYjOPs0KVn7238zbs3AQIAb05shQACCEQlUJCO7+PpPcyVG0RF/vj9IVpJ1//8O/m0dYtsQgDQ5i+UAKBNMDZHAAEEEAhBwLhdvrw83RlqtodQop7LggCg55qUCiGAAAIJFmir4z9RT2MOJbjGkRWdACAyejJGAAEEEJgQ6KTjn9iZD50IEAB0osY+CCCAAAI+CJw4xT/e+XeRnJbLBFnaFiAAaJuMHRBAIBABbR5TTAUYCG3sEvX3aH9Um/p2T5cKxA4i2gIRAETrT+4IINAQ0Pqgtwu+GjvwnjgBfzv+RvUdR5kx904BlvYECADa82JrBBAISEArUz9+w1dAGZBsdALBdPyN+lQlcOTZvg2NNt55WEIbWGyKAAIBCtQzMgSgRgLMgaRDFXDH9+Ulx+fj74GdozcHVal+MNSq9UhmBAA90pBUA4GkCxi7+pzUoZT0eqS+/G6n716T55wIAAIGkWcBPaPqY5wB6MCZAKADNHZBAAH/BcZKtX2S6n7/UybFUAQaHf/4eyg5jmeilbNTW7VyeDn2Tk4EAL3TltQEgUQL1Kq2HMXppxNdiTQWPqKOf4LaMQ9NfOZDWwIEAG1xsTECCAQlsPO6xSUZK+apd0EB+5ruidP7gY/vtyz0qFK1LS23YoMpBbgLYEoWvkQAgUgEmAsgEnbPmbpH+57n5/ecahcbmmdMzezpIoFU78oZgFQ3P5VHIF4CchvgDimR28uwxEkg6tP8TSzkAsCnTGnYvXiUpQMBAoAO0NgFAQSCEZBHvj8iKQ8Fkzqpti0Q046/UQ+tnQe4ALCh0f47QwDtm7EHAm0LrPjOrtnGtv6Ltky27Z2D3+HwGXMX/PDOK3Qt+Kymz6GeU3usmtotWw1MvyVrAxUY7/hjfyLGqLrDBYBd/BAIALrAY1cEvAqYvLVAO87fKaP7ve4T4nab9x975meSX+RH3o+8bt7hFYNP/VJpTQAQ4g9gPKvx8X351HgPO//285NT/457xoilQwGGADqEYzcEEAhAQGvpfrQbjMT+8DOA2keT5PjRfmO2viSxm52mWt4bDVpv5EoA0BvtSC0Q6BmBTMbcK5U53DMVimtFTur441rI5uUyymxQ1ZHh5luwppUAAUArIdYjgECoAnY+u0My5N7uoNTdjj/6+/e7rV1N1ev3dJtI2vcnAEj7L4D6IxAzgU1Xnzeqlf5RzIqV8OJIpz+54094baQy23Wl9svEVyPiChAARNwAZI8AAlMIOPV/l2+PTLGGr9oRaJzmD+nBPO0UrZtt5fT/v5vaUaaN7gZR9iUA6BKQ3RFAwH+BujXi3t71H/6nnJIUGx3/+HvP1XnIVOs/6blaRVAhAoAI0MkSAQSmF9i6eqAifdcPZKskXZY+faXCWNvbHf8JQfNAvTa8OQzOXs+DAKDXW5j6IZBUASfzUyn69qQWP7xynzK+39shk3Ec53t2rTwWnm/v5kQA0LttS80QSLTAQ9eet0crdVeiKxFk4RtH+z02vj89mdmfccpc/T89kue1BACeqdgQAQRCFXAnBTLmFslTHvnKMiHQ6PjH3ye+TccHo+6sHx3emY7KBl9LAoDgjckBAQQ6FBirVjbKru6LJc0d//HWH1Kmvk7n3FMeLH4IEAD4oUgaCCAQiMATb7ngmPxr//eSeCWQDGKfaKrG91u0hvmZHhm5v8VGrG5DgACgDSw2RQCB8AVKlfKPJdefh59zhDk2jvZTNb4/rfeQcurfdNRYddqtWNmWAAFAW1xsjAACYQuk6ixAo+NP4/j+tD8sjv6n5elwJQFAh3DshgAC4Qn0/FkAOv7pfkwc/U+n08U6AoAu8NgVAQTCEejNswCM73v79XD0782p/a0IANo3Yw8EEIhAoGfOAowf7Uvnz/i+l1/RUcb+vTB1tg0BQGdu7IUAAiELuGcBtNE3SrbJnBfgpNP8EgCwtBYwzq2VsWPcBtpaqqMtCAA6YmMnBBCIQqCuj90h+f5zFHl3nOdJHX/HqaRuR63MLl2r3px1KvXUVT6kChMAhARNNggg0L2A+5AgpfSnJaUnu08t4BTcjt9x5HFG7lh/wHn1XvJVx5gbndKRx3qvavGpkR2folASPwV2rF9U6Ouf8WFj9AI/0/UjLZnhdcyuZT51xos37/EjPdJIl8CW1Qu2rFy368vy2/6s1DwTq9q7nb27NN6P/8V/2xXQ5se1saO30UG1C9fe9vi255WYrWdm81m5xui1chptZewKbdRQLet8RcpFABC7xklGgUxF/YPJqt+ThwVdFYsSj3f4HOn70RbuqX+nUvo8T/zzQ3P6NBgCmN6HtQggEEOBLW9eeMSy9Me00rsiLd746X1O8/vYBmNO3fm8Kh192Mc0SaqJAAFAExi+RgCBeAtsfv2Ce402H5dShn9XAB1/ED8OaU7nH63R4duDSJw0ny9AAPB8E75BAIGECDhm6DtyUeDN4RTXPcUvLy7sC4jbbDCl8heZ7z8g3imSJQCYAoWvEEAgGQLuXQG1TPWTUtp7Aytx42ifiXsCI5bIaqeqVW4wtaNPB5gJSZ8iQABwCgh/IoBAsgQevmbJLqP1h6TUB3wteaPjH3/3NWUSO1ngqKo5/8uMHfnFyV/zV9ACBABBC5M+AggELvDQ6+ffJUMBn5CMZJ6ALhc6/i4B29p9WNXrn6qUj97Z1l5s7IsAAYAvjCSCAAKRCsjkErli5uvSd39NytHBzHGM70fQfnXjOF+slI5+m9n+ItCXLAkAonEnVwQQ8Flg09Xnjeqa+aiMJ//Ec9KNo33G9z2T+bRhXZn6t1Vl5Bt0/j6JdpAMAUAHaOyCAALxFHDnB5C5Ad8jwwE/m7aEjY5//H3aLVnpv8B452/Kozeo6siw/8mTolcBAgCvUmyHAAKJENhyzcInVcZ515RBAB1/1G1I5x91C0zKnwBgEgYfEUCgNwRODgIY349Jq9L5x6QhGsUgAGhI8I4AAr0jIE8KGjr25D6nOvYBUzd383CeyJuWzj/yJnh+AXgY0PNN+AYBBJIqcL2xVp27qVC6aUMhU83ZTu3wfpOb/QGVy39Oa/WypFYr4eWm849pAxIAxLRhKBYCCHgXuPz69fbhJXPylWMbC2XL2NpYyjnxr5uuHNmlzWnvM7nC/9SWfoOkGq9HCHuvZhK3HHZv9VOV0W9wwV/8mo8AIH5tQokQQMCLgJzmX7V2kz1slwp7RvoKhfKYZeen3tFUj+5RqvZRVZhxSO5+vk62mjn1lnzrm4BWe0yt/tlq6eg6bvXzTdXXhAgAfOUkMQQQCFxAOv6lX34i56zdVKg65XzWsXW26GECQLnlrFKvfjpXOG2vymQ+LOU8LfCypjMD96rLDapauUHJ9L7ZdBokotYEAIloJgqJAAJKxvfnz9qQn73m7oLOWlnHymh5tQXjHom6M8/l8qftVrYlUwfrRW0lwMatBMbcR/q6T/XjwT6tqKJfTwAQfRtQAgQQmEZg8vi+nbdsIxOYyiFmx4sbBJixQ/+uZ8wdVVbmf0sQ8OsdJ8aOkwTMdsfUP5MZHvmxUWPVSSv4GFMBAoCYNgzFQiDVAm2M73fqZEae2aCzp/3RiYsDr5F0OFvdGaY8e8G501TLN+jS0YedztJgrwgECAAiQCdLBBBoIiAd/8C6bdmxtZuKR8tWrmDZlqfx/SbJtfravTiwZkoftgunbdJav1POBlwo++hW+7H+hIBWTzuO85V65djNtoyt4JIsAQKAZLUXpUWgNwVkfH/Rwjtz/WvuLmptZfvsjFbFcKpq18pjavjpbzt2/79ZxeLblbbeKGMMZ4WTe2JzqWtl/sOpVD+njj23yc65T1NiSZoAAUDSWozyItBLAoODmVVHluRHqhuLdq1oGzkJH1VPomtD+51nRz6pZ53+E53NvktOBLxKqEMKQ5LTqNLx7zKmflOlMnyre9Svc8kpOyU9WYAA4GQP/kIAgaAFJo3vmyOFfNmqZZrdvx90UU5NX7tHsqXD9+tS8UGnr+8qnbHfLoHAb8p2XB+g1FFlnFtVrXqzKR15jM7j1F9P8v6mDZPXZpQYgWQKTDG+r6xaLOviuFexj479i6P777KK+Stl3oC3pTgQkLF9c6dy6t+sjB3byKQ+sfzJdlQoAoCO2NgJAQQ8C0Q4vu+5jE021GZoyIwOfd/o/p+lLhDQZr8c8f9A1eu36dGxR9ygiNMgTX4oCf2aACChDUexEYi9wInx/VJGHsxTkgfzZDM6qvH9bq1SFAhIE5nH5PV/Va12m/Pcc4+5wyJJbbdu273X9ycA6PUWpn4IhCzgTtyzf0G22Bjfn/xgnpCL4nt2kwMBVci/1MpkXiU3Db5EhgcWSWbJvX1QbueTRybfL7f0/VSVS3e6F0RKfRQX+LkKvbsQAPRu21IzBMITaIzvHxkr7nHv33cqVlzH9/1AcQMBNTb0r1LNn5iZM86VyQpeLU8afLV0mQOSflKeMTAmR/pPjI/vy9F++djo9pxdiudFGX40Gmk8T4AA4HkkfIEAAp4FpONf9M0786d/4ZcFpzCaC/P+fc9lDHBD9/S4rgztU5Whr1Xt/HeyuRlLHZ15uWVZl0q2S5TWZ8doToEh6ez3SpM9o7XzgKk562v14a2NCXxy9AYB/lLimTRNHs92oVQIxFtALuxbde6mQukmGd+v5uzazLKc/m7vwTzxrmD7pXMnFDK18haB2CJnBrSeUZyhs/l5EhD8tgQEL5YUL5YzBGfLe5OHFrefZ4s9ytLhH5QO/ym5mO8uU69vVOXKNlUfq8jZGVmnFB1AC8EeX03793gDUz0E/BRwx/cPnakKdbNR7t83di+N7/vpND6fgDx+2FRHHpWA4NFyrbA211c4w2StC7TJzJczA7bW5hKZdXCF5OtONjRTgoM5k8pQkM/N/n12r8kb+dW25rB8HpbXmHT0W6TDf0jG82tG1/foqvO4KQ0/p6XDn7hAwfrVnnxKt0CzH1i6Vag9Agj8SkB6FHd+/vKBocIelcsXchXLjmy+vl8VK0mfxsfWK6WDuqIONsrt9uKWKvY5WctSOjvD2OqMxjqtJEhQ1uSAoLFKrjQ0MnbvPOnIjfnul7qmnlUSaVhVuYRPjY02Nmx0+JoOv0HC+ykCBACngPAnAgicEJCOf/L4fqEo8/OrCjw+Cox32McfnDs8OTiQLLY1y8YNHNyl0cEf/0tCgsYH3hHwKEAA4BGKzRBIjYCM78+ftSE/e83dBa1zWcb3U9PyVDRlAgQAKWtwqotAMwF3fP/wkjn5yrGNBTtv2UZOUDeONpvtw/cIIJBcAQKA5LYdJUfAJwGjrWOHZu6Zc1qhUB6z4vJgHp8qRzIIINBEgMtDmsDwNQI9L+BO8Oq+HCeTqdf7CkWZvIcFAQRSI8AZgNQ0NRVF4ITA+MzubuePCAIIpFmAACDNrU/d0yVAx5+u9qa2CLQQIABoAcRqBBIvQMef+CakAggEIUAAEIQqaSIQucCJU/zjnX/khaEACCAQQwECgBg2CkVCoGMBjvY7pmNHBNImQACQthanvr0pQMffm+1KrRAIUIAAIEBckkYgcAE6/sCJyQCBXhUgAOjVlqVePSzA+H4PNy5VQyA0AQKA0KjJCIEuBTja7xKQ3RFAYLIAAcBkDT4jEEcBOv44tgplQiDxAgQAiW9CKtCzAinp+M2MM9+klXVZz7YjFYtEwLLM3vrRw1/SVq0cSQESkCkBQAIaiSKmSSB94/tu568tfW2aWpm6Bi8g/ydt0/n8GlUlAGimTQDQTIbvEQhTICVH+2GSkhcCCEwvQAAwvQ9rEQhWgI4/WF9SRwCBpgIEAE1pWIFAgAJ0/AHikjQCCHgR4PnfXpTYBoEuBUytqseTcDt+x5FH8Z4Y6+8yXXZHAAEEOhXgDECncuyHgBcBY/TSLz+R08aZoRzreBDgZT+2QQABBAIWIAAIGJjkUypwvbHmz9qQn73m7oLOWtm6c0ZeZ/jfLaW/BqqNQCwF+Bcpls1CoZIqcPn16+3DS+bkK8c2Fuy8ZRtlKTnZz4IAAgjEToAAIHZNQoESJyCn+Vet3WQP26XCnpG+QqE8Ztn5xNWCAiOAQMoECABS1uBU1z+B+tBzemBwa25s7abi0bKVK1i2lS1W/MuAlBBAAIEABQgAAsQl6R4WkKP+QqV0mjV0rNBnZ7Qq9nBdqRoCCPSkALcB9mSzUqnABMZv33Nv43MyMryfc9zOnwUBBBBIoABnABLYaBQ5AgEm7okAnSwRQCBIAQKAIHVJO+ECJybrGe/8E14Vio8AAgicIkAAcAoIfyIwPkufe/Me9+/xY0AAgR4WIADo4calam0KcJq/TTA2RwCBJAsQACS59Si7PwJ0/P44kgoCCCRKgAAgUc1FYf0TYHzfP0tSQgCBJAoQACSx1Shz5wIc7Xdux54IINBTAgQAPdWcVKapAB1/UxpWIIBAOgUIANLZ7umpNR1/7NvaKOdeeVRy7MtJAZMlYFlmr1MuVzU/raYNRwDQlIYVyRVgfD9JbadHDn1Xyuu+WBDwTUDm61R0/tNzEgBM78PaJAlwtJ+k1qKsCCAQsQABQMQNQPY+CNDx+4BIEgggkDYBAoC0tXgv1ZeOv5dak7oggEDIAgQAIYOTnQ8Cbsc/3vn7kBZJRC5QqRXsXDFTiLwgFMBXATN+AV6t7GuiJOarAAGAr5wkFphAo8NvvAeWEQmHLZDtn/k72s58MOx8yS9YAasw48fO0NN/E2wupN6NAAFAN3rsG6iAcWqWNu49PHI9Lw/mCdQ6ysS11qfL9drLoywDefsv4Dhqs/+pkqKfAgQAfmqSlj8Cg4OZVUeW5CvV6iyVyWl/EiUVBBBAAIHJAgQAkzX4HJ2AMXrV2k32sF0qmCOFfNmqZWSCmAxzeETXJOSMAAK9LUAA0NvtG//aScc/sG5bdmztpuLRspUrWLalrFr8y00JEUAAgYQLEAAkvAGTWvzRum0t+sb6Qv+au4taW9k+O6NVMam1odwIIIBA8gQIAJLXZokusSMX9VVMVv/iuYtnzawVTzNZru9LdINSeAQQSKwAAUBimy5ZBa9Lx192cqpqbFU3GVUyhUyyakBpEUAAgd4SIADorfaMVW2M0uOdfdnJjnf8Mtwfq/JRGAQQQCDNAgQAaW79gOrudvxVx1ZlkxsPAJi7JyBokkUAAQS6ECAA6AKPXU8WcMf33VP8FTnir6sMs/WezMNfCCCAQKwECABi1RzJLMzk8X03CGBBAAEEEIi/AAFA/NsoliVsjO+7R/sVOepnfD+WzUShEEAAgaYCBABNaVgxlUBjfF9u5VO18Y5/qq34DgEEEEAg7gIEAHFvoZiUz+34x4/2Gd+PSYtQDAQQQKA7AQKA7vx6fm93fN892q+6HT/j+z3f3lQQAQTSI0AAkJ629lzT4+P70vGPj+9nGd/3LMeGCCCAQHIECACS01aBl9Tt+GsyS5/b8VflqJ/79wMnJwMEEEAgMgECgMjo45PxxPi+dPruNL10/PFpG0qCAAIIBCVAABCUbALSdcf03SN994if8f0ENBhFRAABBHwUIADwETMJSTG+n4RWoowIIIBA8AIEAMEbxyKH4x1/Rp7Ix/h+LBqEQkwI6Fplo1G59018wYeeEDCOs4PHf8W7KQkA4t0+XZfO7fh5ME/XjCQQoICpHH1SVdSTAWZB0hEI0PlHgN5mlkzc3iZYUjZ/qLosM+bkraHaDDVSL6qaw8V9SWk7yokAAgiEIcAZgDCUw8pDJuRftXaTPWyXCt8/cN/p71x4u2XLc/lYEEAAAQQQOFWAAOBUkST+LR3/wLpt2bG1m4pHy1auYNlWXWcySawKZUYAAQQQCEeAACAc52Byud5Yixbemetfc3dRayvbZ2e0KgaTFakigAACCPSWAAFAAtvTGMda9ZX7+0qZDYVMKWc72Yw2CawHRUYAAQQQiE6AACA6+85ydqfpq9f6ylatX8tEPg4t2Jkje8VGwBTOuExn7DfFpkApLoip176rS8/em2KCVFWd7iMRzS2dvnuI35ij12ju3khEu1FILwLashZrS1/rZVu2CVjAWPdIDgQAATPHJXkCgLi0xFTlGO/wT3T+U63nOwQQQAABBDoUIADoEC7Q3ej4A+UlcQQQQAABpQgA4vQroOOPU2tQFgQQQKCnBQgAIm/eU8b3Iy8PBUAAAQQQSIMAAUBUrczRflTy5IsAAgggIAIEAGH/DOj4wxYnPwQQQACBKQQIAKZACeQrOv5AWEkUAQQQQKAzAQKAzty87+V2/OOdv/dd2BIBBBBAAIGgBQgAghBudPiN9yDyIE0EEEAAAQS6ECAA6ALvebuOd/hM3PM8F75AAAEEEIidAAGAH01Cx++HImkggAACCIQoQADQDTYdfzd67IsAAgggEKEAAUDb+Ezc0zYZOyCAAAIIxE6AAMBrk3C071WK7RBAAAEEEiBAANCqkej4WwmxHgEEEEAggQIEAFM02uXXr7efdZya0mp4itWRf2UsXdMtSjFmFZ2ak9murBYbRrDaGD1SrhXKEWQdWZbaMWWVMQ9LAWZEVogmGcug1nar6jhOk/V8jQACvSlAANBoV+mVBtZty44dGSvuKVu5fGnk71Qu893G6ji912ulva0a7p6jK0aVesufF52xGIYASt3z7IoRlWuhWqkfUYX6bcZYhRZbhr7assxep1yuao+65tjQk2pG7U2hF9RDhsc7/zH5vbAggECaBFr1I71vcb2xFi28M9e/5u6i1la2z85oVZRq1yr7VU3tjyOA10Y7HgTEsQZSpladv2yia0P7zfDQh+JYA/do2Wvn75Zf5xyjqiOxPKPEkX8cf2GUCYHgBbz2JcGXJOwcBgczq44syZcyGwqZUs52shktp0JZEEAAAQQQSIVA6gIAd3x//4Js0Rwp5MtWTXp9SzmpU0jFb5tKJkTAOM4OOZ1yS0KK29PFdNui1fVFPQ2Qssqlo+s7Mb5fPjBU2KNy+YJTsZRVS1lTU10E4imgS8/eKyVzXywRC9D5R9wAIWff2wGAdPyLvnln/vQv/LLgFEZzhaKM76tKyMRkhwACCCCAQPwEejMAkAv7Vp27qVC6Scb3qzm7NrMsHX8mfvqUCAEEEEAAgYgEeioAcMf3D50pN46ZjTK+b2zG9yP6VZEtAggggEDsBZIfAJw6vp+rWLbiev7Y//IoIAIIIIBApAJRBwAVrXRng/LS8S/98hM5Z+2mQsYp5xnfj/R3ROYIIIAAAvESMFbNnnaaj6gDgKqpZaptmcn4/vxZG/Kz19xd0Fkr61gZLa+2kmBjBBBAAAEEohbQVma+lCGomU73VXLWtJPZRR0AzFB2zdPc6O74/uElc/KVYxsLdt6yjUxyz4n+qH++5I8AAggg0KmApa050o8F1Q9XTGZ42jPsQWXcqcfJ+8lp/lVrN9nDdqmwZ6SvUCiPWXb+5E34CwEEEEAAAQTaF4g6AMhajjX3ecU+cWHf2NpNxaqM72cdW2eL0wYyz0uCLxBAAAEEEIizgKP0mVFOvhR1AFAw2iycaKApHszD+P6EDh8QQAABBHpIQGt9QZTVaRkAuFcRGkueZBbUYqwL1YkH84xUNxbtWtE2WcX4flDepIsAAgggELmApYp9RqvzAyzIaP7o6dPeBdDyaeYnriLcF1QhjapfsHR46Rx5ME+/nTctA5KgykG6CCCAAAIIhCXg5Kx+eVD4mUHlZ4x5bPNbzxmZLv2WAcCJqwgDG4DXyrqkqO0zpisk6xBAAAEEEOglAW3n3FsAn38NnE+V1JYut0qqZQBw7qyLxowyu1ol1MX6JU6+77Iu9mdXBBBAAAEEkiVgZ39PCtwXWKGN2twq7ZYBwJ1X6JrM1hdkAJC17MzrZDxERv5ZEEAAAQQQ6G0BY/efq7T1qgBrWdOmdb/dMgBwC2iMeiDAgkrS+sp634yXBZsHqSOAAAIIIBCtgKlYWheLr5dSXBhgSUaV0Ttape8pAJDJdh+ShIZaJdbF+n4ra39A26ed1UUa7IoAAggggECsBfSc01dpy3qXFDLIKQB2t5oG2EXyFACU7dx22fYpd4cAl1V6RvGTZsbpA26EFGA+JI0AAggggECoAlUrlzF9c6/QmewnJONgD3aN+uUjr5t3uFUFPd125ya0YnDXLyVeGWiVYBfrtUw28BrLLqxSc7M/ULX6bc5YdacqjwzrXIDzEHRRYHZFAAEEEECgmUClJo+uKVj9ptC3LGdlXqO0vka2Pa3Z9n59r7X5f5JXy/l7PAUAbkL61h0/NEq/UQrobZ/OayIXR2T+WGUz11rZ3G65SHK7jGUMyXwB21TdDGtT3+4oM9Z58uyJAAIIIIBAAALycJ+MPOHPneJX+s3z81rNkTP9S+SU9jzJrRhAjlMluVeGGP5jqhWnfue5M69rtV7GCx6VBII8CzC5fG6UJC99iTtSopU88ne8tPaIlGPa2Y0mJ8JnBBBAAAEEQhLIymF3IcoxbMn7Xx+85gXbvNTXcwCwdfXiAysGd9wsXfGnvCQc4DaeHh8cYP4kjQACCCCAQBwFRuVZerd4Of3vFt5zAOBubFnWHY5j/lw+nuP+zYIAAvEScC+fvfSMvLr49Jy68LTceOEefq6iNh0qqe3HqvEqLKVBAAFfBeTswy9K1cpGr4m2FQDMPuMFjz57aNcGSfx1XjNgOwQQCEdghm2pa5f2q+sumqVm5WSgbNKya6iqbtz6nPrpnlHFJbWTYPiIQA8JyP/1P3jiLRcc81qlk/+VaLGXOyugTAt8q2wW2LMBWhSB1QggMIWAe+Tvdv7vueT053X+7uYL+7Pqf//6Geql54R1HdIUheQrBBAIUkAu/tM/aieDtgIAN+FSpfZjeft5O5mwLQIIBCvgnvZ3j/ynm0HDPSvwXgkQ5hbkgloWBBDoLQGtBr1e/NeoeNsBgHt6Qe4uvFESGG0kwjsCCEQr4I75n3raf6oSLZXrAgbmHL82YKr1fIcAAokU2CbR/41eL/5r1LDtAMDdsa5H7pC3f24kwjsCCEQr0Ljgr1UpMjJU8EI5W8CCAAI9I1CXqXq+uuWahU+2W6OOAoCtqwfkGoDMpyWztjNst4BsjwAC/goMV1tOEOZvhqSGAAKBCcgEff8m0+f/QycZdBQAuBltWb1gi8wJ8Bn5yAWBncizDwI+Cri3+nlZSnV5SPizZS+bsg0CCMRf4IBW5uNb3rzwSCdF7TgAcDPLFbM3K20YCuhEnn0Q8FHAvc/fvdWv1XLf0yXlNVholRbrEUAgUoG6jPl/fssbFt7TaSm6CgA2XX3eqMwO9GHJ/GedFoD9EECgewF3kh/3Pv9jleazZD8p23xhyxE1XG2+TfclIQUEEAhBoG6M+k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"></image></g></g><image 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XLjIAReAEBB055AQu/yxkCOnSjf0tLS9SvX79wkhbHYTYiUU6jykqyPVroqBmlXDvuVbZB76fokJaPaLvdRXrfqusEfLzXWOmjGPCr2traXq92Hz/xpd8YgZwgYMLOSUe5mi9GYOTIkf+wcuXKL+mYy2jo0KHhQI4X31XfTyBqFgjEEu/Tp099M08oN5FVX4Vv/SMFevlriqANOnVslV7PTqjIhmWrNpQVP/4+HXm6aPny5SHWO3vzO4t4x4Etiv0+dfTo0cfWrVsXibi/OmnSpPcpj6caVmEXZAR6gYAJuxfg+afpIzBo0KBo1KhRTMQNIVAkesg6y4R9//33D9JiYoKCuzxHD23YsKFNZPXXBw8eFHeXSyw0hg8ffpaiw+1VDPZFY8aMadOip6R7jgrLfen3avc1kCQ9UgexYNL7kQh7EYe+cFobke/iA2AIbMPihISE3draGo0fPz6aNWtWGDM6vOUd+vwdipS3VONnv3A4EG72HyOQUQRM2BntGFerZwgopvemyZMnz9NhHGWRdwnp9+jRo4mEJ2XyJ39J9pEmd6TUQAI9q2lj7nrkkUfOE2n9N2HwbmKrS/IMUqfIuSySC+xFO9BISMKcLKL+NTUjcptI6xkR+JskgW7R+82NqXH1pegI1VHSGNykNp67Z8+eiLC0+j/asmVLiHgnDCLaXhlTHsIeMGBAuI+oeGhl+I3aWp4yZcrdWsTcr7ZfIxJfUX2N/Asj0BgETNiNwdmlJISAzqS+QgT6KZH2dUjbECqnaiURNhSiY9KfMGFCpPIC6SXUrKqyRWpW4gCUv5A6+H06sWz4vffeW1bI1hIkRihVEViJhQxtIMVqfTQTLD4mTpwYnX766QMXLFjwPWG6XHm+VvcerKoiCd4ct5EipAn43Jo1a86lbVu3bg3nnyNZE+MdVTgX/Q9JVyY0C9IyRJA8iy1IW4Rdmj9/frR06dKJM2fOvE0Ll8v13fLK3/m9EcgKAibsrPSE61ETAiLrPZKuvypV53WoO5GemLwlTdaU36l+BNFhA+WUrhkzZkSa3MOEjyr5VL9p1OeQtaTFb4mI3gghya4f3XPPPSWOAMVWG2scKgmM9nCJnKNx48ZFkjKDJM5iR+17qYj/buF4nfDNBHnRRrXtVrVzoNr1krvvvjuSpB2kapkAwhno4F3Zxsr3fEd7SZg1IG9U5NIyhHazgNHnrVqQ/VDEf4UWMreHm/3HCGQIARN2hjrDVakNAU2yrUhLixYtCrblzZs3B2mr44RdW+7P/4pJHuk6LmfevHnYQbeL8FJ1WBKpvkrS89+KtObccsstQT0sm2wkKTssXlB1Q8JdJUgeiRTJ9MCBA9Hs2bNbzjjjjGmSuH8pcpwrbcKWrn7fqO+k+r5k1apVJdonDUKQlB9++OGg/u6ujdQxHg+oyuNT3fgMKZ3+JQ8tboZILf6rTZs2naY+3tmotrkcI9ATBEzYPUHJ92QaAak1V0iy/sFZZ511JapOvIRRl8aTcj0qjwQWO7gtXrw4kuoYCe39sgU/VI/8a81DmoRLZKeeA4ndfPPN0c6dO4OGARsuBNQdkT377LPhqFBs3ZC1VM2oxgN5g+XChQs/LZvwO7Q4eaLWOvb2dyLUAVpEHFHbnrv11lv7bNu2LZA12hS0B5W26p6WxW/4LQsVFjdoZPgMPMDs7LPP3qFyWym3p3n6PiOQNAIm7KQRdv6JIyBV50GRzXckFZ4nAm2TVFlCamRCx7GIibnWhDc4kjXSu8grqMKlMn5CquI3SD1+c6359vZ3kgxbJFX/ibQJH/z5z39elsNUSVJhhMSJzTqWJrsrh/tiYo9xQuJEfax8ylqUXDNnzpxr5Hk9Z+zYsVu7y6/e32vxNV6q/h/fcccdi37wgx/g8R5s1rG9urflQdJoFnBUw3zAwgXSxkwwbdo0tr1ZNd5bkP37uiFgwq4blM4oLQREOuKX0lf1ukbkepecrfqKwCJsuNizYyKqtn6QFoSNnRfCvvzyyyPt22Xr059D1u3lnuzZVG0hNd4vMp0i6fDTkojLP/nJT0p4SrNIoa2QcG/SY489FiRtqYmDQxaOaVKLb1Ce/RrdZhHp29SPi7BXsyBhAUb9kIzrmcgPDFkIYPaQtqY8ffr0X6m9ozUOHqlnWc7LCNSKgAm7VuT8u8wgoAlV82og7VWSBH9X+2z/mQlXxBo8o5mIUZOjJtZ93apQIWnUwVx4g0uyjORJXJajWUmSdV95Vf8vGk+5jQahvZ1lkdfXkKalzi2hyuY9ZF2LerhjGyAv8GI/M6p2nLNkEjgmovxttfkfO96f5P/SlhxG+qUPecUmjwRc78S4IF/aTTlaJJT27t1Lef+qsq6Mca93uc7PCFSDgAm7GrR8b2YRiMlT5PqV9evXr3rNa16zSmRTlhdwCbssNm28iSEjJmYm6M6S8glBWCBptjudd955kVTC5SVLlpQkVS8ViafqZEY7RSiflL36YrUzeEpjf0WtWw+yrsQESXb16tWBwKRlaBUmv6XvG0rYtAstCdoSiDQJsq5sMxhiSkGiZ0eA/BUuEWlfJtxvqbzP741AGgiYsNNA3WUmioAcwlZLOjxTUuHq2JbNZB8TGx7BTMyVpA1RxxdBRSBr9lvL6QjbdUlk9Tmpw1frnq5drhNt2fOZi7Q+iPQn6TqowbFZd+dcVku1kGZZ5JB3u2khRE6rJa9afwNB0z5U1bzWe1HSsV6MCdrNuEH9Lq3KQJX7e7rPhN0RLP/fcARM2A2H3AU2AgER7L0itFcrIMZNqLXPP//8MAlDPEiOcQQwCID91XiBQ9SowZGuCSSCLVOOR0+KvJ/VPX+fBbIGO6m+W3bv3h20BpBKve25cf8g3ZI/pgQkXGG2TIuetyqsKWrihiVItPJKsmDKYWGAiQGTAG0XviOSLNN5G4GeImDC7ilSvi93CMhp6AeKN32lPH7fJ5LrJ8LpJ0ntEtl8USuXUH0iwWGjxRMcqRoHKzyEpQ79sbZxHRZp/41IfHmWGg9BQyjYrtEaJCFd016wYXEDcYOV7LtDhOHlIrXrtXipvyE5IyDTbmzZFVJ95/aTjNTX1SgOAibs4vR14VoqYhGvlH6ohnOFJOel/5CEeA1EF5MdZI2jGVGv2Gste+0XRdq/H/+mPZ/MTNpoBZD8tBhJxHZd0e5A2pgQIG6kbWkh3qLv/4+un8X3Ndsr+LIo4uI9UreTEcgCAibsLPSC65AIAiLrF820+uw6SdFXSAV+RAFXxmsy7isJdZ9U3i0Qt66SJO3vV1aos3wqv0/jPVIgkm9S0nVlmyAsJGyCsmgx0yKNxCJ9dptwabhNu7JeSb5X25LM3nkbgZoQMGHXBJt/lFcEZJt+WnX/LvUX6TAra27OF/FAoBA1F++TTpSBRA9pI2lL4n6T1MXYscNJX0mX7/yNgBF4HgHOk3UyAoVEQESNzrxppcR6dioSPd7TELcI/DypiwfXM3/nZQSMQPcImLC7x8h3GIHCI4AtN5bqCw+GATACKSFgwk4JeBdrBPKGQCPU73nDxPU1Ao1EwITdSLRdlhEwAkbACBiBGhEwYdcInH9mBIqEAFu7+vTpE86Npt146hWp/W6rEcgCAibsLPSC62AEMowAqnCiwREFjktcvUH/423vZASMQAMRMGE3EGwXZQTqgQDCbXzVI7/u8qAsQrfGAWZ0/3Z5jdd+yHh3Bfp7I2AEOkXAhN0pLP7QCGQbASKzQaCoqZNOEDYnV40fPz6EbZXE/e86uvSJpMt1/kbACJyMgAn7ZDz8nxHIPAIQKPHPFXUshFTl/yQS+bIgQLoWQUdTpkwJpK1yTdZJAO48jUA3CDjSWTcA+WsjkDUEsCePHDkyHFZCvGuCmvBa70Q5nGDGxclllKmFws90/aLeZTk/I2AEukfAhN09Rr7DCGQKAanD++rYzxAmlIAmnCqVBGGjcufkMo4bnTp1KoejrNAZ4a+Q5F3/1UGmEHZljEA2EbBKPJv94loZgVMiIMn32yLOoKJGVZ2ULRu1O4SNKhzpWh7iT5msT9kt/sIIJI6AJezEIXYBRqB+CMjhS5xZetPdd999pSTeIRyxuW3btnBy1+HDh+t6GIiOIY0WLVoUnX766dFpp532rFTjt9WvJc7JCBiBahEwYVeLmO83AikiILIOx3Odc84543QM6FM68rIsKbiESvyRRx45cYZzb6qIoxle6Mo/mj9/fnT22WdHkyZNOiwC/0T7giH5I8J60wD/1gg0KQJWiTdpx7pZzY3AwYMHW+S9/b05c+aUzj333PJZZ50VHMPw6Bap96rxOJnNmjUrWrBgAUQdPMSlhv8QmcYLhl4V4B8bASNQEwKWsGuCzT8yAukiIEewQ5J236Azqr/x2te+9nVbt24NTmh79+4NrxyFGZ+w1V1NCTvKhVc40vX06dOj8847Lzr//PODOlwE/hpJ29+3dN0dkv7eCCSLgAk7WXyduxFIDAFJu0claf+7CPWaQYMGtdx///3R/v37I6nJI4j7ySefjPR9OBbzVJVAImc/N/ZqvMFxZps9e3a0ZMmSsuzXJX3/CpH1T/i9petToejPjUBjEDBhNwZnl2IEEkFAZHq9Mr7+gQce2HXFFVdMg6BXr14drVy5MpC3JPDgiCbpuNPy8TDHA3zy5MnRGWecES1evBgHMzzDS3Jqe5vs44GsO/2xPzQCRqChCJiwGwq3CzMC9UegXVU9/eGHH75z4sSJZ0m93Q87NtIyh3XgPX706NEgaUPcfIf6G+l62LBhQbKOCRuvcOWxTST+OpH5mvrX1jkaASNQKwIm7FqR8++MQEYQQFXdTtovefDBB/+HHMbeJPX2OY899liwQ7P1i/e8krBXExsc5zJJ0SdeRd679f9PpV7/rsk6I53rahiBCgRM2BVg+K0RyCsCsX1ZKuy/efrpp7916NChqyVVH5WX9zFJ2X8q1fi0xx9/PDQPwsZmLXJ+UmFNf0/fD9HVVyS+UmR9Jze1LwA616PnFSTX2wjkHAETds470NU3Ah0REOnu1Wd/F3/+61//+i599iNFRZvEZ6jERdD3a6/1eVJ974nvq3yNFwCVn/m9ETAC6SJgwk4Xf5duBBJHQKS8Tp7jsxRq9ETcBW37ek7HZR5OvHAXYASMQN0QMGHXDUpnlAcEpOrts2vXrqEir9lSDX9WxHU+25/0eTiyEkcsonxpP/Lnjx8//kVplXfPmDEj98TWDG3Iw/hyHY1AkgiYsJNE13lnDgF5Un9ApPzX2HNxwpKTVrRv377gQc0RknhN44yl1/dLbfx+Sac3qBGvr2yI7buVaPi9ETACjULAhN0opF1OKgiIlCc89NBDV4uoh8iWO2j79u1/pr3KZX1eIhoY+5YJOCJp+oTHNB7UBBOR3TcaPXr0ZTfffPPfyUFrl7ZJtYjEn5Z99wupNMaFGgEjUGgETNiF7v7mbrwk4aEi409Iin7Pli1boh07dkS7d+8u6/9A1hyYwR5leVWHMJ4EEUElHl/tB2C0yVnrfTpsIzhrSQp/Vvn+p0h7X3Oj59YZASOQNQRM2FnrEdenbghoK9NFIuT3IEGvW7cuWrNmDYRdInznkSNHAgETbxv7NQnv6fiV98TV5rxpVOWSrMOrPu+nU7H+VvedpCYPP/QfI2AEjECCCJiwEwTXWaeDgAi4n0j56IYNG6Jf/epX0caNG4N0ja06DiCCCry7FBM4Evjtt98e1OecXnXRRRe9bv369WVJ5K9VkJLvdpePvzcCRsAI1AMBE3Y9UHQemUJAcbWPSqoui1RLP/rRjyLZrSMFEgmqb9TgSNU9SUje2LmRxtul83DcJEFHIPGFCxd+S9L2aZLCrR7vCaC+xwgYgV4hYMLuFXz+cdYQ0ClVl4iwo02bNpWwW3Ny1YEDB4JTWU+JurJN/IbriSeeCF7lEP7mzZuDGl0HZPSTg1q/yvv93ggYASOQFAIm7KSQdb4NR0AS8TSR9E9QX3/ve9+L5B0eSQKumaw7NgDiRqV+2223hYUAp1rJk9zhOzsC5f+NgBFIBAETdiKwOtM0EBBB/5W2bhEYJbrvvvuCOjv2AK9HfVCR41Uux7VIMbgDefO/kxEwAkagEQiYsBuBsstIHIFt27b9D6mqf0uq8AjCJjBKNfbqnlYQ0iZfCLsWFXtPy/F9RsAIGIGOCJyILdzxC/9vBPKCgIKgTJTX96vwAudCDc75zxArBOtkBIyAEWgGBEzYzdCLBW+DyHmWCPtinM248AhH+jVZF3xguPlGoMkQMGE3WYcWsTlSTw+RrboPkczYwqUwpEWEwW02AkagyREwYTd5BxeheZKuy0jU7JnmQhXuZASMgBFoNgRM2M3WowVsDyFEY/U3r/H7AkLhJhsBI9DECJiwm7hzi9S0OIxokdrsthoBI1AsBEzYxepvt9YIGAEjYARyioAJO6cd52obASNgBIxAsRAwYRerv91aI2AEjIARyCkCJuycdpyrbQSMgBEwAsVCwIRdrP52a42AETACRiCnCJiwc9pxrrYRMAJGwAgUCwEf/lGs/nZrC4iA9qX31WEoUxUR7qSzu7V//YGRI0c+XkBI3GQjkEsETNi57DZX2gh0j4BCtL78ySefHL1z586ZigT3ScVcL/Er9qz369cvamlp+c7WrVu/09bWtnPMmDH3xDmK4HVLyaemxID41QhkBAETdkY6wtUwAr1BICZZpOlHH330z5944om+Ohv8vXo/mMNQDhw4EOn/EAWOyHDDhg2LRowY8YZRo0a9QZL3rj179vyotbX12JAhQ34psv42dYnz7E29/FsjYATqh4AJu35YOicjkAoCMbGuX79+xV133dXvscceW/LQQw9FIu1wEIoORgmv/E+SZB2JmMMFcUstPl3E/bt6jYYOHXrd6tWrPyCp+1si7s+m0iAXagSMQKcImLA7hcUfGoH8IID6+sEHH9x05513zkWSXrt2bbR58+ZwEArSNWeDHzly5MT54Lo/6tu3b1CLoxrv379/IO8pU6ZEM2bMaJs3b965S5YsOVfnit8vIv9WfpBwTY1AcyNgwm7u/nXrmhwBnf/9KZ1Q9uFt27ZF9957byTVdrRmzZpo06ZNJ4i6JxBA2vv374+QzJHIIXJd199///0HJk6c+Iue5OF7jIARSBYBE3ay+Dp3I5AYApKq/1hngH9YavBIxBoIW1JxJBIPZK1jR3tcNvfK3h1xprhs2hGSuf4vX3LJJbfJw3z28OHDt/U4M99oBIxAIgiYsBOB1ZkagWQRkOr7z+UB/lGk6VtuuSVIx7t37w5qcFTgELBs2z2uBPeLmANRx7ZvvMqnTp2KpP11Sd5fGjt27Fdje3mPM/aNRsAI1A0BE3bdoHRGRiB5BGLCPHjw4J9C0NiqtW0rqLIhWqTjWhOkHRM3ZI+a/Z577oHEl82fP3+ZFghPxx7ktZbh3xkBI1A7Aibs2rHzL41AwxHAwUzbs34XqXrjxo2R9lEHskY6hmzrkY4dOxY8zHft2hXdfvvtwS7OVrDRo0e/W/mHLV/1KMd5GAEjUB0CJuzq8PLdRiB1BKSq/vyWLVsibeMKZCrJN3iB16tiCrISbODYtLdv3x5pm1i0cOFCXvvUqwznYwSMQPUIOJZ49Zj5F0YgVQS0RauEGhwJm21cvVGDd9UQbOE4sVEGjmxaGFy6b9++f+nqN/7OCBiB5BAwYSeHrXM2AokgAEGzBQuPbtmyw/7qJAqiHKRryoCwRdZl2cnfKY/0a5Ioz3kaASPQNQIm7K7x8bdGIHMI4BAGmXLxPsmEepxyIG2px0vYtbXv+yKV25pkuc7bCBiBFyNgwn4xJv7ECGQeAaKVcTUqPfXUU5H2fQcVuRzQ3idV+ahGle1yjIAReB4BE7ZHghEwAt0iEG/5QtqW1N1Xi4XCzB2NXBh12xG+odAIFOahK3QvN3nj4zjZqG9JSauJmxzOwjeP8RNfLFTY5uZkBLKAgLd1ZaEXGlQH7dmdqZOYBsnrt4wHMFfekybUsqJ9TaMtxMCGvJlkTdp579n06h9va8MMoDPFIwWoGfKud71r2mmnnTY0vVq9UDJx37kGDx4cXl/4pj7vyJsUlzF+/PhIh8D0mTx58n31KcG51IqACbtW5HL4OzkL/ae8ixdwQASTEZJD3okNdSXBQ+S5XJbXdIk9yRC3kxGoBQHImmcDr/jDhw8HZ7tf/OIX5+oY0l1EfstC4qQ1jkeFsAcMGBBOXqun2p7jVzn8hbx1zGo0cODAUJ6erYU6fnV9FjAoah1M2AXpeRHzWZp4Rvzyl78Mxy8qWlaQsPNO2HH3aaItIWXTnmZpU9w2vzYWAez0LGZjRzvitWcpIfmOGDECqTcaNGhQkITrRdjkA2FD1iwIJkyYEOYJFglatHxLz9ZS3fNMlvAoUl1M2AXobR21eKnOSL5RkuhQ7aUNoSyRRGObbwEgcBONQI8RiBd9PB9ZtF9DqJh+CEcLeRM2tl6ETdvJnzwhbSR5AvToiNVo0qRJC/T/qhtuuOEpnZteGjdu3DBJ4mfqUJhDPQbXN/YKARN2r+DL/o/1AA6RKu86EfXQOMwkD7vJOvt95xoagc4Q4NmN/TX4vl5kXVkWecYXB8xIFR7piNVo5syZ884555xA6qjNReD2yKsELuH3JuyEAU47eznNTBJpvxObHJfs2HU7JCLttrl8I1BUBJCEGyX9s8DHTIBNH5KGuJG+JYmXFyxY8IwOiBn00pe+1GryBgxGE3YDQE6zCBH0McJL3nfffdGqVatCqEm8qJ2MgBEwAj1BgMUBpM0CAS0dseW12yRatmxZSeekl5cuXfq0yHxma2vrjp7k53tqR8CEXTt2mf6lHjJptEpl2biuhbA5xKHZHM0y3QGunBFoIgRQw8c2fZzxIHDZrnFKK02ZMiXS1q+b1dxZTdTkTDbFhJ3Jbul9pSBrbXO6UVu4fhOnEW17Ck4qrJJZMTsZASNgBKpFIJ47sKFzxKsOgwnbyuS1Pl5zzX/Ipv3xkSNHrq02X9/fMwQc6axnOOXyLq2Er1LQh5a9e/cG6dr261x2oyttBDKHAAt/YsuvW7cuELdU44NF4tdoa+XCzFW2iSpkCbuJOrNjUx599FGORAw2J9RY9gzviJD/NwJGoBYEkLQhbS5MbTi0sjdc28EsBNYCaA9/Y3B7CFTebpN3+D4k65UrV54IlBKrs/LWFtfXCBiB7CKAf8yGDRui9evXY2v7N/2/ILu1zXfNTNj57r9T1l6BUfoRWIHVL3YmtmWYsE8Jl78wAkagRgQwtUHa0uiVcEbTPONtKDVi2d3PrBLvDqGcfS/V9wSR9cflANKmuOERccOxNZmwc9aRrq4RyAkCCAZycA1hUg8dOtQUhwplFXoTdlZ7psZ6iazfJIn6d+QEEk4aYksXK2AnI2AEjEASCOAxfvDgwTDfMNcgZTslg4AJOxlcU8tVq93hbOFiywWStR+e1LrCBRuBQiDAHINgwAV56/VwIRqeQiNtw04B9CSLFEmXOW4SZzOczggn6GQEjIARSAoBpGp8ZTg7nNcxY8bcKTu2hcEEADdhJwBqmllqtdsfmxJOINiTHIY0zd5w2Uag+RHAmZV5Bj8ZHFzlfDZO27yubv6WN76FJuzGY55YiXpwJumw+atwNNMJXcGuxBnRTkbACBiBJBGAtNHm4eiq43zLEhq+o8/6JVlmEfM2YTdRr+th+a6czhYSxAAnEEvYTdS5booRyDgC2LKZe3bu3FlCPS5b9scyXuXcVc+Enbsu67zCIus9IumlCkUanD94eBw3vHOs/KkRMAL1RwBtHqY4TvNqj/3wlvqXUuwc7RjQJP2v/ddTUEfxsEjKDmTtUKRN0rluhhHIOAKoxLFhQ9iclQ1hS2Don/Fq5656JuzcdVnnFWY7BWooLrw2eYCcjIARMAKNQgCtHvEf0OxxjgEE7lRfBEzY9cUztdw43IOHBdu1CTu1bnDBRqCwCKASR2BAeICw9X+QGiQ86LTfkiWIOowM27DrAGIWskANjjqcYCk8ME5GwAgYgUYiwNYu5h7U4Vxyem3T62+YrOvXCybs+mGZak48IByliR0badsq8VS7w4UbgcIhwJyDOhxJmx0q2to1RNf/ksZveuHASKjBJuyEgG1ktnpQhiFhow7H6cMq8Uai77KMgBGIEUDKhrQRIFCPi7Dn6Lo2/t6vvUPANuze4ZeJXyt2uCIC/jp4h6OScnSzTHSLK2EECokAkjYSNj41o0aNihTMaUAhgUig0ZawEwC1kVmKqK+ErAm8z6o23n/dyDq4LCNgBIxAjACELak6mOg4iEgCxHki8HHx936tHQETdu3YZeKXUoH/k86i7cNq1gd9ZKJLXAkjUFgEIGsuIp4pRGm0YcMGbNpXyEy3tLCg1LHhJuw6gtnorETQl2vlOoBVLBI2tiMnI2AEjECaCEDYzEecaYAjLGY6BXHypuw6dIoJuw4gppWFyPoarVxHsZpFBeVABWn1hMs1AkagEgFs2DjAckHYmOqceo+Anc56j2FqOcg2NCTezuXIQql1gws2AkagAwLx1lI8xSFvXcc73OJ/a0DAEnYNoGXlJ3iDY7dG/WTv8Kz0iuthBIwA5jm2l0LcXK2trW+TJnCwkekdAibs3uGX6q8JUIBkvXXr1rCFwmqnVLvDhRsBI1CBAIcPIVDoBMGy/GzeqvnqHRVf+20NCJiwawAtKz+RhP0s6iZCkiJl24adlZ5xPYyAEQABhArZsUuKwChftPLfG5XeIWAbdu/wS+XXihd+mqTpz8gD88x4O5c9xFPpChdqBIzAKRDAWxzCZo4SYZdQjTv1DgETdu/wS+XXeghGS6J+Ldsm8BD3udepdIMLNQJGoAsEYsLmQKIRI0YE57MubvdXPUDAKvEegJS1W/C4JHY49mscOyBsnYiTtWq6PkbACBQcATR/mO0w2SFtO/UOAUvYvcOv4b/eu3fvQBH019esWRMpwlk48IOVLJeTETACRiArCDAnIVDs3LkzBHWCtPXZQAkXz2SljnmrhyXsnPXYgAED+mjQz2XwE5QAu5BV4jnrRFfXCBQEASRstIHMV7xqX/Y3abrmMKsEaxgDJuwaQEv7J2zfwi6EDZvABBA2q1knI2AEjECWEGBeYr4iTgSkresqbfF6j6RsT1g1dJQJuwbQ0v4JextxNoOwOanLEnbaPeLyjYAR6AwBCJvtpqjGIWwEDM1fH+zsXn/WPQIm7O4xyswdqJHGjh17CMJGFY6KKXY6y0wlXREjYASMQAUCkDaqcQibLV5ylh2oz+ZX3OK3PUTATmc9BCoLt6FGIgDBqlWrgv3a8cOz0CuugxEwAl0hAGHjIU5ExoEDByJxT1L6sH5zXVe/83cvRsAS9osxyewnBw8e/BKOZhA1NiFUTcQTdzICRsAIZBkBzHbMW5jy2gWN57Jc36zWzRJ2Vnumk3pJBf4OtnJxxizqcNuuOwHJHxkBI5ApBGKVuASOaMiQIdHUqVMRNkzYNfSSJewaQEvjJxr0gxSA4ChkTexwbNdORsAIGIGsIwBhowl8/PHHYxs2hD006/XOYv1M2FnslU7qJLJ+q0i6lVUqKiWfzNUJSP7ICBiBTCKANhBHWRzP2lXiV2su+81MVjbDlTJhZ7hzKqsmz/A3Sw3en61c2scYBj8rVycjYASMQNYRgLBjL3E8xbUdtUVz2vdE2mdlve5Zqp9t2FnqjS7qInJuQarG6QzVkiXsLsDyV0bACGQOAUib7V1sS33iiSdiocNCYxU9ZcKuAqw0b8UjnEFOXF5WqATUdzICRsAI5AkBbNnscMEPBwe0MWPGjM5T/dOuq1c3afdAN+VLsg4xdyVRH4Ow2RZBtCBL2N0A56+NgBHIHAKY8XCYReggvHJra+sf6bMBmatoRitkws5ox8TVIljKtm3brhFRn4U6HJUSg97JCBgBI5A3BGLnM/xwFASK+ewVIm5renvYkQaqh0CldZucM16lrVz/sX379jDAUSmZsNPqDZdrBIxAbxCAsNEUbtmyJcSRwHN8xIgRPrmrh6CasHsIVFq3aQU6JFaF42XJgJfUbdJOq0NcrhEwAjUjgMCB/83evXtDmFLs2WxVdeoZAlaJ9wyn1O5igEPY2HvYvwhhW8JOrTtcsBEwAr1AIFaJow5HLQ5hjxw5cmovsizUT03YGe9uHDQgagY3R2lC4E5GwAgYgbwiwByGKpztqUjXOhhkvQSSV+S1PY2stwm7kWjXUJYG81GIGhUSg5vBbgm7BiD9EyNgBDKDAGY9HGjRHO7atQvP8R9mpnIZrogJO8OdI2Ie3L9//99gGxcDO7ZhZ7jKrpoRMAJGoFsEEDo4cpNtqpyPIGHk8W5/5BsiO51leBBoEA9VwJR3oxLHjo163NJ1hjvMVTMCRqDHCBAMCkEE7aE8xXv8uyLfaAk7o70vYi5pQB+QOryMZI3Nh5B+JuyMdpirlTgCHvuJQ9ywAuhLJGwinu3evTsEUmlY4TkuyBJ2RjtPavAJeFLKvlNqP93G519ntK9creQQwKsYSYzIfvbfSA7nNHKOCbtv377RjBkz0qhC7so0YWe3y74GYaMu0ildlqyz20+uWYIIQNI4J0HavDo1BwJI2PQn0Rvlp4PHeB99NkDOaEeao4XJtMIq8WRw7XWuWn2+HGczLtThSBpORqBoCDDuka5ZtOLDAYE7NQcC9CX7sPHPkblvsDSJ/ykCP685WpdMK0zYyeDa61yZpLDv4EHJgLb9rteQOoNeIpDGGOQ5iPfr8hxYyu5lJ2bo53EQFXx05KvTR062V4jAv6B+npehamaqKibsTHXHC5Vh9ckEhf0aZzNL2C9g43eNR4Dxx8W4hLhblBpRC2majjP+0TJB3mksGhrRziKWQV9i6oj7l/lOfXym+vqMIuLRkzbbht0TlFK4B/Xfnj17QlABJAwnI5AWAhA1wXs4i72trS2ophmfSSapRoepjMc3bNiApqmsid0HRCQJeIp5ozVBMOFAkKlTp3JGdoq1yXbRDVklZxuC7NROK84wKem1Hx6U7FHE8YyVpyXs7PRT0WrC2GPRyFhkTELWffr0OZwkDprEFxBUg0WCxn/J4z9JtNPNG0mbA0Ew/+GzYwHl1P1hwj41Ng3/hrOv5RV+zdatW68nDGnsaOPJquFd4QIrEGBCRSXNZAqJtl8fFpEmZmuULfNGyHrTpk2OoV/RF834lvkNoYT92Mx7JuxT97JV4qfGJpVvtNL8bR308UrOv05a7ZhKA11oLhFAAkLrg5lm/fr1ZW3Feb9OWbpMjVlYrwahYWLRqnLOWr169Vhd0e233x4OvqFsp+ZEAL8Itndh/hg1alTU2trqrQCn6GpL2KcAJq2P5TH5DPYcVpzYdpBunIxA2ggwqeL0BXFLCiqhvpQGaEFcL8g2fl/raztZL5Uk/1/YzJHoubxwrRXRfPyOOY4FGZI1WsXRo0efno+aN76WJuzGY37KEjVw52lCnMvpNQRMwXvShH1KuPxFgxFAdYkN+2c/+1l0/fXXR6tWrYo2b95clkboBsi2t9XRWO+/du3ar9x6660Tbr75ZiT5QNhM5jYL9Rbd7P6evmUhiI8EGpxBgwb9hcbZXo2HXi8Cs9vq2mpmwq4Nt0R+JbvdhZKq5yJVoCJiojJhJwK1M60RAWzZ2JbXrVsX7dixI0yykoyu1pj9kMbslFqy1RgfKq3SNEnt31A+Z2zcuJGFQFgcEFgD6d6puRFgexdaRSRsJG2Ns8kaCxOau9XVt8427OoxS+wXGrRjNUH1IWDKQw89FAg7scKcsRGoAQGkIVTUTLCShkMOU6ZMaZk7d+5fDh8+/C9F5r8zffr0b0jiPoSE1J3kLU3SW2S7fKXG/XVIV+SJZI2GiYnbZF1DJ+XwJwgm8djCHILAMmDAgJ+qKfNz2JzEqmzCTgza6jPWgD3KQEVywUZolXj1GPoXySLApIrmB8Jevnx5hDQ8bdq0aOnSpdHkyZOjZcuWfRmnSUnMPxdZf76r2kiC+qiuT2pXRHT33XeX16xZU4Ko8UKHrCmD8pyKgQCkjUYFgYX9/kOGDLEtu0PXm7A7AJLWvxqsg+TMsxCVI/YcLjudpdUbLrcrBGISJaQkpErQM3mMh8l26NCh0YQJE64ZPHjwq6U2f71Iu18neeEJ3ke+GstYmLJ1S7siSvExi+TLYtWpWAhA2CwGcbrl8hh4cf+bsF+MSSqfaFX5KpH129iHiEoICQPCdjICWUUAr3HGKJMsY1bbcSI5jOE0BIH3nzRp0oX9+p3M10zKsbc5i1NJ2GHvbWy/jKX3rLbZ9UoOAcYG42Dbtm1hEYjGxulkBEzYJ+ORyn9SAY7VavLfNXmVNWBLrCyRXBjATkYgqwggaTNGIV7s2pKmgxMa5xsjcaPa7oywGd9okHhlguY9ecX5edxntceTrRf9zjhi3AwbNiw4oCVbYv5yN2FnoM9k8yNWMo42JVRBSBmetDLQMa5CtwgwTnEMq3QOg7j5H0laIUwDkVeOZ6RyvmPMx1J6twX5hqZHgDHC4g8/hoEDBwYtY9M3usoGmrCrBCyJ2+URfpyJC2cznM4qJ78kynOeRiBJBJh4cR5Cgu6YKom78n3H+/x/8RCIxw1SNoSN8KJ58Y3yFv+2vtM6sPd7/fOOqvdhZ6AHZ8yYMQvVIGTNCjN26slA1VwFI1AzArGau/KVSTm+as7YP2xqBBgfaF/w45H28VuSuN8DWSsVPpCKCTsDQ1+ryf9NlB+8ZFlV2tksA53iKhgBI5AaApgFiapHAB359vyjiPvdlrCjyISd2pB8oWCtHM+PgwWgSrSE/QI2fmcEjEDxEEBowRmRXTNEP5Nq3IdkaxjYhp2BZwH1D56RxBC3DTsDHeIqGAEjkCoCEDZb/pgT2S7IKV5OlrBTGwOxPUZBIs6JtzLgHelwjKl1iQs2AkYgIwigEoewiVtPmGbmRScTdmpjAHuMpOl3yWb9E2w1eNSKxFOrjws2AkbACGQFAXbKoBKHtHHIhcCdTNipjgHZZv5EK8g2YiljpzFhp9odLtwIGIGMIIBKHPNgHK5WPj5PZaRqqVbDTmcpwi+SPoztmmD37MM2YafYGS7aCBiBzCCAhI2pkF0zqMTHjRv3OZkPz8pMBVOqiAk7JeAplj3XrCLxhOwsyESKVXPRRsAIGIHUEEB4QYhB88hhMOyi0Q6aldreNTq1SmWgYBN2ip2AjQb7tSXsFDvBRRsBI5A5BCBs1OIIMpA2UjbELcm7LXOVbWCFTNgNBLtjUXg+QtYETWH/tVXiHRHy/0bACBQZAWJSoImUZB2EGx0s0+UZ682OlQk7xR7WQOyPhI0d2yrxFDvCRRsBI5BJBBBiIGwEG0nZHJL0qkxWtEGVMmE3COiOxUi98/9rMM5lywIX6h9L2B1R8v9GwAgUGYFYwoaw5esTjh4uMh6OdJZC70uqfotU4B/Eho19hhWkT+hKoSNcpBEwAplGACEGc2E7YQdHtExXOOHKmbATBrhj9lolniWi/jcNwLJU4SXIGunayQgYASNgBE5GAAkbwo63v+L3IxIfrMBThdyXbcI+eXwk/p/2FT7HFgUFSynhSGHbdeKQuwAjYARyigASNnMkcyZRz9gCK1s2cUoLyV22YTd4IHPQB6pw9l+jDrd03eAOcHFGwAjkCgFIm9CkSNrtPj8lSdwfyVUj6lRZE3adgOxpNqjAid6zZ8+esFokOICTETACRsAInBqB2PkM1bi2wbZI2HnLqe9u3m9M2A3uWwiblSKkjaRtZ7MGd4CLMwJGIJcIIGVjwyaAisKWPpfLRvSy0ibsXgJY7c8hawbcvn37gk3Gp9BUi6DvNwJGoGgIIGGjDmfe5EAQzaMLJPR8rmg4mLAb3OMaeC2sElHtIGVj03YyAkbACBiBUyMQb+8iKuTevXuDpK25c8ipf9Gc35iwG9CvGmwlitFWrjeMHj36TRA26nC8H60Sb0AHuAgjYARyjUDsLY52Eo9xTvKSw245142qofKFdI2vAade/UR7Bssi6Su0JeHbDDRJ12WpdEpI16h6nIyAETACRuDUCEDY+P8QQGXUqFFBPV5EYccS9qnHSF2/kTT9hXgfoQZeCdu1ybquEDszI2AEmhQBCJsdNWgnMSUi+GgOLdwWGxN2gwa4Btrw7du3R1u2bAnOZo4d3iDgXYwRMAJNgQASNWZEnM8IOqVYFr8jbeVyCUKTmqKBPWiECbsHINXjFrzD2/cQhoAplq7rgarzMAJGoCgIMGdC2kjXSNmKeNZH+7HPl+Q9tigYmLAb1NOoc3CY4GKVaMJuEPAuxggYgaZCAO0kUjYmRl5F2sebqoFdNMaE3QU49fwKr/B4DyF2mCI6TNQTT+dlBIxAMRFA+EElvnnzZqKeIfycFu/EaXZETNgN6GENphYNshZImxUhAw4nCicjYASMgBGoDgGEHc5h4DwGNJaDBg36Y+XQr7pc8nm3t3Ul3G8i6SWytXxeA6sNG7behz3YVoknDLyzNwJGoCkRQOBhLm1tbY2mT59O8KkLpL3s05SN7dAoE3YHQOr9rzb5v1ED7ELU4FxI2Y5uVm+UnZ8RMAJFQQAbNpL1gw8+GIKoQOCQdxGSVeIJ97IG1jDOcCU6DwOLweZkBIyAETACtSEQe4ojAGFiRAjq06fPmNpyy9evTNgJ9pdsLG+Srfr3dZRmGVU4hG3bdYKAO2sjYASaHgHMiey0wY7N9i4EIs2ru0Xeo5u98SbsBHtYBH2EFaC2H5RYDVq6ThBsZ20EjEChEIglbbSXXIog+W/NDoBt2An2MBI1q7+tW7eGE2bY8G8JO0HAnbURMAKFQQBJG1v2jh07ohEjRkT9+vU7t9kbbwk7wR5mBUjAetThDCyczUzYCQLurI2AESgMAhA2QhDbu5CwFU2y6R2ETNgJDm8O+MDOAmEzoEzYCYLtrI2AESgUAgg/hHxGi8mF81mzJxN2gj2s1d9xJGui8TCgIHBL2AkC7qyNgBEoDAJI2JA08yuRzyQc9dH8OqyZATBhJ9i7AwYMeCUDCqJGPW6yThBsZ20Eyk+55QAAQABJREFUjEChEGA+xU8o3t4lDSY+WfOaGQQTdkK9q1Xft0TU75U6vIwdm4HFitDJCBgBI2AEeo8A8ykmR85oQMqWcDRYu3Le2fucs5uDvcQT6huR9Bu1/xpns5JV4QmB7GyNgBEoLAJI2MytbJdlP7bm2ha9ntHMgFjCTqh3cTJj1YcNG3W4kxEwAkbACNQXAaRsnHlxPmv3Fj//wIEDn6hvKdnJzRJ2An2hQCl/d9ddd51E2LZfJwC0s2w4AqVSiTCQUUtLS8R7Lt5rD2x4X0uFeDaYeCtfWeTGVy15+jfFQCCeV+PtXezIGTx48JBmbb0JO4Ge1YrvfVrtlWVbCRHOLGEnALKzTAWBvn37RkOHDo3kUBn1798/4v8hQ4bEgStqIm1Umvh4VF44a8ZxolNpqAvNFQKoxiUoBXv21KlT36Oxc+OwYcN+matG9KCyJuwegFTtLSLsslTiJbYa4MFowq4WQd+fBgJIy6RKybmjJK2zh6PRo0cjxUQDBw4MpyQRZWrSpEnhfZxHT+uPhARhQ9A4Z8YXNklOYyK/OKRvLIGTd+zAyWv8vqdl+r7mQ4DFXmyGlLQ9VATelIeBmLATGLsMnvYY4sG2YsJOAGRnWVcEIEZU3ai2IWKk5uHDh0eSUgIRxwQNYbe1tYV7kLC5kLjHjBkT3ldL2DQCQkalGV+VBzvgAwKJI0HxPe95vuLPeM54H5N6XUFxZrlBgPHBIg+VePvYaMotOSbsOg5JSQCar0plJhwmEgYQzhCWAOoIsrNKBAGkVwibc4VHjRoVTZ48OZo+fXo0Y8aMoO6eMGFCNH78+PA9pM69McmjFoe4JY0/qHyO1FDBqSxqeU545YKAcSaCpJmEeZ54feCBB4LWSo5FwaGTnRg8Y07FRoCxwuKOMcJ4aNYFnAm7juMcstZEsnnTpk3Bds1Kz2RdR4CdVa8RgGQhWIgZSRqJGWkaSZoLqRppGXKeMmVKJHtgIGypwX8tEh8lQu7TRSXKXXzX1VfP6+I7uUP1PS5S/pYm5Ddio4zJG+JmDy6EHZueIHUWy5ihmLSZxLmcmh8BJGwIGzMK40JamFrHYqbBMmHXsXvkZPYDTSRz2vdf23ZdR2ydVe8RiMka9TYSM3bnadOmRbNnzw6SNEQtdfcxkfn9ciprEaG36vXrkrj/O6W3a5Aaukexvcw3qfg3aQF8ta5/kvQkXj7M8/WciHm8iLo/oX/Xr18fCP3ee+8NRM7EDYk3q7TV+xHRPDmwMGOhxoIOW7a0QP9TGs7b5F/xWPO0MopM2HXsTU0eTzForAqvI6jOqlcIQNKor/Hq5kKqxkkMlTcSNCrvuXPnRqeddhq26FtGjhy5QqT94c4KVV4Nl1oqy9RC40bVi+tEklT1ERHz+ar3KzVpl7CnQ9K0mecQIsfmjWqdST1Wu5/IwG+aAgEt7IKfAws5jQfMki+RmeZaNe4fmqKB7Y0wYdexN0XWbOUK57MyWWCLczICaSEAaWFbRtUNOUPSSNZI1Ki88fYW0aEW/4VI+raJEyd+jLq2S7UNJ+dqcWqv56f4nVShf7Jo0aLf0+Jjxumnnx7IWtqu8s6dO0sE1Iil71jqrrYs3599BCBtFmcaC6WtW7eW582b9yXV2oSd/a5Lp4ZaxQ/AzhbbUUzY6fSDS41CMJPYGQzCxha9cOHCaNasWeVzzjkHSfS/pCr+G0mtA2XDXifS3hfjVinVxp9l8bWynuPGjfuMyPgmabmmSIOAqvzokiVLfr527dqyIg6WIGrsnDyTbCGzb0kWe7T3dcL8gS1bi7WSxgRq8lnyy9jW+5yzkYMl7Dr1g1Z3i2+77ba52FDwYGVSMGHXCVxn02MEkKrjQCbYpFF5I0nPmTMnqL0laZckdZ8v9fgavW+qA4S1CNkgoLhC2r1794L58+evBwf2k4MBDmo7duwIanPex8+pCTxGLb+vSNjMudiw6Vs0SpK4f6wWzcxvq06uuQn7ZDxq/k8P/oUaMIGwYw9WTwI1w+kf1oCAJM6gAh87dmxQe0NQF198cVB7i7SOS/3dImecK+VEdnsN2efuJ3Ko26Bnsk0q8a/o/eskeR3ftWtXH0ndkcg8uuOOO0L4YOyeSN9M+E75RgA/BQgb06S0SfTraflu0cm1N2GfjEfN/2klN7Td4SGs3nlvwq4ZTv+wCgSQHlF/43CFBzje39O1h5oJS3Y8Plsjsv6AJO9bqsi2KW7VIuZxNeT1NEZq8d+XduHvsXOiieBwHp5RVKg4i6JO5TJx57fr6U+20+JDxKsk7qZahZmwezk29XCXNDgWalV3DZI1Di54qcYeqb3M3j83Ap0igDTNBVGzlxq190UXXRTIGqLG61uOZE9Jmv6mJO73kAljVampJrBOwenkw/a2f0H+JeecccYZpwujc9FA7N27NzikrV69OpiyeH6Rtp3yiQALLvyISLw2W1+asHs5LrVtZLBWcd+RSnwuK3VsYmwh8Sq9l8D656dEICZrpESka5zK8P5etmxZNHPmzEje3mvlcPNZkfnDkqy/H2dUVLKm/bS9nbTfqdep8jV5tcwEkPbb2eqGGpXJXc5qJ7Z++RmOR05+XiFsHAxJCE4xYbf3fe4XqybsXo5Fqdf66yGfy0od73AeeD/ovQTVP+8SAaRqHKmQotmupe1MSNbl8847ryQP6SdFQFdLqt7RZSYF/DJesOh1j5r/JT2n/yLtxJ0veclLvoSWgq1fW7ZsiWTnDjZu1KpO+UIgVonHkrak7JLs2d9Rn7+hGUjbhN3L8cgqDpJGHY4KBtu1kxFIEgEIG4kaTc7SpUujSy+9tKx91WxdGiCpu4+k62eSLL9Z8tYkfkST+D9q8fMPsmeDYVnYlfCyB1ueayZ+7/bIT48jLMVOhGg8mZPVv6+XJvRf1d9vzU9LOq+pCbtzXKr6FCcWiLvdyaGq3/pmI1AtAiJs4nqPRR1OaFGRd0nbtIZqr7WNr1WCqUn8uXbJq7RmzZoy6tR4ooesec+zDWnbibRKcFO6nX6KiZv+xEwps1FrStWpa7Em7F7CiXcp9i/UaHidQtpORiApBNrJZZxUt/dIAnxaNupDut4lT/Cm2lOdFH6d5SvSDrZNmRgWKcjGV7R3ffq55547ZuPGjdHy5cujFStWhGfc0nZn6GX3M+ZmTJX4KBAgKLs17XnNTNg9x6rTO2OVOKTNe7zDnYxAUgjE5CJnqXOSKqOo+UoVvlZtXyY16u/Jw/6LaDDYz8tinOcaSS2W3oqKUZ7ajeYTDQmXFrqna327TL4KK/LUho51ben4gf+vDoGPfOQjT+KcEkvY2L2cjIARyC8C2gr3r5LKLiTm+oUXXli+6qqrogsuuCDEX+cscC2a8tu4gtQclTjqcKJOsuiSXfs0XWfmvfmWsHvZgzfeeOOXvvrVr4aBgYODH+ZeAuqfG4GUEdAzjHlhuZ7nyYpHvk8mh7ICrpQIO4y0htnLmrSUO6kHxeM4yJxMQCGkbfXZ0B78LNO3WMLuZfdIon4XqnA8E1nV2TGll4D650YgIwgoGM1+qcY/pC1zJbbQLViwIMRmhwAsaWekk7qoBoSN9hNzJYSt5H3YXeDV9F9JDV7Gs1RbBko4pDgZASPQXAiItD+tBfmNUo2vVLjXQbqi7373u5GO7QwqVyRtFupO2UMApzNixuPhj3pcpJ37jrKEXeM400PaTw/y05C1t3PVCKJ/ZgRygIC2BG3StrnXa5/2UU4/05nb4XAVqcpDpLkcNKGQVUSIQvOJpM0crahnMzRv988zGCbsGntPqpbPyj7Sum3bthNbPmrMyj8zAkYg4wgoctwPtTXoNTpT/Nnf+I3fiK6++upA2rJtZ7zmxa0eknUcbhZbtoj7fdrmNSfPiJiwa+w9qcLOl8qlhXNXsWE7GlKNQPpnRiAnCCgK2o8VsObP5IgWnX322SfOGmefL3HdnbKFAKYKTBbYr1GPY8+WQ+H/p89zG0TFhF3jGJO65Th2ETxH2Z9pwq4RSP/MCOQIAanB/1Lx2o/pgJXo9NNPL59zzjnhwBVikTtlDwFIG9U4QhVnPYi8r9uzZ09ug6iYsGscY6zc8D7EqYGVm53OagTSPzMCOUNAtux+qvK0iy++uMQebU5Jw57tlE0EEKYQrIiVwbY8BcTJ7UZ678OucYzJNvIczgys3FC5sJJzMgJGoBgIKCpaX2IuaMsXx5lGUpeHw0JQvcZHOhYDiey3kq22OJ3RNzihMWfnNZmwa+g5kfO8tWvXjkOylpd4IGurxGsA0j8xAjlFQCrwg/Ic/4j2Y0/USX2v0zUeW/a6deuC5s0L+Gx0bKwSZ56W02Aga5k0slG5Gmphwq4SNK3O5uqQj+/Ifj0V9QrStZMRMALFQkBHNj6pFv8lrb799tvPUACl8ajFsZOieWNe8CI+/TFRSdg4BiJly1H4ePo1q60GJuwqcZOD2TIR9ULOv8bZzMkIGIFiI6BjTdvY3oVa/J577jkhYcfRD4uNTvqtZ+GEvxGqcdTh8vAfpVo9nn7Nqq+BCbtKzPAMZ08fhM17JyNgBF5AQJLmeZIu58mxZ4gcMV+pSXKupJyJusK+J9l9j+u7tTrT+zuSeFbql8dlD96ozw++kEu+3smOfabU4ZMkYe8ReQcHJ8gBW7al7PT7kj6AqHEMxoyp8fafqtUZ6des+hqYsKvATBL1eBH1H3DuNSfAoF5xMgJFRYDQvCxakSTRNkFQP/jBD4I6GJUwqmE+4wQ7VJNcHFkp++8y2RGXIZWKuKPW1tbo1ltvZSINF7G6RYDvV1jQv8sDtlpsPKd67tW88InFixd/nDrfd999wdEJDJzSRYBxB1kzFpmzRdoLJXRdpfEFcecqmbCr6C51+Bh1/FIImyvP3oZVNNu3GoGAgIh5prxtTxNJH5N0vHDHjh1B08QkiFMPnrhxXAIInOcDwuJ7JkwShI3TD57VOAFB2gr9GSmSWLhkGw6qZU2mn9eieLgI/Q4R+Ebde3/IIMN/JGn/+c033/xxFiGoYIk3brV4NjqM8cciknGpvilrQXijapa77V0m7CrGkwi7TKczOXHOqlXiVYDnW3OHgCQTCY+lsgh3lsb7OzZv3vxqPQNLHnnkkUDO+j88CzwHfAY584pEzcXkCGF1fE6QoFFN4lXNqVdI1jqD+qQLBy6R9ychcEngt+qQnZ/zfsKECZ/MKpCbNm0aLLxC9cCGNsVbiqwaT7fX6IdYNa75u6QxdUTj8lKNxVvTrVl1pZuwq8MrrNIg7FiSqPLnvt0I5AaBdrL+hEj3tfJ+XihCihSLOdq6dWvw40CChpDj8I9MiPyPNMPF53wGiaGWJPE+XvTitcuFRKroU0E1rq1S4RXpG8kbRy5FE7t02rRpl6I6V8osYetQkKe1sPmQjuL8qylTprC4CO1GFQthxBjQCKfGIhCPQRaPzN0yywzQOGQsmbAb2xWNK02qlCdR++F0xsV7JyPQbAhoXE/UxLZa0vKR1atXj9f7fthkN27cGAh7y5YtQZKGhEiVRHSq9zFGfA+BQeaViQmVFL8ieRPuE1U50vr06dPDRPurX/1qv6TvFqnVV4jM3677H6vMJ833qgvbhT6tBf3A2bNnf2zevHkRCxC0CbS5Eps061nEssGei624mDPR7mhxOSBvWFjC7mGPrV+/vr/UJ7ukmgv2KWxUPIRORqCZENAe1UGa0PYrMFBwnLr33nuDnRrSiVXcLFQ7Em61GHQkr47/sxhASkdiR5KHvBUSNNLhGxOJLnbRRRe9RqT4qKSlPxWp/0W15Sd5v+zvf6u6XvCyl73sUqTs7du3B7NAvMBJsmznfWoEGGOYbPAtYCGVR4dAE/ap+/ekb2SPmsPqjNV+rAa0XeokiPxPjhHQRDZZaum+WoRej38Gqm+OjsUWyxZGxjyEE19JN5XJNVaxY4LCWS22i7NgYPuUpKSy1Omf1CL6GyLJ43Jk2510vXqSvyTtR7XgOQBZx7Z6nOtoT8eFSU/y8z31Q4BxjFqcMZTH+duE3cOxoIlsrbxWy1KFl+LJq4c/9W1GILMIiIxxlFqkxSjR+yYR+AOVN2SNrRryTms3BOTGFUumsVYLSZ/PRdSlRYsWlXVG9Xa14ZAWHedqYb0hC2BLbf8z2dzfLCmuJK1AWPBgRovrnoU6Fq0OjBnwpx8YS9IS5e4ACBN2N6NWnay5oFSmo+V4U4onjW5+5q+NQC4QEKnM1di+HUcc2asJsxnImhCbjHUcxLKSqAtqcp5FVJvYuSXBlvhcpD1EZP0h1fWtade3fc74iur2iBY7/y5Juw9SHSpYtAR5lOzSxrRe5dMHaEpZhIqwn3ecqFfmDcjHx2t2AzJkrc69mg7GWYHVmR1IugHNX+cCARHLABH1D+WfEa1YsSLYrJGsZccOqlwmtyyRCxJSbNdm0uVZZC84fiVoBvR6hV5XS51/JaSZVicwZ1C2HONu1Ja157C5I2UPHz48qPbTqpfLjYKmgwUfCyiN7UVa+H0gT7hYwu5Bb2ni+hwrelSF2PZQiTsZgTwjIOn5zSK7r0nt3fenP/1pIDzU34xzpECIEYLMaqJuXDgQQd44dul1jGzbYy655JKbVO9BulIPM6btaSVt9wpb1/AFYP7IOrZZ7fN61AttDOOFS0JYP4319yvOwHItplbUI/+k87CE3QOENTH0h6SZzIjaZO/wHoDmWzKNgCaqr4u0+6I1QkrFXo1kjSaJSS1LknVXQFJfFhq7du0KbYC4eU5V/yXSHAzp6reN+E7OZrsI+MJFgBic55zSQ4BxzfzNfI7jmcb6ND0L89OrUXUlW8LuAV5sYYGoCe4Q2/WyLH30oEm+pcAIiOAeR4V81113BXUynuDxQjQvRB13H88hEzD137BhQ1CTy2O8rAn5dsKfajKerC1h++P7G/mquuH/MlsBZzbJo30uW+QgbeYTFkVO6SDAmIGwWehhptB4GZpOTaov1cu9LjDjgeNrvAnbnc6CKoUHzskI5BEBLTg/LAl0IEFQli9fHt15553BNyNPUnVH3JmAsbejLcAGry1VpZUrV4b3em5TO+AhtmUrgMo82bE3EgSG/eRs8XJKDwF2HbDIwweifcvgWzROxqdXo56XbAm7C6x44OSU84eaCEaygkeFwsosb1JIF030VwVCQER9uWyoH5CDWYhctnv37qAxgvDirVN5hYM2sOjgFY0B77kUJnSK1OXfVqCMv5G3dmp2StmtP6byv4PzGYm5JO+Y53msIHRhOoGw9f5czesj1J4Hst4mE3YXPaRV1xnq0D/USmwAARAga1ZmTApORiBvCGjROVdOWqN+9KMfnVAf4wDVLInnkucTKbt9Io503OVYff4GqT6/o3amRtiyXT+JHXvmzJlh/sC8Rn09lzR+9MXjBMJua2sL87qeg3Bee+NrU12JJuwu8JKa7fVapc9od04IK2I/YF0A5q8yi4C2sSzUwvOTEBl7rPGShdyaLfF8oh4nxRIUdkppxVJdmWjPeAl1OHXhYBNp75oN+ly1B+2G/BvClScTp23YXQwzEXVJ0c0iVIfYO+hkE3YXgPmrTCIgNfgZ69atW/vjH/+4DZs1HtUQdp4mqmqBpW1Isd/97nejG264AZXYf8i8dUG1+dTrfsiaUKULFiwIUjanlDmlg0BM1owPtgWy0yAvi1cTdhdjRirwFjoTz3CczkzWXYDlrzKLgKS5NVp0lokjwAIUfwzsu81sQ8XPhOAYqMd1ldiyJrXnL9X2WWl0lGzoz3EOOI5nqGHZ3mUpO42eeP50OUxBLForTZ3p1Ka6Uk3Yp8BLarWpeqAuQYVI2EaIG8I2aZ8CMH+cOQQ0VoPeFYKWVF3CMxxPaqSJZiZrOoLnFCmbSZnnl/3ZLFgUvOTbaXSUPMNXa2vXrUjZijNusk6jE9rLjMcGZM34aHckTtVk0lM4bMM+BVJsplfHno+9j/16dCwd7WQE8oCAxqrWm6WytENfI/QohCW1eNAUNbMqvLJvkKLQjGHLZo82RypKsj2z8p5GvZd0fVBzyHtU3ve173c+KnGk7GZfODUK32rLYdHKnI72FFu2/Apm6ZnZzDNTbV6NvN8S9inQVsf14YFHskZCiR1ZTnG7PzYCmUKgnaw/I7K6Di0R0gTjmMmpKCTBAhvVOBIUUjahQZmk00pShe/QomE/9mwuFhC2ZafVG1EIEQtxt8cV/z7PDAvd9GrUfcmWsE+BEQ86JI1jAqEbmfCKMtGdAhJ/nCMENBEtVYCt37rjjjtasONKJR72/kJiRUq0l0UK2gU0C+PHpxsfQ6R9kzzFL9R+7FakOxYSaAGcGo8AYwPCRoNKaF6ZTIJaqvE16XmJlrA7wYpVlsh6LA8Sqy9ecdJxMgJ5QUBj9m0aw5NYcGK3RlPEIrSIi07ajXaBnR7tJPmUNA/D0uhLSdafkyr8MbZ3Eaa0b1/LTGn0Q1wmzwPbdtG8MNdnPZmwO+khrbhGabL7HCq0mKxRjzsZgbwgoAVmX0iKqF9I1xB2UROTMlI2kzLPtDzmB4m8N2u726I0MMFjXHbssCcbtbhTeggwNpjjWcjl4RkxYXcyVqQmmaMHeigOZ3QmahNW6U5GIC8IaMwehqDYZ8pWrjxID0lhy6SMhowJGXs+mOj5Hq/PXpNUmV3lC0njKY6U7bjiXSGV/HfM65WELe3q0uRLrb0EE3Yn2Ckq0V/hWcqEB1nzwDsZgbwgIGnyXI3ba5CwIWpUfkXxDO+qj5icwQLVONjIGS2VUG+owYl21traapV4Vx3WgO9i7QuLOYhbr+9rQLE1F2EDSifQ6UG+CNsfwRZwPHMyAnlCQBL1TC04p6H+ZeHJZGSTTjh1L6g+cSLVnugg4abRrxA2wVOGDRtmCTuNDqgok0VcbCrBIVEL25dXfJ25t5awO+kSpJF4xcV7vAmdjEAeEJCUME6E8H4k6/aAEEFD5DH8fDAVNGZIUti003IkZSsX0jXqcEc8S/epQsLmOUHzwqv+zzQnZrpyaXUlHYd0wpYLHmpPdmn1hMutFgGR0ViN2XOx1bLoRILw+H0exVglDjbtKvFq4a3L/RA10jVqcXuJ1wXSmjLhuYCwWcChiWJM8H+Wk1XinfQOanC2wrA/j/ee8DoByR9lEgGR9XNMQNhpIWyP3Re6CcJG88CzPX369NQc8eQjE9TxVom/0DdpvcNUhEocjQee4oyRLCdL2J30DlI10jUrcUvYnQDkjzKNABoiSJtXE/YLXcXkHBM2EhVq0DQShM1BIEQ7s4SdRg+8UGalhB1rpF74NnvvTNgd+kQT3RlI1UgoRDfD5uVkBPKCAOM1ttNaO3RyrzE5Q9IsxpGq0iJsSBp1OI5vJuyT+6jR/zEm4j36LObksxQclrTQzWSIUhN2hxEiqXo1DzOrcUsnHcDxv7lAAK0QEjavWbfJNRpQnun4ShMbxa32iV2N7vwuymMssMCV5mWCAuv8mLjiXdye2lcm7ArotY3rfpF1H1QjJuwKYPw2NwggXceEjUqc5IVnbrrPFU0JAZ4Rnhu0L5K0XyH/pc+kVJUuizVhV8AjFfjjqMKRsLPufFBRbb81AicQYBsipI26F4khTSnyRKX8xghkHAEImwUufkvt5pJMBuAwYbcPJHVYmzpqIOFIcUhh4nMyAnlEgIkHe1wsYeexDa6zEWgkAhA2cz5+S2hYs/rsmLDbR4WkkbdJKhmLSgT3fqvEG/m4uKx6IYB0jXaI8csrE5GTETACXSPAc8KzE4fzxREti8mE3d4rsl8sVScNjE9tsUo8i8PVdeoOAYJyxGE32TaEc5OTETAC3SOAhI2zJmQNeWcxmbDbe0W2vj50FoTNKsvSSRaHq+vUHQLs8WXLEKdBsdeX0JdORsAIdI0AEjZaKUxJELZV4l3jlfq3OOdA1KjEsWHTeU5GIG8IIGFzfOOQIUNC9Cbqbyn75F60meBkPPzfC3HmmfuxY6e1R7+7vvDyux0hJOpYws5DxJvuOtbfFxcBDpeAuHk1Wb94HMSYpK198MLhxX2T5ids64Kwmf+zasN2LPH2EYKETSfRWRC3VeJpPjouuzcIED0LKRvSTpuUetOOev8Woo7DgqYZZSyea1C72lem3r1cW36x01nWvcRN2O39y4MDWaMSp9P8INU28P2rdBHo6HSWbm2yVToLmeHDh0eTJk2Kxo4dG2z8adSQ/fHxbpSs2krTwCXNMuNFlM6Sj9jaq3gcmTy2yyrx9lEign4OlQhStgNOpPnouOzeIABhI0UiQfI+Vv/2Js9m+S1YgAsOedj4eZ9Gwj8GG2ksYVs1nkYvvLhMvMRxOkPDOmLEiNnql8EvvivdT0zYwl+rqk+qk95CZ1myTndAuvTeISAp8lgsRUJMqMRN2s9jioSNBz3SdVtbW3jfO7Rr+zVzDEIBAoLnm9owTOpX9AeLKY2RN0vT+oci7UxpoQtP2AoffqEenI9qO1fZD1BSj4HzbRQCY8aM2SyCfsOECRMCKeF45vQ8AixekKpHjRoV1OFoINJIqF+Za9jry3unbCEAacs8WhZxf0oxxSdlqXaFJ2w9OM+iApF0XeIBsnoqS8PTdakFAUnYG1pbW9chRQ4bNsznLreDGNuwp0yZEqRsNBFppDhAB5Ic752yhUC7BqRE/2iR96ks1a7whI2jGapwAr7bASRLQ9N1qRUBSZAbFDTlZ6h+Ywcrop4VPeE5P3HixOjss8+OZs2aBWmnMv+hDpdmD8cmzzkZG5QIbCyi4IV2retbslTFVAZslgDAAQTJmoeI95aws9Q7rkutCEjd2xf1L9HOcLBCuix6im3YsUpci5iH08AktpPGc04adXCZp0YgNllIgCtlTQNSeMKWY0GJlS6u/HiI26Z06oHsb/KDgAi7PHr06OjMM8+MTj/99GC3zU/t61tT7PgsWnDCQ+sg+/7j8gJ+rYj7n+tbUve5SSAYKKLuK9toOMrRWr3uMWv0HSyk9u/fH+3evTuEqm50+V2VV2jC1sNTkgQyI1aJ4whiwu5quPi7vCAg6fHfdN0/ffr0aPLkySfiihfRYxxnMzQNIungiDd+/Pgr9P67PP8p9OfF0uQNxASH70xWD5lIAZdMFImGNVaJE/UMO3aWUqEJWx3RV5PaMuwVxBGHsJ2MQDMgoHF9h6TKJ6dOnRrstjhYoRKGvIqW8AbHls/iRV70bHO7C7JWavjZo5prXiFCGAQRoNHDDOeULQQQ2lhIcWG+yFIq3tNbgf7BgwcHKMjE2/Ua1FNWiVeA47e5R0Be4n+8cOHCaO7cuWVJlWHfMeRVJCk7lq4xDZx33nnRzJkzQ7+mQdYULKI+R/NMHwQESNuEnb3HrMKGnTkv/kITdlhil8ttSNZx1CGrxLP3ALlGtSEgm+1NUgUv0WsJdTCXJO/CEDYLExYotJkFCx7i2LHTSgpH+nbZR1+KYID0ZifXtHqi63JjtTi8kLUFVaFdR7X6LtE5OBlwZc0jsOth5W+NQPcIiKTv27lzZ7Rs2bKg3tu0aVN09913B3Vs97/O7x1I1tqLHo0bNy6aMWNGdM4550RLly7lOW9Lo1VybL1MhP3V7du3Rzt27Ah9wdzjlD0EEN7QurL1Dv+mLKVCEzYdgY2CDoKwee+HKEvD03WpBwKSMD8j++2foIZFuoO0kRxYoDbreEe6JgwpQVLUdjzDj+maqM8frwem1eYhibofkz/e4TgzNSvu1eKSxft5NvBrwjGQ5yVLqdAqcTqisnMgbj9IWRqerks9ENiwYcOHJWnu0vauZ7HhInXiNd3Me7PZyoUJ4KyzzgraBUm1rxZZH6wHnrXkgTAACcSnAWJ681xTC5LJ/oY+YSEb7xxCkMtSKryETedwnCarXjrHD1GWhqfrUg8EXvaylx3TuJ4pkv62JqLXrV279oSEHdvommncQ9ZI12xnu/DCC0NUM7U9FVV43H9yMDvCPMPxjahb7SsTI5O9VzgB6drburLXN4Gg6aB4D3YzTVwZhNtVSgkBSZfPyaa7gu1N8+fPD3ZdHLCw8zbTASFoDXAumz17diBqnM20pe2wCHxfStAzxwwUzm+GBCol7LTq43K7RoDFFJyQxW1dhZawRdJIHoGsUYfH0kbX3elvjUA+EdBE9EXtyz5w1VVXfR6Jk0UqExOqWoJ4NMNilQUIDmZs45o3b16k9t6n/eiXaItbahEwkKo1+b+byFlbtmyJHnjggSBhNwPe+XwSuq41hA1ZV3qJq6+05m38vv2ONS00YWsP9sshajubdRwW/r8ZEVBYzkNq17+KQP5MzlhtOGNB1pqIwgQFeedVVatnOUQxI044XuGnnXYakvazOq3sBknYj6XZn5L6n0a6xukPvG16S7M3el42Ahy2bIWtPqJnZEDPf5ncnYV1Otu7d+9IPTz/yYksdIxXu8kNMuecLQQkbf4ppMZWLy6ioeGExmlWeYyEhkqfo0Q5hQubNa9Sie+XV/gmkfUnkY7S6gFN+GPxDEfKjv1kkN6cso8AghyaJ2lE+u/Zs+ehNMdRjFZhJWxNTI/I+aPM4R9IFk5GoAgItKv2viFb6rWyYf8mB4SQmJxQ1cZHPiJpZ3kRi1aAxCKDxQYLkMsuuyzYrs8444wj8oafzPft7U1tw7Mm/Ac3b94c9l6DL9uEPN/QM9lPsWc/Cy6NozEyt3xPtf7NNGteWMLWQ/OEVrrDYjVglienNAeIy24uBLDDtZPYa3Qi0Re01eu9RANDyl69enV07733BuKGWLDhZTFB1jiXIVmzTQ0HM64lS5aEdmjf+R+pjS267znam2Yb5B1+THbrvtiucTizn0yavVF92Yw1EvygRWzqUVQKS9isnlBNQdh0hpMRKAoCMYnJY/z3Fc3pe7L1PqFDMb4oO/ASmYgCUfNMxIvZLOHCBIraHsmaC8JGrb9gwQLe/1zS9idU7xW677m06i3sVPzzCwURdnnbtm3B2QztBfOOU34QaCfqmLBTr3hhCZuOqLxS7wlXwAikgIDI+scUK2n7sFTJgazZv7xr165IdruwZxgnqXhhyzOTVho4cGCIC44KHKKOz/uGrOVEd0iLji/ru5+nVb+4XMhaqvDLZLP+O0nW/VCFs/carUVenfrithXplb7igBZ8Dxh7BOJJOxWWsNMG3uUbgSwhIGn7PDlG7ZMn+RhFBet/3333BfX4unXrgioXooa000oiwUDWRGljn/Ull1wS1OBS5R/VXuuyiPoGXdenVb+O5Ur9/U5pK07H/glZEzSFhY8JuyNS2f0fbQhOZ/Qh2wUZe2mnwhI2kw9burDTpSk1pD0AXL4RAAE9A6hxg6OWpO2DUjePhiQhGDywUZWzxYXnheeG5ydJ8kHtzVat+LQtXgn6QmzwadOmBTU4+6z1/xDVM6wk2tuQngqgfSipHgPuuuuuEdqJEjQVkDUTv9Xh+XrWGN+MdbbiIWlnwaejsIRNJ7BywhHE2yzy9SC5tvVHQKR3guikup0pr+s1CjgybdasWUE6xGkKFTnqXdTl7CtmMktqsQtBs6caqQaHMqm7A1njDY4qXFL2Vi0q/qeQOFHvyjbUH6Ge5yiCfo/mlys5FY3TuZjsnfKLAAtXriykwhI2K16cQFj9pqnqy8IgcB2MQCUCIsgnJE1fqXCel0rN/LTI83LZta/duHHjiaMh8dLmGYo1VEgjkHd8kV93ZB5PhEjT8Xte+V/7pwNBx05leLEjWasub1cwlKGq289F6Osr652F92rzYAXauI5FzZo1a8Iix7brLPRMbXVgDDO24/FdWy71+1VhCRv1FFsseK2cZOoHrXMyAvlFQES9QbXn4vm4XiR6LXY8BSMp6yqhIkc7hZoc2yyvaKqQJlkA81x1pwKG9CXFh4M6YvU3h3ao7Ig45+326rJU3yXZ1pG479br/80qqvK4HyQtxFYJAhMIQ8p5yggFDsyU1R7rul6QNAtStEksTrMg2BWWsDt2FSt7iNvJCBiBkxHQs/G0zEdjFy9e/G/at/0KtlFBzjt37jzhUIV5iUkNEkeiZHLjHia9zhLPW+zIAzFr73Qgb95rQRDU4ZC5rpKk6o+I3D+v0J7peb111ogOn0lb16I6TmAbV4wN4Ui997oDUDn5l7HLWGZxignVNuycdJyraQSKjoBIlLOkr6jEQU7lvxZJlTWZDZEk2T+e3LBtQ9aQdleLYPZRY4/m4j3StaToQyLtNfrs/Mqy8vBeE/ooji4l+MzWrVtD7PAsTPJ5wC6LdWTsMo7RHsWL0LTraQk77R5w+UYghwhoMpOQXBpJ1UXaHxBJv1Eq8MNIk0gmXLzHHt1Z4nsilUHUqMN1X4uk6CekCn+r8n20Pf/Mq7ziekptepaulcuXLy/fc889JdmxMyGRdYa9P+s5AoxhFl3xArTnv0zmThN2Mrg6VyPQ1AiIVE+QqQJK/G81lqtuqTL/umWaQEbUU+rvq0TQN8qDvqzXEnZrJDIWJU75RQAJmwtfjKz0pQk7v+PJNTcCRiADCMh+/1ECzch2XcI7PHbAY7J3MgL1RKBzfVU9S8hJXn64ctJRrqYRyBACmjf6y9Gu/4YNG4Ltmm2iXTnbZajqrkoPEIAXKq8e/CTRWworYaPmwCkGGwUdItVWlw4yifaCMzcCRiBXCIikZ8tuv4UTzpCqOUITz3C2tWVFfZorQDNYWfox3vnAQgxnyrRTYQkboqYDcCigYyBtJyNgBIxATxCQvfo4+3MJJoNXuOKvh33XSNdOzYEAQh3mDYXqDdsXsxCxrrCEjXQNYXN5RdwcD5hbYQSSRkAT+ELZrOfIsSwER9m0aVOIZsZ+6yxIYEm3v0j5I8Qh2BEYCMEOAk87FZawecDiY++8Kk57GLp8I5APBDRvfFmnb72UKGY333xzeeXKlSWCxRBYw6k5EciS9rWwhM2Kia0XPvKuOR8yt8oIJIGAbJqTIWvs1jqNq8SJXNg5szSpJ9HuIueJf1NWUmEJmw6wKjwrw9D1MALZRkCEPFy2zG9q+9bUX/ziF8FmDWmjBjdZZ7vvmql2hSbsZupIt8UIGIHkEFAY1g9IHf4q4oTjZMaxmXgOY+N0MgKNQsCE3SikXY4RMAK5Q0D26SFyUH2TyPqjkDVS9SOPPBLU4Pi+WLrOXZfmusIm7Fx3nytvBIxAkgjIM/iftH3r2jvuuAMnsyBdczIZvi9Z8BpOsu3O+2QEtDhL3U3ckc5O7hP/ZwSMQIER0KR8wsNIccH/QUcrXivHsjLnW7PXGrLGWdWq8GINEvyddNTrDI2P4Wm23BJ2mui7bCNQBwQgGQ6hqENWhc8CHGWv/rDU3u/QqVuziWSmIzNL2KwhbUvWxRwimD90oNx5Ghf/rj3479Dxr/vTQMKEnQbqLtMI9BIBeSyX2ZoogvmQrk/3Mjv/vB0BSdBv14T8KezV69ati+66665o1apVEVHNkKotWRdvqGhBHPpdmpUWRTt7uYh7slAwYRdvKLjFRqB6BBSkY/SKFSsiEUs0adKkv5JE+HGdLX3OhAkT1lefm38BAlu3bh3Q2tp6GClaccHLOsyjRBQz4oPjDc7iyKmYCEDY+CugXSE86bBhwxbrs7u1UG74+amFlbAFNiqOcOgH752MQF4QUHjMgz/84Q/LsqmWFi9eHF188cWtEydOXCfp8GXjx49fpfHssFs96MzYlKDXkZKozycuOBL1mjVrSlKDExglnLxlqboHYDb5LYwByJoImSLsL8u34Xo1ueHPWWEJe8CAAdHQoUOjwYMHB+Ju8vHm5jURAtpmdEwTRl9J1mH8jho1KtL/5fnz5/9MgTy+LaL52pQpU37QRE1OpCla2JS1bev1Iuu3yMHsapF0WZJ2iF7G1i2cy5CunIwA4wDHs/hKC5HCEvaIESMiTWpB1aGHNC38Xa4RqBoBVvsQyp49e4JanL3BkrBLl1xySTR16tQ3Ssp+RpmasLtBVmT9Di10vnz33Xf3v/XWWwmGUmIRhL0aacpk3Q2A/rrhCBSWsGWvwk3/hISNWtwPaMPHnwusEQFCYhLDGhUdRwBiZ5Xnanjft2/fN0jKvmLgwIF36LPXUoTGtoa4PcnBQoudb0h6vkwYDVVc8P5SgZ/YXw2ODogCSk5ZRKCwhA1BV15Z7BzXyQh0hgALSy4cYSAXpEGdIMX2o3ACnQh8kCTHQePGjbtGUnh50KBB/0dj/b91lleRPhNmo+RE9vD69esDXlu2bImQqHEuk0o8LIA4dhdsnYxAFhEoLGFnsTNcJyNQLQLY1PBe5cJrHN8MiHv27NnRwoULI3mPR7Jxv1MOaYv69et3ld4/KvJ+utpy8ny/FjMTtYg5LpX3JkgaxzKCoCxfvjwQNgsfcGQBb7LOc083f91N2M3fx25hQRCAdCAfVOWc0QwpyT4LYWPbPltbwPZJHfyP+u7rY8eO/YXISRzVXGryyjZJy/AqtXe61Nx/JNKerb3rARN8Vh544IGgmQCvmKTj14IMFzczhwiYsHPYaa6yETgVAqh0cUiDtNlTTNAPbNtz586N5syZU543b95vn3nmmW+UNP6vIuv3nyqfvH7OAkTpUpH1lXIee78c8vpxfvVtt90W9lSDDWQdmxJY5DgZgbwgYMLOS0+5nkagBwggJUJGXDijYduO7bMi6RKxsEVmbfIk/4Pvf//7Z2q3RN/hw4dHI0eOHNrW1vYKbXO8vwfFZOIWtTVoCNTOhVqgfF2aA/mSPX1ch3TMlIlgPCYCIpahbVCY0QjiZqsWl5MRyCMCJuw89prrbASqQADyjlXA8oyOCBAiwua6QGryaPr06fEWR8It5iaKENK0vOSvlNT8H1qEDFCEsqBdwE5Ne/GgR6KOnfJ4RQPhZATyikBhCVsP+wkv8bx2nuttBHqCACQlQgsX9zP2x4wZE0h7xowZwVkNFTqSp+zeZTmuvVvbxtZIOt0g6bsPNnCSJPCy1OvP6PfHwgcN+iPJeJDINsxVELC0BscWLVp0ppzo/kxe8C9Hg8CF+h8NAk53eH/THocUbVAnuZiGIFBYwtbDHjxqeSVEqZMRKBICkBnqcpyuFJs8OGNBzFwi838mCiDviVWAZMorkrp+82Hh9FeNwkoLiXFSbd8i9fcC6kldUPvfcsstIcAJbWAxgjSNyhszAKpwiJq2sTjhficj0AwIFJaw2e7Sv3//cPFQOxmBoiAAgbENDAImqhdqcp4FBVoJl+zZkfZw41kezZo1C/t2kMiJDiiJ++OSXsco8NBOLXZ5cDAkH9bVousZ2cB/otcDPcVSdZktUj5bv+kjgm3T7/rwW7QCIt1jkpyvFCkvQLJGeubiPR7wMVlD0hA07cGJjNfY+9tk3dOe8H1dIaDxGRZ/8fkTXd2b5HeFJWwineFsg+QAeTsZgSIhAKFxkSBvEs+BoqQFiZWIX5A5rzrsAHV4eNXzMkCS9x9B7kjhED2pfUJ7Tr+/84477tilia27VbC4tDz49ttvn6afz9f7Fl19qROhVyFdorkhVSM584rkzMV7FhnUD1V+XP9QEf8xAgkggCZW472sxSvj+kN6fSqBYrrNsrCEzaEfSA5MRHSGkxEoOgJIp5AlJAhhombGFszzATFD5pXvicXPojeWOmT7blFM85fq2Xppd4tgkXMoA9LlolykalT1EDCv1INXLuqDFM3FfbEDGe9ZLJCfkxFIAgHGFwGJdHxtST4cf6sFa2rnzxeWsJmAFLIxqABtw05imDvPvCEA6cWSdyzdnqoNTGIdCZtnSoQdtFbdPVOUBdkiwSMxx1J1TNbxKwTtvdKn6gV/3ggEYsKWKeZ6tEuNKPNUZRSWsE8FiD83AkagewQgXPY3Q7hxgqQVqCRI4vFnXb3GpA1x854rXjBA0rw3WXeFoL9rJAIan6mrcQpL2FLvDUS9h5ovVuk1svNdlhHIOwKoqbmQQOKEqrry//jzU73GRM338XzY8fVUv/XnRqBoCBSWsKW++6DUG5/F89U27KINe7e3HghY+q0His7DCPQcgUJuQNYKviRb29/icCav13J3DjI9h9N3GgEjYASMgBFIBoFCErZUdsEWISn7G9qeUrJKPJnB5VyNgBEwAkagfggUkrBj+CRp/zb7sbG5xXaz+Du/GgEjYASMgBGoxicjabQKTdiyXUsb/nywCJzPstQxSXe88zcCRsAIGIGuEUD7io8TF+/TTunXIGUEIOo4khOb452MgBEwAkbACEDQRMJUwJQQV59gW2mnwhM2EjZhFgmiEm/xSrtTXL4RMAJGwAikiwCEjcmUELyQNYGB0k4mbBE2nRHHRbZaPO0h6fKNgBEwAukjABcg0KF5RSWeBW4wYZuw038yXAMjYASMQMYQgKDRuiJlI11D3mmnwhN2rPZALU7nOBkBI2AEjIARAAH4ofJKG5VCE7ZO6zqq7Vw/HD9+fMTlozbTHo4u3wgYASOQDQRiCRv/JtTilrDT75djOnjgboiaCzuFkxEwAkbACBiBmLBRh8MNJuyUx4Q65JjO/L0LsmZrFx2D+sPJCBgBI2AEio0AXIB0rTMnwrnv2LLTToVnJx3h9wge4hC292GnPRxdvhEwAkYgGwhA2Pg2yXQaCDsL/FB4wpZUXWJblyXsbDwkroURMAJGIAsIxCrxLHmJF94tmtUT3uGHDh0K6g+rxLPwqLgORsAIGIF0EYgJGykbc2kWdhEVnrAlXfehYzgXm5UU752MgBEwAkag2AjABRA1Gli4wU5nGRgP6ohtUodfr/OxD+N8Zgk7A53iKhgBI2AEUkago9OZbdgpdwjFjxs37sFJkyb9lkj7Z/HWLpN2BjrGVTACRsAIpIgAEjWSdVtbWwhdrfep+3ylXoEU++OkotU5j8bOZ7jyZ0H9cVIF/Y8RMAJGwAg0FIEKlfhz2ou9p6GFd1KYCbsdFNkrSniKS+IO++4gbNuzOxkx/sgIGAEjUAAE0LTicIaELe3rFu3H/mDazTZht/dA3DlI2Zaw0x6WLt8IGAEjkC4CCGwIbpD2/2vvTMCtOutzvzYzIQkZmQLhBAghEAiEABnIZEbNja213mv0sbfWaG/V3lZtvR201evQ2kFb2+tQo9bWeKPG9vo4xDjHmJhAEiBhnmcOMxwOwwEO+76/L2eRAznAGdbea3rX8yzWPpu91/q+37f3ftd/+P6frOvl6bbmpatbsNtGgdJzWNhDhgwJGeNZSOHPwgfEbTABEzCBMhJAsDHeJNif0VoTv5YFBqWf1hUPggYnuMQvuuiiSOVKHcOOwfhoAiZgAiUigFUdG3C4w5WU/K6sdN+C3TYSGqTDuMOZj02p0jiGrdW8sjJWbocJmIAJmECNCTB9i4JarOCIFmRps0u8bTSUsv8d7XsZKFzjCLY3EzABEzCB8hDADY51jQ4QHrVgZ3TsdVf1iDIBtzBIrM7iGHZGB8rNMgETMIEaEmAq1+DBg4O3lYSzLG12ibcbDQl135aWlmjjxo3hLou7LW8mYAImYALlIYCxhpcVK5uksyxtdom3Gw3dVc3DumaguMuyW7wdHD80ARMwgRIQiBPOyGeyYGd4wOUOfzuCTWZg29w7F0/J8Hi5aSZgAiaQJIE4ho2FHetAkufv6blsYbcjqMHaI6t6GAPFgFFb3LHsdoD80ARMwAQKTgDvKl5Wql466Szjg81iIAj16NGjmX8XllZzLDvjg+bmmYAJmEBCBPCujhw5MrrsssuCcCd02kROYwu7A4zEMMgSxMrmsQW7A0h+ygRMwAQKRiB2iVOTAw2Qh/WeLHXRgt3BaJBoMGrUqGBhs7yaBbsDSH7KBEzABApEgPUkcIdjYZNwJtFeoN/+J7PURU/r6mA0GDDc4Tt37gyDZ8HuAJKfMgETMIECEYgFG4Pt6NGjz1188cX36re/OUtdtIXdwWho4D4WLwJCmToLdgeQ/JQJmIAJFIQAv/GEP0k4Zt+6deujem5r1rpnwe5gRA4fPvwFLOzhw4dHdol3AMhPmYAJmECBCCDYWNYkm5Edrvh1JheRsEu8gw+dUvmP8DSJB1jYTO06cuRI5IVAOoDlp0zABEwg5wTaW9hM5cJQy+JmC7uDUVHsYrME+mbc4iy3STEVpnp5MwETMAETKB4BBJvfeKZy4V0l6SyLmwX7FKMil8hgpnUh2Eyix9r2ZgImYAImUDwCJJzFs4NYVpPf/CxudomfYlSU3r9X2eKHNL1rwLZt26Jjx45F8fEUb/HTJmACJmACOSSAhc3soBEjRkTysBLD7pvFbtjCPsWo6A7rCf3X58ePHx9deeWVYSC5C2NgvZmACZiACRSHAL/teFFxh8urukOC/VQWe2fBPs2oyMpu0sAF9wjuEov1aWD5v0zABEwghwQQa6Z0kWDM77wez5PB9p0sdsUu8dOMisR6T0NDQ3To0KFo3bp1FuzTsPJ/mYAJmEDeCLCEMkYZbvC22PVBJRk/nNV+2MI+xchoCldFg/hJTaL/cdu8vDC9y2tknwKYnzYBEzCBnBGIk80QbXZZ2HuUbPwlfv+z2BUL9ilGRe7vMHFebvH/o9jGfgaTCjjEOSzap4Dmp03ABEwgRwSoscFsICxsft/lDg9e5/j3P2tdsWCfZkS4y1ICwv/TQDYxoCQkcCTW4c0E6k0gvutvbW0Nl9bf9W6Cr2cChSFAThKCTb0NQp9UttTf67LcQQv2aUYnvsuSld2XuzAGlPl5+tvx7NNw83/VhgCfR00tfFw5FX20OIE/g7XB7LOWiADeUoqk8NuOMaYKZ5/Ncvct2J0YHc3P+xoJCVOnTo0uv/xyF1HpBLOyvEQi2q9efd21a9e7d+/ePYN6AKp3H2oD1Ovavo4JFJEA3tLRo0dHU6ZMicaOHctv+5ey3E8LdidGRy7xP8LCpjB87BLHneKtvARwR+/du7equ/Nle/bsGVMPEgcPHrxp//79A3XdUNu+Htf0NUygqARwh1MznNh1vDpj1vtqwe7ECEmcj0iwd3InRsZ4VgvDd6IrfklCBHBJy+KtrF+/PpJgz03otKc9zYEDB442NTVxPQv2aUn5P03g9AQQa+Zcs9AHa0XoxnuLPKkfPv270v9fz8Pu5BhIsN+kwvCPbdmypSq3SYWJ9qzgRclSb+UigHVN4teOHTvC/Hx9+etSeFiCHe3cuTPavn171NLS4tXjyvWxc28TIoB3FMFm5g85SXhNtfDHe3V8mMRObZnN5rSF3ckPge7CfijL+hsaYDLHw10Zd2jeykkAwcY1rYXug4jWgwLWNTcJumm0YNcDuK9RSAIINgYXljU7q3TJ+FpAZ7Ms1rTPgg2FTm6aZP9xsglJPKPGOMLtrZwE8K5s3rw5Wr58ebRhw4Zo3759X6s1CQl167Jly6KFCxeGmwV7d2pN3OcvIoG4WMqYMWPCbzkxbBljvfPQVwt2F0ZJbpRW4tfclSHWFFHxVk4CWNjEkhsbG8MuV/WNuNPWrFkzIEkinFMu8PGy5H+sjNa3cD3c4mSJ45r3ZgIm0DUCWNhkhzPzBwOM33FZ3LmIbTqG3YWxlljv175TS25eiHsSC8tb+QgglFjYuKYRTr78EupL9fgYsTAR+VRPqSDUuOfWrl3bXwluy7DiFyxYUNV1Kps2bQr17Xt6Db/fBMpIAAsbN/jEiROjCRMmkHz2VYU6N+SBhQW7C6Mk14l+L9d84Iorrvgs7kj9mIbiFdyx2T3ZBZAFeCkWNpYuVjbxsDlz5oR5nA0NDbfLCh6ou/ePd7ebsVhLmH9frvbBixcvjl544YXo+eefr6xevbpuMfPutt/vM4GsEqBQCrlHeElZNlmhzXWysP9Ov+H7strm9u2yYLen0YnHsqYekRvlrbJ6Zs6fPz+4U/jxJmvXot0JgAV8iaZ3RU8++WSwuPWDcK+mh9y7cePG2zRv/87udBfLWtb7h3RD8JfcFOtx1lwAAEAASURBVOhmoDpv3rwKK8aRKe7NBEygewSop4ErnOm5PNbv+Y81pSsknHXvjPV9lwW7i7xHjBixQxbQDRLp+RLuq5jHx48orlFv5SPATRqi+vTTT4fpVuQ2YHHL0r6jszRii7r965ubm++UNycktOnGsIIFTxjGN4XtKfmxCXSeAJ7QWLCJXUusF+r7+o7OnyH9V1qwuzEGGvhWFcxoYf4emYbxNBssbW/lIiCxDXOyJbAh+Wzu3LkUVEGwo5/85CfM2Q+VlDTn82eKm/033eBtP5kQFrXKjX5L5/gN5lir/Gj085//PFq6dGk4J8VZuCn05+tkcv7bBDpPAMHGFa56GpHykHCNt+i5XCSbxb20YMckuniUFdULwR43blywrqnv7K2cBLB6CYnwGUCwiTNzI4f4UpgB8dbft0l0Z6i06Hwo6Yeil7wy/STSezX74HuaXz2LJMZFixaF98ulHo6INzcAJLl5MwET6D4BBBsPGN9NBFsJxLmbJWXB7ub4y53yfcVBpigBrTc/zCtWrOjmmfy2vBOIrWyJccSOcCtZLIg4go3LXKJb1Z399+TW3qn+kgI+SEI/ECHmtRRgIfub5LJVq1YFyxrPjVbmyjset98EUidAZjjVzWKv6KWXXhop1+T7qTesiw2wYHcRGC9vizl+QFnifz5p0qSqimeEUqXdOJXfUkACCDjZ49zEMW+f2QRyiVeIn8lFfiFdjkUe6xzRxuWNWx2x5712gRfwg+EupUYAsWZZZBlZVWWGV+Qa/7pupj/Q9luem4IGFuxufIRkHYUBVkxylubxPaOsw6pikxXcl/wAs3srLwFizSSIIcC44bi7Z+cx+8kb4h3vvJfPD3/7c3QyKf9tAl0nwFQuckmYey3LuqKs8Hc2NDQ8zJni3/KunzWdd1iwe8Bd8ZA5KmjxdrnFv3D11VeHAhq4MbGQvJWbgG/cyj3+7n02CHCDrPBlmHONG5z4tXJHvqHnd2ejhV1rRe6C7l3rXu1fvXLlykeIU8ZrZSsuUvuL+gomYAImYAJnJIBgM82Sedcsj0zdcN1M58YFfnIHbWGfTKSLf2vh8164WiZPnhzijtR6xsr2ZgImYAImkB4BwlDEralsJhd4+I2WcbVVzx1Nr1U9u7IFu2f8yDo8rKShZYqTXEHc8sUXX+zhGf12EzABEzCBnhJAsHGHk+yp6bfVadOmEb9+jYpfNfX03Gm93y7xHpKXYO+XG/wNygKei9uFeX4sjE52MO4YbyZgAiZgAvUngGVNuFJeUAyrin6T/1PJZ2vr35LkrmgLu4csJcrEQ17UXOzvajL+DFZ/IVucObW4x12ytIeA/XYTMAET6CIBjCWEWtNuQ2UzcoxkWD0hY2pXF0+VqZfbwk5oOGRZf0PJDQuxsikuT1wbl4w3EzABEzCB+hPgN5jf4ksuuST8Hkuwcxu7julZUWISPTzqjm6p7t6WXnXVVZFiJZHiJCE7sYen9dtNwARMwAS6QQB3+OWXXx6mdCmW/XXFr/+pG6fJ1Fss2AkOh1wwz+ISR7BxwRDH9mYCJmACJlA/Ang2mcqFYI8dOzas96DHS+rXgtpdyTHshNi2lbj7hGLZe+SG+RyuGNzjlJg8ePCgq1YlxNmnMQETMIFTEaAEKStysewxC3wQx5bn85NKOvvwqd6Tp+dtYSc0WiSfIdr6YHyeDwmreOEeR7iZC+jNBEzABEygdgRINCMzHC/nzTffHFF9ktCk1r5+X+2uWt8zW7AT5I1oczolN6xSvCTc3VHD1slnCUL2qUzABEygAwIIdmxhx5Un9fu7qoOX5vYpu8RrMHSysMepgEqVVZjWrVsXqd54cIuzoIM3EzABEzCBZAnEcWtKj2pth2jWrFkh4Yzf4mSvlO7ZLNg14i9X+M9Vs/ZWuWOwuMPKTQi4NxMwARMwgWQJINhxVTNi14i2Es3OS/Yq6Z/NLvEajYHmZd8toZ7PHECKziu27Vh2jVj7tCZgAuUmQFY4YUhyhqg2qb+/or9zP+/65FG1YJ9MJKG/FU85rP1/kfhwyy23RFOnTg0rxTienRBgn8YETMAE2giQFc6c62uvvZY1r8kbYgnN/UUDZMGu4YjqQ3Q+d3tKgKiSOc68bBZT92YCJmACJtBzAiSasRN25LeWrHAqnGnBj9aenz17Z7Bg13BM5J75ulzhn5k9e3aFYiq4xZl2YNGuIXSf2gRMoBQEEGri1ljXWNUzZsxgOleT8oZ+R3OvHysiBCed1XhU5aZ5186dOxv0gXoNCWh79+4NRVSam5sjZ43XGL5PbwImUFgChBeZNkuhFAT7sssuo274LxS7/nJRO23BrsPIKlvxD7XPVubiudwVtra2RocOHYqcNV4H+L6ECZhAIQkg2HgtJ06cGIpUSbh3SqzvK2Rn2zplwa7D6EqkV2g7ev3114dypVjXsrqDcGvqVx1a4EuYgAmYQHEIxO5wLOtbb701rN+gxwOL08OOe2LB7phL4s8qCWK75gdecPTo0SDaTPBHrKkzjsXtzQRMwARM4MwEyAEigVdey1BNUqHGqvKFKnKPf/TM7873KyzYdRo/fchuVyLEhUo+W7B06dKosbExlCzdunVrEO06NcOXMQETMIFcE2BtBhZWImY9fvx49oqyxKfKJb441x3rROOdJd4JSEm8RFMONqko/Qvbtm27lg8aOx86piN4bnYShH0OEzCBIhPADc6Gdc1v55gxY8KKXDKEtijZbKn+v/ClJG1h1/kTrrvAFrLDSTrjuGPHjmj//v3R4cOHnTVe57Hw5UzABPJDAMHGHa7w4vF64azMtXv37hsaGhpa8tOT7rfUgt19dt1655VXXrlYIv0XSpD435yAxUFIQtOHLoh2t07qN5mACZhAgQkg1sy5po4FiWbMuVay2ZaxY8e+Rv+3tsBdP6FrdomfgKP2f+jDRVr4NzTZ/0ty4zRPnjw5YicJjQ+lNxMwARMwgRMJEDakkhmlnhFrWdSszfBD/WbOP/GVxf7Lgp3C+Gru4DK5xt+mzMb18RxC3DwW7BQGw5c0ARPIPIFYsDFupk+fziIfm5Ql/g+Zb3jCDbRLPGGgXTmd7hj7sQwccZn58+dHTU1NwT3OVC9XQesKSb/WBEygiAQwYnCFy8AJibr8Xo4bNw5r+5BqhpfKumZ8Ldgpfsrl1rl87dq1e+XaOUePK3v27Im2bNkSYtnM1/ZmAiZgAmUmgGVN+VGWKSYrnGlc+q1sUpb4uDJysWCnOOqyonUDWRm8cePG7+mD+Jp9+/aFcqUcKaZiKzvFwfGlTcAEUifQp0+fSNO2glizfCaiLbEe3PbbWU29gXVugAW7zsDbX05iHT5wDz744OtVtvSg7iSrqn5W2b59e4SFTa1xi3Z7Yn5sAiZQBgK4wrGuqVPBkpmscz1lyhSyxB+i//FvZxlYtO+jF2duTyOlxw8//HB/ifWNWjO7QZXPqhs2bKgwLxvBdq3xlAbFlzUBE0iNAHk9TOEiM/yqq66KXvva1+IK/xeJ9++k1qgMXNgWdgYGQRmP+7UYyGsl2p+cNWvW25iXzfzsp59+Otq1a5dFOwNj5CaYgAnUjwBrXDOFS7+HYTUuTXv9jOLY765fC7J5JQt2RsZFWZBNcn//g443aurCBFxBixcvDpnjWNveTMAETKAMBOJEMxVFiWbOnBmNHDky0rKZm8rqBm8/5hbs9jRSfqwP5EJt85VcMQGXkFb3CrFsluKkfKk3EzABEygqAeLWuMGZwjV69OgwfYspXKpRMU/P/aCo/e5KvyzYXaFVh9dOmjTpgyoI0F+Zka+TeIcPMJY2tce9DGcdBsCXMAETSIUAlrWKSoXCKEzfwiWuuPXDcoffn0qDMnhRC3bGBkV3mSvVpN9QDLuqmrlVCXWFpThZJKSlpSVY3BlrsptjAiZgAj0iwPQt5luzxrVEmiIpYY1ri/WJWC3YJ/LIzF+KYb9h9uzZ31SiRfXAgQOVvXv3RhRWYXfmeGaGyQ0xARPoIQEs63iutUo1V2+66aaKwoIVFZR6Qw9PXbi3u5Z4RodUU7wekYX9mFb3qhDLVtJFuAMlzuPNBEzABIpCAOuarPC2amaV4cOHf0fTt6ZKtB8pSh+T6oct7KRI1uA8srLvV7LF1xXLuRMLe8WKFRFH4tkuqlID4D6lCZhA3QjEhVFUEzwkmN1www2h9Kga8O8yUBbUrSE5upAFO8ODpfjNbs3D/sg111xzkz7UA5iXvXz58rB2Nm5x1xvP8OC5aSZgAqcl0Ldv3xCzljcxVDF79atfjaV9TJnizad9Y4n/0y7xjA++kjCeUDLGmzTN4QhTHFithqQMXEjeTMAETCCPBHCDU2uCOdb8rinJ7Khc4T/TFK5fl4A/msc+1aPNtrDrQbkH12grcv+fWiDkdaqn+23Nz+69ZMmS6NFHHw3ucSeg9QCu32oCJlB3AtSYQKyxrG+++eZIU1kjxav3qwzpq2hMWRf26MxAWLA7QynF1yjJTJ/fsKrX97QoyM9UV/cO5mPPnTs3GjBgQIhlE8/2ZgImYAJZJ0DcGlc4cWumcMmyxsKuKuFscNx2fvPixz6eSMCCfSKPTP4Vf4B1B/pqWdQ/OXjw4M0kaGjaQ6g5jsXt5TgzOXRulAmYQBsB3OBUMmPWi1YnpJpZVWWYmQVTUSLtAIM6MwEL9pkZZeYVEu6jsrZvl0W97LrrrhtDoQEs7bVr10YScYt2ZkbKDTEBE2hPQL9dwSOoRNqIGuF33XUX1nVFVvZ9Euzvtn+tH5+agJPOTs0mk/+DaKuQyp2U8NM87ZC0oWQN6u1G3MF6MwETMIGsESBujVgPHTo04veKXWLdrLDeiqy1Ncvt8XrYWR6dU7Tt05/+9EHFgpboA3+9hPocXOMknzFHG0vbmwmYgAlkhQBijTdQFcyiG2+8MZoxYwZrXG9SdbPbJeCLs9LOPLTDJlkeRumkNsrKPqSnvtbU1LRPq3j9h1xMfZRFHq1evTrM0eblzh4/CZr/NAETqDsBXOH9+/cPSWZUMiPJTMdjMjQeVHGUF+reoJxf0IKd4wHUh/47mzZter3uWr+thUGqWje7QlLHtm3bgnAr3p3j3rnpJmACeSaAWOMGx6Imbn3LLbdUKbUsy3q1nv9wnvuWVtst2GmRT+i6qrlL3d3eWjO7VRZ3OCtflLh8KdXQLNwJwfZpTMAEOkUAq5q51vptCstlTpkyJZo6dWpFwk0Ytrd+o2xNdIrkiS+yYJ/II3d/tX3wqy+88MJBrXQzkOldzMuWtR2sbETcJUxzN6xusAnkmgBJsYh1g6oyqigKCbJHNPf6W/q9OqaOsXvrBgELdjegZfEtuoM9S4VV5ij7cpi+GKO4w125cmW0Zs2aKLa8s9hut8kETKA4BEgwY7aKjIdo1qxZoZTytGnTmhS3fkBu8G8Wp6fp9MSCnQ73mlxVd7UzZWl/QvXG30/GOJZ2c3NzcInHLvKaXNgnNQETKD0BZqtodcGQYDZmzJhgWVMnXLk2/2axTubjYcFOhmNmzqKyf5/Vl+SAqqJ9QNO++vAlWrZsWaTkNCeiZWaU3BATKB4BCXOk9Q5CJjiVzFQj/JBc4K+Wt29j8XqbTo8s2Olwr9lVlYW5Vif/sJLQJsjKfmMcwyamjZWtbPJQEa1mDfCJTcAESkUgTjCjGIp+f4I7fPz48RR1eo+Mhp+XCkaNO2vBrjHgtE4v19T9CxcufKNEulVFVnozL5uEtM2bNwfh9jzttEbG1zWBYhGQNy/CBc4cawRbHr5Whed6S6w/V6yept8bC3b6Y1CTFrSt8MVSXyP0RdrE+tkkg1AJbceOHSG+bdGuCXqf1AQKT0Cu7oiVtziSBX733XdHWNVKfm2RYI/U8zvafoM8fSvBT4MFO0GYWTqVvjDhiyKX+CFZ2z/WYiF3kMGJW3zDhg1RY2NjiGl7yleWRs1tMYF8EGCJTKZtUb2MlQOVCR7WNVBt8LsRa3oR/wblo0f5aKUFOx/j1O1WqqrQLrnBH9BSdvfqC/T+LVu2jKagARnkcXy72yf3G03ABEpJgJt/xagjGQIslXlMM1OeVCz7U/ptWVBKIHXqtAW7TqDTvIzuhNfp+p9RvfF3q9pQKBeIOxzreteuXWHqF0lp3kzABEygIwK62Q8ucEofywgIlvXVV19N9TLi11UVSLlHrznQ0Xv9XHIELNjJscz8mXRHPHHJkiVHVHWojwS7qr3CWtrr1q0LFrdLmGZ+CN1AE0iFABY1bnAqmE2ePJkpW+xVxawrWnHr7yzW9RkWC3Z9OGfiKm1JIH0XLVrUb+bMmS1a6Svii7hv374g2PGULwt3JobLjTCBTBAguUyx6VAQZdSoUcGq1u8HyWYV7f0l1nbP1WmkLNh1Ap2Fy+iLFWds9pObfLvWp7142LBhERnkTPdavnx5pBh3qEOehfa6DSZgAukSQKxxg5MJTrlRdixs5cTsVqGUVVu3bu2rFlqw6zRMFuw6gc7SZeTKatYSnDfoi/geifUdShYZj1v8wIED0Z49e+wez9JguS0mkBIBxJqpoFQwQ7C1PGYQa7nFN+q53x0yZMj3U2paaS9rwS7p0OvLtlJdf5cS0b6t43gVOQgJaBRXYdqXFhKxpV3Sz4a7XW4CCDUljbGsKYpCMZRrrrkmWNeq6dCiuuC3yUXO74e3OhOwYNcZeNYupy/n/crwXKTEkQYVVakqA7Qyf/78aM6cORELiJBN7ph21kbN7TGB2hBQ2Cwkl0mUI93UB4v6jjvuiCZMmMASmaxxPUJivas2V/dZz0TAgn0mQgX/f8WyD0iQx6qbVc3NPnb++eeHBBMqoik+FWLaJKdheVu4C/5hcPdKSyAWaqxqXOCUGWWlLSxr5bpE8sCN1m/DJr2utbSQMtBxC3YGBiHtJuhLGBaUlyCfoy/rVt1ZV/VlHcQKX4888khYVxvRptiKRTvt0fL1TSB5ArjBuVmXty0iE3z27NnR9OnTwzQu5bncIMFen/xVfcauErBgd5VYgV8v4W5W9wZpbvYnlWzyHgok4AZj2pdW/zpeGQ1r25sJmED+CSDUzK/GsmYta6qXtVnXx3R8Uf//IYXLfpX/nhajB5VidMO9SJLA7t27b1HZ0lmyqD/61FNP9V29enW0ePHiaN68eaH+uP7fS3QmCdznMoGUCDClU1O0gvtbx6qs6gr1wWVlr1Vm+CTdxBMy0+H4lNCUWurLQsAWtj8HryAg19jjevLx5ubmx2699db5ZInqi11VTJvVv4LF7WS0V2DzEyaQOwKDBg0KFvWMGTMQ7YoE+2lN8/zw2Wef3YhY0yGLdXaG1YKdnbHIXEv0pV2g6V2XKDHtQypB+HbN3a5qyleFuZnM18ZVTmzbmwmYQD4ISHzD3GqEWt/rYF1TD/yqq66qysLmu/0+WddP5aM35WulBbt8Y96lHqtIwma94R2Ka/9cX/CH1q9fH7LI16xZE+EqJ5scq9vJaF3C6hebQCoEiFezWh+JZffdd19whWulraoW8ECs36zvu8U6lZHp3EUt2J3jVPpXKXv0a6qEdlhf+K/IdXYWiWdY1yzRyUpfFu7Sf0QMIMMEEGo8Y1jWcXKZhDoaO3ZspPLEPxo+fPjdGW6+m9ZGwEln/ih0ikCceKJs8Q8+99xzo/X32xYuXFjVQiIVrO4FCxaE0qYsIMKynd5MwATSJ4ALHLFmzQCEWq7vaNasWVXN/iC57GFZ1PtVzexBZYk/nX5r3YIzEbCFfSZC/v9AgMSTNtH+CE9okZC9SkR7L3fs7Ig2G8loLrISUPgfE0iVQByvxgVO1TLc4IpXV++6666Kpmx+QdO13pFqA33xLhOwYHcZWXnfgGjHvdcd+ccU036vRDwINkdW+lq1alVYXxsXOdY2z3szAROoPwHWB1BsOljW06ZNC4ItC7uiZNIvSsB/t/4t8hV7SsCC3VOCJX2/KqLt1sIh/TVXc6LcbQ8pw3SiEtOixx9/PMS0WTwES5sdq9ubCZhAbQlgUbNRDIV4NRY1K2xR/OjGG2+MFKfeopvsEbqJ7tP+5ru2rfLZkyRgwU6SZonO1faFZx3c+Tt27HhaRVbGa+/Dj0PsHse6ji1tx7VL9OFwV+tOALEmVo1Yy4IOXi8KoFC5jF0W9c42sdZLK04yqfsIJXNBJ50lw7G0Z5Eo8wNQ1bzst2le9lvkFj9LBVZmkIRGdbQVK1YEN/muXbsowGAXeWk/Ke54rQjwvSJOjQucfJLrr78+rK41fvx45lkv1cpb+5UNfm38Xa1VO3ze2hOwhV17xoW+AmLd9kPwRXX0izxet27dT3v37n0r7nBNBYtUcCUUWdH/Bfe4XeSF/ki4c3UkgEWt71oQbJbERLQnTZoUUblMFvZKTcG8Q9/RTTSJ72odm+ZL1YCABbsGUMt2yvY/BDxeuXLlb2nhgB/rbn+8po2EGJqei4hxb968+bibvGyc3F8TSJKAvmuIcqhYRihKQl1V3LpCKWHllWxWfYRb9BoKH3krCAG7xAsykFnqBlZ2Y2PjRXKDt/br12+dRPrspUuXRk8++WSYr83KX3v37g3C7SlgWRo5tyXLBBDo2KKOi6CwXvXkyZODWF933XUV5YxM0nzrQ3KRH1DcujHL/XHbuk7AFnbXmfkdZyCAla2XbOdlEuevScDfcejQoVB/nB8d1tlWnDvsxLadkHYGoP5vExABRHrAgAEhqUzFTkIxFMR6woQJTN9iutb/1OOl+o55WkZBPzG2sAs6sFnoFpZ2m3gTx/6lluUco4zyC+Ue76cqadHcuXODxU150ziuTZzbmwmYwMsEuMll11TKUABF4aZIq2pRsYyVtloVu94qi/ovFX568OV3+VERCdjCLuKoZqRPsVjTHLnnZiPg+tH5iaaf3IYrHKHWEp6RRDxY39Qk19SwYHFbuDMyiG5GagQQaaxqMsCZqqXvUJiipdoHVQqgEKvWc38twf5Aao30hetKwBZ2XXH7YnKR36X49gQJ9j+y2pdi22HJzvnz5wdXOS5yRDy2uE3MBMpIALEm+xuhplqZRJqFOqo6VvhblQa/qUIoT2mO9T+092SVkVWZ+mwLu0yjnYG+KiHmh2rGD5V09rSmoHxc80RvR7ixqInPKUktWNxMB8PaRrhtbWdg4NyEuhAgqYwCKP379w+WNVY107RwgU+cOJEFOx5RBvi/6LszV6K+x2Jdl2HJzEUs2JkZinI1RIsPzFGRlQf0g7QGQeYHiUQaCq2o5GkQbZLTSFZDuC3a5fp8lLG3WNUINSKthTnCTpUyCqCQWEapUYWUFuum90cxH73HSR8xjBIc7RIvwSDnoYtylT+oGPb9L7744lkkpC1btizUJSebnHW3EW02C3ceRtNt7AoBhJqNeDV1C1iog4QypmzJDX6QZDPdzF4rMV/clfP6tcUjYMEu3pjmrkcSYf1mvWQpyMLeJMt7BNY1cW0WEVmzZk2olkaSGgJOcpo3E8g7AVzflBLVMrXBmlZ4KAi2XN/BotbfeyXUl8qibsp7X93+ZAjYJZ4MR5+lBwRiseYUWqDgtYpjv1s/Yvv1+DrN0Z7+y1/+MlqyZEm0YcMG5nVbsHvA2m/NDgGSyignSrUyrVMd3XbbbaG0qL4P/yj39wUKG33MYp2d8cpCSyzYWRgFt+E4AVkcz+mPt/KELO+hyhqfr+pNw7BEiHEzHYxMcs3pDpXSsLZtcR/H5wcZJSARDslkWNUkVyLUuLqxqtkl2FXV/6b4yecl0n+Y0W64WSkTsGCnPAC+/KkJ6Eduq0R74hVXXLGL2B6uQqa2yE1eoeiKFhkJFjcC7tj2qTn6f9InQPY3An3++eezLnWIT7NQR0NDQ5hbrSSziizuGzQrYmH6rXULskrAgp3VkXG7AgGJ9u5Fixb1Zz6q3IQzVTDiCeLbWNWINDvztrG84ylgFm9/eLJAAKuaDbHGqkag46zve+65J3iM5DV6WKJ9v3I3+uv/WrLQbrchuwScdJbdsXHLOiCgoitvlIv8ISWk9SIZjSlgrAKGi5zHWpM7CDgi7s0E0iAQu78RaaxqpmTJzR1WrSNerXnU0ZQpU1pU/GSL/v/PJeRfS6Odvmb+CFiw8zdmpW+xBPsWWdnvlUgP0JSvu1SbPFRLmzNnTqQM85BRLmE/7ia3xV36j0zdACDWWNTkXMQJZffee28Qawn1aonzCiVU9tNc6+/JPf73NEyfT73N86nrNkg5vpBd4jkevDI2ve3H7XH1nZ1pXt/Uj+BvUmgClzjrbWNp82MpYQ81yjkyjxu3uTcTSJoAAs0cao5Y0rKcw/QsPpMUO7n22mvDUZ/Jf5eF/aGTr2+xPpmI/z4VAQv2qcj4+UwSOPnHTSVM36Ifwmdk0XxMVku/PXv2ROvXrw9zt7GyKcKCiOMyp9wpmy3uTA5trhqlz+Hx9lJOlxwLhBpx1nREVtGqKkGyQtwa0VZC2Tsl6pTl9WYC3Sbw8qeu26fwG00gXQIS4N7KHL9Y9cn7KJ69b9y4cXsQbbLIf/WrX0WrVq0KrnKyySl1Gpc7ZR1urHJvJtBZAsydZmoWR9zelBKNs7/x6tx+++1M06oqbl3R/92pz9dSPX9MyWWNEnl/2DoL2q/rkIAFu0MsfjLvBLT+9s+VeHaLhLwqC7uClY2IU6s8rpxGERbE25sJdJYA4kytb8RZMxZCbBqLmkQyFTrh2Cpru7dE/U16/H87e16/zgQ6Q8Au8c5Q8mtyR0A/qrfSaFnVf6y44kf0d39+UNniuuTEtGWVH19cJJ4WFl7kf0xABLCk2YlPc2QeNeKMaKs+QNhxeatOwCqJ+Xq5wF9lcCZQKwIW7FqR9XlTJ9CWoPa3il9vU/zwdlk+90u8+yi+GKxtMspxm1PylGlgiDcJao5xpz50mWgAsWksaUSaPY5Ha950SCpjjWq5ujfqNYuUTHYPjW77zHkFrUyMYPEaYZd48cbUPWpHoP0PqKztN0uQz2tpafkvSk67hwIsL7zwQvTUU09FJKjhMic5Lc4mt3C3A1nChyzKwc1dvCs3ItJe1d8VzbH+V8Won1ei2XxZ1k+UEI+7nAIBW9gpQPcl60egfVa5LKSHuLKE+Euyrn+hH9trqZiGdc0UHKwp4tr8zZKe8f/FVdXq12pfqd4EcHlT6GTgwIEh25uEMqZojRkzJsKiJpEModaRet/X6fOyWJ+tffVup69XbgK2sMs9/qXtvdzgAxV7PCgru0r2OCLNY1nYldWrV4fkNCWuHT/GVndpgRW847i/L7300jAFS9OxQnUy/mbBGerYY23rNZ+SSP+VaoFvLzgOdy+jBGxhZ3Rg3KzaEkCs29zl4aZVLvFXyQL/CfO4+fFmyhdHXORyoYdscp7DTR4fa9tCn72WBCS8x4udkEyGIJNIhkgTmyYDXJZ1s57fofj0ZbVsi89tAp0lYAu7s6T8usIT2LFjx28pg/wPNQXskFzmZ2nK12hZ3ecR31b505CcRmIaiWpOTsvvxwGxZnoW07GwoMnyxqpuy/ZuUmhksYT7bLnDb9Zrd+e3p2550QjYwi7aiLo/3SLQZm3/m97MjiXdT4J9v+Zxf0IiPZTEtOeffx63eSh3ioscS7vttS7AEkhk9x9Emjh1bFmT9Y1IE6O+5pprolmzZpEJvk9C/lblNvxHdnvilpWZgAW7zKPvvh8noB/yE6bi6O/D+s+vyLpeph/yL6ty1QRc5Ag21hlxb2LcuNBJUJOwW7SP08zWA1zezMFn3JiaxXrUsqxDNTKsakRbVvWXNL5fklg/ma3WuzUm8DIBu8RfZuFHJtAhAQnzMAn1OfrPS/TD/zOqppFNPm/evGjp0qXBVc5jYt1xqVNi3d7SI4AlzcaRjG/i0kzPIjZ9ww03hBkBEvDHlGz4pzoeHD9+/Ea91muypjdkvnInCNjC7gQkv6TcBJSM1CgC7Cu0Otgw/fA3YrEh2qy/zbQvXKxY2pQ6xV3OHot3uenVv/eINFY1O2KNZc20PWLWcbxaU7h+qmSy+/TaI/Vvoa9oAt0jYAu7e9z8rpITUILaX0u836pjH+0XzJ8/P6wKFq8OhpCToGZLu74fFOLUcmuH1bNGjhwZzZw5M4j1lVdeiRu8UVO0jqr4yZs039rFTuo7NL5aAgRsYScA0acoHwH9+P+Jev0nEuQr5SL/lizsK1kVLLawse7IJI+tbQt37T8jrEnNzhQtsr9xgc+ePTtY1Xq8UBb1NbFFrfHQwxPzFmrfQl/BBHpGwILdM35+d8kJ6Ed/iazp18vVupikNIRZwhBpmc9o2bJlUVNTUyRL/Pha3CXHVZPuawxClTK4E6ogiYzHxK0Vm+a5rcoveL1ed9z9bbGuyVD4pDUmYJd4jQH79OUgoHj2ncoYryqJqY/E4Muatz2MtbhZWOTZZ58NR6aBeUWwZD4PiDQbFjUlRSl6ElvT1113XVViXVHm/k2aTz1IpUQ3yQNCKVGvR50Mfp8lJQK2sFMC78sWi4DqTv8o7pGs7GslzOsk4L1Jeopj2UwFw+K2aMekundErBFq4tVM00KssaiJU8vTUdWylxUlmo3VcXX3ruB3mUA2CViwszkublWOCUhQNkm0L5ZVt0sJaUclHH2eeeaZCIt70aJFIaucNblxn3vrOgGEWlZzWKgDq5qiJ6ykpUIoRzWfWlreZ4qywi3WXUfrd2ScgAU74wPk5uWTgESbkpa4ZW9jChEJaBRawdpmx9Jm3jZJaRbuzo0xVjV5AqyoRfETptJNnDgxmjp1KglmTZq29ZtifdzT0bmz+lUmkB8CFuz8jJVbmkMCsgZXyj37XSVCzbzxxhuHsITnihUrIjLKmceNkDOP29uZCRBeIPub1bOmTZsWFurgOGzYsGck4t9HrHXzo3slZ3+fmaZfkUcCFuw8jprbnBsCim1vUGPvU4nTBxVrfRsZzNQlx63L1C9KmsZFVmxpdzyscREUhDquWHb33XdjXR/TnOs/k2X9sER6ncW6Y35+tjgELNjFGUv3JMMEFFN9l5p3nxLOhkh0qrKsK7h4JTRBsHGPO679ygFErJmqhXXNzc6kSZPCoh0NDQ3EsX9frvHPxO+yZR2T8LGoBDytq6gj635ljsCaNWsGyA3eKpE5jGX93HPPRY899lhYBYwMcmLbWNveXibAlC2yv6ladvPNN0e33norLvCfKBv8DlnUvSTSnqr1Mi4/KjgBW9gFH2B3LzsElBh1qK01ldWrV98kS/sXxLJZ8QtLm3g2u7cohAtIMCOxDGuapTDZdbOzVOKNWAuZxdqflXIRsGCXa7zd24wQUPnMg4ppz9XKUUM0l/jSJ554orJ79+4TVvzKSFNTaUb//v1DDXBEGstaVvVcFUE5JGbvoUESa8+JS2VkfNE0CViw06Tva5eWgBKonlXnZ8pSnCTL+xeKX1+gxLTgEmfKV1ld48T1iVtTDIUpWzNmzKi+6lWvqkyZMuV1EulNfGCcXFbar03pO27BLv1HwADSJCARWqS1tvuzTjNCrb+jF198MSzVmWa70rg2mfMs3IEbnBrgrLSlqXAVJZd9Ue3ZFbfJ1nVMwseyEbBgl23E3d/MEVAG9ChZkLuUYFWVeFeWL19eWsGmghlFUVQdLrr++usj3ch8Q+7wBzI3aG6QCaRAoFcK1/QlTcAE2hFQMZXdEqp7JFQV5hlPnjw5LA3Jus5Y3GXYsK7JCCcb/Nprr42mT59OnfD5mg735jL03300gc4QsIXdGUp+jQnUmIAKrDy2cePGv5dl+T7VHw8lOLXyV1hfWzHbGl893dMj1sSuuUHRdK1QG1yW9WoJ9irdsBxNt3W+uglkh4AFOztj4ZaUnIBKlP6TsqLft3///qqW4qywpjZxbUqXFlm0STIjdk2i2ejRo6MJEyZEChGM5ePgBLOSfync/RMI2CV+Ag7/YQLpEZBrfI8Srr4rd3CFDGm5gyPVIY+Y4lRk1ziWNVXMrrnmGuZaV+lzvDnBLCbhowlEkQXbnwITyAgBuYD3SrDfItGaqfWdF2FtkoBFWU7cxkXcYuua5TGJW6uqWUWx7P9RxL66TybQUwLF/BXoKRW/3wRSIiDB3iOBnqtYbpOKqkR33HFHSEDDyi7aRtya1cu4Mbn66qurmsLF4h5L9fxXi9ZX98cEkiDgGHYSFH0OE0iYgIQ7uIn79u0bLVmyJCShUba0SLHs2LpmFS5VMavopuT3tPLW5xJG6dOZQGEIWLALM5TuSJEIaJrXYlnZ0yRiA+QeD+tAU/0M0VZCWiG6yjQuCTVzrSMJNdO4+haiY+6ECdSIgAW7RmB9WhPoLoG2zOgHtILXfLnHX6f47qu00lekpTmjrVu3FkKwSaKjSApzzmfPnk2t8I3KFJ/fXWZ+nwmUgYBj2GUYZfcxVwTIjEa0ZXH+s0T6rVifFBS56KKLgms8V53poLG4wgcOHBgy4CXUwcpWpvg8ZYc/0cHL/ZQJmEAbAVvY/iiYQAYJxNOZDh06tIsM6paWllBcZNu2bRGreuV1I9udrHemrJFspulcrVr8ZJzWCS+Gnz+vA+N254KABTsXw+RGlpWA1oTuJUs7atCa0BRSwTJF9HCP53Uj452kOjwGOlbVp7V57YvbbQL1JGCXeD1p+1om0EUCsq4PaOrTByTYh2SJtuIaxzolYSuPG+5wlWENiWYkm1HdzJsJmEDnCNjC7hwnv8oEUiEg1ziu4o8ppv3ZhQsX/kAu5Bl79+4NNcYpWZo3S5u510zj0rzrsISmHvs3KJVPli+aRwK2sPM4am5z6QhIuHdJtJfgRsYqzVshFbU/xOBx6cd1w+mHXP7/VLrBdIdNoJsEfHfbTXB+mwnUm4DmY/fZt29fxHxs1sxmVa+8WNi4wqkZjjuceeWqaNYq6/oBZYl/pd4cfT0TyCsBW9h5HTm3u3QEyK4mAQ2XMjFsRDAvG4lyeAWwrvESNDY2/raKpvxrnA2fl364nSaQJgFb2GnS97VNoAsElGx2DJEmDsz8ZYleWH6TKV9yl3fhTPV/aVzVbNq0aaHkqqztXfVvha9oAvkmYMHO9/i59SUhQCEVbW/R8X0S7M2K//YePHhwdOTIkbDjJs/yhnVNhjuVzVRyNVjZWW6v22YCWSRgl3gWR8VtMoGTCMSuYx23yVrtxSpXuMeJC+dh6U0WMeEGg6ptxLFVltTGwklj7D9N4EwE/KU5EyH/vwlkjIAyq7dpetfQ5ubmkIBG5TMs7SxvxN9Z6GPq1KkUf3lGFvezWW6v22YCWSRgwc7iqLhNJnAaAhLsP1MM+4taHCTatGlTZpPP5A0IbVN7Q7IZHgFZ10tV3ex2/d/+03TR/2UCJtABAbvEO4Dip0wgywQkeF+aOHFiNH369FCPm/gw4pi1jTaRFT527Niwy42/U22fbbHO2ki5PXkhYAs7LyPldppAOwKUJyUznCleFCMhczxrbnFi67SNuDW7BPuwxHpnu274oQmYQBcI2MLuAiy/1ASyQkDu8P5K3DpKIhdWLGtLZ21eNhY2gs3NBVXN1M7suQGyMqBuhwl0goAFuxOQ/BITyBoBucRblS3+R4ghc7IpRkImNiKZlQ0Lm5sJlgfVwiXBG5CVtrkdJpBHAhbsPI6a21x6AhLmVrnElyKITJNimheCnaWNmwfi67jtuaFQpvj5WWqf22ICeSNgwc7biLm9JtBGQBZ2bzKvSepqaGgI5UqzNCcbweaGQu2r0ka57Ed78EzABLpPwEln3Wfnd5pAqgRINCN2jUu8qamJla8y4xLnxoGiLm0egIpi7X8gK3trqsB8cRPIOQFb2DkfQDe/vAQkiKtkZS+k5Ofw4cMzI9gkvyHUZIYTY1c7D0msP13ekXLPTSAZAhbsZDj6LCZQdwIS62WyXL91xRVXBLc4Fm0WMsVpA6562jVhwoTo61//+ti6w/EFTaCABCzYBRxUd6k8BJTI9X2J9k4EkilUuMXTjmNzfUqRMpWLZLjrrrvOyWbl+Ui6pzUkYMGuIVyf2gRqTUCu5jkSxplyQe9GsMkUz4JgY+3jDldlsxBnrzUHn98EykDAgl2GUXYfC01ArvHV2g+11eoO1m2aHT7ZwiYxzpsJmEDPCViwe87QZzCB1AnIsq4w3xmrFnd0mgVUuHbsEsfC5rE3EzCBnhOwYPecoc9gAqkTIHZNcRLixmkLNklntIEbiHPPPZfyqYdTB+QGmEABCHgedgEG0V0wAQQbgdy7d2+IGadhYXNNxFru+dAGKrCp0tkH1K6VHiETMIGeE7Bg95yhz2ACqROQYAeX+L59+1JL8kKwuXFAsGVZY2X/Stb1Q3q+mjogN8AECkDAgl2AQXQXTEBCeYT49YEDB0LRknpb2FyPymskvtEOjrK2n1O2+EaPjgmYQDIEHMNOhqPPYgKpEpDr+U+xaknywsKtt2DTebLDqXDG3Ou2Ii69U4Xii5tAwQhYsAs2oO5OOQnI9fxVifV+LFvEMo0NC5ubBtrAVK40bhrS6LevaQL1ImCXeL1I+zomUGMCKgV6iURyD6KJWLJrCc4aX/Wl03MtirZQ03zUqFHH1+euy8V9ERMoCQFb2CUZaHezFASaFMt+CuuW5C8ytuu54RLHusctTtU1/V3fBtSzs76WCaRAwIKdAnRf0gRqRUBJZ39DHJs52Qgnlm89Nq7DDUIcw1ZM/bBuGpbX49q+hgmUhYAFuywj7X4WnoBEs6ppXWuwbnGLY+1i9dZjw/XOtSTUIelN4r1D07qeqse1fQ0TKAuB+nyby0LT/TSBlAlINFuJJZMpzrFeGxY2gs1NAi55XXuXrv1sva7v65hAGQhYsMswyu5jqQgg1PFSm/XsOIJNSVJlrHM8KBE/Us/r+1omUHQCFuyij7D7VyoCJJvhlkY42xK/6hbHJoYdW9g6DiwVeHfWBOpAwNO66gDZlzCBehHAumYe9OjRo6OWlpaQCHb06NGaXx6XOPOwqR+u2uFH5Bb/dM0v6guYQMkI2MIu2YC7u8UmgIWNZd3mlj4+H7sevcYlzrVl3R8ZMWLEF+pxTV/DBMpEwIJdptF2X0tBACs7TjrD8q3XxrW4YdC1D9Trmr6OCZSJgAW7TKPtvhaeAIKJa7rego11zd4WP3914UG7gyaQAgELdgrQfUkTqCWB2MJGvOuxcYOAK5yEM64tl7inc9UDvK9ROgJOOivdkLvDRSaASFM0hSUuWTWr1uVJsaqZd33JJZdEI0eODKJdZL7umwmkScAWdpr0fW0TSJiArN0jiDbTunCLE1euZRwbwcaqpiQpNwpY295MwARqQ8CCXRuuPqsJpEJAMeSdEuhHY/c0jajHil3xTUF8TKXzvqgJFJyABbvgA+zulYuArNydra2t30CwSQCrh4DGVjaWdj2uV64RdW9N4GUCFuyXWfiRCRSCgNzhzQh27BKvdadilzhu8VrHzGvdF5/fBLJMwIKd5dFx20ygGwSGDx/+iLK2P8kymwgoFnCtNs5P0hkJZ046qxVln9cEXiJQu2+yCZuACaRGQO7wgSSfIda1dlMj2ljz7LawUxtyX7gEBCzYJRhkd7GcBBBr4tjstRLS9glt7R+Xk7h7bQK1JWDBri1fn90EUiPAFKs4+axWgs1NAecmjh1b9Kl12Bc2gYITsGAXfIDdvXISQKwRUBLBiDEjqLXasOCJl1OopZbXqVX7fV4TyAsBVznIy0i5nSbQBQJYvVjXSkAL87APHDgQ7d+/vwtn6NxLuQ43BKNGjYoaGhqIY/9O597pV5mACXSVgC3srhLz600gYwSampouVPz4FSY0yWa4rGudeBa7xRFvrcHtOuIZ+3y4OcUhYMEuzli6JyUk0NjY+Gv79u37ro4/OLn7JIEdO3YsWNi1SgjjvPF12q7V/+R2+G8TMIFkCNglngxHn8UEUiGgWPVyWdIrdPFftW8AVi/xZOp7S9BrFlvGiuc6ipVXBw8eXJFob2rfDj82ARNIjoAFOzmWPpMJ1J3ARRddtEQW7gO68NH2F5d7usICIMSwDx8+HG3YsKH9fyf2mBsDltbUdSoS7I/ouD2xk/tEJmACJxCwYJ+Aw3+YQP4IyMo9fHKrJdgrlb19eMiQIf2am5tDUZOTX5PE31jYZKLLyv68xPpj+vuEG4ckruFzmIAJvETAMWx/EkyggAQknHy3K3GMudZdlKXNzb8NgFqD9vlLTcCCXerhd+eLSkCCfURifazW/cPCZtO1/FtSa9g+f+kJ+EtW+o+AARSRgJLNvquEtM0UNFFsOZQnTbqfFEwhTk4MW9te7a9wzSd9TZ/PBMpMwIJd5tF33wtLQJbvSgnprgsvvPC4YMfWcFKdRqiJX3PUuVt03takzu3zmIAJvJKABfuVTPyMCRSCgBLPqlQ7Q1ApVZrkhvhjYcfrbh89enS7nqu5Cz7JPvhcJpA3AhbsvI2Y22sCnSRA5TGWvERYeZzkRjIbYo27naNuCKpJnt/nMgETeCWBZG+7X3l+P2MCJpASAUSaoiZY18yXTmqLXeucmwVGOCZ5/qTa6fOYQNEIJPctLhoZ98cEck4Awca6TlpQsa4RbXaE2mKd8w+Km58bAhbs3AyVG2oCXSMgIT07toBr4RKPLW0E3JsJmEDtCViwa8/YVzCBVAgotvw2lr7U1KsqVnaSG2JN7JoscY5Jnz/JtvpcJlAUAhbsooyk+2ECJxHQXOynxo4dW7n44osrJJ/FbuyTXtblP+PzqI55NHr06EjnD7HsLp/IbzABE+gSAQt2l3D5xSaQPwKIdS1c4u2ndSV9/vxRdotNoPYELNi1Z+wrmECqBEgKwypOOtaMSLNzbieepTrEvnhJCFiwSzLQ7qYJ1EK0k74J8CiZgAmcmoAF+9Rs/D8mUAgCCDVzsUkMS7LimcW6EB8PdyJHBCzYORosN9UEukMAwWZ6FyVKEW3+TmLjPLEr/NgxVyVNgqnPYQKnI+BKZ6ej4/8zgQIQQFQRapLEWluTW5+Dc3IjwB4LdwFwuQsmkFkCtrAzOzRumAkkQwBhPe+886IhQ4ZEmuqVSMY41nX7aV2y3l09JZnh8llM4JQELNinROP/MIFiECCTm+ImiDVu8Z5aw4g1O0VTWG9b5z4kK7uxGLTcCxPILgELdnbHxi0zgcQIxCKbxAnjZDPOifjr2KTndiZxbp/DBEzg1AQs2Kdm4/8xgcIQaC+yNeiUNFvq7c0ETKCmBCzYNcXrk5uACZiACZhAMgQs2Mlw9FlMINMEamwAH5MF73ldmf4EuHFFIGDBLsIoug8mcBoCiDWJZxRN6WnCWfvLcN628+1VJvqO9v/nxyZgAskTsGAnz9RnNIFMEUBUyQ4nq5u52ElZ23H1NJ1vmzLF12eq026MCRSQgAW7gIPqLplAewJY16zYxdrYCHdSgk3BFKaL6bhN53SWeHvofmwCNSBgwa4BVJ/SBLJEAAsbyxqxTqIqGYLPzvkGDRrE3i9L/XVbTKCoBFyatKgj636ZQBsBLGyElWpnSVrYnE/Vzpargtp9hm0CJlB7Arawa8/YVzCBVAlgDSPa7EkmnbW52uek2jlf3ARKRMCCXaLBdlfLTQDhTnLjfLoBcEnSJKH6XCZwGgIW7NPA8X+ZgAmckYB/Q86IyC8wgWQI+MuWDEefxQRMwARMwARqSsCCXVO8PrkJmIAJmIAJJEPAgp0MR5/FBEzABEzABGpKwIJdU7w+uQmYgAmYgAkkQ8CCnQxHn8UEMkugLZs7TOliWhd/J50xntnOu2EmUCACFuwCDaa7YgIdEZA4b9HiHKHaGfW/kxDr+Bxapat3R9f0cyZgAskTsGAnz9RnNIFMEZC4/pRa4tT97uniHwg14s8ua72q4ikbMtVZN8YECkzAgl3gwXXXTAACW7ZsefcFF1wQDR06NDr33HNDxbPukkGozz///GjYsGHxwh/Lu3suv88ETKBrBCzYXePlV5tA7ghIrCvxal09tbBxqWOps1Qnj2Vh/yx3QNxgE8gpAQt2TgfOzTaBrhCg7jcC29Na4rwf0Ue0OZ+2lq60w681ARPoPgELdvfZ+Z0mUDoCCDZucZbp5CZAMe0jpYPgDptASgQs2CmB92VNII8ESDpDtNl57M0ETKB+BCzY9WPtK5lAYQgg1prSVZj+uCMmkAcCFuw8jJLbaAImYAImUHoCFuzSfwQMwARMwARMIA8ELNh5GCW30QRMwARMoPQELNil/wgYgAmYgAmYQB4IWLDzMEpuowmYgAmYQOkJWLBL/xEwABMwARMwgTwQsGDnYZTcRhMwARMwgdITsGCX/iNgACZgAiZgAnkgYMHOwyi5jSZgAiZgAqUnYMEu/UfAAEzABEzABPJAwIKdh1FyG03ABEzABEpPwIJd+o+AAZiACZiACeSBgAU7D6PkNpqACZiACZSegAW79B8BAzABEzABE8gDAQt2HkbJbTQBEzABEyg9AQt26T8CBmACJmACJpAHAhbsPIyS22gCJmACJlB6Ahbs0n8EDMAEOk+gWq1GR48ejY4cORLx2JsJmED9CFiw68faVzKB3BM4duxYEOzDhw9HPPZmAiZQPwIW7Pqx9pVMIBUCsoilrcei1tbWHoss5zh06FB04MCBINyysi9NpVO+qAmUkIAFu4SD7i6Xi0C/fv2GILLNzc1RS0tLj0Qbd/i+ffuiXbt2hXOJ5Nhy0XRvTSA9Ahbs9Nj7yiZQEwKyeivtT3zOOec0INRYxbiyexJ7Jna9d+/eaMeOHZyrIgG/uf21/NgETKB2BPrU7tQ+swmYQBoEKpVKdfv27X988ODBj+7evbvfs88+Gy1dujTsmzdvjl3Z3W4abnG2ZcuWRb/4xS8+NG/evA+dffbZ0cCBA3G7XzB69Ojd3T6532gCJnBKAhbsU6Lxf5hAPgls3Ljxz+Sy/tiWLVui5cuXR9u2bQvHNWvWRI2NjSHDu6c9w0pfuHBhsNaHDRsWSaSjwYMHV8eOHbtr//79I84666xtunF4Sdl7ejG/3wRMIBA4wXVmJiZgAvkgsHPnzutlQY+Um7uVmDKubmLU/fv3v1Ri+akNGzZEmzZtihYsWBDc12vXro2wrvWe8LqeZnhLjKMJEyZEw4cPjxDs8ePHRxdeeCHPVS+++OLKnj173qu4+WpZ3r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icon-zap"></i></div><h3>Blazing Fast</h3><p>Pinot is designed to answer OLAP queries with low latency on immutable data and mutable data(Upsert Support)</p></div><div class="col col--4 feature_Fxce"><div class="featureIcon_NFNa"><i class="feather icon-unlock"></i></div><h3>Pluggable indexing</h3><p>Pluggable indexing technologies - Sorted Index, Bitmap Index, Inverted Index, StarTree Index, Bloom Filter, Range Index, Text Search Index(Lucence/FST), Json Index, Geospatial Index</p></div><div class="col col--4 feature_Fxce"><div class="featureIcon_NFNa"><i class="feather icon-rss"></i></div><h3>Near Real time ingestion</h3><p>Near Realtime ingestion with <a href="https://kafka.apache.org/" target="_blank" rel="noopener noreferrer">Apache Kafka</a>, <a href="https://pulsar.apache.org/" target="_blank" rel="noopener noreferrer">Apache Pulsar</a>, <a href="https://aws.amazon.com/kinesis/" target="_blank" rel="noopener noreferrer">Kinesis</a>supports <a href="https://www.json.org/" target="_blank" rel="noopener noreferrer">JSON</a>, <a href="https://avro.apache.org/" target="_blank" rel="noopener noreferrer">Avro</a>, <a href="https://developers.google.com/protocol-buffers/" target="_blank" rel="noopener noreferrer">ProtoBuf</a>, <a href="https://thrift.apache.org/" target="_blank" rel="noopener noreferrer">Thrift</a> formats</p></div></div><div class="row"><div class="col col--4 feature_Fxce"><div class="featureIcon_NFNa"><i class="feather icon-code"></i></div><h3>Horizontally scalable</h3><p>Horizontally scalable and fault tolerant</p></div><div class="col col--4 feature_Fxce"><div class="featureIcon_NFNa"><i class="feather icon-shuffle"></i></div><h3>Joins using Trino or PrestoDB</h3><p>Joins are currently not supported, but this problem can be overcome by using <a href="https://trino.io/" target="_blank" rel="noopener noreferrer">Trino</a> or <a href="https://prestodb.io/" target="_blank" rel="noopener noreferrer">PrestoDB</a> for querying</p></div><div class="col col--4 feature_Fxce"><div class="featureIcon_NFNa"><i class="feather icon-search"></i></div><h3>SQL-like Query Interface (PQL)</h3><p>SQL like language that supports selection, aggregation, filtering, group by, order by, distinct queries on data</p></div></div><div class="row"><div class="col col--4 feature_Fxce"><div class="featureIcon_NFNa"><i class="feather icon-list"></i></div><h3>Hybrid tables</h3><p>Consist of of <a href="http://pinot.apache.org/img/dynamic-table.png" target="_blank" rel="noopener noreferrer">both offline and realtime table</a>. Use realtime table only to cover segments for which offline data may not be available yet</p></div><div class="col col--4 feature_Fxce"><div class="featureIcon_NFNa"><i class="feather icon-bar-chart"></i></div><h3>Anomaly Detection</h3><p>Run ML Algorithms to detect Anomalies on the data stored in Pinot. Use <a href="https://docs.pinot.apache.org/integrations/thirdeye" target="_blank" rel="noopener noreferrer">ThirdEye</a> with Pinot for Anomaly Detection and Root Cause Analysis</p></div><div class="col col--4 feature_Fxce"><div class="featureIcon_NFNa"><i class="feather icon-bell"></i></div><h3>Smart Alerts in ThirdEye</h3><p>Detect the right anomalies by customizing anomaly detect flow and notification flow</p></div></div></div></section><section class="topologies"><div class="container"><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="ingest-query"></a>Ingest and Query Options<a class="hash-link" href="#ingest-query" title="Direct link to heading">#</a></h2><div class="sub-title">Ingest with Kafka, Spark, HDFS or Cloud Storages</div><div class="sub-title">Query using PQL(Pinot Query Language ), SQL or Trino/Presto(supports Joins)</div><svg width="800" height="400" viewBox="0 0 800 400" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" class="svg 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BAAAEEAiSgw1w+6OXD2bugu2X+x8sxEhsCuQqQwMpVjnoIIIAAAggggAACCCCAQIUJGKNP9emQJXTzRYm9x6fxZx22JLGaEo3Rs7OuQEEEfCJAAssnB4owEUAAAQQQQAABBBBAAIFyC2jfLuBuOupbOr+tjbpADPeU27HI/Wuj1U/MHBUucj80j0BJBUhglZSbzhBAAAEEEEAAAQQQQAAB/wpIYsSfdyA06jFHPbY0+UdZhv6z/j0CWUd+Wry29vKsS1MQAR8IsIi7Dw4SISKAAAIIIIAAAggggAAC5RboWlRzRHqX9ZLEESp3LO77N1fVtXZ+1ann3Ekx0RT9ozx8q/t2fFVjRyYVPnliW/tWX0VNsGUX2HzZhGHhVHqSfLNMspWKamXGyLfNeK30KPn+GSYBDt8bpHlZ9vUYbW+Xa3S3a222yusbwxmzofaWrkShB8KUwkKL0h4CCCCAAAIIIIAAAgggEECB9MuhyZL+8WHySqLWe2dgOYdFZnGYrrD94XTa+pc8PTKAh2r/kI4KR9LOgu6X7t/BVwT6CziXmnYcV3OapUPTJAF1liSgpqqe9AnytfeKvb2znvb+b5z0b7+td598g/WW2Pc1Y2kVb4o6ye51ctnuWml3VW1X5yq9QqX7VXf1dG8UrqpQGAEEEEAAAQQQQAABBBBAoNIEZNbSf8jp6/V+HLeVMSdFb+3c0Df2jubaL8hiUd/quy+Aj40x1uz6pfHVARwbQ8pBYH3D5KqRkZfOUsqeK+ncs6WJKfKvFInc5yX/daey9K2xlsT/STLq0GzYEOMhgTUEEC8jgAACCCCAAAIIIIAAAggoJQmsH8kZ5yd9aPFy7MTkKL1YydVQBzbnRH5EZMdjclJ80oG9AXxk1D9iW5Jn5jv7JYAyFTEk+Z7VycboaUbpuUobSVqp2fKvFAmrQX3le+5JiefHOmVujLUldw9asN8LJLD6gfAUAQQQQAABBBBAAAEEEEDgUIF4Y/QBuU5ozqGveHyPVg/VtSRlxsmhW0dTzVytrP879JVg7ZHLuD4pC9j/JFijYjSDCWxumHCcFUnNsiRpJQmsd0i56GBly7rfqE656vDrdScmr++fYB4oLhJYA6mwDwEEEEAAAQQQQAABBBBA4CABWdPmOdnxmoN2+uCJMeq6+qXJRYOFmmiMtslJ9PsGez0g+19Mm8jJxy/d/GxAxsMw+ggkGqKjVVidY7Q5TyZcObOsJvV52QcPzeqMHVk48Zb29sMFGz7ci7yGAAIIIIAAAggggAACCCCAQOLSaK1J+y955Rw5WU/6scMdQcvY/19GW/OkTFkvqzpcjAV47eiwSv+3tHNZAdqiiTILOHcJtFKpGb0zrIw6T2ZZvVFCkhss+HWOkp4ZstJ/72isXVS/tPM3g/H6dXSDjYf9CCCAAAIIIIAAAggggAACBRaQ9a/eISfJvy9ws6VpztKz65YkVh2us47G6H9prb59uDIBeM1ZDmlOXWtiZQDGUlFDMIuVFd9Q9wZZeH2mJK1m2Mq8XZI5IwOIIBMm1RfqW5NOsvWQjRlYh5CwAwEEEEAAAQQQQAABBBBA4CABrU51f8+wg1oo1xOTCYceH6rzXemjvj8qsuP9cvJ88lBlffy65OjMD02DepNuUxkfj6MiQo9fEnutsey5ziyrxAZ1jlZ27+W7Rr4RAzwTSd6j6up4U+2IutbOr/Q/0AEed/+h8hwBBBBAAAEEEEAAAQQQQCAXAVn/qkXqNeVSt5x15IS3I9aanJBNDB3Ntedpo5dnU9bXZYz+eN3SxM98PYYABt/VWHNsSutz9i28/hYZ4sQADjP7IQ3wPiWBlT0fJRFAAAEEEEAAAQQQQACBihSQBJazjtRp/hu8vlsumXt3tnHHm2O3KmMuzra8T8u9EEmlTxrftnW7T+MPRNhdi2qOyOy0ZssNBJw1rORumOoMGRg5mgNHNy0L2J3b9/JfLiE8gMMjBBBAAAEEEEAAAQQQQACBfgLrGyZXKbXjpH67ffLU/oebQC1lf9ZW+h1SZ4Sbej4re0wqHL5aYv4Pn8Xt63Dl0s1QPFL3elnHSpJVem56l5kl6apqZ1BkrQY8tGFlm19vaxhz+ti27bucEtaAxdiJAAIIIIAAAggggAACCCCAgAiMCu9w1oWSJJb/NlkResj1r/qOKtrSmZRFeK7quy+Qj7W6LNlce1Ygx+ahQW1urDlZboBwucxgvCMRiT4v61g9IskqSR6auRJmb/LKQ+F6MZSJu6uqv7o/MGZg7ZfgKwIIIIAAAggggAACCCCAwCECtjZnyIyRQ/b7YUc4ZFzNwHLG9FLPUT8cWfXiZcroU/wwxhxjtGxj/VRmBb2ZBd1zFByg2qaF48ZGMuGznXWs5OW3yaWB9fKPLQ8BbdQn2hfU/2DCko4tJLDygKQqAggggAACCCCAAAIIIBB4Aa1P8+kdCF+p2dO1ye3xmdy2vqe9sfZyS6v73Nb1V3nzxkRV7YeV6vyFv+L2TrTbP3jsyFf2VJ9tGT3XWcdK7u042YmOpFVBj9EwbTKfkRb/nz/T6AW1oDEEEEAAAQQQQAABBBBAAIHBBOTypz/Ka28b7HUP73+4rjU5Ndf44k21t8jqRJfkWt8n9VjQ3cWB6r+OlaSqZkt1X15e62LYXii6PdaVrGEGlhcOBTEggAACCCCAAAIIIIAAAt4VON27oQ0emcyCce6cmPOWSUU+HYqk50kDo3JuxPsVj+kJh78pYS7yfqjlibCjqe54Z+F157LAhDJvlXWsjtobCfOsSnhExiRqYucxA6uE4nSFAAIIIIAAAggggAACCPhJoKux5ti0trb7Keb9sUp64VP1rckf73+ey9d4U+xzMsvmO7nU9VEdWxID02KtyYd9FHPRQu2cXxPL6NBcpZ2F1s15MgtvXNE6o+GsBYxWP2EGVtZcFEQAAQQQQAABBBBAAAEEKksgZekzZBFlX26WUa4XcO8/0FhX4oeJmuilsv+0/q8F6LlQqZ+ZxbKg+2JlB2hcWQ1lW8OYEbsjkbMsZfWuY5VR6o2SuNo32Yc5P1khlqCQfA6dRQKrBNB0gQACCCCAAAIIIIAAAgj4UsDoU30ZtxN0Wj2eb+x6hUonm8ynbKXvz7ctj9efEt8Q/ZBSyWs9Hmfe4W2cd0L18GN2T7clYSWJqrndSk2RNFXIp3navD181IDcDZUNAQQQQAABBBBAAAEEEEAAgQEEZCHzG+QSqg8M8JLXdyVlAfdYoYIUh6XiML9Q7Xm0nX+HjX1SzdKu5zwaX85h9V3Hyijj3JAgyOua5ezk9YrMwPL6ESI+BBBAAAEEEEAAAQQQQKBsAroiF3Dvzy0Lun9m34Lu+xbw7l8iEM9Hp3XoKhnJR/0+mvj8WI2x7BnOwusys+pdsgh7jTMmSV75fWgVHT8zsCr68DN4BBBAAAEEEEAAAQQQQGBgAdOgQolIdKe8OnzgEt7da4z6n/qlySsKGWG8OfZZZcx3C9mmB9uyla2n1t2SeMSDsQ0aknOzgZTS58osublaK1l4XcmdA9mCJsAMrKAdUcaDAAIIIIAAAggggAACCBRAoMMaf6Llw+SVM3RLq8cKQHBQE7HOxI/2Lejuy1lpBw1m8Ceylrn5uSzoPtXLC7onGqLD7Ygzw2rvwutppd4gs3Pk7coWZAESWEE+uowNAQQQQAABBBBAAAEEEMhRQFuWf++8V4QElrOge3yBvlzZ5kEhDfLVTGcmN0Q/IAu6/yrHt07BqzmzAeORutfLpYBzde9lgWamVtYwLggsOLWnGwzyN52n4QkOAQQQQAABBBBAAAEEEPCygCxcLush6S97OcZBYkvtTB01YnLb+p5BXs9rd7wp2iINNOXViPcrbzfKOqm+Nf5CuULtXFBzUsaE5krC8DxJF54jcRxdrljo1xsCzMDyxnEgCgQQQAABBBBAAAEEEEDAWwJGne7TeUZPFit55RygTCr8WVnQ/Z3yMMgLuo/ROvMNGePHnTGXYtu0cNzYSCZ8trPwuvT3toyt6p1l1336HiwFWcX1wQysijvkDBgBBBBAAAEEEEAAAQQQGFpAZho9I6UmDl3ScyVa61qTzcWMSmanfUYyK98vZh8eaDtjW/abJyzp+msxYtnWMGbEK6Gqsy1tzVVaZlkpdar8I0dRDOxgtJlmBlYwDiSjQAABBBBAAAEEEEAAAQQKJrCx+YRRynRPKFiDJWxI5uwUfAH3/uHHujp/khgffb+kW87o/1qAnocs2/qFLOh+ViEWdO+/jlW3MrNk1fXq3llWAUJjKEUTeIIEVtFsaRgBBBBAAAEEEEAAAQQQ8KdAlXpFFnCXe/n5cDO2+Wexw+5d0L1Jf1KSLyukL186ZWn0psTG2kVKdf4iy/IHFetorJustD1XElVz40qdrZU9cm8Bll8/CIonQwsY9RcSWEMzUQIBBBBAAAEEEEAAAQQQqCgBbUKn+3VmTFWVebwUB6uuNbGyoznaoo1aUIr+ytaH0d+PL4ytrrs5MWRiMNlcG80oJZcDarlboJqrjH2cE7eTrgpylq9sx6aCOjbKPEwCq4IOOENFAAEEEEAAAQQQQAABBLITsGUGli9TDi+Ov6krkd0Y8y9l94Q/Lwu6v1taGpV/a55tYbjKmDtlNtW765fG1/eN8umG44+KhFPnOGtYOQkr26iTffmu6TsoHntSwArp+0hgefLQEBQCCCCAAAIIIIAAAgggUFYBSWD5cDPqcUmglOz6tIlt7Vs7mmuv1Eb/wIdabkI+Xmv77x1N0XvlcsBNRuvjlDGTleo5WRoJOQ2VDN1N1JQNhoA2T8SWdG4igRWMw8koEEAAAQQQQAABBBBAAIGCCEgiQieUdu4I579NF38B9/4odZ2d18iC7pcFfEF3Z9hhSQ6+ozdRZUhX9X8f8Lx4AsbWdzutS/KUDQEEEEAAAQQQQAABBBBAAIG9AlsurYnJo6P96GFMada/6mvjLOhulHW57COr0xeGxwgUSMAY+xanKRJYBQKlGQQQQAABBBBAAAEEEEAgCAKZtOXP2VeCL9eyPVaOYyBrQ62W2UlLytE3fSIQcIHHJtzS9TdnjCSwAn6kGR4CCCCAAAIIIIAAAggg4EbAVsqf61/JDKjd1vCDFhl3M+58y6ZT4f8nbezItx3qI4BAHwGjf7n/GQms/RJ8RQABBBBAAAEEEEAAAQQQcAT8msBqn9Sy6aVyHUJnQXe5hvDKcvVPvwgEUODfw9LdN+0fFwms/RJ8RQABBBBAAAEEEEAAAQQQkLXIfZvAKsvlg33fMnWp5DWSxPp73308RgCB3ASMVteMbdu+a39tElj7JfiKAAIIIIAAAggggAACCFS4wCOLpkSE4CRfMmjzz3LHrdtURhnrExIHC7qX+2DQv98FXq7qSV/TdxAksPpq8BgBBBBAAAEEEEAAAQQQqGCBMTu3nyjDr/YjgbHLs4B7fytnQXel9M399/McAQSyF5DZV98b37Z1e98aJLD6avAYAQQQQAABBBBAAAEEEKhgAa2NX9e/Usa2H/fKoUub3gXdX/RKPMSBgM8EnutRw77XP2YSWP1FeI4AAggggAACCCCAAAIIVKqAtk/16dC765/dstErsR+/dPOz2qiveiUe4kDAVwLafGWgGzKQwPLVUSRYBBBAAAEEEEAAAQQQQKCIAsY6vYitF7Fp/S+9QqWL2IHrpqPp5M9Y0N01GxUQeDTW03ndQAwksAZSYR8CCCCAAAIIIIAAAgggUJECxq8zsMq+gHv/t0vvgu7avlz2s6B7fxyeIzCwQFpp/dHe750BXieBNQAKuxBAAAEEEEAAAQQQQACBShPY1jBmhIx5gi/Hrb2xgHt/u/qWrjWSv/p1//08RwCBQwUk0/vNupbEXw59Ze8eEliDybAfAQQQQAABBBBAAAEEEKgggZ6qKmf2lfbjkI2tH/Nq3D2h9BUS2wtejY+4EPCIwKPbR4z75uFiIYF1OB1eQwABBBBAAAEEEEAAAQQqRMAo7ds7ENppyzN3IOz/djnh5me3SVaQBd37w/AcgQMCL1sZ03TmtY+mDuw69BEJrENN2IMAAggggAACCCCAAAIIVJyArZRfE1jPTWxr3+rlAxZNJX8uSay/eTlGYkOgXAJaq89Eb+3cMFT/JLCGEuJ1BBBAAAEEEEAAAQQQQKASBIzy5QLukhjy7OWD+982zqLU2piPy3MWdN+PwlcE9gr8LtaSHPCug/2BSGD1F+E5AggggAACCCCAAAIIIFCBApII8mUCyxjvrn/V920UXdq5TvJXN/bdx2MEKllAPnOe3KOHfSBbAxJY2UpRDgEEEEAAAQQQQAABBBAIqED7gvrxMrQxvhyepTy7/lV/TyusPi/7nu+/n+cIVKDAC8pS509q2fRStmMngZWtFOUQQAABBBBAAAEEEEAAgYAKWMb26/pXzhHxTQIrelPn80rrKwP6NmJYCGQrYMvsqwWxJclN2VZwypHAcqNFWQQQQAABBBBAAAEEEEAgmAJ+TWDZVVbPv/x0SGKTEj+XeB/2U8zEikBhBfQVsdbkH9y2SQLLrRjlEUAAAQQQQAABBBBAAIGgCRh7sk+H9PRxNz/7sp9i14uVbWnzKYlZbvzIhkClCeib6loT381l1CSwclGjDgIIIIAAAggggAACCCAQKAF9uh+HI7f08/wdCAdyjbZ0/ln2/+9Ar7EPgeAKmNV7XqhelOv4SGDlKkc9BBBAAAEEEEAAAQQQQCAAAqZBhWQYp/hxKHJC65v1r/r7WmFzhex7rv9+niMQUIFnIqnMRZOWbdqT6/hIYOUqRz0EEEAAAQQQQAABBBBAIAACnVbta2UYR/hyKEb905dxS9B7F3Q3X/Fr/MSds8AumTn4B1nM/3PaqIU5t+Kviv+2Mmbe+Lat2/MJmwRWPnrURQABBBBAAAEEEEAAAQR8LmAs7dcF3JXS/ryEcP9bJtbTeZ08fnT/c74GUqBHRrVS7rq32Bhr1rYR40bXtybfWdeS+J68f53kcdC3Hm2p90Zv7dyQ70DD+TZAfQQQQAABBBBAAAEEEEAAAf8KyP3sT5OTaz9ur0RPTD7tx8D3x6zbVCYxX33cWGqt7GOCyX4Yf3+Vxfn135Uy98sMq/si4dSqg280EO8d3eaGCccZlb7c30PNInqjPxJbkliRRckhi5DAGpKIAggggAACCCCAAAIIIIBAoAVO9eno/unc0c+nsb8aduyW5EMdTdEbJIn4oVd38sBvAjK7yNxntLo/FFIPRG9KPn+4AcglhDoRSd8gZY49XDm/v6a1ujrWmijYzQpIYPn9HUH8CCCAAAIIIIAAAggggEAeApI44Q6EefgVoqqVUleYiHqPtBXohEYhrLzRhnlWK2ulrczyjMr86fjWLR1u4ko0xb4hCa95bur4rawk6X4bm5T8UiHjJoFVSE3aQgABBBBAAAEEEEAAAQR8JNC1qOaI9C51vI9C7huqbxdw7zsI53GsLfnvjsbaL2utf9H/NZ57QuBFiWKFLLy+3E6n759w65Ynco2qo7n2/cqYL+Za3yf1Hpak7MJCz5AkgeWTo0+YCCCAAAIIIIAAAggggEChBdIvhV6nLBMqdLulaC+kzGOl6KdUfdSd1HldYmP0A8qoqaXqk34GFUjLK/+Q2YnLbWUv3z5i/INnXvtoatDSWb4QXxCbpWzzyyyL+7OYUZ06oi6KtSZ3F3oAJLAKLUp7CCCAAAIIIIAAAggggIBfBCzl1/WvVCiVCcwMLOft4sxW6WiyPq6V/bA8ZUH30n8PPbM3YWWW9+jhf5rUsumlAyF0HXiY46OOprrjlW3/VqpX59iE56vJZYM75Y37jthNyc5iBEsCqxiqtIkAAggggAACCCCAAAII+ELAPk1SJ76ItF+QXePbtm7vt8/3T+tb448mmqLXSyJgke8H4/0BbNFKr3bWsbLC6vfFSro4DImG6Gij7GXycIz3WXKOMKOM3Rxb2lW0mZEksHI+NlREAAEEEEAAAQQQQAABBHwvIAksX26P+zLqbIJOqS8ouQRLirKgezZe2ZfZJcnaPxtJWMkEt+VOsjD7qrmXfGTRlIja9WybtHBi7q14v6akwT9dt7Tr7mJGSgKrmLq0jQACCCCAAAIIIIAAAgh4WUDr02TNJd9tRquizfIoN0bvgu5N0S9JQiDYayUVH7oo61i5DXvcrmevkW+xc93W81N5ea/+WNa8uqbYMZPAKrYw7SOAAAIIIIAAAggggAACHhToaqw5Nm3UcR4MbeiQjAnuDCwZfd2JyetlQfcPsqD70G+FfiVeXcfqiGF7/jjmhud2Hng9/3WsDrSV3aN4Y+xLMuMr2JeDavXHaGfys9mJ5FeKBFZ+ftRGAAEEEEAAAQQQQAABBHwpYDuzr3y6yW0TA53A6rOg+0NyiHx5l8gSvbVeXcdK2/qeulsSpc9SDTLQjuba9yljvj7Iy0HZvT7VUzVfr1DObLeibySwik5MBwgggAACCCCAAAIIIICA9wQySr/Zl8u3K5VOVUWe9J5oYSNy1miKN8eulyTIfxa2ZV+39rwsvH6/s/D6vnWsnvHiaBLzo1ONUTdJbHJTvsBuW0K2Pa+u7ZkdpRohCaxSSdMPAggggAACCCCAAAIIIOAhAW3U2/15A0K1YeKN7d0eoixaKLrHfNHsXdA9yHevO5zfbll4fc3+hdfrToz/zZmddrgK5X7tmabx9RLvnRL38HLHUsT+d2tbXVh7S1eiiH0c0jQJrENI2IEAAggggAACCCCAAAIIBFsguaD2DNtWM/05ymCvf9X3mDgLuicao1+UReuv67s/wI+d5NRftVbLnVlWdiSyxk/Jyo3NJ4wK2933SGJ4XJCPkSS/F8ZuSTqXt5Z0I4FVUm46QwABBBBAAAEEEEAAAQTKK5BoiI6W5NWvJAp/ng8aK9DrX/V/d0RPSt4gC7p/KMALum+S2UrLjbbvs3r0/U7Srr+BH56bOSqcUN23SvLqVD/Em2uMckfFL9YtTf421/r51PPnB1Y+I6YuAggggAACCCCAAAIIIFCBAmaxsuJP1bzTaPNjSRhM8C1BKNgLuPc/LgFc0H27UuZ+bfTytAkvn3hLe3v/MfvxeaK29keSZHy7H2PPOmajbqhfmvzvrMsXuCAJrAKD0hwCCCCAAAIIIIAAAggg4BUBmS2hE/NjU2Qp6QsTG+wmrfUEr8SWaxy2rqwZWI5T74LuTbXXyuH8aK5uZaz3ivS9ypllZduZ++pP7vqH19excmvV0RT9lCSvPua2np/Ky2fJA9tHjvuIUsmyhe3Tm06UzYuOEUAAAQQQQAABBBBAAAFPC5gGFYqH66ZZ2m6Qk84LJdiYpwN2F9xLsdbk0XIiK0OrrK2jqe4YreynZNReX9A9IzH+XY6RrGNlL7erqlb7aR0rt++qRHN0ntxx8G6pF3Jb1y/l5ZtN3nfWNEmkvlDOmElglVOfvhFAAAEEEEAAAQQQQACBAgjIulbD7bA919LWu+QOaBfIbJeALiKt19S1Jny6+Hz+BzrRFP0PSSZcn39LBW/hmb0JK7PcSun7/LqOlVuVjsa6yVrba6TeUW7r+qj883Jsp0nieGO5Y+YSwnIfAfpHAAEEEEAAAQQQQAABBHIQ6F2MPWLOs5Q+35lppZU1Yu+0pCDPU7Afy4EqMFWirckb4pLEchIKZR7Udq30it47Bdrhe4OyjpUb0/YF9eO1nfmD1Aly8iplKdMQbe0se/LKOTYksNy8QymLAAIIIIAAAggggAACCJRRoHN+TSxtWfMspZyk1dskiRDZm7QqY1Al7bry1r/qyyuJK9Nu2Zdr23pY9pfykjVZx0qvldl9y+VSsuV1J8b/FrR1rPo6D/XYmfFoTOZ2KVc3VFkfvy5XRpr/kOTVA14ZAwksrxwJ4kAAAQQQQAABBBBAAAEEBhCQtY+OV8o+X5JVDRllpksSQ1dW0uoAijH68QPPKvPRhCVdf4031/5SGV3MRcMPWseq54UjVk1atmlPZYofPGrnbp6Jp1SLrMI29eBXgvXMaHVVfUvnzV4aFQksLx0NYkEAAQQQQAABBBBAAIGKF3BOkOMb6t7Qm7TS5mJl7FP2olRq2urVt4Sxq61/vvqsgh8YE/qyvD/eJ8nMsQVkeHUdK5nctbzcC3YXcFwFbSq+MXq11r03Ryhou15qTJLlbbGWxGIvxeTEIu93NgQQQAABBBBAAAEEEEAAgXIKHHTnQKPeK2dqteWMx6N9x+tak/Ueja3kYcWbYx9Uxvwq144lHbpN1k970FnHKqMyfzq+dUtHrm1VSr18zX3i9Eh4hH12zbVdctmotzYSWN46HkSDAAIIIIAAAggggAACFSKw/YPHjnxld/U8ben3yOVI75BhB3kx6LyPqsx6+X2sJfmuvBsKSAOSgNKyoPt9clJ/TpZD2iWGvQkrbck6VjcnmM2WJZxTrKO59jxt9DJ5GHFRzW9FN/eEUmedcPOz27wYOJcQevGoEBMCCCCAAAIIIIAAAggEUmDTwnFjq9ORdytLvWd3tzpPEgrDJHnFlp1Axa9/1ZdJElcmqc2lttJ/kffQcX1f2/c4LTmuh5yF1yVJet+2I8b++cxrH00NUI5dQwhsbqw5WZJXbVIsyMmrHUZb7/Jq8so5RCSwhnij8jICCCCAAAIIIIAAAgggkI+Aswi7NuY9km94j8qo6bI4coikVU6iJLD6sUVbOpPx+bEpyjLfkZfOkPdYlzLWeqMy9x8xvGfFmBue23mgSuLAQx5lLdDVWHNsWlv3SIVjsq7kv4JppfXF9S3xf3k5dC4h9PLRITYEEEAAAQQQQAABBBDwpUBHY91kpe13yWLI50tSYboMgnOvPI+kMdap9Uvj6/NshuoIZC2wvmFy1cjwjj/Jd++crCv5saDRH69bmviZ10NnBpbXjxDxIYAAAggggAACCCCAgOcFeu8c+FTddEuSVnJF4IVyh7gT9wbN9YEFOng9u9IjNxaoLZpBYEgBZ42xZGTH9fJ1zpCF/VzAmO/VLU16PnnlEJPA8vMbjdgRQAABBBBAAAEEEECgbAKbL5swzOrpmWkp6/zEBtWgtT2edFWRDodRT0xuW99TpNZpFoFDBGSB/Ctl2uTCQ14I0A75vPpDXbrzCr8MiQSWX44UcSKAAAIIIIAAAggggEDZBbYuHHfknnToXEtbDaYnfYFS1iiSVsU/LMZSrH9VfGZ62CfQ0Vh7sSSvvhpkEBnf34al9lwiS9Nn/DJOElh+OVLEiQACCCCAAAIIIIAAAmUR2NJw3JieSGiepXRDT0a9Ve4cWEXSqrSHwlIksEorXrm9tc+vma61/rUISI4nsNsWy7YvGNu2fZefRkgCy09Hi1gRQAABBBBAAAEEEECgJALxS2Kv1Za5SO4YeFFKqTfLmaxF0qok9IN1wgyswWTYXzCB3juGKvsOaXBYwRr1XkMv25b9rrrWLt/dlpIElvfeTESEAAIIIIAAAggggAACZRDof+dASVgFeQZGGYTz6DKkHsujNlURGFJgY/MJo7Tdfad8148ZsrB/C9jK6AUTlnT91Y9DIIHlx6NGzAgggAACCCCAAAIIIFAQgX1JK1mA3VysjH3K3kaZa1UQ3MI18u/YTcnOwjVHSwgcLPDIoimR6l1bb1Nan3rwKwF7ZvTn65YmnBlmvtxIYPnysBE0AggggAACCCCAAAII5CJgFisr/lTddEvb75I01fuUsl/b245cK8jmUQHD7CuPHpnAhDV219YfyYTLtwRmQAMP5FeSvPr+wC/5Yy8JLH8cJ6JEAAEEEEAAAQQQQACBHAVMgwrFw3XTJGnVkNioLtbaPo45VjlilqEadyAsA3oFdZlojH5ePg8+GughG7ViZ/qojymV9PUwSWD5+vARPAIIIIAAAggggAACCAwk0LWo5ojMTmueswi7rFT8Tq3so3qTVmSuBuLy9D7uQOjpw+Pr4OKNsfcYba729SCGDv5fmerwhZOXru8Zuqi3S5DA8vbxIToEEEAAAQQQQAABBBDIUmDzZROOtnpSb7GUPj+9S10oizGPyLIqxTwsYBvLlwtOe5iU0ESgfX7NG5Q2S+Sh5EgDuz2nLXXBxBvbXwzCCElgBeEoMgYEEEAAAQQQQAABBCpUIHlp7WsyafVOSVo1mJ70W2UdmyomWQXqzZCyq6z1gRoRgym7QHx+rEZZ5i4J5MiyB1O8ALpt275gQmvXpuJ1UdqWSWCV1pveEEAAAQQQQAABBBBAIE+Brktr6lJp6+0ybeJ8O63eLsuvh0la5Ynq0epyXNfL7JFuj4ZHWD4U2NYwZsRuy/xePjeiPgw/25Cd21J8aMItXWuzreCHciSw/HCUiBEBBBBAAAEEEEAAgQoX2NxYc7Jl6Qu10Rel0+pM556BJK2C/6bQyvw9+KNkhKUScG7okIgMa5X31etL1Wc5+tFGLY4tTbaUo+9i9kkCq5i6tI0AAggggAACCCCAAAI5C3Q01k1WcudASVa9SxqZQsYqZ0rfVtRaP+Hb4AnccwKJcOy7kvo+33OBFTQgc2t0aedVBW3SI42RwPLIgSAMBBBAAAEEEEAAAQQqXcAsVlZ8Q90blLLPl6RVo3w9sdJNGL/aiAEChRBINEY/JHcc/HQh2vJuG2b1nheGv18+PwM5QZUElnffeUSGAAIIIIAAAggggEDgBZxLeuLhummWzLRKbFDv08quCfygGWDWAsbSJLCy1qLgYALxptjbjDI/H+z1gOzf3BNKv3fSsk17AjKeQ4ZBAusQEnYggAACCCCAAAIIIIBAMQU2XzZhWCideavKqIsS2shsK3t0IKcLFBOxMtq2dbd5ujKGyiiLJdB7KbKyb5X2g5z/eCFj7HeccPOz24rl6IV2g3wAveBLDAgggAACCCCAAAIIICACXYtqjkjtVOdZ2mowPekLZNco5azEzobA4ALrY23J3YO/zCsIHF6gq7Hm2LS275RSRx2+pK9fTcmlkQ0TW7ue9PUosgieBFYWSBRBAAEEEEAAAQQQQAAB9wLJS2tfY2esC7QxF6Z3qbdoraqZaeXesXJr6OWVO3ZGnq9AoiE6PK3V3dLOa/Nty8v15e8AH61r6bzPyzEWKjYSWIWSpB0EEEAAAQQQQAABBBBQcnng0aFU5iJZQrjRTptzZC3hEEkr3hjuBUx7pir8dff1qIFA7wrmOlGlb1TGnBVkD2PU/9QtTf4qyGPsOzYSWH01eIwAAggggAACCCCAAAKuBZyZDnaVeae2dZPqSb9DGqh23QgVENgr0C3Jz1ad1p+f2Nr+IigI5CKQaK69SpJXF+dS10d1fld3UvILPoo371C56jxvQhpAAAEEEEAAAQQQQKAyBRLN0dOMMR9VSjeLwKjKVGDUBRDokfXQ7pfZJLdbKXWbrHv17wK0SRMVKtDRVHupVvrXAR/+I+ER9tk113a9EvBxHjQ8ElgHcfAEAQQQQAABBBBAAAEEDiewvmFy1YjIixdoZS2SC3XOk7KcUxwOjNcGE3hF3jj328q0pVPVd7627ZkdgxVkPwLZCsgdB2dqbTtrpwV5FmiXpc3UaEtnMluXoJTjh01QjiTjQAABBBBAAAEEEECgiAJymeBoO6I+KV18VE4ixhaxK5oOrsB2Gdpdcse0O+xIZPnEG9u7gztURlZqgURTdJKst7dO+n1NqfsuVX8yvp0hy8yKLun8R6n69FI/rIHlpaNBLAgggAACCCCAAAIIeEzAuQ19SluXy4nTpyRxdbTHwiMc7wvE5X3zR9vY92wfOf6PZ177aMr7IROh3wR6E+xK3S3vtcAmr+SY2NrSC6JLkhWZvHLekySw/PadSbwIIIAAAggggAACCJRAoH1B/XjLtj+fVmaRnBQeWYIu6SIoAkb9U2lzu22ZOyYs6frrgWF1HXjIIwQKJOBc1mzCO34rn1MnFahJTzYjf0T4TP2SxF2eDK5EQZHAKhE03SCAAAIIIIAAAggg4AeBvWtc7fiosjNXSbwj/RAzMZZdwJYI/iYn2PfITKtbJi7terLsERFAxQiMiuz4ibz35gR5wHKDg+vqlyZ/HOQxZjM2EljZKFEGAQQQQAABBBBAAIEKEOhorDlf6x0/lKEeXwHDZYj5CWRk/f4/G1mE3QrLnQNvSnbm1xy1EXAvEG+q/aIkr+SGEoHe/lS3JfmxQI8wy8GRwMoSimIIIIAAAggggAACCARVoP2S8adYobD8dd/MDeoYGVdBBF7WRi2T+07eka4K/14WYX+xIK3SCAI5CHQ0175PGe3MFA3y9vgePexivUKlgzzIbMcml4myIYAAAggggAACCCCAQCUKyMwFHW+KflhOCn4g4z+iEg0Y85AC/5b3x+9tZe4enupZNrZt+64ha1AAgSILdDTVTdHKXindBPdzS6utaZM56/jWLR1F5vRN8ySwfHOoCBQBBBBAAAEEEEAAgcIJbG6YcFwokr5BWpxXuFZpKSACCTlRXMadAwNyNAM2jMSl0VqTUg/JTMDagA2t73C6LW3OibZ0/rnvzkp/zCWElf4OYPwIIIAAAggggAACFSeQaIy+3ej0Uhn40RU3eAY8mMC/nDsHqox1R+yWxKOSwJIJes7GnQP3OvC/FwQ2Np8wyqS7nctYg5y8kjXbzaUkrw59x5HAOtSEPQgggAACCCCAAAIIBFagoym6SDIT18gAI4EdJAPLVuBf8l5oM5nMrRNu3fJEtpUoh0A5BEyDCiXMniXS92nl6L9UfRptvlTf0tlWqv781A8JLD8dLWJFAAEEEEAAAQQQQCBHgd6Tv0jUucPg5Tk2QTX/C6Rk2bMHZW7V7Tpi7uTOgf4/oJU0gkQk+iOZGHh+sMdsbpTk1beDPcbcR0cCK3c7aiKAAAIIIIAAAggg4AuB3sXaI9FfyGVhH/JFwARZSIHdctzvs5W6OxVK3XHCzc9uK2TjtIVAKQRk5uinpJ+Pl6KvsvVh1Ko9Lw7/SNn690HH8lnGhgACCCCAAAIIIIAAAkEVcJJXiabYz2TmAidGQT3Ih47rBa30clvZ98idA3/HnQMPBWKPfwQSzdF5sijUXRJxkCfgPBNJpc8a37Z1u3+OTOkjDfIboPSa9IgAAggggAACCCCAgMcEEk3RH5O88thBKU44SWn2Dlk/5466zs4H9QqVLk43tIpA6QSSC2rPyNjqVpl5E+TcxfMyvreTvBr6fRXkN8HQo6cEAggggAACCCCAAAIBFog3xT4qySvWvAruMX5GTnzvsY3VVrc0vkYey4Q7NgSCIbC5YcJxtp2+S97XI4MxogFHkbKNuXjC0s6NA77KzoME5L3AhgACCCCAAAIIIIAAAkETSDbXnmUbmYejVHXQxlbh4+m9c6BS1t31rfFHK9yC4QdUINEQHW6q1AOSkp0a0CH2DktrtSjWkrwuyGMs5NiYgVVITdpCAAEEEEAAAQQQQMADAslLa19jp/RtSpO88sDhyDeEtMw6WCmN3K7l8sBoS6dzqSAbAoEVMIuVldiglgQ9eaW0+WaspZPklYt3MgksF1gURQABBBBAAAEEEEDADwJ2yvqenBzV+iFWYhxQoFuSVrIIu7o7YyJ3Hr9087MDlmInAgEUiD8V/bbMTLoogEN7dUjaqNuiJ3Z+9dUdPMhKgEsIs2KiEAIIIIAAAggggAAC/hBINEbPNlouvZG7D/ojYqLcJ/DqnQOPGLbn9jE3PLcTGQQqTSDeGPuAJN9vCPi4HwmPsM+uubbrlYCPs+DD44dawUlpEAEEEEAAAQQQQACB8gg8smhKZOyuZ/8pvZ9Yngjo1aVAl6y7fqcxodu3jxyz4sxrH025rE9xBAIj0N5Ye66l9R9lQJHADOqQgZj2tKk6i1mVh8BktYNLCLNiohACCCCAAAIIIIAAAt4XGLtz2wKZd0XyytuHarPMIri7986BJ8XX6sVKrhR0tvjeL/yPQAUKdC6oOSljy7p9AU5eyS1Cd1pGX0DyKvc3OAms3O2oiQACCCCAAAIIIICAZwSchY/jG8wVXGLhmUPSNxDuHNhXg8cI9BFwbjqRSeu7ZdcxfXYH7WFGLo1sirV2Pha0gZVyPCSwSqlNXwgggAACCCCAAAIIFEkgvqH2vZK8OqlIzdOsO4GM3EFtlaxFdnskbN9Rc1MX06vc+VG6QgQ2zjuh2k7vvkOGOynQQzb6k/WtyXsCPcYSDI4EVgmQ6QIBBBBAAAEEEEAAgWILaK0/KEkTtvIJdMu6+auNMvfYqfCtE9vat5YvFHpGwPsC8nGlk8d0X2eUnun9aPOI0Ogf1i1N/CyPFqi6T4AEFm8FBBBAAAEEEEAAAQR8LtDVWHNs2qjzfD4MP4b/ssx6e8BWpq1HD79jUsuml/w4CGJGoBwCyaboVyWJtbAcfZewz2WxdOJzJewv0F2RwAr04WVwCCCAAAIIIIAAApUgkNLWRZJICfCduzx1FJ8T62W2sdt2pY/50+S29T2eio5gEPCBQEdTbYMkr670Qaj5hLg+lapq1G0qk08j1D0gQALrgAWPEEAAAQQQQAABBBDwpYAkVN7my8B9E7Rp10rfZSv77rqurhV6hUrvDb3LNyMgUAS8IhBvjr1JGXOjxCMfXYHdtoRse15d2zM7AjvCMgyMBFYZ0OkSAQQQQAABBBBAAIECC7y5wO3RnFLcOZB3AQIFFtg8f8IEZVJyx0F9RIGb9lJzuyUz957aW7oSXgoqCLGQwArCUWQMCCCAAAIIIIAAAhUr0L6gfryyM9GKBSjcwG05qV4ni7DfHcqY26O3dm4oXNO0hAACG5tPGBUy3XfJ99m4AGvYWqvmWEvy4QCPsWxDI4FVNno6RgABBBBAAAEEEEAgf4FQJnOGCfKFOPkTHa6FPXKyuVzu3nh7OJW+a3zb1u2HK8xrCCCQm8Aji6ZEqndtvU2SV6fl1oI/ammj/ivWmrzdH9H6L0oSWP47ZkSMAAIIIIAAAggggMCrAnIL+vFKMjBsWQs461fdY4xZesTwPcvG3PDczqxrUhABBHISGLfr2Wvks+otOVX2SSX5FL6+bmnyOz4J15dhksDy5WEjaAQQQAABBBBAAAEE9grIJW/jmICV1buhW2Z/fF/Z6qd1tyRYpQ4XEgAAQABJREFUfT0rMgohkL+ALNr+WUkYL8q/Je+2IJ/B920bMe5jSiW9G2QAIiOBFYCDyBAQQAABBBBAAAEEKldAaz2WGVhDHv+NRlvvqW+J/2vIkhRAAIGCCcSbYhfIHQf/p2ANerEhbZ5IRyLvO/PaR1NeDC9IMVlBGgxjQQABBBBAAAEEEECg0gS0MvxOP8RBl0t7ZIH2zFsTDdHRQxTlZQQQKJBA+4KaN0pyvUWaC/Jn1HaVts6feGP7iwVio5nDCAT5jXSYYfMSAggggAACCCCAAAKBEZBL49gOJyCX95ykjf6BiaiuRFPsNx1NNXMlqcWVl4dD4zUE8hDY1jBmhGVbt0gTR+bRjNer9mhLXVx3a+JprwcalPi4hDAoR5JxIIAAAggggAACCFSkgEwt6iYTk/Whr5Y1wxq0shriTdGn4lpfF7Yzv65Z2vVc1i1QEAEEhhTYHan+gXwuTRqyoI8LyB0HPxdbklzh4yH4LnRmYPnukBEwAggggAACCCCAAAIHCbxw0DOeZCXgzMqStXm+m9ZWMt5Uu7S9sfZcZmVlRUchBA4r0NVYc6x8f1122EI+f1HGd190afIanw/Dd+EzA8t3h4yAEUAAAQQQQAABBBA4IGBss0lbcjrFlqtAtVxNOF8I5yeaohsTWl2/x0rdeMLNz27LtUHqIVDJAulQ6BRlm0DnGmxlXy2fupLzZiulQKDfVKWEpC8EEEAAAQQQOCAwc+bMY4wJy5oz5hT5/W6sXLIzSis9QkoMk+e75GTxJbml9g45YUxkVOTva9fe/4y8xi+CBwh5hEDWAiFLb+CbJ2uuoQpOMkb9d1UmclWiMXqXraxr606K36cXO4vAsyGAQFYCWgV+VqiVsv6alQWFCirAn2oKykljCCBQLIFp06aNDlmRb8nprfvPLaOXr173YFuxYitHuzOmzfq03DZdEgMF29LS3vPyV/zntTbPG8s88cILLzy+fv36noL14IOGZk6b/Sl5h70uh1B3r1678tM51AtMldlTZ0/KhM152ljnSR5qhgxsvMvB7ZTyj0hS666ICd2x4s8r2l3WL2nxWdNnzZePo3PcdGoZ9YuV61b+zU2dXMvOnD77S1K3zk39VGbP5x966KGX3NQpRdk5b5pzXDpif60UfQ3Vh9GZz61Zs8Z5r3pqe2TRlMjYXc9KYlhVeSqw4ATzjNHmeqPDN05Y0rElOMNiJAgUR8AsVlZiQ/Qpaf2E4vRQ/lYtbWLRls5k+SOprAjcnwhWlg+jRQABjwjMmjH7O/IX0c/lFo6+Z/XaB8/Pra43a82YPnuzfIBPKHJ0u6WPB+Qk/bYjdlX/5t7H7n25yP2Vtflp0+acHNL24xJETrOTbZUet3bt2oq63GTatHNrQzq9QMwuk38ny7+CbfLe+4uRO4aFq3XbihUr0gVruEANzZo++08y4+WtbpqTmRynrV274p9u6uRYVksCy7md9ygX9XdKEvYoKe+5iTwzZ559vlyKcpeLsRSr6A4xOsaLRs6A443RlZKAn1WswdNur0BKvkPukc+na6MnJe9lVhbvCgQGF5B15T4js62/P3gJv7+iP1DXmrjR76PwW/ws4u63I0a8CFSggHOSLMmrj+U+dHNG7nW9V3Pq1KlyKZaqL0Fkw+VM9h1yPnvDKyO6k7NmnP3VOXPmOJeABXILa/u/ZWA5Ja8cEMuuOj2QMAMMavr0s6fNnH723ZK86pCXr5Z/BU1eOV3Ke+9NSpvWdI/91KyzZjkzujy1SXynuQyoZ8eO7Rtc1smp+OzZsydIRTfJK6cfJ3krw/LeJnd5cmtdnEFo5SQfPWnkDNhodX9xBk6rfQQikiS8UKyXJZ6KxmW9rKufaRpf3+d1HiKAwD6BYame6+ST6dmggmhlPhnUsXl5XCSwvHx0iA0BBHoF5CT56/LgiDw4Ym9+83mvyaO+p6pWW9WnSkCSwyrpdrRc2vU1SSasn3nWnLkl7bkEncl6TVPlrDS/WXqWHahE6UDsM2acPUtm9iy3lFkrv5S+S8qEBipX4H3HG23VFLjNvJpzLmmWBlxeIqmfKNUluSZt3CdTdW8CKy+XYlWWP2B4IoGlbe8a9doba3mxjgHtDiCgVa383LgirEKb4o21d7bPr32naSjJZ+IAwbALAe8JjG3bvktm8X/be5EVJiL5/n+Dc+fSwrRGK9kKkMDKVopyCCBQFoEZM2acJB1fmm/nVVUZT5wA5TsOp37GyuHktBAd722jTqYa/XHGtNk5Xs5ZuEAK2lJGO7OI8ksKauU+aVDQQRSvMWcNopnTZ90kC7I/KL3IGlel3Wyt2kvb4+F7C4WqczjW9mOHb7Vwr8oJg/vPO28nZ9yPp3Ccr7YkJyuluPzz1f7cPtg+csxDUme723qUz1sgrLR+t2XpexKR6OaOpujizvk1sbxbpQEEAiDQ88KwX8gvVx0BGMqAQ5Dv+2/Jz4b8fn8csGV2DiZAAmswGfYjgIAnBLQJO4mFnC/renUQdlmTPq+GUYgHcic3ZwZWObeQ1uo7spD8V8oZRKH6njlt5rvk5GNO3u0ZHcQZWHrGjFkfkQW0n5DfzxaKUVl+SctkwvG8j08BG9C2+4S4fM84l+iVZsthxpLcDbJ08blQmDx5sixKbia5qFK0ol412j/gM699VNZn0i37n/O1LAIx+ZC8MmNZ7fGm2P91NNU2OAvslyUSOkXAAwKTlm3aI39UWeyBUIoTglFTE02xdxencVodSIAE1kAq7EMAAU8IzJo2681y4nJBQYIxwUlgyQosOcz+KIjiQY3IXQu/Lnfty3t23EGNlv6JpbR1VWG6NafsPdkuTGvlbsVZa02O7y3a6J9LLEeXMZ7dDz10n6cWx5e/trqeEWTsUOkSRDnMBuxOR9aX8RgP2vXoEaOd9dUkiVX+zatGB8mE1a8Oes6TcgnIOZaZK39w+o3cHbLDWSurfWH9xHIFQ78IlFMglkrcLP3/q5wxFLdv800uHy6ucN/WSWD11eAxAgh4SsDoAlzWtX9EWgVpdky5Z2DtV3Xm4/xU1o+qO7DDX48kQePcQe/1BYq6at/JdoGaK18zsmj6GyOh6r/J8b24fFG82nOHPJKckYc2o10nkY2VKsklhHKjhWEidYJLreTDD9/3vMs6pSke8kbCXgbb6VmjPkei7uaEc5njI3128bD8AuPlA+wKK5PZFG+OLks0Ry9kVlb5DwoRlE5At6mMzEwMxKz9QdQmxyO1zYO8xu4CC5DAKjAozSGAQGEEZsyYLXe/U+cUprXeVibLiV3+lyIWMKBcmpozdU5U6jm3cffKNkLZoe95JRg3cfTOltLqSjd1hizrnZPtIUMdrIAs1H6OsfQD8vrxg5Up6X5t2kva39CdyaQK9bqhix1U4t9r1qzpOmhPkZ6k0+nJ0rS7zzrjzcsHHSI58Xc9260otNqUbgZdngMw2jh3VGXznoDc/0K9XW5K8LuxLz/bewfD+CWx13ovTCJCoPACsdbk76TVPxe+ZW+0KAm6r22cd0K1N6IJdhQksIJ9fBkdAn4VcH7J+0aBgx9ud9ueWEcln3GlwrbrmR/59JddXfPe6dPneGdWWHZBq6NHjf6YFC1oksY2lgePT5YgUmzmtLMvlIXa/yAPR2Vfq9gltTMDyzObzDicKMGMdBnQP1yWz7m4tnNYwN3LyRmTw3hy1hu8orEt3ySw6lo6fysjKcmMv8HFeOWwAkYd58zKUiGzYf9aWesbnPXe2BAIroA26r8CPLoJ1aP3fCi44/POyEhgeedYEAkCCOwTmDV9VpP8JeMNhQbJBGB2jGXKvoD7QIdFW8r+6EAveHWfzMYbIWt4faHQ8Vna+PZSVfm+m6+0uU1MnEvQPLMZZTyVwMopQVTCZEIuM5bk89a7yRntjRlYJV2EP8/vPjmeMsnHFPqPQHlGRfVBBORcbO9aWSMjO7bKWlm/7Gisc2ZRsiEQOIHY0uSDsvbE8sANbN+AjDFf3f7BY93+gSuoHEUbFwmsotHSMAII5CLgXNYlJ2CLc6k7VB3L9v9d4myvXE5zKPb7ZJdvfqZkUub/SbxjDx1GfnvkvVuo9bTyC8Rlbblhwnlyl6AbpZrnjqEkjDyVwBIn15e0mVLe4S+HBdyN5c3L4yTR7Nw8oNbl27koxS1jnLWlfLPVndg7C+th3wRMoI7AMfIzZJHW9uOSyLpPZmbN55Ik3hhBE9DKfEnGJG/14G3yx4Oxu7urPxG8kXlrRGFvhUM0CCBQ6QLHHHXsR+TnWlHWhJB1QXx9eZfz3pAfjq5PnqXaX6XmIL8sGOeShdHyL9+TxLFyadVpq1evLtmlUhJzTtusWbPGmIz5dE6Vh640durU88bJXfOeHbqoN0rMmDHnTGPsOySaQq/d8Ix8Lz+ktfVPW5lOaX+n/EvLv+HKNuNlBtwk+RV2irypp8i+QX8fkUsaPZXAkphPc74R3WyWXcIEkftL7tIjR4580s14SlU2lTKnCbVLbfWcVCnwe8YYq9p6olTjLkQ/ctN6O9GoPizJ00elvUG/vwrRF20UXMD5gX2ufH6eW31M94uSzPqNfBdcE2tJenemZMEJaDCoArIW1sPxpujtMr6LgjlG/flEQ/QXsbbkv4M5vvKPih9o5T8GRIAAAvsEnMu60j32F4sI4tvLuxyTKVOmROQX2pNd+mxbvXalkyA47DbnTXOOS1VlPqCN/rIUPOKwhQd50WSss+QlzyewjK2vlDhHDjKMvHeHw2knUfp/eTdUggamTZs2WpuMXDaoRxSmO/O0Mvr6jLJuW7duxaZs2pw6deqocLjaWXvrQxLHzP510jrS3n9fWZ+7v6TNHv7y8PWliHlvclaNc9WXURuWLVu2x1WdEhWWs3jXCXtJjP5k1ZoHv16iED3djVyu81i8MXaN/P2iWAl7T48/IMEdLcmsRZI4XyQn/Y/K42sjI+wlNdd2vRKQ8TGMChQIWfYXM7b1bhl6EHMRR6sq9XkZW8GXqajAt8qAQ/bcpQIDRslOBBCoCAFJXn1OBuru5MudTPTNbz7vNe6qeKf0sGHDTpRo3M6Syeovtiv+smLrmjWrvq0se7b0sSuXUcsaMZ5fJH/27NkT5UTgw7mML+s6tm/WwdJhHflfSRrVZz22wQv+U2Z6vHf12lWTVq9beXW2ySunuYceeuilNWtW/lrqzpJfSs6WXTJj8NWtZ926+7e8+qzMD+bNm+d8/7l9nz9972P3vlyK0E0q5DpJ7+W1nSSp6TqBpYzt+SR6Kd4L+/sYlu7+ikxhK/CMtP2t87XEAlPkWP4yvctKSGLyBx3NdW7vhlricOkOgYEFapd0PSV/kL154Ff9v1cWIfxk+4L68f4fiTdHQALLm8eFqBCoOAFn5oAM+jPFHnhVVcb9CVGxg8qyfctYrmN3e3IqlwA+Kj94v5ZlSAcVk1+sowft8OATO22+KWEV9U5P2ieXqs6cfvYn5K/5zl9A89kk2ak/JbP8zpAk1O+kIWky923l2pUrx9eOe7O08BX5l5HmEvJVln7zxvbyiy87J4yu/mIsIKW7G5zl/jJpiS+rJHc5joCxcpiBFdal8y4Hiss+x7Zt3yXfQZdItR6XVSnuXYHRzqw6bez1MivrkY6m6KKtC8cd6d1wiQyBQwXCYbNY9npy9u+h0brec4Rl0s4VDWxFECCBVQRUmkQAAfcCdqb3hHWU+5oua2Rs1zMUXPZQvOLGOt1t48a4P5lLZZxZOe6TBrK4tad/gZ41dZb4aedErrib9v7NAuZMnSPJxvzuUiaJj79ZYXX66rUP/lhAC5Zkamtry0hC7Bsyk+Y9Wmn5K613Nhmk+yRyCRNEckxcx6cs958RJToistavcns3tpdWrlzZXqL4fNNN7JakrEXX+zPWNzETaNYCvbOyejKRTucOhsmm2tdnXZOCCJRRoOamrrgsOfDzMoZQ3K6N/nD8klhR1vQtbuDeb50ElvePEREiEHgB57IuOVFd5H6g2v26Mto3l3cdyqGN25M5+d1Au55d8fDD9z0vJ445XHJiIocG7Z09xtJXSzRuf+7JLCD1hKtRGHWyczdNV3VKXDgVsn8oXY7MtVuZbXerbVIzJFmwOdc2hqq3et3qe0JV1vuHKlfK13UOM4Lkm7BkM4LkuLhOYIXT7j8jSmE+ffq5ddLP0e76Mo615PHY+gtEW5LfkX139t/P88AIHCVv/EW20n/bPytrW8OYAq1tGBgjBuIxgbDKOLPiX/JYWIUKJ2L2zjIrVHu0s0/A7S/ywCGAAAIFFzBpdZU06nZtJzkvVJ+Qei+6C8j9LCZ37RevtFza5/bkNNPdvfNfOUWk1Q7X9YwqyTo/ruOSCrOnz54td3Ga57auJARukfPhZS7rVR1zzDGnuKxTsuIzZpz9FhnXe3PtUGbaXX9c7bjmdevW7c61jWzrrVixQu4o550th+9BuQ6yNAmihoaGkLxX3a6Js3PFn1d0eEf4QCSW6XH7eSeVS5csPBCpPx7J97y8fa0PyKVn7hLy/hgeUR4s0DsrqztSnYw31/6UWVkH4/DMOwI1S7uek8TrD7wTUWEjkRuRNCWaozn8LCtsHEFrjQRW0I4o40HAZwKzp80+TX54NboP26xes+bBB+Rv7S4vMTKnyt0OXa1h4z62wtdw7tQmCZgJ7lrWmx599NEc71RkjnHXl5wdae3Zv6LJe8yZfeV2y6SN9Q2ZtOXyPSbd2CHXl3u6DS7X8rJuyldzrSvfb79Zs/bB/3Qu88u5DX9XdPeLqCR1161b8UwphpxMbp8k/Qx305ckNf4p5eXbw3ubzJh0Z+0MxJRwvTHvkQ0ZUX1r/AX5xf+tUjA5ZGEKBEHgKPnB/DFmZQXhUAZ3DMNTe74rP4S2BXSElrHzW64hoC55DYsEVl58VEYAgXwFZE2Z/5Y2XH8WyV81ehdHlBOwJ13GUJ3JZE50Wafsxaut6lMlCBmuq8315YNO63svf9PjXPXkFDZ23HWdElSYNX3We+WXo2muu9KqRZIPT1rKuH2PORieXGtNZqKdK2+jma4t9lZYO/LoIy+Vh/JtW3nbtGnTRsuoa1yO3EkQlcTLsmzXSVP5vsjpM8KlQU7F5TPefQJLMwNrKOxoS2fStuwLpFxOd5sdqn1e96xA76ys3ZHqLmetrI7Gulx/Dnh2gATmTwHnRhPaaOdcIJib1u9un18zPZiDK8+oXJ80lidMekUAgSAKyKVMs3K5rEtOuu5ftW7Vg46JbRn3s2MyyvWJXrn9ZbqL+5M5YzvrwbjeRo8a7SR7hrmtqLW1yW2dYpd3LquSS96cS1TdbhmjMt9yKqVVOocEljffY5JJ+aJbiH3lX7BVuGnZsmV7cqzv+2qhULX7zw3duyZTScaeW8LHeDaBJYlWt595JpPpdr8uYkmOjrc6mbCk66/GWPPkZ+lOb0VGNMUWkL+CjZTjvkhre5WslbVe7mB4RaIh6iTn2RAom0CmOvQz6TxRtgCK3LGlrVyuAihyVP5tngSWf48dkSPgewFtTE4f6LLQy9f2D17bluvkglzq5snZMfvHNNBX+bB2ZmC52rSynNkfrjfxaXJdSSpYxvw9l3rFrLO1a+sHpH3X61FJ0uvmNWvW9CZH165d60xt/7fLOF/vsnzRi8ui2K+VTmQGVg6bUZ9eu/b+jhxqBqaKtjNuEyrOlMmSJYjkpNR1fJZduvjcvBH23QTB1UxZsX76oYce8uxlzG7GX4qy9Uvjq5W2SWKVAtu7fbxOvm+ultuvyB0MY7/paKqZ691QiSzIAhNvbO+WP8J8PbBj1GqWzHp8S2DHV+KBkcAqMTjdIYDAXoFZ02a9Rx65n1KrzfKVa1eu3O+ow7brGVjyQ9L9TIr9HZbpqyxY7/rk1NYh1zOwZk+d7ayjc1kOw/z3ynUrPTX7QdY6G2aM/moOY8loK/PtvvXkl3y377Mxc94057i+bZT7cUhn3i8xyFBcb6tWr1t5s+taAauQS4JIVpdy/T2YK1sun2t7MlU5JblzjTHbeqNHjD5ZylZlW94px/pXbrT2lq1v6VrjJLHkmcubobjvixqeFhhmlGmQP3r9X7wx+vd4Y+xjTzccf5SnIya4wAlEtyRvlJ+zbn/X8o2DzHr8lowvl9/BfDPGUgXqu4WMSwVDPwggUDwB57KuLZ3bZHFs+Sh3ucldlA76C80rr7yycXj1kSlpJpJtU9Kr72ZgydjcJrB2yYyZzdmaOOUk4TMi3WP/Vh66OnHs7UOr++RrSdb66e0vi/8yPeaTUiyWRdH+RX69evXqDX13yi/3MtNPT+u7b6jHmXDGeZ9tHapciV6XCXL2pbn87qRt8wWJ0f03a4kGVqputNKnukXIaF09c+bMKcWO0cpY1bbrmzyorocfvu/5YseWS/uygLvrGadyWvBiIa3lM+BvErunPtNysRyqjpPEctZD0jpzj3w+TBiqPK8HXEA7vx+Zn0YiPd+TWVl32ypzbX1r1/KAj5rheUBAr1DpjibzFfkc+o0HwilGCGcmG6MXqaVJ5/dstjwESGDlgVcpVeWkNtzTY9XKLzd18hfeCVrZRyttjTTGOHdFO1ocRsqveCPlB95IZ/9+Fznh26Hldlf7nztfZZG+V2QmyYtyKvSi1H9R1sx53mjTLmsxxI3ZE5fbsru9TKdv8zz2icCW5LPvl/fOZLfhyp8t7l295sFVfevJXfZSM6fPdhI1bi43qZX39bErVqx4rm9bXn08bdq5tbISk9s1KpyZFQd9/x1ufHLid2I6ZbdJmdMOV26w1+Ry0FsHe60c++X4Hi3JuCty6DulLPubh9bT7v8qaPXO9PvToW2Vfo/c7fMMeTPU59Dzg6v+vGpNDvWCVkV+/Ln/zJJp7n+ShfqKbiHJK/dbCdfnch2czmmW7AfF+oOu+xq4wguy2+1n7sAt+WCvXE64vqux5k1pbd0hv8vN8EHIhFh8gf2zshpk0fcn5efHjRFj/6pmaZcvfm8qPg89FEOgrrXztkRz9CE5T5xajPbL3aacA3/LzFF3Osm6csfi5/5JYPn56BUwdmdGjHML7rDOvF4Z63TJNNVJAkpOdnS9nATWWHJP+P1/f5e1YeT3G/lV3tn2fXF2Sdk+O/Y+k9v3Oi+8uvUW31dH673lnbMCSYrJfxEliYiXZO96qfaY1H1Mvj7e3b1LchSPvvJqIzzwtYAkFobJe+rKXAZhLOuqgerJe+ZJeRu5SWCpVMo4iZoHBmrPa/tCys4hqTT04sxvPf2tR+4a0T1DTq8vkW/BhTLurGex9TN6Lm3Sf+i3r6xP5T32XxJADieg5kaZefFM/+C1zMDq/ezr/8LhnhvvrLVmLDO3/+fx4ULf/5p8BjsLq1b8Nnv27Al2Wo0KEoSxrZKtz+XWTT7TT5PP9LJt0rdnbYqF4iQmuhbVvDW9UxZT1ub9xeqHdv0nIN8PJ8v35NWS4LxSklm32ca6tncNNf8NhYg9LiDvMxNXztIPxhN//CsC14mJ2tilsl79DUVou2KaJIFVMYf6wED3zqgyb5AZE2+UE9fXywnK62VGzGkhrY7sPUFzMkq9m3yMlH4bJb1Pk8TYNCc75oTiXB4mia2/SCir5CRy1fBdw1fc+9i9L5c+NHoshEBqj3255C7rXLdl1LLVq1esHqjevsu73j3Qa4Pus23n8q4HBn3dQy/ITMbT939XZh+WXijfN+87TPnIK6p7RCHmhkgy+ifr1q7bfZi+SvrSjBkzauTj4xM5dJqywvqgta/2t2Fr+0ltQvufZvVVjtnpWRUsQSGJ5dwcunkxErHuyqFe4KqYtJFjWZafiUWzlM9hzyZp5P2aQ9K+cFRetincKA9tqebaLuePhZdJkmKVHIOfyOPhh5ZiTwULDJf3xUJZy2ehrJW1SqfV22JtSc/87K/g4xKoode1JO6Vu2M+ID9xzwnUwPYPxpgrN847oWXSsk179u/iqzsBEljuvHxbWi4POl4mUc2VjNBcmZkwV05aj3F+F5cfRHs3b/9e7swKcRb7ni4prSteGdHdPXPGrNXK1vdYEfW7lStXJvaNgi8eF5g7Ze5Re3TPf736vnMTb8j+2qDFjVze5fI9LCconkkuDDqufS8YZct6MC4HqNQwqe78K+omUT2ftnuuKWonLhvXxlosVY5wWc0hvkE+TzYPVC8SiTwtn5098pqb9cFOmTdvXvWyZcvK/UuK5P51DpcF6eVymW33QB6Vtk9+9pQ1oVIMb1tpTyawnJ8T3aonWowxZ9um/FHEuQS7YrdYa/JXicboX+QPnEsEIXDv/Yo9sIUb+KNpnVl4fNsWkleFM6WlPgIhbb5oG72uz64gPayrGr37ozKgHwZpUKUcSyH++F7KeOkrS4Fp06aNnjlt9gdnTZ99i8zC2CbrQjwt6apfSsaqQZo4JstmvFpsmJyMSTJO/VAu6eiYOX3WqlkzZn3I+aXXqwET116BPVWpKyR59ZocPH4vl3U9NFg9+SCTBbbdbt65vGvoyOWyXo9utjZf9tLadc5aXpKJ+kAOXKlwxrp6sHqSyHHWK5DPUVdbeMeO3ae4qlGEwmISk2ZHum3aaPs+t3UCW94E7iQ+XVWVy+dm8Y9w9/C0kzBxnbEvZGRWxruz0wo5zsO1FVuafGxn6qgz5Q+fzpqArNdyOKzKec2Wof40UxWeeXzrlo7KGTYjLbVAtKXzz9LnnaXut2T9Gf2F7R881vXvZSWLz+MdkcDy+AFyE55cGnj0rOmzLpPE1R9COrJVfv37lSQLLpE2xrhpx2dl5ZdcPdMYfV13dc8WSWa1zpw5Z6bPxlAR4c6aNWu8/FX7k7kMVi53/frh6qVUKocElpo8ZcqUXNd8Olw4BX3NueRXks8nF7TRAjUmny/3r1mz6toCNVeQZmRdn29JQznMLjbXr/jzivbDB+F+IXe51MK5VLWsm87onJJocnONR8sauJc61wFLYBm1wbOz62zncs3ybqFhofXljcAbvU9uW99T19L5Zbn771nyu9ZfvREVUZRDQH7ePyU/E86ua01ePvHGdmbmluMgVFif8n77kgw5E8Rhy8nr2O7dwz4TxLGVYkwksEqhXMQ+5AT8CElaLZCk1V1yectWuczhfyVxNU+69PyJeRFYhssvWI3Ktlc5a2bNmDG7efLkyW4u9ylCSDS5X8DY+kp5bx65/3nWX426e9W6VQ8frvy+GUDbD1dmgNeqhw37/9m7Evioqut975uZhJAAAgICCQEUNxCXqDgzmUmKiMWlrbWpQtXaxaXqX22r1VaraF1rV9tqta0LCqhpa60LrSINWUXFHXfZkrCICztJZubd/3dDwBCyzHvz3sx9d879/QIz79177jnffe/Nu98959x+8NZRuyQSCaljrnJacrYuEDO+Bb3kiqwSJRQqOwYvBV+3oUybyQK399mOC8tEKcjXjE/G8btghwAViUTLe31ikgUVZBgozJygk6kq53jCPSM9sDJZGkHubcykAqr1XTxv9dKiAxuPwSLUt6Gb1d9a1cwhfawhEMfv6p2BAvMoStxuDTiqnRoCcndUzOvmpiZF3dbYPfiKD88eMVxdDdXVjAgsdcemV82CwfID4G30ayQ4b8Lk5CEQA6eigXqT3F6tcPXk0UgA//DgQUM/ioTKfkBElqtY9yk8OiU6AeGr3+2zYjcVTM673Xmwm6rWyQXTn3FyoRs79jyUUDJX12aeECdXvVS1bk9lM/vNYOJWaIB3bYuFs7/U1y9a1VcrTKytEzoK5FrjjI/oy7Zuzm9esmTJ5m6OZ92hjjBQG1596kIFbwol819JxISRWW83PECUxSaTVxSfzcziec1zDL84BLmx/gBdZE5AKlojwF/B7+oxyIl2WUeCf62tJePUQyBh+q6HVlo+a/BbMyAnEbhSPdTV14gILPXHqLOGBrytTt4ZImhiIsV/hJNez2fV2T43PhdixfCuwYOGvC/zZHkhZMwNEDIt0/Szm6FDwLoe/F/19YvlDpRJFOveMcjtkfHwrr4MA0E9sa86aT7/OTOMk2teqFEqnCQSjJwIHI63gUWLP95z7qvO8kzus0ySIl9fxq8xYYiBne1I8vOWJOtpX80QpvpEt8VRAAGhKkmDDQdYRp95KpN7FofZleqFc5o/LZ7bdKnfb07ABEyGkGsZ4uMKeN4R2oL74IYtsYHBwnnNr7mkNi4fKoRA7wiMe2TlSvxe3dN7LU+fvaT5zFEyTykVCwgQgWUBrExVleELIK7+D2FxH2Ay+1RHiCCNnaUB4cUyTxY81l4PhaLTLTWlyikhEA6XH40JyTdsCBHYJeuG5NtZz08EvZSfmOINTyEdxUcJYYRqa6tqkx+XtNTkgvNbbPZ0b9WSqqZk2gYCtpJeD5P535KR72Idy4lCMXmhpM0dA8Iz7BHkxnXh86lJYHVsOLCPGzYnKxMMWlbvQJgsTqPmrFkNz5wLBDcm43fqIbSjZ0ay4Kldr9pIiMOL5zXNljnQnFR1ypQpA8PBsl9iIX55OBwucFI2ydIXgYQZkIvg2zS1sJ9pGNdqaptrZhEJ4hq0qQuW3kJ4yJ+zZeO2d0Bc3QmJ41OXmvUSDsFF/9/ScOS56HFRW4mNsx5BiwBwlrAX1iXY4/X1VUmv/GHSYd07hrOMe8f0BScSTKlBYHFW6c/xHd3QUGUd576MTPF8abDsTIg4yoaYloTw/zLZdu15cZD7K9n6u+qJuC+z15ng/Xfpkuz/mJAOTbau7vWEfjsQbq2url6p4rhxk2c6/xVCGIWq3mkqDhkrnrv6bRBZ5yRM/wQo+Cf87VBSUVKqLwQQMs4vwliWFz7a/H5fla2eR17akwK+3Dc5F1diIX5AXV0deflaBTFL64+fv2I9gst/q6v5WDD8buOsQvn8pJIkAlrldEjSZuWrSeIqLyd/JsKbroOy+yuvsBcVFHyaabBX4NV2+47WbTcvXbo05kUzVNe59LjyaUyY0+zoCY+azRifq5Jtawo+Ai9GyVbfVW8UdvnbF8TEJ7sOqPQ/VigHwKTijOok2NvISXNlXV31MxnVo4fO28OCuYk8aaBcLBf+kY/Hz8J1lnxLwSw/K3BdShLyP8l34nBNLtrgbWi1DMC90U/ZneqsWpNa/YyTKqmpv1dr6WFk/YrYS4zzB+ApfZitW9k5VeIDBgxQjqR3zjz3JMlQH0i/5KOK8df4A23n4rdLEhWj3euRJDuIwAKEhF4oveoclNkuCl6Vg5mJMH3Bzu8ke3mnz/SREOgTgVgs91eBQNsPUFHHxTU/0t3cCNtm9gkEVWhHgAgsxS4EeBKAuJKTMUlc2ZmQKWaQ2ur0g3rX98/NPwlEwXewGoTdLqg4iABnhrjNrjx4VJ1rpa0N8qpdvNnWnt9mkZW+0lVXCP8k4JCJBwGcTtj/8Aj6fV199VOw10yXzVb7AdkvX4ptEv1C5tqxfY0mq6vIdK41IeB6b+MyamH7wcaVydqpY71gMDgEdo2yZBtIX7jxXGapjc3Kwkh8CUn6f2atuboeRtgdcZJVZg32X4Rdbj+whkH3tYVPtC5YsKC1+7N0NBkE9q9cvgn1fr+sYuLdBYGNZ2J8rsB33UjgZKDwQp3Pcb9djXDBe91QFnOaCmYK6ZU3bE/5nAisPQGhb30gIJ8rjTMLb8cbcdJe832IVOw0P2PlmaN+OfaRNa8qppiS6hCBpciwgECZyE3jj/C6Krc10VDEDi+qgR/vY7jwLYUXxjW19dW/gQ1W35+9aLbrOsNd/JtMiBLXO0qxgw5yQUkCC+Gu6Xzp34grvwFw/pf5zMframsdX4lNcaj2aj598vT87UbLtcrfsRnOtcY53yQZSaslzs0Q2qy02k6n+n7mP8wydFwsqW2oWpgOHPC7VWq1H1CZyobIAWurIdPgruIP171A4UhWrwO363fkT5qDfuasmjmm1ODmVRjfk/HdBpvutrbZJx/EYqU/Frt4ZOW6DU5bL/M+4sb8E94BT+tONrzzVnR3nI4RAr0hEM/1/8HXFr8UdQp7q+fRc5wbxk3QXT4jqfSBABFYfQDk9mmEv/RHYvGfYAL2U/yk57jdH8nvEYFcnPkVcmN92d/mO7vqpSrLeW56lJyFJ2RYF15QpCeh8sUUhtUJU9psEsyEB5bFd33BHkOTvl4O4zBiC2RvRsjOCsM0P6peUv0RjinradUd6Nvyd/wI2dull5Dq5eCMhuMJATLS4nUkEeWsDP/OUx1cN/VDqPlheJZZKvAiSh9BJPNzWR1awd6wZFCaKreHAzN2kMXuVlAuHYuIZaB68fzVtei2tiPPyyW4pb6P75Zz82VAdR27XItn1MVFcxsfd8E4XhoqO08kxB2QPbBH+X2/o/TYlE5kLwLjHljZsmrm6JuwKPdnHVHAT/lJjWcVlhc93FSlo31O2kQElpNoWpQFD5Uz8GL8azSjHAEWsXOtOnJjxQPmy+Fw2cy6usU1rvWjueD+uQXfRzy3RxISisPVHQ5uWbcEM65pqK/6UF2bnNFM5i6Lt5kyNMULxW+2mnLTiEy5htsN15gGnSU9YpHC8cKQJKcjCFLLXpAi4UsfgcWte2m2JnJkDizlSn5O/sFg0C0t5OHCTB/WyiHmPYWQIFyGel62tmK/m9oC/u/i4SK9KayF6HrPbFU0lvkIHkYWx8uLKps+c1qpUGjq/pzFEYoopvYpW5h2f5P6FE0V9EZgTLz5r02Bwsvx7D9YR0vhuXgT7LLsWa0jFr3ZZPR2ks65gwBca4eFQ9F/gbx6BD0QeeUOzKlIHc2FWBQJRf4vFSHZ2hY5Y/JAXl3jFfvxQndox8q/cirjAT3RolLbGxqqsuLFMBEz5TXW8wqvReDcrp7JPFjYVe0dm/aNx3PwJJttdWk22aohMZEeAks+a6HbeIv6rXnxxec/tdgmLdVBXlkmC/H8JgIrLaPjbCcybA15l25v/bzfeLwvfBsUuZKkqrNWZ1TacvwGnSB3i3SavMJikh87pl9miPjruB/7Jq8AA3LNrcgoGtS5ZxHglSwhGL/Bswb0qbgIw1M129+7+kSJCKw+IXK2QigUnS4S/DU85L/qrGSS5jAC2BGC34n8IveoSm44bK9j4nw88CMI8xIxm5ubO8Bq2IpjePUkKBicOhorTEN7Ot/DcTkJwDxQ74KV3mLkdJK70XimZDJUtba2VpKa6+2AZTIu7+dsLfipZodaNP6TJUuet4W1xX6Yz+eTuvkstlOX8EG4pkVb5BYXRHxYBU2h+hMWfNhaPK95zpj5TYcJYURwwz0F9fDTR8UhBOLA9M4cX2xy8dzm5x2SuVtMZEpkMjyhG+Cn+zv85e8+0fuH+I4dOxp7r0JnCYGeESia1/gonMNf6bmGt8/gAXibmM2Io+llGAmcXsBx8hRIkP6RYPRuAP4fyCV3aSfBdVfW+Xn9+j+DJPsD3O1GD+nt2yUz9mOvWWOI9p0IlVLbx0zLkzm89qs7OXUQXYPHb4Q4mbfOOyXjOxGyF+2AhcnP1Egwcrydtl5vE41Gx8IGS15+ePFMW34pbloPb0xrfi6rF4DM52WxGIa6OypaNCXrq8s8WfAQOtVIiIMl6QJAdmQ9KKkB8CbjPARML9vvofXYida5InM6YoF3tvDxlyD1aEuSBWtcunRpzFIbqkwIdEIAzweBncev7XRIt4+HNb5f9E3djHLSHiKwnESzB1nhcPnRebkFr2Cl8EJUwX1HxVMIIC8WF/7/hUKh4Z7SOxPKmlz+oAzORNcp9ZlpcqEb5bmRsDyZgxjtCaxQqHwSiLpvdQOZ0ofwY2s5n5mTBiHp6QK78vCm+OCxxx4/1G57r7ZLJKwTKviBT5tHEMgyy88IYSr9jLBqT+v27ds/8Or1RXp3j0Dho83vS9IlEfOPxzxV5oP5pPuadLQHBFowvb/244IRJWPmNkqCydESOS4ShteVzOd4Pf4s5axrV4SzrEhz4CjoJGwvBIrmNsl3muq9TmhzQNz48vklAW3McdgQIrAcBrSruAh24+DCrMOPsHIhSl11pe+9ISBKDOF/IRgsP6C3Wtl8Toa8gZ/1VFjX7vHilrdu393UrQ8IkbM6mWNGOnc/c8vwPuQaTNyKKlbDpvqQ6v5pkA1D4cmZOe9bn/kvWGk3vHR0jj/+V7R37Z0BK/oF2DxjhvsjkXwPyFNp+R7EqnAaSWTrHlgYwDTqlzzW00qmDULtouRbyJr8XfLksIaYl2qPq1y5bsy85p8jT1Zhe54sxt72kv6Z0BUelvVmInHUmPmNNx99r7NeTtMnT8+H19VtwuCSNLCdQBvpOVZkAhvqUz8EBDd/pp9Vuy2aMGzr+u/s/kYf9kDAtZfRPXrJwi8yb1JpOPpH/OhiRw4bKxRZiJnyJnM2zsfNxZiEEhnZzWAZPCFXSvO6OaX+IWF9tz/3jTIsJ4+Os3javD/ct3/vHkpLy7Ezizhl7zNeOeLPmBdWTU3NWqBUYx8p8TVMXv5SUVHhOHkYDUWjWNFfiheSY+zr53xLeFNZJrCQKDmdBNEki1bHjVzDbkJ/i11Zq96SF5dYW/JQBymcTqytGUS1HUNgV54seGVNEsw8ARcJ5cnaG93tuB+uLmxrio59dK3j97hcXNhe0CIJxKvwl9LcEQttRGDtPX50xAYCxXPX1OG6f8ZGU080wbPu+jXnj+rvCWXTrGRKD6E06+qZ7mSoRb/c/P8gzOVizyhNiiaLwCgufIuxk6TVxL7JyvdkPXimIWeFOMuTyu9UeqRKIaJyVx8QNdZWODlbV19f/7GHx6Bv1YV5W9+V1K3BTZExAqsdFcH/lCI6313bvP7pKVOOH5GinPbmyJl3IBZ65sItrAoHDmTCXO2EXKdk2PCCFChp8RLBDoRDYOdIi7Z+UFVV1WKxTVqq496wTBbiBZYIrLSMjhqdYDIniuetWdieJ8sQR+K7XCBW8npOK2Kc/SfOEofKXR3lDm1O9i3zmsrNjLAztyQJxjgiW5gUQugIkCREIgBC9Gr8Z9e7XHUQR8W3+i5SXclM6EcElsOoyx05sNvHS/hhneqwaBKnDgIjsJPkouhx0UPUUSmzmvi5eTs0AOni3eITPssTKLesTSQSB0J2riX5midwD4ejX8GiQNgSJqpV5sKyV52TJuxo2yrDCJtSlHliwBd7qzRUdtHEiROt5z/B+6ZMCh8ORf/OTGMZxnQW9MFPpvyHK0NgzZgxIxcKyfvQSllRV1e3xUoDu3V9vlzr15LCzwh41lh+/ppcaO1xavfayIZ2hQ83vw4i64K4CIyFB8YNsPnTbLC7i40bYfsFyAV00vh5a1d1OZfy19JgWQWe0e9B0PkpC+skQBiCPLA64UEfU0MA1z8WMsRjqUlRubX42Ypzx+6jsoaZ0M3TE85MANZbn6XB0lMQ2/0IXnqT3Uq2N3F0Tm0ERpgGezYUmlpaX7/I8RcHtU3fUzus0E1BYuBT9zya7Df+49r6xb9JtnYy9WQulZbcto3J1O1cR3BDesc4vs105z6S/Wxid7H2GX2yDdrrpTV0yZJmDlQ2uOA34iXFhii+DNeYnOw7ukIHAqcS+nzDkkIZDlWV+YKg9y+g9z2W9N678r6Q8afBg4ZeWxqKPGQw/nT+oPwlCxYsaN27KjPwnBznY7GgyYwyeGqeilEc0d31HROGMgTWpk07DjEskvKwK20eQdxMHIb3jW7g7uWQ0jnyuNVwSMZNtm/pceXTerHY8qkEN95paFjUbLkhNcgIAuPnr1iPjmd/MOOAW3MG7zgDOaCuRgJz7RcXcec/JUx+QfEjjWucBh4RBiMh+09MiNOcli3lYUMR8sByA9gslonFr+vw+3s6INAx6flgIxb/IWy7PouHeC/TicDaCxJ7B7BSMZNx8SBa63jz2ANF/1aFPhZfiNCzsPahW72NZYLfhnmUxZlUu8C1O1q3/rk30XbOLVy6cBPCktaB69jPSnu4yFv3aLDSgYW6tpJHp3HybMEUR6pGQpFzkE/QVvgdchJdByUcJa92GiXkyrS1Ao8euf14JsO4/Dn8vngb+xEIKCdy+Y3EdOQnAPcnWzZtS0RC0RUIu/uUGWILJpFYMeQD0M84xuI5kmwBedUbXmKfffIae6uQznPYLMCyRxAegmkjsICndcKHKU1yW8YbF9SDksVysvgEmwF5RGA5CWoaZMk8WehmjpjNHl79/qipBjMuw9PGw/kSewANqQJwyV9SNL/pHz3USOUwxwLHeSIh7oCQgakI6qXtVuRj3NDLeTpFCFhGAN6YH6yaWfgACOzzLDf2QAPMCX60fOa4uzoIew9o7L6KWGCkkioC4XDkQpBXD0MOkVepgumx9nhBOsDH/E9hUlrgMdUdUVd6HWI5rdyOMExmb4VHyHY7bftqg0n0+33V6XpecGaLIOkqx4nvmAhbn8yZ7A0n+lZNhgxTE4JLEspOebOurlqGzTlfbFxjUMKfaElkNH8eyLM4yBmZU6FXNskGYJj7swNAKkwBeSW9Yo7uIMmSDTNc14MHlw1VUm9iI/8VzGZpC2mz84xA6E7aCDYrIxCNRuXug0qESCSYoSRGVvDM5rp8NjN35ckyTfMo3CcPAY+YBpjg8cIe4m1sohvkFbxk90dY90I8s+8BVm6RV3IYVmgwFmSCggjAS3821NqhoGpOqFTgZ3GZ64tKBwJEYKV4KYC8+ilumrshhrBMEUuvNsek7Zh4q/mYG7tzKY4JIocMhCPZKmviIv5XWy2TaARyzLp3DGOH2szpk4RG1qrYmDwn4jz+jrVevFF78KB9LwEpMs6Wtga/Bu2cddHYpYjPtHONMWG0h6rukpKR/6vrqxehY/m7pU4RbLU6ysB3zLCek8lIH0GEBVk20RJegm2rra1daalNmionEjYIe3d0+5zCB90BNhNSxz6y5lV4ZpyTiPnH4H65ATp8lgk9HOhzBXLETZe2FFU2OWoDFl/9pcHoZYaIvw6CbKoDuvYqAuOwvNcKdJIQsInAmPZwWqHWe41NW7ptxsVFK88utvcu3K1Abx8k0sX++MHVNnI7yKtb7IugltogwNmMdWvW/0obe5IwBC89ctfBI5KoulcVEDQ3NjQ07NjrhGMHuB1yIWfowKFOhFWlZEU4HB4AwmasNSH8Q3fxtKaNU7XbsWDiKnvy+NLa2sVP2Wvbdyu/32/nGoPDohqhqv239vsJJhOv9m1pmmpw3pimnpLrRnCrXpCt27dv/yA54anVKj+uvBgSrHlJ7Mx/5Q6Zm5o5MgpwcooiHGmO+4G8rxxBUi0h4ypXrsMOfbP7xVqLMcYX4M/WszsDVpkgle6F3pOlV5nT/ctNpxJtZj3eN36Hv3yn5XcrT1D+q25xoYOOIOAX4lYI2uyIMPWE5BjxxLXqqZUZjYjAsok7tpX9rcz9YbM5NdMQAZAyl0eQP0BD0/Yyqd1TidtNKChWbdz86f17CXXygLDnHZMwMj+REsIvc9vgvdVC4ULL8EEufJK8Gm4BiS+qCi5/6DFXcacgFG8jJH9sVTqeE0qEqj77xrPbGE/IHDHNVm1wp75Y5Y5c61KDweAQtBptrSV/VybJt9bGXu2433p+LtwK6pIzhhoeWMifoi5G9i4VatUJgeGVG7aCyLp3zIFNhwphfgU/s46TQp26S+2jYG/hJSAod1qUeqcmbM/W8LrqhznMbOHjL+EH8pg9z7r7zeDmCnd7IOnZjMCo+Ws+wTWN+bmmhbNvrzxjpPabVCQzekRgJYNSlzqRcNmNOHRZl8P0lRDAbFn8CVvEl+kOxT4Dh8gcOuNt2Ynd5JYtW9Zmq22yjXzWc2C1izZ5xskFPJSten4wYSo8OU12zLrUw+YIkri6tMvhZL/W1zZU/SfZyinUs7OSn/FrbJe9dXV1a7AzoCSxPtl1LFP/CyGaMtV31379zG/9Hkyj9452mzwI68+8rmPmxHf8fqcth5kT+pIMewi058mav+bJMfMaTzANswREkcyTFbcnzfFWMehze+vGfkeDvHrRaemR4yLheJv5CuRej79k8xM6pgae80RgOYYmCeoOgbxY669AYlleXOxOloLHfD7DJzmIrC+YK1GxggB26LgUD+CfW2lDdbMKgYDg/LFgcKrF1XvvYCQT1mMb5J/a0RgvZh/6c405dtpaaYPwLplnwbI3hArhXdgN0fLkGQ9y7TwHDOabjTEcYGXcd9UFHul5RttL5D5EpedDfX3VayCxvgTs5Hb0mSsGX525zvfs2bThEZTOexDeI9JL01IRhk/JZ0RJSUkAhmQ8dFuCaST0e45aukiysPLYh9e8InNLmYZvV56szzMIwwtCGEdCn6s7dlV0TBXcZ/3hdXWbMHg1hGbMg4P7KYTQsUElQd0iID0WMUf5ZbcnNTiI3RxObzyzcIoGpqRkAt65qCSLALZyPxdu+L9Ltj7Vy1oEhvt5vLLjxVw7EBIxIUNnbYV1Yev3G+UuaG6DsrMPGy9KCuxEiB8nywRWggeUnJzaHWfsSjYO4R3fs9de1HYkKbfX3EIrbtjaLIAZRlwZLyxpbn191Vv+hHE0Pr4gv2eicG6uykS/3fWJ3JaW70Ekq0njPWhdP84Ty7qzNdPHcnMHSPIq7Z4g3dgtfP18SmLUja50yGEExj68aq3Mk5XXr6U9TxbEW97JOAWVtsNj5OqiWFNp8fzVjl+D4XDZjLzc/HegnwzJz+S8TyBP4MoUcKKmhEBSCCQCvj+holp5NZPSPKlKXBjsxqRqalwpkw8yT8EK8up0TL7/CqW5pxQnZTOCAF5Ggv1yCm7OSOcudhqJRIbBA/Fym12878/h8222tdzM1k6Egu3XEbpmuT+nGuABY827AruL1dcv0sotX8TZrcDT1qQW4VXXOjUWfckxma3NAiBWKEVgSTurllQ1+XOMLyGM6g/4mvZk31gxXS31UKHgHrRMYAXiPC0EVsfCyIEWcVpbU1OzwWKbtFQ3bHicuqRYY0deO5fEk1gvIDDsvk+2yDxZRQc2HdKRJ6vOZb2r8bw5An3ezitZwsm+SktLB8Pr6h54dT8DuWOclG1LFmfrkSdwu6221IgQsIDAuAdWtmAedpOFJl6rOn3lzNFTvaa0k/oSgZUEmqFQdDrIKznx9iVRnaoQAu0IIBztChCfX9UJDmHy62GPrbAueBZdlw7vq114m8IeuWCYORnbEQu77o3Cj+7QXTYk+b/M25J2wiFJ3SxXw0v34cCgwnLDnQ2eq2moWWyzrY1mCTs5sBiWzzJ2jfVmJO7Plrr6mksFN8JYq3m9t7oOn9sOgiXjebg6bAIHyiZatG8TCMBmi21sVc/PyT8YDS2Ru5ggp4Vcs2OQHbLQTj9JtFEWoyR0pyoOI8BnM7N4Z56sUsGMo3GdOp0nayOeMxcgXLAcfx84rD4rDZZVMNOQv0/nOy3btjzBlttuSw0JAYsIjIk1/Q337bsWm3mmusH5rXiGwMTsLERg9THumFAeBJAeRbVAH1XpNCHQFQFMhPgDodDU/bue8OL39rAuwc6zpztfVldXXWmvrb1WhmHaCwEwzIx5xximYdnzAznX9Jp4mcYdGHFbv00gXq6zd7XYa9XS0iJfyC3nWlPRA6szAnV1VS/U1i8+ihn8K3g/Wtr5nMOfP4e8e5GDS+ZzwLtY5kv5ceXF0GKgNU3aN1FIi/5gqq0/IxQmsOyETFsbmyRrpzUENEmdqJoSCBTPW720PU+Wz3cgZou3Qym5A63tAhlPcT+bJD298NnR5wa85EeWhqP/xCrJY1BwmG0lXWgIW1e4IJZEEgLdIiA9GgU2jer2pB4Hj208q+hUPUyxboWtSYL1brzZQrrfYhv3J6H9Pt60gLRWAIF9DB7/J7Zlz1NAl5RUMOPiZgiwtPK/q0PBhSQWMPdKX8Gk2JZ3DNYzMucdY1jvGy+F2hBYHTt4nmDnKiVC5sYAAEAASURBVAEOz0jixU5bu20QDiHJq5U22h/ogWeCWVu7+EkQWccgaVcENt6LP0k4pVY4W4c52/0gLk7/fNOn+9XWV19QjxxcqQl1rnXMZ9q5/9N2D3LGrYUYAxpgnTb9rI+EaZmQs95H3y3AIihzDfatLdXIBAJjH1q1QiZYR54smfD9cvzmrLKmh1iPEO1vQsapRXOamq217bM2Lw1GzxEJvgyU2Gl91s5EBW4jL2km9KQ+tUGgaH7jI7hPX9XGoK6GJMTNYra9Bd+uorz23e81hdOlL3Za82OrWekxMiFdfVI/miIg2GQfD8jk/xd42UK/6ftZ3B+3lV+orrY27StvLS1bX8jLy7Ps/YZcPDsyNU6tsZz7cnJa/2Gl//6b+6+3Ul/luq2JnLdgv+Uxkza1xWIZCUETPFGOa6afVVxz/bk2PLes9uJIfVFbW1ULSbUVFRUXrVmz4UhmmuV4KTwGxMgE/C9/Iwt66OkznH8Pk703QWK/ibDqhtra2ldQF4fULPF4a1UgELB6DaZO7CULh9/8LRNC5uNMupiJxNqkK6e5ouHnXzJNpMDPcFEZowxDQ913QUDmycKh32Pi+IfV7406mXHjp7iAg12q7fEVxHMl94sfFM5p/nSPEw58kV7+nMXlAsNUB8S5JkII8sByDVwS3C0CuC/FKiygY2OWJ7ut4PWDnE1qeq9wFmNND3vdFKv6Z/ylwarC6aqPxIdyB4OL0tUf9aM/Akgq/p2a+poH9LeULCQECIFsQgCh9gMCbYH8eG68f4fdn4OokmE2yhJV2TQ+ZCshQAi4i8Dqs4oiwhSXYVJ1MnrqvKDxAnJJXF00v2mx0xq0L7S3mhdD7s3wHM93Wr7T8pBMvjy9OSqdtoDkeRWB1bOKsAgnwl7Vvw+9l2+JDTpkYuWytj7qaXWaCKxuhhNuuBfjx+CP3ZyiQ4RAKghsh7fGsXV1dctSEUJtCQFCgBAgBAgBQoAQIATUQuCDbx0wMEdsP9YQxiDMI95wI0G7tDgyJTKZ+fhfsUJwjFoI9KKNYRZjYWN1LzXoFCHgCgIgsKIgsBwnkV1R1o5Qzi8ZM7dROt5kTSECq8tQh0JlxxhMyHAJW7l+uoijr4RAFwT4sh2tW4+lrYS7wEJfCQFCgBAgBAgBQoAQIAR6RABeV/0SreI6hGRfiUr+Hiuqd6Jt5OgR/SsrKxPqqaauRkjKPywej29taGjIWGoLddGxptnqbxUugE/4l6218kzttTm+2IT9Hlq/zTMap6goJXHvBOC0kmmDDCHkjoNEXnXChT46iYCYmJeTf6eTEkkWIUAIEAKEACFACBAChIC+CESD0RBy874C8uqnsNJL5JUclFVEXlm7NktKSvqzBH/CMHInWmtJtbtDwODiahxP62ZS3enh0rGRrfHA/7kkW0mxRGB1GpaW3Na74fI7rtMh+kgIOI8AZ98rDZWd7bxgkkgIEAKEACFACBAChAAhoAsCkshAXt7bsNVBDWw6xIt2IdxnhRf1zpTOGPMAFrv/jhDRIHKHKbFTa6awcKrfwoebX8dmCpY2SXKq73TI4Zxd1VhROCQdfanQBxFYHaMQDkcuZIzPVGFQSIdsQEDcFQyWH5wNlpKNhAAhQAgQAoQAIUAIEALWEAiHy2bk5ea/g1ZX4c+7czbBlluzPKtr8365BffCoWLGThQEeWA5dDnwhCl3Uo87JE41MfsIP79CNaXc0se7D0MHEcEOShOxxeavHRRJogiBvhAo8HHzsWAwmNdXRTpPCBAChAAhQAgQAoQAIZAdCJSWlg6G19U98L55BhaP8bzVnJMHVpKDGAlHf4ldy8/dVR3ea7Y9sD6qGD9olxz6n7HCR5vfBw4PaosFF5euPKt4pLb2dTIs6wms6ZOn53PhqwQmu7b/7gQPfSQEXEXgMB/3E3HqKsQknBAgBAgBQoAQIAQIAW8gUBosq2Cm8S60Pd8bGiejpUkeWEnAVBqMXiwE28OLBmGEk5Jo2m2VQE7bf1fPKvyocVbhPatmja6Qu2R2WzGLDvr95o0wt1VTk/MNM3GNprbtYRaI3ewuWOH4PRC4NLtRIOszioBg36ptqJ6XUR2oc0KAECAECAFCgBAgBAiBjCCAaJABTPgewsTsqxlRwM1ODfPo2trapW524XXZIC5nMi4ehh17OZe0xQP7vvji859atXH1zMI3EYrYmQBrxfVVh0zmz/qY+O/oeTIvFPbmy7KyembRb4H15Zqa3SaYcUjxvNVak8Z73SSaDma3ZoXD5cfhxMXdnqSDhEC6EODsz3AXPzBd3VE/hAAhQAgQAoQAIUAIEALqIIBokPlakleAOJFIUAhhL5da6XHl00CoPIAq3c7Lc31tnUmoXiTteYobbNWeR1gu2KqpuM5uMxl/FZ5Z6xtnFT22albh+Y3nFI7uUlfbr36WuBk4bNHUwByDmbM1tW23Wd3eKLvPavxhxowZudw0/wYTfRqbSaZ5AgH+PNTc4AlVSUlCgBAgBAgBQoAQIAQIAccQiIaiUQg72TGBagna1NDQ8JlaKqmjDRawS5hh/hMa5fSklWDcFoEF36q3epLZcXyYYKIChNY9Is6aOsINf79q1qhpH8w4ILePtp49PWr+mk+QZ0xGYGlZQM59q+ms0YdraVyHUX6djevNti2bt82GW+WhvdWhc4SAywi04sflqtqGxXeiHzxvqBAChAAhQAgQAoQAIUAIZBMCgrNTdX0LxMut1qFMqVynodDU/ZkZfxoyBvQmBwSTfQILjS2U8RivSzkzLs0d3LJ99ayiehBcC+EYthAhaVqFgMZiub8KBNp+AGyGWsDHK1UN0+Q3QNmveUVhq3pmpQdWKFR+BH4ofmwVLKpPCDiFAH4gVmLFpQy5r+QKAJFXTgFLcggBQoAQIAQIAUKAEPAQAkjcfYiH1LWqKhFY3SAWiUSGGSwud5kc0c3pPQ4JLmwRWJyLvjyw9uinyxdsbiamgf+6jTPz5dXfKlyLkMM5Mhn8qlljBnep67mv+1cu38Q5+6XnFE9e4a82zRwdTL66t2pmHYFVXl7uR2yoDB0MeGuoSFttEBDsn4Ec40gktFyijU1kCCFACBAChAAhQAgQAoSAHQTG22nkhTYGZyu8oGc6dZQJ+0WC/wd9Jpn/lh+GutZ8qdBgx+d57+C/OP5SL4Lth9X2sznjj4HQ2oBww5dBaN0mww1fPr/Ek3PqeMAvI2CaUgdHTQkm57epqVnqWmUdgRVvM6Xn1VGpQ0cSCAHLCOzA788F8Lo6vaqqaqPl1tSAECAECAFCgBAgBAgBQkAnBCQxUayTQZ1tEYITgdUJkJKSkgAS9lfikJW56KDyKeWWk6xPWPBhK5LDf9Cpe6c+yvzRJSC0rkK44XPDt67/DGTWkzIZ/PJZIz1zLY97YGUL5mW3OAWKgnKikmBUUK+UVcqqHFihUGg4EPtZyqiRAELAMgL8PWYkzoDX1euWm1IDQoAQIAQIAUKAECAECAHtEIA3zkgkkkC4lqZFcAoh/GJoeV6//L9ivE/84lByn+JGex4sy95CXBhvIY+V2yGqBSCzTgETe4ofe6PBO2s5Pi80kT+rjef9d8LcDzcnZ2X6a31cMPyvIOCkc8v+6e/d/R5BMN6KsXke44H/9ClZRWAZ3H8jhm+gPsPnqCXywv4Q+HwItv4TfN7ABN8Ah9VdF3wejvXDThhDcWgkPkt35wn4y6prCPbaKOIhf45xUVVV7VYbjakJIUAIEAKEACFACBAChICGCBgJY5zQOB5GGLEVGg6bLZPCwbJfMyHOsdOYGwkZRijDDq0VgTxYnFVYa5RybZkM/nyEG56fK1riILRex/enkAz+yTHzVr+iEply9L1LY40zC2djI4WHUrZaTQFHN80qPI3Na5I7XWpTsoZ8QOL2SUyY39dm5FI3JIGHSZ3BxVMmM17Oawm8snDpwk1WxE6cODFnyIAhk4RhlOKBXIYH5HS0L7AiQ/O620H2XVxTX/OA5naSeYQAIUAIEAKEACFACBACFhEQnGvp+dEBgzlw4MCVFiHRsnokVPYTeEL90LZxJp9oq63B3tztimBLQMqNJNdQAtKqhDHzeoQabmhkvEp6Zxl+9nTRnKbmlHtIUUDhQU3zGt8rvALz2MNTFKVkc5OxW0Q5+zevcigfmgJWZg2BhcTtvwLeMmY328sL2O3kgbgZ+NeSJc+vTwWMZcuWtaH9Kx1/dyJBfr9EW+JEsNgXwntLusfieZWthS/jPvObNTU1b2crAmQ3IUAIEAKEACFACBAChEAvCHA2rpezXj+1ZsGCBa1eNyJV/UuD0Vkgr1JKqI25lT0CS7BUdiJM1fTu2g8DFhWYIFYIpJfvCDeEM4X5ZNvn/Wva83Z118rFY3w2M1fP4tcjyu5fLnaTMdHA+qDGUUVnMdb4QMaUcLjjrCAYSoPlX2bcXOAwdl4SZ2Kgn4Kn1B21tVW16VAcMf0HIUnhpehLuspml1eWYH/b0bbt0qVLl25PB9bUByFACBAChAAhQAgQAoSA9xCIBKMPgJz4tvc071tjRHpU19VXl/VdU98akWDkeHjZPQMLc1K0cnttffUAyIBDTfJFzGZG4/uFMgdVfvKtMlYT8yZeD4JrIcINFxbPW700nZogCX095svBdPaZrr5g16qWz/sdlAmC0A0bs4PACkWrAV7EDQDVlylqeYJdXLOk5o1M6BoMBof4eOAX6PsC/OnuAbdVep/V1VXPzQTW1CchQAgQAoQAIUAIEAKEgHcQKNV4jsIFe7Cmofpc74yGs5qGQmXHGEwsglRHFvJN5j+gvn7RR1a1hJfTS2hztNV2Ga/P2TpcQ88h3PBJTCElofW5mzqtnlUUhRfWYjf7yKRsEMqXFc9rujOTOjjVt8ZpA3dCJB8e+JSN5NXniHm+vLa+pixT5JUcgYaGhs+wYnAx8mwdjYDCup2jouG/gr0teOI4Iq80HFsyiRAgBAgBQoAQIAQIAXcQkJsiaVmwqLtCS8OSMCoYLD8A5BWIF2fIK9kl53F7YYQMidy9WATbD6TL2UgG/xhn5gYQcS8jh9Ztq2aNmvby+SUBp00aM68RDi/iOaflqiIPXkvXbvjuvtKLz/NFewILBv7E86Nk0QDc7ItMFj+4tqH692hqydXUYldJV6+vr3qttq46gofQRWjUknRDD1TE6sCf/blGSV1d3TIPqEsqEgKEACFACBAChAAhQAhkGAGZOxYqyJ29NS18uaaG9WpWJBIZaXBTEiEjeq1o8aTB+CSLTXZW54Y3Caw9jZVRPCWY417FmfHc8K3rPwOZ9STC/s5fPmtk8Z5VU/hmGj9Da3SjZRm2oyXvMh0s05rAikaj43ANnqbDQCVpg7zh7mxp3fbl+vr6j5Nsk85qoqZ+8d2GYCF0qsOP2hYkq58F9+gfVFVVaUXKpfOioL4IAUKAECAECAFCgBDINgRaW9lY2KzvXMzgK7JtTKdMmTLQTPCn4e0ix9bRgtxQ9jyw1Evk7gQuBZj0ngKc7/Ez30p4Z30EQuueVbNGV3zwrQMG2u1gzCONL6Pt43bbq99OXNF0zuih6uvZu4b6PjRht5lgP8Z/uudd2jXCbXDVPRPhepcheXhs10EV/69uqH6VGSZisflTKuqXnE58KWLRj6xtWDw/ufpUixAgBAgBQoAQIAQIAUKAENiJgGGY43XGQoiYDovVSQ/RxIkTcwK+3L+DVDky6UZWKgpuj8Ay2ZtWuvFo3fEgtM6X4Ya5ouVj5LN6rnFm4ZVNZ40+HMcxJMkXwY2fo7YSEUzJa510zUGJBL8q6dqKVrQ0oIra0K1acMstiLea63DJ5ndbQa+DrZyJ02vqa572mFkGklfeDZ3P95DeAisgfxw4qOBK2hrYQ6NGqhIChAAhQAgQAoQAIaAQAuFw5BIu+B8UUslJVVqwqN4fAsEfZEUxSsPRR2BthYvWtvpzjAJEfcSt9gEPpU/QxvOeN1bt7qi/AcRWFZLBLzT87OmiOU3NfckB+VUJx5Bv9FXPo+dbDC4mFM5tbvKo/szvVcX70jvehtDBLCGvmMEramqrvUZeySE08eN2YSQc3QxW6Iq+xlSB85vxMDuvrq7mMQV0IRUIAUKAECAECAFCgBAgBDyKAHKojvOo6smovQKVsoW8YliQ/43L5JXEPBdhpwfg/3flF4tF5umNWmyjS/VhcD6ogNdOhQD1BzLvbXx+0mTmwrbP+9dMWPBha1dDORc3C8ZPx3EdnX36YXO1a2HbhV3t9sp3fUMIuTnLK4OQgp64/nhFbe3iJ1OQkemmoqau+kooMTvTivTav2BL/KZxOHYZJPKqV6DoJCFACBAChAAhQAgQAoRAnwhwri2BhVm/JLCyooC8ugaGpiU5tp8n7IURsqwII0z2ejsUzGp7MvjcwS2fyXBDJIO/atWsMSW7BBTOa34N3NXzu75r978Q32s+a9RBXrVLSw8s7P4wTCT4NK8OigW9b/Q4ebXbVHhi3VAaisDVmKu2a6RcPfrD55s/vXLZsmVtuxWmD4QAIUAIEAKEACFACBAChIBdBARHDixNnZSEFps19TmykVDkLIzgL/qs6FAFeAXJnQj/YVkcF29h4ynLzbKgAeaeYhqQAW9gSu+sFYzzZ5Ga5784fhcihHTlE/wJ07gO4/stL46xnh5YCXYmBkNLcu6Li4w/BdInbQ/ML/p171Ntfc3VcNSc414PliVvwgPsm8D5MiKvLGNHDQgBQoAQIAQIAUKAECAEekRAaOuBJThf0aPZmpwAeXUyCKX7YU76mCHObHlgCdP3liawu23GOCbEBSCu/ok/3aNuzmg6Y/SBbgPqhnw9CSzGZ7oBlkIyt8YS/u9DH912SBCfb/z0PKxkLMo41pzVIdHfYUiM//eM60IKEAKEACFACBAChAAhQAhogwA2m9oXxgzUxqCuhgi9QwgjwcixQvBHYXZ6HSZMewRWPO5/E7piikXFAgLpHVsLijlU1Wca3tyRUDsCqz18kLHjHBpYVcX8esmS59erqlwqeklPp7zWnK9DxvupyEmhLRZT2G3+gFFeXV3dmIIcakoIEAKEACFACBAChAAhQAjshUCiNTF+r4M6HfAllutkTmdbolOiE+Bh9iTmC+nf6Z6zA2fMmJHbWZ9kPu9fuXwT6vW5+14ysqiORghwdvaKM8eO9ZpF2hFYIm6UYxDS58qZ/hH/WPDEr9Pfbfp6XLh04SZkp5c7P2xPX6/tPX2C8PBTauuqf2pni9o060rdEQKEACFACBAChAAhQAh4EQHOtCawYrGYliGE4XB4lOkTz+GSG56hy86/beM2u2Ff0guLCiHQGYGAzxeTm6l5qmhHYDFufslTI2BRWSHEn+vq6rZYbOa56vX1VW+BTDo/jYovTgj/Edhl8Jk09kldEQKEACFACBAChAAhQAhkGwLc0JnA+mTJkiWbdRvSaSXTBnHhxzyBF2fSNsGFvTxYnFEerEwOnKp9C/7d1WcWjVJVve700o/AYlxrAosZpu4J5XZfpyCT5iJa+2+7D7jzwQQpeOPI0SOOb2hYRK617mBMUgkBQoAQIAQIAUKAECAEOhAwBRunKxgIg9HO+wo5y/q15LY9iTRSh2d63JDrxBaBBd2XZVp36l9JBPoJn7hASc16UEorAgv5r0bCzoN7sFWHw2/B+yqrHj472rZdih/CD10avA1ciJPqGmqur6ysTLjUB4klBAgBQoAQIAQIAUKAECAEdiOAXa61JbCwe5tWBFZFRYUvHjPnYfAiuwcwox/4JFvdC9qJ0BZuWdCIC3bey+eXBLxiqlYElohr7n3FxLNeubCc0nPp0qXbQWBJVlg4JbNdDhcLY4nAYTUNNf91VC4JIwQIAUKAECAECAFCgBAgBHpDQGicA8tgWiVwX9O87reYhZzW23Cm95yYbKe/QEH8HbTTbQd7O1BQm70RGDli2/pT9j6s5hG9CCzGj1ATZme04sx41xlJ3pJSXV+9CGn573VIa+lpdcPIUft9WdedHB3CicQQAoQAIUAIEAKEACFACDiMAMLR/HivLXJYrDriBNfGA6s0FL2eM/5/6oDbrsk4JJMfYFWnUfeukZtjaTM2Vu2n+r0jAM/Js3qvoc5ZrQgsw2ZSO3WGow9NhPl+HzW0PR2Lt/4Exq1O0cD12PZ2Rm199WwKGUwRSWpOCBAChAAhQAgQAoQAIWAZgbY2oxiN/JYbeqWBybXwwIqEys4D5LMVhJ2jHGpLL0F5sGzhlh2NTl5x7th9vGCqVgQWmMNDvAC6XR2RYC1rWXO5mwnIpwvtYgfX3wXcJw6rq1sst76lQggQAoQAIUAIEAKEACFACKQdAcM0tc1/JcE0DcPz85XS0rJTBRN3pf3iSLJDLvhhSVbdsxo3aCfCPRGhb18gkGvEYl/94qu6n7QhsKZPnp4Pd1y5oqFtMU2zRVvjkjAM5NMCJJl7MImqnavE8eXq2obqk2tqajZ0PkGfCQFCgBAgBAgBQoAQIAQIgbQiYIjxae0vvZ0lWls3pRoxkV6Nu/QWDpcfx0zxCA4r6yUnbBJY2Dzg7S7m0ldCYDcCmGd/efcXhT9oQ2BtH7hd7j6ojT3dXTNwF1X2Qdqdvm4ca00Efgy5yRJRqw3ByhAyeDvaCDf0IZmEACFACBAChAAhQAgQAoRA0ghwjXcgZKwRGzDFksZCsYrILTWRC/NpqNVfMdX2VEfYS+TODUEeWHsiSd/2QICfKCqYb49DCn7RhhDhJj9Ed4bCiBv74hpao+B1lDaVXnzx+U/D4eiVYIgf6LVTwZ5MsNi5tQ0Nn/Vaj04SAhYQmFYybdD23HiRjyWKTcELOWf7YhVsqMHEEDx/hoBCHwKqNA8id8WQF+Bz121ppSflDtkt2mzCf5/LP6yKyWv1c8hchxON+FuNtb9GeA6uxXEqhAAhkCEEkHC5oLWVFfpZYoRp4L4XfATu3uH4G8g4z0f6gnzOxSAmuLzf8zv+OmnLt+CL9AZGMXHvc9z3YjPabsZ9vlH+of06YfqahC/WCG/r1Q0NDe3PiJ1t6F9CgBDQCgETOxBii21Ni2fzXwWDU0dzEX8G4zJE+bHh3NZOhDs+zXs3d3CL/D3ShgNQfqy8peDgVbmjDgfd8IrKamt08Rqj2qeDKqOdqm5G+5a7b6Qqxuvt6+qq54RD0XPw2z+1G1vkQ/lmhAzeiP9pq9huAKJDvSOAyeo+8Xj8oJ2kOIdnJz8ILSbg+VLUwtoGSjdPgTdPEE3tBRNPfO8ouz/sOtD7/3u+v+78hsnwziK/Ys9M7IDTCh1WMpl4k7O3BWdvCWEsa23d8p6XVzl7R4bOEgJpRcAoP658TMwnJuB2lvf6gbipD8T9Ng7/F8XbzHwf7sf2+779/ux0k+Jj+52LG7Pnsqu+rLGrXrvAnU3aP+Ifw2Rc+JiP++R9Lz2NG1F/uRDmG9iF+C2T+96or1+0Asfpt20ncvQvIeBNBHj7+7w3de9DazxD5TPKc0UuULYYbc/gQT/GI8oPiUQiI60uck5Y8GHrqlmFH+EXR77bUiEE9kLAMH1TcJAIrL2QceGA4GIwHjpaFyQxByPK/qW1kckZJ3wmu8Q02GuonrO7CX404cVyZk1DzYu7j9EHQqAXBKLRaJGIi6NMwY5CiG4Jqh6JySrIcPhU7W73xafdh9L7IRdT54Mw75UvG1/HyyE+miwvNz9WGoy+wg3WIExen2C++oaGRc3pVY16IwS8hcDEiRNzhgwYMgkO8keBLD4KXlNH4YaaHGdmnry3dhe83beXXf/vPpG2D8PQE/6EfDZ9Q9JnBpy4QGxtxcdlUOtVsF113M8XV1dXg+iiQggQAl5BAPfvuM6PG6/onZSe3HsEVjAYzGvhrU/h98CWV1NSuLhQKZFoT+S+1qpoXH/L0IYILKvAZU198xiYerfK5mrjgYUwnn1BXqiMdeq6CVEOITekLsj7EqpfqH4HL/J3wJJrpDUY+Se4z/xOTW2tDMeiQgjshYCcuA4dOPRoYYgwCJ8IJq1TzDhDGNAX3lR7NVL7QEDagEn4FExkL/ftnNyuxt3wPMju/3BuPldL94PaI0jauY5AaWnpYMZ8pUjIW4bOIvg7Ar8XObtfF/Am77FS0H7fM9z3jF8o4HMMj+SV8AitBq+9mPnMKtz3ng3h8dhYkLqEgGUEpkyZMhDPoKGWG3qlgeCeev5UVFT41jatfxjwlnoF4l16IiJA7kT47K7vyf4vPfmxYPP1ZOtTvWxDwOYOl2mESRsCiwsxBC91ehfOQ8cee/xQmQdKb0OTsy4hYjf7WOA0EBJ319XV/DG5VlQrixAwsJPMUUjGeSJsPgF/xyLuJg8rbJKz0rXA9Z1/By8m32HCSIDkXQJDF2AzgyeqG6rf1NVososQ2IVASUlJ/7ycvKmMG7jneRmIK7zgCxn5q23B42wsCLmxeK6dg/3r4aVVtkww8wn4bP2rvn7xyzAc82UqhAAhoAICuUbu/ngX0bYIg6/wkHEc5NU9eHZ6kszBu54ksCwXLNtiJ0L6WbAMXPY0GK+6qdoQWHj46Lua8cVVlJPjj5+Br3d9cSh7P8kkt/CqOXLZsmVt2YsCWd4ZAZm/KhYzT8aE7iT8Np/AhCnDcLK1yF1EQvLP5OwXCDd8mxv8UcETj8BD4/1sBYXs1g+BUGhqMTZWOAkv5KfglfxLsFBupICSrS/oYiImKPgTPwOJLcOKn8Ai3z8RXv8/fNZ57iwHnYr6CBjwjBwk1Uwkcvr7fG25ppnDDaNt1+YnKVsghNiK0Nsed8KLxXK3BQKt3b47+v3+LVVVVTKfqislYbBxIB60LUK0LfeKcXg+/gK6fs8r+nbVE+uxtggsRCG8xXW+CLsCRd+tIjBk1awxg4vnrVY2qgnzPD0KHkIyubmtG9ljCLxfW199CHSml1CPDRyp6w4CoVBouMECX8NkVa6gycnrF3nR3OnS81Lx7vwqksLPMVl8DojgzzxvEBmQdQgEg+UH+Lh5JhavKsBTeSpvSeYGS6yCV9r9JvM/gGTw+EyFEHAGASwe+RM7EofC++YILJSMhefjMPg9job04bg/R+J/hPK279Cp/O8z9lI4VKapgL6ulHAwegVCfmUKDP2KYNuwiZLcjVX5Eg5HLsSOskrn+UkCxFZ/jlFglXB9+fySwPCt67dCvvL3YxIYUBUXEEgI85Bx89e864JoR0Tq44GVPduBHghPijPxAzHPkSuAhBACHkQAL8sF8LQ6HQtIZ0P9cpBX0tuISpIIYOXiSGyjeCRCcG8tDUcfwwv7PQgxrE+yOVUjBDKCALw2xoiE8U1M/uCJbB7droTGngzOg8yLIXM2ksFfh0W/5+GhdV/BoILHFyxYgJ1OqRACySOAe3E8Jv9TmclL4AVyFDY/mYztM/tJCfB+Ak8qPyQvT6GaIsZjK93UB8+vcW7Kz6hszjzhfRUJRb6K3Mk6pB7JbW1lB2DMLRENR9+7NLZ6VuEHaDcxo9cLda4sAj7DN0BZ5aCYTgQWNpzPksLZbcjz8a+lS5duzxKLyUxCQCJglB5XPpUZiXPirebX8RKYT7CkjEA/TDLOQYjhOZjQvomwoz8UDOo/hya0KeNKAhxCAGR1P3m/YzZ8HibLZbjv5dSYSmoIyJxgJwjGT9iyadt6eCL8PhDw3Y1V/I2piaXWuiLQnuh67dojkGPtVLBTp8DDqqSdn9Lvblwr01O4Oo5cjG/PxelqJ5kRjsthRWZ6Tr7XSDBShmffI2ihxcKnj8k8j9YIrHa0OF8GtpkIrOQvnayqiTyaA1U2WCMCi4PA8uZyj40LpKh/Tv7taPd/NtpSE0LAUwhgArsPJrDfxk57uN7N/duXdvV7aVZhTA4TTNyLCe1NILPuhlv672hCq8KwZKcOCBE8GCGC58Kz43ugrPbd6dKRnVi4bPUIeNLcApxlvqz7DD/7VXV1daPLfZJ4byDAo6FoxGTi+2ub14O4MjpyVGn8rs3TQMAIrnyCZNuXp+IeWKFQ+SRMzB+Hfe3egrbtVKghN9rT51RaVYmbYpnc04gKIdAtAnBR7Pa4Igc1IrCwh3wWFVxWF8MF9j819TVPZ5HZZGoWIRAKlQUNLi7BxOp0TGBzaQKbtsEfjp6uB+6XYkJ7VweR9UnaeqeOshYB6eWxrnndaYLzy7EBQzhrgciM4TJvzaVmnF1YGorMNfz8FyCylPemyAxUeveK8MDBzPRVYFH4EiRbhXeH0vMYZwdDCLdD4KT3Y7GzSqsjDXNeZZ8Z5VPKC+PMfAZoyXxs2hRE7OIetVE4e8tGK2qSLQhw5q4naoo4akNgIZeDqfGaUHfDjPQ//GGsUh/T0FD1YXcV6Bgh4EEEsDtR2cm4nS+Fa/O07HGqVHKk5EveNSCyLgOR9adYovWWJUuWbFZSU1LK0wjInHbxNjFrbfPHP8Jk+SC67zM6nEjqy78DIuss3PdI+B7/eX19/ccZ1Yg6TwsCIK4O56bxU2Gy00BeZWVyZ5ABrhIwIFFGgUTBgpymxXAXP7uo4TdmX/zGLET7Irsy1G1n2iKwDMNcljAln0qFENgbAZFgSqcp0obAAvRZ5YHVcantgxCLf06fPD347BvPbtv78qMjhIA3EJgxY0bu5o3bzkV+mx8jt8aErFrxVX+IpGfGVQF/7rcjobIbt7du/Svy7/W4Pbn65pCGqiAQiURGIq/Vj0CSngedBqmiF+nRjkAA/55vMH9FJBy51Rfw/QEhxS2EjX4IRKZEJgs/vxZ7W38DC8FZ5G7VzVga3FUPrISR2JkGoZuudTgkRNxV/OxghJzB/fEb8wTaHmSnvfpt+BjoKO9bS34co1rXfNgYKJTPdG3CKdUfK+9oaAif0gvW2lCvgotN3rksHNX0sO35LXMgMbtfOhyFlISlCwFJXGFXzYuRd+lDkFd/Rr8gr6goiYBg+yFH1l15ufnLwuHoV5TUkZTyBAKSuIqEo78VCf4RPB6ugNJEXqk7coMRFvTLeFvi3dJg2Ux11STNrCKAZNbHwsvuKeHjr2Hqi5BBeo/0ueyBBYTHWR0nD9UX+VvyV6qkL8irQP/cfJkfKqSSXg7rEoAjQ3+rMnklS+C+f89qO6qfFQiYrZtzm1S2VBsCCzfhWpWBdlU3zr5eGir7oat9kHBCwEEEJHGFna8uAXH1EV7o5FbGhQ6KJ1HuIjAB8ctPgHj8d/lx5WPd7Yqk64QAiKthmDDfBuLqQxBXl8O2PJ3s09sWXoyNNOaVhiML6L739kiXH1O+H/KczUGuuRdgycn4owXQjiGNCf9Hro4uN8a7Kj+Twjlbr1g0CM/L7X8P3JJOyiQsaej7Y9u4c7YsDfpRF95DoHnCgg9bVVZbGwILD6jsJbDarzDxy1AoOl3li410IwSAAMcqfgWIq7ex89Uf8H00oeJRBDg7NW6Yb4OQmC0JSY9aQWqnAYFpJdMGdRBXq9DdVfizvFqcBjWpi2QQEPzLcW6+FQ5GLpdJ95NpQnXUQAB5gPxYeLgsHjDfxU/x2dCKiKs9h6aloWGRq3MJJOsdt2eX+nzDwtYKlawpDUdvwSX+HZV0ckcXIcMjbRVEL1Eid1vIad9IuVDgrohrQ2DhZ9jVH52uwCn43YfBfCgajWqYoFBBtEklywggXOF4TGJfxir+Y2is7yqkZWQ83UB60Fy/ZdP210tLy0s9bQkp7zgCMnwD3sHnt+S2yTAFSVyRx5XjKGdAIGf5nPPfrmle/xISf5dkQAPq0iIC+P0tQx6gV/Gu/Ds0pZDdbvHjkmA3uz3l0EFsOKXtuw92R1eGwJJELSJzrnZo2FQWs5EZ4ia7Chomf9VuW2qnMwJiqerWaUNgGdkcQvjFVTY8EWdPIBY6/4tD9IkQyCwCmOAcGAlFn0a4gtwB5qjMakO9u4OAOIiZ5mLkNboDq/yUENQdkD0lNRKKnC7zpSGv7D1QfISnlCdlk0IA7jtHMtNoCAejMo8ZvlJRDYH2PJMybJfzRdBtkmr6KaWPEOnwOhinlM1OKqOIBxZydJ6Bp9FvnDRNUVlxLsSZtbW1q+3q1y+vpQ5tZSJ3KoTAbgQQIVO/+4uiH7QhsAB2tntgtV9i8oWyI6m7NmOr6L1DavWBgNz5RYaXYYLzRhbkIOgDjaw4bciE3PFWc2k4XH50VlhMRu6FQHRKdALu+2cF43/HSdqUYS+EtDsQwAYcdyA31rMyt5J21nnYIOScOxTh+jLPlfR+pHfCvsbSYK4SWMFgUHqg6nuPCHfx62v45PlwuOxLCGV8EB91v96xpw67oKah5r/J4NJTnWH3fbIF88bbejpPx7MTgYTPRwRWuoYeW9OuTFdfyveDpO7hUOQXyutJCmqLALwvzoT3xfsw8Hr8UX4kbUe6G8M4O5QLswEkxvWUI6cbfDQ9JCdnkrA2fexNmHiCpmaSWT0hIPg05FZ6nXJx9gRQWo9zGbqLzRJeQq9HpLVnL3fmsgeRYbQncAdfoGnxGa4SgH2hFg1GD4NH0j9RT/d3TmwMKy6qbai+ry9MkjlfNK/pBjjQXgRv6ZXJ1Kc6miMg2OtjH16lvFOQNgx1XV3dGvwqfKr5ZZW0eZzxn+IFRibppEIIpA2BcDg8Ctfd4/C+mI9OKUF72pBXriM/NJqNHDmL5DWhnHakkKMIIGTjKz7ufwdCibB2FFnPCRuOl8pnkOD9BiKvMzN2U6ZMGYjf4H93hO7SZgkWhsHtHE6c+8dbUMdzVU3TyFgOLKSqGG8a7FmAto/ngLOoMOZ3V9fV1fzZYrNeq4+Z13h30YHN+wtulmIufTty1crfcyrZiADn//CC2doQWBJshCnRbgpfXHV4Bom/ysSdXxyiT4SAawi0r/hy4XsX193XXOuFBHsKATyEorgmXoNbP3nkeGrkklM2FAoNx2S5EiEb2AWJFyfXimppjoAPCd6vW9u8/gmQ1wM0t1Up8+QkPuDrh9APcYpSinlEGc7Nj1xVNaFvAnfgFhs9emiTq/j1IBx5N/dlpu8ZTAL1Dc/8wvbZNfWLf/nFV+c+cST8KJ67pg4eWVePmdt8qOnzjce8+gK8xz2FXtqc64kkqYwA8iUSgZXuAUJA8Bvp7lPx/nJwIf49FJq6v+J6knoeRkAmaUfoUC1emmWyZpqweHgsXVJ9GNz6F+AauQbytVo0cQkvT4gtDZZVGMyPRSPxDU8oTEqmG4GTQV7Xlx9XPjbdHWdjf5HjImGZUB/348RstN8Jm2Ox2Aon5PQog7NxPZ7z+AmQHKsqKysT6TZD5lrF7ppP4ro/KN19Z6C/39fWV9+Qrn7HPrRqRfG8pntBaJ2a44sNEcL8Csb5IfT/ebp0oH7SjsDS4rmr3057rzY61GoywZlBHlh7XwT7Giz++LSSabRt8t7Y0JEUEcBWxefgpXkpxIRSFEXN9UbAB/NuKg1H/067pHp7oEFYDy4NReYgxOAxWDLM29aQ9i4jMClumC/BAzPicj9ZLT4SKjtPGPx/AGF4VgORgvGYmH+6ZMmSzSmI6LMp+hjXZyXvVkh7/iuQVwHkWpU5r47zLmzJai7uB3n1w2RrO11vv4fWbyuev+ZJkFnnFMWahglhRHA9U6ih00BnWB686X+XYRWS7l4rAgveRuSB1f3QH9aS2/aPiRMn5nR/mo4SAtYQmDLl+BEgr/6NrYofRMsCa62pdtYiINhp2wta66LRaFHWYuBhw7E5w8kgrBEmzM/2sBmkenoR2BcemM/CY29mervNjt7g2XoVog/uhbWB7LDYNStdJ2AQjjXeNe0zLNjt/GHdmMf75+T/BcdP7OacVoeQU/bh2vqa78MoXEKZL7ySJYrnr67dFWoomLE/FLsc7wULoV0s8xqSBnYQwBh+3LKxX6WdtplooxWBNXBg3qsAcUcmgPRAn8cPHjTkAegJ0pwKIWAfgdLSMulOvAxX0qn2pVDL7EVAHG7GWT12DDoyezHwluVy8SMcLPsNXqQRqkFeHt4aPSW07QePvbnhYPQKJbTRRAm56ydMuU0TczJthusEFgwcm2kjXevf5R0cu+qNa/8OkGbf7npcv+/8X4Ec/h3YZapqW/G81csRavh7JII/wfCLkSDUv4mJpgw13KSqzqRXNwhwceuEBR+2dnNGyUNaEVgLFixoxUtSnZJIK6EUn4mH/o1KqEJKeA4B6a6N6+dXzBRPgKkf6jkDSGGVECg0Oatu9+hRSSvSZS8EZA6jwYOGLuZcyPAFWgDZCyE6kCQCyO3O7sBvCBEuSQLWSzUuCWWcv76XOnTKCgKCu0pgSa91qKOvt7rL+HUeyg4i/Medj2n6+bkBg/qfWVVVFfeKfYVzmj8tntdc2R5quKZp345Qwzuh/2qv2JCdeoqVbZ/l3e0l27UisCTwmFgv8tIAZEDXa7Fr1EUZ6Je69DACMuQrr1/+/2CCfGmgSayHx1Ih1Qvg0fNEex41hZQiVb5AAGMzCzmMZGh+FuQY+cJu+uQqAldFwtE70AP9jtiD2UAuwbs7CGV7EqjV3ghwtmLvg84d8fni452Tpp4k5GBzFb9dFstQZBDht+/6rvH/9f239jut3THDo0byKhbvCDW8bMy8pmKDCXjd8+tgzkv4w3SdiioIcMav9ZL3lcRNOwLLMNnzqlwQ6uoh7oTnw1fV1Y80UwmBSDByIkK+XsPPTVglvUgXLRDwYRp7XyQckfkdqCiCALYl97d7ynA2FyrRzqKKjIsuagjBrpAkDOzR7h3U7TEC+fdr/BZf4HY/2SZfmO4SMCActSawTLPVVQ82eT1GQ9GpiLK5Hx+1fm6A2XnVn2Oc/Owbz27T6T4snNf8GsIMfwEy69hALD4CoYbfBnFSCXu36GSn12zBStLzhfOa5nlNb+0eAvsV7id3RNvotYFIs74+mRQwHC4/Os39UnceQwAeGJdhc4SnofYQj6lO6noHAZ8Q/F4QJtkQEqD8qIRCoeHYllwmY71KeWVJQe8iABImHCr7W0VFhdyhlEoSCMjQKZB/SJZMxXEE/HF3CRhTawJrU0NDw2eOj0kngXK+YjL2BA7ldjqs48c3Y/HACQgb1HoeO7Jy3QaEGs4pmtf4TSPGQGaZJ4BIuRPkfLOOg6qwTZt9fvO7wN5zHnHaEViVlZUJDMMihS8WVVQr4MJ8OjolOkEVhUgPdRCYPnl6fiQUfQTeMb+DVjTBUGdodNVEhhP9CiQWkSYZHGGZWN9gvhehQlkG1aCuswQBzsS565rW/w3myvufSi8IhMPRM7IkdKoXFFw7Fd+xY0eja9IhGGM3zk35mZSNma+r5F8oNHV/zFeego365hDbOYAfcJ848cUXn/80k+OZ7r6LKpt2FM9bsxB5sy4rmt9UhJvlWPApN2Eu/3q6dcm6/gS/fNScNau9aLd2BJYcBGGwx7w4GBnQebjpY8/TlvYZQF7hLuX1sL2gtQ4vJWcorCappicCt1GOvswMLMLKz0Ji/XpMtYozowH1mo0IyJ3EEE54SzbanqzNCOMv44I9iPpavrMni4Nr9QRrXLp0acw1+VKw0JfAwrW5wi3sIpHIMIPFF0D+CLf6UERuo8n8J9TU1KxVRJ+MqIGVDDFmbuNLY+Y1/3zM/KYj4iwxFnORC3BcEphtGVFK2075n8fMb7zfq+Zp+WPY0rLtSQzIVq8OSpr1LkJ+o2dKS0sHp7lf6k5BBCJTIpNxPWAnT3G4guqRSlmBgPgjvA2+lxWmKmJke6gw43OgTj9FVCI1sgkBwa5GHrwrs8nkZG2VXvII48+G0KlkIXG8HkhU1wiY3cpyPn73Z80+cMMdD6xwODxAJPh/AJfukSLrBU+cUF+/aJVml0bK5oyft3ZV8byme+GddWoixz+CCT4L85NHIXhTysKzWAA8Quu3xAZe5mUItCSwsJKyHRe4JLGoJIfAJGYaz5SUlPRPrjrV0hEBmaxd+HgNbCvS0T6yyTMIIN8tuwe7DVV4RmOPKtqerD0c/XNHqDAWOakQAplBAHnwbqfNHPbEHvdnv4SPycnaoD3P0DcnEUAo60dOyusqa+LEiTk4NrrrcV2+4951nACUmBnC93dgdJQuOPVgxycgr46vq6t7r4fzdLgDgXEPrNwIj6H58M46syjWNFQII4KXFrkj5QcEUvIIwKPtvZgZ+PrEymWe9mjTksCSw4iLWv7oU0kegePycvIfkROa5JtQTV0QiITKftCRrH2gLjaRHZ5GALsTiofC4bKIp61QWPlpJdMGxWOJBQhtoR3NFB6nLFKNYyL8ZxDXp2WRzb2aGm81/4h32SN7rUQnU0bAbQ+soQVDi6GktrlEXcCPDx405K+YaE9PeXDVFrBJcGMGyKtlaqupnna8kiWK56+uhWfW1djV8ECQWZNwvVyN2T8iSPBWQ6UnBD4Qhu9L4+evWN9TBa8c15bAKhhUIN1OP/fKQCihJ2enxtrE3dCFVuKVGJD0KCETZ2M727vQm7YvWOlBknpxGIFcQ4jHg8HyAxyWm/Xiyo8p36+lX1s13PGnZT0YBIBKCEjieh68gZHEN7sLwnpn4U2MQqnTcBkYLuZwkuoLQ+sdCGGhszs4RsLR32AacnYahj6TXWzHovGpdXVVL2dSCV36Bpm1DKGGt4+Z11jqM81ivNtcDNuexZ+nvYwcHp83uZ99aezDq7TIs6YtgbVgwYJW/Gzc7/Dgay8OrtTfB6HxW+0NJQMlAhxj/Sv8fxvBQQioiACW0Yb6uPkkPEP3UVE/L+qEfIdj4gFzMdYoJ3tRf9JZewT6CYM/kc2by4RC5ZNAXv1F+5FWxUDhTg6n3eZxY/zuz/p9EIFAYJVTZoXDkZ8KwS53Sp6ictrAap5eV7dYpuyg4jACox9Z04hQw7vgmXWiv8AcLIT5FXhl3Iv3yY8d7soz4mD/87FYTqRoTlOzZ5TuQ1FtCSxpt8kCd+K/RB8Y0Om9EbgMxMY1ex+mI7og0J77JhR5EPb8WBebyA5tETg43mZSeLMDwwvyajxLGFUQdaAD4kgEIeAOAoLtl4izJ6ZPnp7vTgfqSpW/zQYTD0BDykmapmHy5fqWu9mVqfEOhMBtTVVVVYsT+GHzlm9xwW92QpbCMmLM4N+obaiSUUJUXEZg1L1rthfPX/MkQg0vGLOmabQpxPEgc36Pble43LU64rm4a33BiBn7Vy7XKvG91gSW3NEBjKvcepOKdQRuQkLVH1pvRi1URwDJ+gPxNjE/C1y0VR8K0i95BE4EiUWegsnjtVdNkFeHY7OOenh2jNvrJB0gBBRDAJOMI7fnt9wHtfAxe0qiTfwI0QMl2WNxxi3dAgLmE1e14FqHEDpC/oG8OgmbtzyAcdD5fk8gtO3btbWLaZMxV2+47oXzKhYfO795Ecisy+GdNV4wY39wBPD20zZv1ibG+Zlj5jZffPS9S2Pdo+Ldo1oTWO3DwrlkWqnYQAAJVX+NPAzftdGUmiiKQEVFhS8vt/+DeEH+hqIqklqEQE8I/CgSiny1p5N0vGcEosHokSCv/ocaI3quRWcIAcUQ4OybeAe5VDGtXFOn/LjysUKI61zrgAR3gwB3hIDpRnDnQ+M7f9HpM0inlPELhcqOgRy58ZbOm0gh1z3/QW3DYiweU1EBgeJ5q5cjb9bvZd4s5IYqwrP3Qui1AH9IQeTxItjjBheTxsxt1HZDO+0JLMQYy5f2Nz1+KWZKfTxv2T1IqPq1TClA/TqHgPS8Wtu0/jGsNsx0TipJIgTShgDecfn9cpKXth416Ajk1WEmb09mOlgDc8iEbEOAsztwDYeywey4Yd6Ld66sC5vM6NhykTIB05f+cCka11cdr55PdQdCuUkLQmZlpEyBVzFIUu8ra+oXU167JMFKdzWZG6p4fvM98Mw6qV+sdV/BRQXu24egx6fp1iXF/j7knJ00Zn7T1wvnNjelKEvp5toTWBJ9PGBvUnoU1FbOj50yHi0Nln9ZbTVJu94QkHk18nLyH8HL8dd7q0fnCAHFERgc95kPy+tZcT2VUC8cDh9kGu3k1b5KKERKEALWEQiAgJU58LS+hhFC9W1Ac4J1eKhFKggIk69MpX1fbYPB4BDU0XcTkhR2cMTv0yiDm88Bn+F94ejl85zza2rrq3/tZRuySffhlRu2Fs9t/jtCDc8pijWNwMppORNCjt8yhXH4EOGC3/u4YMShRXObpBeZ9iUrCKy6uupKxLm+qv1oumdgDuPmP0pLy0vd64Iku4iAEW9L3EfklYsIk+j0ISBYONaWuCF9HXqzJ+S8OpAL3/+w2+B+3rSAtCYEdiNQlGgz/7b7m2YfQM71wySJFlozMa6G6aoHlmHkjs+EWWnr02fYwm/KlCkD8fv0NLxcxqZN1wx0hFQsv62pW3xLBrqmLh1AgFeyRNH8psVj5jdfAe+sSX6/WQwPp/Mh+p/42+xAF6mIwIadDFFmfCaItoMRLnifjrmuegIoKwgsGC/govrznkCg40kh0J+Z5lPtuVSSqk6VVEEgHCz7FR5wZ6uiD+lBCKSKAGKbfyLzZqQqR9f2odDUYuS8eh72jdTVRrIruxDAi/pXdM3JiQ0qLsNoFmbXiKpirbs5sLgpxqliqRt6JBLGCqtyJ06cmBPw5/wD7Y6w2tZT9Tn7U13DYmzKQEUXBEbNWbMaHk5/AZl1etGapqHt3llcyJ0zq/G3PQ12CixKvg5a4xdIQn8AcnhNRQ6vRyTRloa+leoC5Hf2lHAouhgGR7PHYlcs3ZAQRrShoepdV6STUEcRKA1Hb8XD7mpHhZIwQkANBN4ZMCj/yAULFng/4aaDeB577PFDc/yxWog82EGxJIoQUAGBrSbzH4Edpj9SQRkndJhWMm1QS26b9GKRoWZU0owA3mcPcfN9tjQUvQom6bqDbgtC42TONtPCsBmloTIkltZ8IyHO5tTWVX/HIjYWYKSqqiHw8vklgRFb1x8Jr7ujmcEmIyn8ZOg4CbzDgBR0jaH9W3C1epUZ2C0xwf4z5pHGNSnI06ZpVuUR4YZxPbyI4G5HJQUEhvl44j/RaDRSXV3dmIIcauoyApFQ2Q/wACXyymWcSXzGEDhk88atP0Pv12dMA8U6Rr6VPB+P/RtqEXml2NiQOo4gUGCw2AOQVIY/K5NmRzp3Q8iO3NafwKOUyCs3wO1bpsCGY6v6rpZCDc7GYRFR17IShlm6D0Ho/VZ78kqwf/pzjO9ZxUbXiyRb7OoI33sR9sq/3WVFxdj9fDmJInhjjjENUcSFMQBJ4gcYgg3aXQkfTME2Il/aVnxs5EKsE37etKVl4PsTK5e1da5Hn3ciAGIvuwo8Uh7Dj0lFdlntirUfcJ8oq6mpWeuKdBKaEgKRUOR07NiGHQexDkCFENAXgRgzzGNqa2vhUp3dRSa2RyjSv4DCydmNBFmvPwL84tr6xXd53c4pU44fETBiH9HOgxkbyTXwIBrtZu8gbJ6FfD2T8wu2oLah+qRk8QsHIz/HBP3GZOt7sR4m1c8WDMr/CnmGe3H0SGcvIZB1k1t/m3EpBmiTlwZJUV0niAT/rwxXUVS/rFUrEowcC/JqDgDIuvs7awc9ew0PcNO4G+Zn3WJM1yGPtQm5RTeRV12Boe8aIiBuKT+m3PObEwR8sR8QeZXRyzMNoahifEYtdLNzgy1PVnwkHPm+7uQVHO0W+XKMrxJ5lexVQfUIAfsIZN0Et+qlqnUIq5ptHzJq2QmBw5BrZSF2uxrc6Rh9zCAC0SnRCYLzp6FC/wyqQV0TAmlDAC+NwdJg2Zlp61DBjpDc+mrOxLkKqkYqEQJuIDAonmN6elv6kpKSAID5vhvgkMwkEeBsRZI1bVWrqKjwYW2lyFZjLzQSyeFXGiw9BXmB5EKTzuWFAMirqqqqFp2NJNsIAVUQyDoCSwI/qnC/P2DS86oqg+BxPY6AB8TTCF8p8Lgdnlc/HA4PMH38cRiyr+dYZJ4QAABAAElEQVSNIQMIASsIcPHL6ZOny2SyWVfC4bIZ8OK4KesMJ4OzGwHBZoXD0aTDl1QDq39u/9Ohk6vha6rZrJw+SRIwdvXe0LhBklc5dtsr307wPj2wsMA9BcEAj8AWjXMu89cTInYyyCuZv4gKIUAIpAGBrCSwKisrE4YQFwLfeBow1r4L6QERjyUeB4nVT3tjFTVQrvQZwoeXBDFRURV1UguXPFuPvzcZFwu7/skcCKhQjVx7b6POBvzJ+lTcRaBwe0HLle52oZ70UKh8EpJ9YkcnhpV+KmlEYAf6+rzjrxF3+IpO3+l+T9NAYAvzX8vcb2nqztFucJFc7KhAEmYdAZF8CJx14UjKbJj6hg8CEJNz+dzrsUQikUMBwjOah8m+H0v4T2xoaPisRyDoBCFACDiOgCd/+J1Aoaah5kUkFLwFMdnXOSEv62UIPk0mEJ4xYwbFf2fgYljbvO4WuKp7djU6A5Al0aVYhVxir4GQek1w9gbn5iqEH68NBAIfY6XNCvltIFnvsFwjNgovfJPw2jeJCX4YXuok2TgmCUWoSnIIXAkvxHvr6uqyYothTNz3jbea/8Z1lMoWzckhm121NuKefwfbVr8NbFeCnFptcNYUE8Ya0/R9PmRIzsa+cpwgPKx/Xl7evqg/wsfM4QifGQuiezzjHDuS8UPBaU8ApFm5gOjwpXRwok2cB5meCk+SxDN+B0odxoLEWUXAZ/TpQWRVZOf6Cc7GgWTVtvRv9fdIYAWDU0eLROwZGK/xDpviI8HNLy1ZUi0XNKkQAoRAGhHI6sS3cuUu0WZW4/clmEbM9e5Kbh+ba5xhcYKvNyYuW4f8N7Mw0Zrrcje6i4/BwBfw9yxmlfVYOX0VO9tJDwtXSzAYHGIYgXI8iKdjonwCOtN6xdZVMCFcMPGHuvoauVGH1kV6XK5pXv8srpupWhvqvnFbgGED3gFeBEn9kmn6lzY0LGp2u1sZ7rptQNthhkgcg5yFEVy4UfQ5wu1+NZW/XvDEBBDXW7xiX2kogvxd/Ede0VdXPRPCX+jm/Y4dCGVo9zU64ofn5qc19dXdpquYVjJtUEtubDF+kQ/X0fYOm5oNP4tUV1f3SOJpbDuZRghkHAE8g7K7ID57PFxcXwUKA7MbCSetFw/V1tecC4km/qi4iIB00RZx/iIIrHwXu9FV9LsgPJ7jhvGc38//p0L+glBo6v4+lphuMnEmHs4RAJ/1z2iLF18LJiUHuDkpsaiPK9UxMfoFBF/rinC9hSZAVtXhpnrWZHxRTg5/SZXFlvZwUJ44lZv8q7jrj8EwkIdW8tfiTbX11T9Pvnpma+L+/Qga0GJFZoehBdeMfG9y7T0VROU8/ITPzKyZrvX+MvCTz6k9Chbl8nw88F8clO8vupYNhsnKql+ofkdXA8kuQkB1BGhyhBGKhCLnIlToftUHy2P63Ycft+9DZ8wXqLiBgFzF357fIskrhKRQSQ4BsQovlI9gxf5+rNi/l1ybzNSKRqNFZpzNwkP6+7iJDsiMFt7rVXcvrNJg+ZcZN+VOo0RwJHd5tuD+eZoz/kRb3P/Miy8+/2lyzTJXq3xKeWHMnzibC+Pb+Ak9KHOaeKRnwbZxvxhXU1OzQXWNI1Mik4WPv666nlmg37t4Rz3ETTvhHf8C3s+muNlHxmQL9lhtQ/UZnfuXnsFrm9Y/Bpu/3vm4Zp83cVNMrXmh5hXN7CJzCAFPIZC1ObA6j1JNfc0DkWC0HCEEeFmk4hAC342Eo5/V1FVnXWJlh/DrU8z2gh13g4wh8qpPpNhaztmjzBTzZe67vqurUQOu6Y3Q5Hb8/TISjEQFN67AZPZkfKeFh16GCETFeci/cbuOXlg7SU3zIZhP5FUv1wBOgbNiVbhVHurXGvjnwqULN/VeXa2zVUuqmqDRrfIvGopG4S32Q5j0FXynce9uqOCBLMz2kLyfdndapWMgr05TSZ9s1QU/oq7mv2rHlWvsZWfsjd+6pvW/05q8AlHOfMYpNfVVRF5l64OD7FYGAXoZ6hgKX65xIV55lygzMhoogiS4V8BVfrYGpihnAlb2vovJ2dnKKaaWQouxQ9tpI0ePKAKR+kMvkVddYBTQfXFt/eJTTWYchefUYzjvWthDl769+LWfj8e0yy+DxOABEQcRy1i3eUe8OFAu6PwJEqbfkRDGgfCumIp75n6vkVddMamur66GHacZCXYw7v2/4byVDSS6itP3u2AXy5yCHjDwax7QUX8VXd6BEDl2CwDiMG2BFHxFZ9si4bIb4QRwSedjmn3eIcmr2tqqWs3sInMIAU8iQCv5nYat/Jjy/eIB82UcGt3pMH1MHYGr/5+9L4GToyjbr+qZ3c0dIoQrkEsUAUUxkmR3djcrgWgAAcGoKF4oooCKfor4eeJ93/4RPEAQD6J48RmFEDa7OzNsMIhoEA8gEELCTUKu3Z3p+j+1OdhjZnamp6u76u23f79kp6ur3uOpvurt98BiQnuS8BYCAu0L2p/np4T+AqRfkHgbjkA/PBV+KX3xTcou3qi2d6QUqU9gQavza/B9fPg5oPeeTjd6h9uQ12y0aMFaBhcISn082Gjyo+7B4umrU6ZM/OlYFQJdR6K5ueMFnvQ/i4teh+nwtT9kQlEl9tPZfPcnhzRZ9RP37UOlShkvEmCV0nuEgUvkek+J1cJTPfgQszHlq82e7z1WaChsj0PeYrG4K5/P7zTFm3qoKIpPLMlmV9+s8cs0t58PL/fvm8LSArr9UqgzEa2jQ/d5YwQYAQsQ4JefEZOApO7zkNS9G83jRxzi3eAIYG0hL+zOrUbIG2/1IKC9MMY3TdRfgObXQ4fg2G1Sye+ItP8d5EHZRFC/kirtDi8S38XBF5XskOhGeSE8V/4fBQgymY6FUvn6ucRh/8Mn9C/wsvwcPBR/j+ZEeSXi2j8BCutn6vOHQ5LovacmbBt3+E133RSLUWQs5FubF71aSHXDWP0IHX8auvwEHpHfz+c77yGk15iqIOz/DBh5fjNmR0c7+CJ9RC636t5Mpv00ierjUCPlqCpjiV3EAuYN2WyX9nznjRFgBCxBgEMIR0xET0/PWtys3j2imXfrQwDPN/VdhL29uT4yPBrGq08ABTZePXsqDODnlekB73nd+dX/myTjlYZAhxfB00iHFV6MXafy/Gj5zW7qfaDv/DNOF2uA8eoa6MLGq2dPmL/r8GB49s6H8eq3aE6U8UrDgGt/Fa59Xab+s/jHYYUaFCGmbZ+809rQenwxPn63mMT/17mChLgUxsTDcI1enDTjlZ5d5KycS3iWi1u2PLJBf0DDy70Oa6dqvEJEujifjVeEz2RWzVkEqN506pqQDRse+NvMw2dp77SOugjx4KEIwAlLnHb4zFn/Ar7rhh7g39Uh0NKyqBkn5Y/R2/lFeXUaV+yFvMbi58Lzz+rJdl+7/uH12yr2Jnxw/fr1/oMPPdA79+A5V/tp/4WIKjqCsLq1qLb/YYfPyuF+c28tg2zre8gRM74DmZbYJldM8jwMY+1FqH51wYMPPfjPmGSwhi2u/cKDGx64ddaMmatwP1yMa38/a4SLSRB44s7F/dBKb++Zs2ZeijmibNjQMa2/Fyn/VDyX/3jvI/fqD0yJ3A4/fObZeOldQFT5vvFNE9bDuPMT6Ec1lQWMV+qCbLb7B0TnkNViBJxGgBfCZaYPX40+hUMkwk/KqBhHcwpfa65tbW49NQ7mLvNcunRpkyeUTuDLRmcxmENjXk+2643wmLzP5XkNU/bO2zs3Y9FwspTqEtBN7MJhKKZYTL1z6L5rvzOZRUthsHFah5Aw3wU6n4PH0ZEwXl2D34nzuKqEI/L9ZdONqZfAOHJjpX6JOCbF0QjfOsFCXZFGR77MQrnCEklX/rysO9d1Bp7LD4ZF1FU6MF7NdVX2KuQeD0ulNl5RNph/BMYrynm9qphm7sII2IuAZ69o8UuG6mXvxRP5d/FLQkqCBiG967EwezkprQwrs+3pHTox7VGG2dhOfosOlevJdS/K5TrvtF3YmORT3dnuryB8oQXGm//GJIM1bIHBq+bPX7y/NQLVIIiuYoUwOf0CDTUSvd0KT8sX46PSxygl5Q97RoHN08j5drquwhg2bdfoKU9eaJvMyK/6PMhEdcG/C8a5s/Z8+NWGLN6EoGzAoj6/l+Fc/hJ1JVk/RsBlBNiAVWH2li9fXtzVt/0N6HJbhW58qHYExmNhdmNra0dr7UOTN6K9uf04uDJ/KHmaP6sx3oh/rWTxaHhffAut7H3xLDQlf2WznX9p6mt8GQx+K0p2SE5jY0Nq4DUuqlvo9z8HuWe6KHtIMj8N2925WEgshkfHv0OiSZ2Mn82vvgRemOdB0SJ1Zcvqp8QpbW1t08sej+GAUh7Cu0luiCRT5/XkV5NNWB5g1vRHh9kBxvGQ+BH49h5DbPySsASMACNQFgE2YJWFZveBtWvX7pApdRr2/jNGVz5cGwIThO/DiIWqj7yVRQBeGOmiFDp0MJkJnKXYjJfjM7K5rtdks9mHywLFB0YhsHLtyi3pJg/3LnXVqIMJapCeeJNr6iIEShdqsM6LJCocYbBe5aXFsfAo0ucudnmrBQF4Yf4QVoU3YkxSQ4kbVdE7uxbMTPdF+gTtgUVug7H0w9257p+SU6wOhTKZzCEYPr4OEjw0FgTUVTBeXRwLa2bKCDACNSHABqwq4EJls8cQwqDzNj1eRXfuUj0CU4XvrWhf2J700LiyiBX6/AvxKe+4sh1oH1gtPfVSvBxzGG/AeUZYUQEhl+diOJIHJ3RTIgND+fNd0X7evHkNKL/+Q8ibxHx32uDyQRisT+rq6trgypzZKCcqZ/0SYV2vg2yJrFCIysdvtWxenLkH1YDbn3XIeg39E9FVyoa5iVCUlJLyV4fMOFh7rvIHE1LzyspQRYANWFXOrA5hQNjbKbi16fLAvIWHwHTfEzd3LOyYHR5JGpQ6ju84GNlvLqOhTU1a+EqpTyMH3WIYjzfVNJI7l0RA53NQSrwLB5MZfln0ziwJjIWN4xsn/g/EepGFopkVCd6WwvNOwLn6NTBK5nkaMsI6rEuHd4Fs4hZl+sMPwu8tuo4UNQPWTl+kE+slWvFSLfpswKoIkF0HcXP8XbpRnq3TxtglGUvDCDAC5RBgA1Y5ZEq0d+e71+C9+vU4lMgvmiUgCatpRtHzbx402IRFkQCdYoP/VagxlYAqtajwmFDeKdl89yf5ZaIW2Mbum813XYFcaslccEjxqrERir8HQk8OhdH6o/FLErkEtxX99Mt6ejp7IudMnCE8WK/WYV7E1Sypni/FWSUPxNIoqRmwvpTLrbo3FihtZyo5gbvtU7RPPqlWNjR6r9fe6vva+AcjwAhYjwAbsGqcop58D8pU21fhpkY1rOuOLyBHFBr8mxDqM8064WIQCDlwFgETXUAgSdsdSNT+kp5855+SpHSUuuqy0FLKJBpIFra0tBwYJdZBeEnlfRHjJgUZ6+4YdW1RDZyQz6/a6K4Odks+GOalBnMp2i1o2NJJ8eqwSQahd+K8E/WHKOvvPzXo1u+LwuU19E9WVynmJEthZ7XNpRtSr4bxapezGrDgjEBCEWADVoCJR2LZKzGMS6wGwG6MIS9CTqw/Ljl2ycQx+lE/7Cnp6TAaREEkZMNXsIFi38s5Ubv5+e7Orv68lEJ79yVp81IidbLNCsN4vwCX/Dk2yxiybP1SyHciR9ub8/n8zpBpM7kRCKCgw0V4oNw+opn2rhLHtrSc8Ny4lexr7DsybhnC5K+EvD6Xyz0aJk1StBQbsByYzzvTjd4pMF5tc0BWFpERYARGIMAGrBGAVLuLPB0fQVWZn1Tbn/tVjcDCHZN2/Wbp0qVNVY8g1rG1ZdEbkbIkSdUZf4yvYEt7e3u3EptKa9XpznZdAvPoNdYKaEAwpaTNYYQSxvtvQu2kGK2fQU7Jk7tzq39gYKqZZAkEtJeBTA+G1D1V4jDZJk8UzohbOd+TtCoQJuzZEeD8mRtgDA+JDoG/9xcaTsQ98enoWDInRoARCBMBNmAFR1Pt6N+uk6PeHJwEjyyDwEnPPL39Z8uWLUtcFa7m5maUXlafLYMLtWadWPhTMAa/HS8SnH8g2tlV6QbvfEzAX6NlGyM3KU7q6OhIxyhBWdaZTLsOF15YtgOtA49KX3Ugp+QttNSyXxtd2RFJ3S+wX9JQJVwaKrUgxHxSCdx3TJkyoSsIDEkYg2fMOOh5SBJ0dVFHfCH6r0ypV6xZc8sTLsrPMjMCjMBuBNiAVceZsHbt2gGEPb0GH83X1kGGh5ZCQIozNz/0yHdLHaLclhIN74N+MynruEc3OMSo98J4dVkCdLVSRe2RoUR6GYTbYqWA4Qs1ub9fHRc+2foozps3rwHeSEm5DjYKz2/rvq37jvpQ49FBEUBS919gEXdd0PEOjsvsMSrEJrqUHqUE7t0rVqzoiw1Myxn39YnZEJHXVnbO04aU753E1a3tnByWihGoBQG+ydaCVom+OuxJpnx84ZP/KnGYm+pAQEnxrtaW9qR4I4nBBPZSJKVa1IeQUDxxBso6LgcjQwerSCn5NhDX3nDkN0+odtuUHDduwtvx/Ig9T08EuDxUVF5HT0/PvyPgxSwqIFBQA+/F4ccqdKF0aJzf77fEqxApD6xk5VGr8cTxPH9ujUO4ezQIPIoiQSd13ta5Php2zIURYARMIsAGrBDQhTX/MS+tTgKpDSGQYxLDEfhoW6btQ8ObaO5JlXo/NNuPpnbDtPoYPK90knreLECgJ7/6N3CH0/mX6G9KLLJJSR0yLJX8mE0yGZJlI4xXL8/nO/9riD6TrQEBJM1/EkbTpHwsEUWhFtcAT9hd4fAmyOTAQgjqnWEDRIueYgOWfRP6OIxXJ6BIEDsa2Dc3LBEjEAgBNmAFgm30IJ1bwlPiFBzhpICj4amrBYvrL7U2t59bFxHLB8+fv3h/pZQOH6S+fRbGq89RV9I1/absN+EjkPke1+SuWV4pWjHGmudeSjbofEQzatbDrQFPYvHwCjZe2TVpqKZ8NRwve+ySyow0qHbZYYby2FQzmcwh6DV57J5u9JC+vNsNSeOREsWd5sTDmbmWQWArqnovhfFqXZnj3MwIMAIOImDNi7yD2I0SuSvf9Xd9o0QwzvZRB7mhHgTw/imuRKLj0+ohYvPYplThfyDfFJtlDEG2b8N49fEQ6DCJkBEYzGnie+8JmayN5Ka1LWh7oQ2CIS/PJMhxqQ2yGJMBz0I8E0/hxYMxhOshjDSEQj93khA+fJzONVcPWEHHStlAKf+V743z7g+KRTLGeXOToacTWu7AIvdV2WznX5yQloVkBBiBqhFgA1bVUFXXETfK24TwX4/eXFWtOsiq7ZXCy/bP25rb5lc7wJV+g95XUtE2Hiix4pAZB33AlTlJopw9t3WuRKzLdeR1T4mX2qBjoV+9E3IcYIMshmRA1KB47e5noiEOTLYuBFAJcg2sVzfURcSNweMnNEx4USyi0qpA+JAu/hELju4wZQ8sO+aqX0n5mq5cV5cd4rAUjAAjECYCbMAKE809tHryPTciAflbsesbIJ9kkhPwQPptS8sJsyiB0JgeuAj6aG8Mqts96SbvDcuXLy9SVZCKXv3FBu2R8RQVfUrpoYR8San2KNt2e4MQDxmGd0822/XHKHFlXrUjID3/fzGK/Ac3PyVi+fglpSKT/wrGTs5hN+YlxjmwxoTIfIcC1mDLstnVK8yzYg6MACMQBwJswDKEOl7cr4N/fhJyGhlCsCzZQzxV+ONgxb6yXdw5oJM4Q1qdB4fq9iTcMF6Fr7acG86BGe7tveURJOn9pAOiBhYRi7DjAg8OaeD4pknngNTMkMjZR0aKK3ryXd+yTzCWaCQCg1Uhpbh+ZDu1fXhwx2LA8hWdBO5I/H8ftfMiTH3a2tqmgx6ZfGdhYhMhLYWg6POxBvt9hDyZFSPACESMABuwDAKezXZ/F+klvmyQRTJJS3G08L3lyCGTdh2AlGx8G3Q40HU9yshfkEq9lpM3l0HH0uZJUyddCdEeslS8usVCmKT2wMKf2DbYCNUHY+NumrESvU89/cR7TbNh+uEhIAvqS6AG2y7dDYk0XxiHdrjRPDcOviZ44nl+rwm6VGiiEM9cKrq4qodS4hJ8PPmxq/Kz3IwAI1AdAmzAqg6nwL16ct2X4rXwR4EJ8MByCCwuDvhfKXfQhfZly5alsJJ9vwuyBpERHoifQI6VW4KM5THxIaATuuO8pGx4nwIPzjlxIdzW0nYazGdHx8XfMN/HvQaxbN26df2G+TD5EBHo7u2+S0j15xBJWkcK1rmjIFTUhmvcSulUpUNOu/utm1iLBEKFxtieKxbBEJ8oSvwhm+/6anwCMGdGgBGICgE2YJlHWh1y2EHn4+Xp1+ZZJYsDvrRcjMqEb3FV680bN5+K8+IIV+WvKLcU2UMPPZiyEaSi+q4fTDWmfgAdHnZdj3LyY6GhF7OxbPq+FQtj80xhs1bndHV1bTDPijmEjoCfoh7yOQn5MyMN20VI2cEwmU0Mfa5iIqgUhxBWhF5yBcKK+Jg+KMWJuMbJeDyahovpMwIuI8AGrAhmTyevbmj0zoGxoisCdoligbwWl7ctbLOiqljtwMsLah/jxIgt6aJ3Didtd2KuSgq5u9KU/FrJgwQalfBiecltX9h+lJByEQEIR6kgpfgWPC5Je/GMUppQQ0++U8/dfwipNEoVT/mRGq6VSsVynxmleEgNjY3y/pBIkSSDfGfsgRXvzI73ROF78YrA3BkBRiAKBNiAFQXK4KEXhOP7Gk/DzzsjYpkUNuPh1n69a0ndm5s7joBB80SSk6TEeztv61xPUrcEKbWzb9v3oS7R5PsqloUlKqFdCEyjDmMyf9YqcXfBH9DV7HhzFwEU+VXa85Lw5h8ZqXJFf26k/MwyewbvsY+bZeE2dcSLsgEr/il8RWvLojfFLwZLwAgwAiYRYAOWSXRH0F65duWWgWLDK9FM+ivnCLUj2JXPRVL3a8HImYVhShbfDXkJXn/yV0igeU0Ek84sDCOwdu3aHbiifmmYTVzkIzdgZTKZyciH8+a4FDbIVxdreFM+n99pkAeTjgCBVH9KP0eLEbCKh4UUh0fJGOXQIr/PmNOPwwfHwhYfJQnN91ja2nxcfae9vZ2NiTZPEcvGCNSJAMEFdJ2IGB6uy9R7abEYbB40zCpp5E9pbW53IiQP1RPHwdb2VoITtDU9IN9DUK/kqiT9q4gqH/lCQ/opbbwiWGJdfb37tu47iJ4niVKr8/bOzaiQ2U1WaRWtAUtQyokk1X1kz4sQFNNVsfEF9bAQSDGJ+hGY6hfFtbpQUv2kmAIjwAjYiAAbsGKYFZ3kVsniyXjYPREDe7ospfhqS0tHLKWyawG12F88Ff2fU8sYN/rKywYXQG4Iy1JWgUBPT08vvIburqKra13011ncgiPcpDwvQm4RsVL37uzbcVlEzJhNBAhI6V0fAZt4WMhoDQweJQ8sJdiAVeGsLRQKM3E4XaELH4oSASUymx9+9KNRsmRejAAjEB0CbMCKDuthnLLZ7Dp86TwZjduGHeCdehAYJ4V/zTHHHNNYDxHTY5FA+i2mecRA/5/ImfSdGPgyS8MISE9dbZhFHOSbkDdvv6gYty1oO1YI9eKo+EXGR6UuGgw1jYwhMzKNQFEM/Bo8qIYRRhxCSCikTIn7TZ97TtMvpOc6LT9B4ZVSH29pWdRMUDVWiRFIPAJswIrxFEDFpjWYgNMhQl+MYpBiDZeK454z5YBP2apUS0vLgVjIvsJW+QLL5XvvxUJ2IPB4HmgvAp74KYTz7RUwsGTTA4+sdWBavqnWIfb3lzeict2f7JeTJawFgVwu9yj6d9YyxqG+kV3zCCmbBFzwvKexoVgOG7AqTaWn2IBVCZ94jqU9pa5bsGDBlHjYM1dGgBEwhQAbsEwhWyXdrlzXKqEGFzdUv3hWiUR43ZRUl9j61UWq1BugaUN42sZPSQnx657bOlfGLwlLYAKB7u7uTaB7lwnacdL0it4BEfHHO7R4fUS8omJTULJwaVTMmE+0COB8XR4tx8i4jWtubh4fBbfiziIpg4bvexxCWOnEkVyBsBI8sR2TYk5DuomjA2KbAGbMCJhBgA1YZnCtiWpPfvVyJcXbMQi2AN5CQCCF3BNXzps3zzpDkRSS2kK2KGSR8wyEcNLaTEIpebPN8gWRDYbuSLwxMplFiyEfreS+UlwxGAYfBHgeYz0CvkjfaL2QAQX0PG9awKE1DVOe99yaBtjdWTU1ifV2ixizdL4gZbCMGc1w2aP6b2vzorPDJcrUGAFGIE4E2IAVJ/pDeGezXT/BgooNAUMwqfPnCyeMm3BRnTRCHd6xoOMwpI2eHyrRuIlJ8UssZP8VtxjM3zACniBnwMK1GIkBSwp1juHZiZr8TuRF+1zUTJlfdAjk86s24nvavdFxjJRTNAYsSgnchXi4s7NzV6Sz5BgzCU8fx0ROlrhSXd6xsGN2spRmbRkBugiwAcuiuc1mu78AT4evWCSS06IAy8uam0+YYYsSBc8/C7IgTReZTXm++CIZbViRsgg0NMhuHNxZtoODB5DUy7gBa9ALVInTHISnrMhYqH1vT1hp2T58gAICcjUFLUbqgNDhSIo3SEkpJ5Li/FcjT6TR++yBNRoTm1qmFrzitcuWLUvZJBTLwggwAsEQYANWMNyMjcrmV39YCflDYwySRXhyWha+Zo3KUmgDFpkN8a43dOW7/k5GIVakLAK7v76rtWU7uHjA94wbsMaNm9QBaCJZMEc0BTuEp74cES9mEyMCSGvQFSN7Y6z9lBdJlWKkC6ATQigl57+qcEZmMpnJeB/av0IXPmQFArJ108ZHOHejFXPBQjAC9SHABqz68DMxWh0648B3wVHntyaIJ40mXipe17qw48S49V6wYPFBkCETtxwh8lcpJTiMKERA7Scl77BfxuolRBic8QWHVP6rq5fIgZ5SXAXvq8cckJRFrBOBhqJH0gNLKRVVbkw6HjmKKxBWupxwStExVlZSlMaxT2UyHQtpqMJaMALJRYANWBbO/fLly4vpRqkTDt5qoXjuieQpHeYWa+hegzfwCshA6Xq7Cd5Xf3XvZGCJAyOgBC0PLCFMVyPDPUe+KjDe9g30i773TfvEYolMINB5W+d65MF6wATtOGmmhHkDlg5TwsezmXHqGSpvJdgDqwKgKDM7p8JhPmQXAml8WLpOe83ZJRZLwwgwArUgQGlBXYve1vfVITsDxb4zICgpr4d4gFfz2prbTo+H9x6uUr0yVv5hM1fyirBJMj27EfClR+tepKRRTwzcc47HjBKqPih/n893/tfus5SlCxUBJXtDpWcBMaXMhxBu2rRJ596MJFQxCkiVJ++Pgo+zPEjlO3N2FmoRfK5U3rdqGcB9GQFGwC4E2IBl13wMk6a3t3erTGnDh+Qqb8OQqX1HSfkZjIrrfAdfeVLtUls6QorNO/u3kS2zbinqsYs1Y8b0f0IIOonclWoyCqrnnWqUfsTEfaEuj5gls4sZAenJdTGLEDp75SG7l/EtPdc4iwgZ+H6KPbAq4c0VCCuhY+kx+ba2lrbXWyoci8UIMAJjIBDXgn4MsfjwXgR0vhEvrZZif9PeNv4bCIEXZjLtrwk0ss5B8MR4GUgcUCcZm4ZfvXbt2gGbBGJZzCOgQ5vBhY4x3TMcSqRU7Ln3QjsrkAMnl+taGRo9JuQEAjBa3uOEoDUIKaU0/+zySYWU7crnV/H7Z4VzTCrBIYQV8LH1EApmXd7a2kon1NdWoFkuRsAAAmzAMgBq2CS7urru94W3BHSfCpt2kujhJeMzHR0d6ah1hvcXHe8rJEVBHpwfRY0h87MDAbgu0PHIUNKYB9aCBQumIAeODiEkscFr5QdQxCehDCtRAwJFOtf7Hq1l0e+vAYBAXSUtj5z1AIGv/QpnAu71pDzuKqhK7dB+wvd+qnPWUVOM9WEEqCPABixHZjiX6/wHJkvnxNrliMg2ivn8Yn9RYxjphkV/e6QMDTLDi9qtnAfHIMC2k5bybttFrEE+YzmwGhrGLYIckRvLa9C9lq66cNvPahnAfWkg8PTTT/8HmhRoaLNbC9/zjBuwfEIeOXh/uY/S/BvQBRCJWQboMsloEGjb9NAjH4qGFXNhBBiBsBBgA1ZYSEZApyvX1SU8+VqwIvVCGQF0+1jAZfiCfTsR/NhTjWhBBKwiYeFJ9fNIGDETOxFQPiWPDGNJlqVSJ9g5gUGkUtlcbtUDQUbyGLcRWLdunTb2kErcH0UIoZR0QgiRMex+t89is9I3N59wKDiYrmhrVomkU5fiMwglJPOenvTpZP2TgQAbsByb556e1X9AQvBzITacYXgLgMDLUT73mADjAg3ZvGHzizFwaqDB9g1CJKv4P/vEYomiQqCgUnQMWNKch5RSYnFUc2Kcj5K/MM6DGViLAF406OS9A8pe0d9uHGxCHlh402QDVoUTJpXy51Q4zIfcQCAtfHkd1gaT3RCXpWQEGAE2YDl4DvTkVl+Ll4r/dVB0K0SWKvXOqARB/qu2qHgZ56PE7SgqwMlcjQNtL4PDDpuuFzM0KhH6ZvRAnr39gFFkRnLDZ4uSaXWDYR5M3mIEkM9ps8Xi1Syan/K31DyohgG4/seh+yE1DLG7q5IcQlhphor+3EqH+ZgrCMjnSuV9zRVpWU5GIOkIsAHL0TOgJ9/1RYjON9tg8/eWJccumRhsaI2jpFxY4wh7u0txo73CsWRRIKArEcIjg0hlMmXEEDcwoI7HXJB4tiK5y1/YaB3FlWUvD+WrR+yVLpBkRovh9PWJ2ZAKlw6NzZeSPbAqTSWthP0jNU1Y8n55HqqVnzkSBN5nBBgB+xAg8ZJtH6zRSNST6/oQcjr9NBpupLhM3Tmp7w0RaXRcRHyMs5FF9XvjTJiB9QhgZbbVeiGrElAaMWAh/xWZXBowVnLIcFXnEt1OUshHCWmn0un0Myb18Tw6+a80ThP60mzAqnDCoLr1nAqHHT8kUcRBXeu4EjWJj/n8IfJhzaxpEHdmBBiByBFgA1bkkIfKUO3q23YuFpR/DJVqAogpoc42rSZCCSbh4f8803yioa8e6O7tvisaXszFagSUmG61fFUKh+TEO6rsWls3JebXNsDe3jDGrbBXOpYsCgSQkJySB9b2zs5Oo0VwpK/mRjEvUfDAu+UTK9euNBpyGYUeJnngOUJmvkfhpNR94/qa3oP32AdGHaPbME343jW6ABNdFVkzRsB9BNiA5fgcrl27dmBH3/ZleNHIO65K1OK3zZ+/eH+TTIu7ijqBO5VrbJVJrJi2OwggJ85B7khbXlIplBkDliRjwNqWakrdUR5BPpIEBIh5YD1tfM5ohZRx/quxTxi6BixP3KcNmEp6bwIMxbGhINNj0eaHN3+AjDasCCNAEAEqi2uCU1O9SjBi7egrNLwKI/5Z/ajE90w3pfs1ZsY2PyXIhA8qJXuNAcWEnUFAf5VEWNk0ZwSuKGj4IYQtLSfMAksiBj6RM+2tUnF6+KAVCAwoj1IIoXkDFq2QMjZgVbgKly5d2oTDdBL2j9R1TwXKbHZ1Nw59c+Rhyvt45/1cS8sinc+SN0aAEbAQATZgWTgpQURas+aWJ9JFbwnGbggyPoljfCHPMKk3vly/0CT9KGl7vmIPvygBt5TXgw8+eQBEI/HcgCdZ6B5Ynup/kaVTV7NYMFR21TyIB5BDIJUqhH6dxAjSY6Z5I6RsjmkeUdGHLpz/qgLYW7dunY3DJJ6HpdQcOv/pRu9jqH5+d6l+RNsaPKGumTdv3gSi+rFajIDTCJC98To9KwGF7+ztfEjJ4lIMN1plJ6B41g1D2OUSo9UIlTjaOqWDCfTMwTMPXhdsKI+ihEAqVSSR/0rPiVIidG8MKVNUrnmhpFxD6dxlXYIhkO5L9wcbaeEoJYwbsPBeQcaAhZsAG7AqnsbpuRUPO35QKW+fBx68cXeJlH8uVEpSKOELJjRO/Jrj08jiMwIkEWADFrFpzWaz65B499VQaxcx1UyoM37nxJ0nmSC8h+ZRBmlHSXrN8uXLk/TSEiW2TvGiVGELyak3hg2+LwSVax4GvoG/hY0P03MPgR0yNeCe1GUk9swasFC4ZT9w1v9obL7cZ8CgoVC4WlBK2F8KmZEVKHt6enrhufyNUn2ptsEL7V1tLW2nU9WP9WIEXEWADViuzlwFubvz3auRoFgnXcR6irfKCMjFlY8HO7pgwWKdB0eHWzm/oWIj579yfhZDUsCn412gfLU5JFT2kcF9l4oH1qZcLvfoPsX4R2IRaGjYxR5YVc4+CrfQ8shJF9iAVWnuCYWLjlQTnoQlK1BOmjLxY+ibqHy7SsgfZjKZQ0dixPuMACMQHwJswIoPe6Ocu3Pdv4KHwQeNMiFAHGEybSbUaEoNHGOCbhw0cZP4exx8maeNCKjn2ihVEJmkkg8HGVdhDN75xQsqHHfnkFTsfeXObBmVdMqUKZQMWI8bBcujY+AHTsWdO3dyTtVKJwythP0jNS1pvFyxYkUf8se+XZ8fIwcQ3j9AqtTV0E8/43ljBBgBCxBgA5YFk2BKhO5st3b1/bYp+jToqhftcfsPVZ2iJOOJIXyZ+m+o4DAxhxGQZDwMUsVUqAas5uYT9BfaKQ5P7j7Rle+x0XofGsn+gQUrmRBCZTiE0FeSTP4rFHHYgArXZObeyFUsBZnn4Uh8kCOybP6zXG61LurzvZFjiO+f1JZpu5i4jqweI+AMAmzAcmaqggnak+t6PyqH3BBsdCJGecW+4rywNUUFQhqeGABGysK9YePD9BxFQCkqL+zF6bOnh5rQWcriTEdndZTYyHOyflQjNzACjiMgpReq0XokHLhuyBiwoFtJD5yROid8fzZZ/b3K87+zb/tH4I6UqI+bSskvtLR0vITsnLNijIBDCLABy6HJCiiqXxQD5+BBo7+Y8FYCAYQRhv9A8hWJEEKcN08gcSdXtSxx3iSt6ZhjjmmEA/0RRPR+NOzCBCnhzyKCDabZf4CKLqxHfQjAQ3lCfRTsGe37ntmQOFIhZZzAvdKZ29zc/Bwcp5Owf6SyY1SghHfeDuV552EYnPUSszV5yr9u3rx5ZO6JiZk5VpQcAmzAIjeloxXK5/M7U43eaTjyn9FHuQVGmuNCR0FKEtXI8GbC3lehnxxuEtx/yv7aKNvopvSjpA69AiGsPoeP4uJqQ1GYXei7iksC5S4Wi+OJqO1v2fLIJqO6SDoh1p4sH0JmFENHiHteExVv5NKIV1GBsqens1MqcUVpAkRbpTh6/LiJXyaqHavFCDiDABuwnJmq+gTt7Ox8XMniq0BlS32U6I2GkSZUY9P8+Yv3B0q6CqHzG4x7bPR0fhbDUaDoiZeGQyl+KqgqdE/YUviKjgFLNSg2YIV9gjhKr1hsoOFtIMWj69atM5mQHo9LRcYLU/lswKp8yfqkDVi+591fWf/dR8dvH6eLRSUr3FSJC1pbF+n1FG+MACMQEwJswIoJ+DjYZrPZfykp3gzefhz8Leb5vDBla2goHB0mvThpwbj3UJz8mbdFCCj1YoukqUsUeBeEXgZcCo+KB9YAhw3XdXqRGqyUT8MDS8nwvS6HzHQmkzkEu+OGNLn9M1VMllGixtmC59GcGoe41L3Y17flwWoEvumum7ZLpd6JvnhdTMyGKHv1o7a2Nn3N88YIMAIxIMAGrBhAj5NlNtv1e6XUp+KUwULek1taWg4MSy5PCTIJ3IEJ578K68RwnI6UkowHlvD9u0OfDqWovMxuCx0bJugsAmlVJOGBJYUyasDyih4pgwbu92zAqnTV0krYP0xTWKJqqkDZne++BQSuGkaE/s50UZRXQ014XvLGCDACUSPABqyoEbeAXzbf/VmuTDh8ItIqHVpyaoQSherRNVzSaPdQSvnpaDkyNxsRGEzg7ovwix3EpKxKqfANWFJMi0mdsNmyAStsRB2m56e8iQ6Lv090097ESGg9dx8z939s6+7uDrVKq/uQjNBACUrzPUK52kMCx/U1fgBEEuWxj3vKktaWRe8ZCR7vMwKMgHkE2IBlHmMbOah0k/cWCPZ3G4WLQybk9zksLL6eVEeGRSt2Oh4bsGKfAwsEeM7k5xyP74wkFrKAsy+dTpvwLtBVqShsbMCiMIsh6YCqW6F5J4ckUiAySiqji2tJySNH1m7ACDQpDg+C2w0pj7uhU4HwyPuH7lfze+XalVvg5fiuavrS6qO+3NraSia9Aq25YW0oI8AGLMqzW0E3JHXf5ov0q9Fla4VuyTnkq0PDUhZfZZ4fFq246Xi+Yg+suCfBAv7Kkx0WiBGWCP/G/a8QFrEhdKYO+e3yz+0uC8+yh42AR8KAJX2zObDgeU3GoAHP65oNGGGfdTbTW7ZsWQrveTNtlrEu2QJWoOzOdf8fCqT8tC7e7g1uEkXvZ83NzTRyBbqHP0ucUATYgJXQiddq53Kr7kVS9/MSDME+1fH1NJT8NR0dHWkQpeRabmKhvw93/uEIAlJ1OCLpmGLiy3no4YNI4DwZjBvGZO5EB9wNeWME9iDgK/9gCmCgcINZDyyhyBiwhOL8V5XO+Y0bn9Ae+42V+jh9rI75HyikL4a39man9a9VeCmOTsmGL9Y6jPszAoxAcATYgBUcOxIjkdT9+gR+MRk1d1LJg0Y1Bmjo6xOzMYzSi402yPGWYAR257+SzVQgwP1uTdi6IOExlfxXgEZRun+FPdWJo+cJScIDy/d8owYsnBhkDFie9NkDq8KVLmWR0kfK0ZrWUYFyzZpbnoB32vmjiZJveU9rc+up5LVkBRkBSxBgA5YlExGnGA2N8t3g/584ZYibt5IylPAfz/PJhA/qOVFCEfEqifsMc5f/c6Y8p41Q/ivhC5EPeza8Ao1E13twaQobH6bnLgJYjJIwYCHv3cOmZmHevHn6OTnDFP2o6aJSNRuwKoAufULediX09P36DJi62jleHq8vQZpykxTS+1HH8R0kPFYpTxTrRgMBNmDRmMe6tNidD0u+EUQG6iLk9GClQ4Dq3jxBKIE70MATmT2w6j4r3CYA4+6ZbmswTPq+qVMn3DGsJYQdP+2T8VqCwYKMLiFMLZPwRCjeyTED+aR+zzElQ2Pj5FmgnTJFP2q6Ms0hhBUxp5Swf7Si23K53KOjm2trQaGoCzGibjq1cY2994GFxuJVkILD8GOfChaAOgJswKI+w1Xql8utvh1f3T5TZXdy3fC0mRKGUkh++rww6NhCw/ckL2ZtmYx45NAvYqfHw9oAVyn+smLFir6wKft+isx1ggmfFDY+TM9dBPBMI+BZJI16FEnpUwopUzt27Fjv7hkbgeSSVJ7TkYCFcq3AYPw4qhK+byRx8vtKvrK1uf0C8nqygoxAzAiwASvmCbCJfUNT6guQ506bZIpKFrykTwyDF/LrHBkGHXtoKBLhI/bg6ZYkLS2LFkJiAgvYPbgr0WNiBjxPUgq7238w75kJoJimUwjoamswaOqE1a5voSzKy4EgCVUghI6b165du6OcrtwOBBRdA5YS4r6w5hhVCX8BrG4Ii54zdKT4atuCtmOdkZcFZQQcRIANWA5OmimRdWl5X8h3gj7SxCRskyKUawEv+6Q8sJDcnuP5E3YpDFUXC7NXD913/bdU6jYTOng+nRBC4COnT5rOhmsTJ4pjNDdt2qSN186HkeMDlVkDFqWQMhmeAcOx070WcefU0tmpvnVUICylZ7owGEr4ZKljhNvGqZT3M1QlH0dYR1aNEYgVgVAW7bFqwMxDRUCHEmLRem2oRN0gVnf+ij0PKzreKrvn7RA3po+lDBuBQe8Lqd4QNt0Y6amiLOZM8Ef4NWzXdLaBtGDDNZ3pDKyJUqlZgQdbNFBKsd6oOIRCynArM2rsMzoPERBHwv4JYEPWwB92BcrO2zs3wwvr/RFMjWUs1DEDferzlgnF4jACZBBgAxaZqQxPEZXyPw1qA+FRdIGSqvta8Hf5+qtc3XRsQgurcl7I2jQhEcqyeePmV4IdIYOsvCuM5LSlpkBKSex+6R9aSk9uSxoCaiYFjWFeNmuUUXIuBZwGdVB+aCFkZDAZokhT02Q916Q+WAxRT5ioQNmT77pGCfG7oXyS8FtKdXEm035yEnRlHRmBqBEgtdiOGjyq/Hp6evQLTMK8sOpfgPrSfy61cwKhF7Op6cT6VIcAvsSfW11PN3rBs/SPpiQtiPrvH6ZkC0KXWjXVIBjwGKzSydz/C4aNMoqMAcszbexz/MIilrB/1GwYq0Apizqx+VOjGNJugA1LXLVgwWIKlVxpzxRr5xwCbMBybsqiEbiovK+AEz6aJGODojvr19QjZ8DCd8bncxx//WeGaxRaWloOxNy/yjW5K8mLikg3VjpezzFyHli+PKYePHgsGQQoeGCphoaGB0zNyInzTpwK2tNM0Y+arhTSsLEvao3C5Sd9MSdcilZRM1aBMpvNPgxrziVWaRuNMAc2eANXgRVZr71oYGQujMBwBNiANRwP3tuDQD7feQ9+5pMCCJ4s9RuwKCVyfXbi036ff9Szu/wrCQh4Iv026NlASNfHDzrsoF5T+khZIBVCiJArNmCZOlncojvLLXFLSrsJBWp2lTwSQmNfQx+tD1dpw+GWIWAeKwma73l7ITVagbI72/0jMPrzXmaJ+SvF0kym7fzE6MuKMgIRIMAGrAhAdpeF/KG7stcouVQ7ahxRoruk9SK7R8OiJ7gccInZptq0dOnSJuj2Xlr6qRXLly8vmtIJeUOMLZBNyVyRrhLaaM3vBxVBSsRB9z2wTIfEeWIuoTOhv6urayMhfcJXRZGa7+H4mK9AqXyR1oacZ4Yzpr+Hit7faG9ufxF9TVlDRiAaBPgFNRqcneTSX0j/HoIbW/TZBAoeLk/ULY+ikwdjKBZSKTZgDQWE+O9tW3a8GSqSSuIND0tj+a/06VAojHua1GkhxcS2BW0vJKUTK1M7Ako4b8AyXlWPlkfOepwkfu0nSoJG0JrvYRNn/FoBt1xu1QOIprt0GONk7IzzpfgZp+RIxmSzluYRYAOWeYyd5bBmzS3aqGMs7MYmYBAy83id8iB1BNXcCLK9Tmx4uCMILFu2LKWE+pAj4lYrZkF5ymjYwrZtm8klp/XTgq/7as8wgv3a2tqm45k20XXVTCclR6GTOa5jtE9+qe7b95t/lENgdrkDzrdHVIGyJ7f6clSIWOk8XrUr8MLigP+Z2ofxCEaAERiJABuwRiLC+8MQkFLeMqyB6I7yVV0eWM3NJ2iPlfFE4TmutbWVTJJaonMUilqbN25+NQg9LxRithDBizIqqxo1MK1bt64f6pIKI4RFng1YtpzDMcjh+ykK+a8EDEz3m4VP0gkhNI6V2ZkwTX1PNblJpvnERd+0sXeIXspLyXdif9uQtkT8xP3oA60LO05MhLKsJCNgEAGSBqy2lrbTW1o6OPwhjBPH9+8Ig4ztNDzpba5HxgZZIJn/ag8mKVmUi+rBh8faj8Cg95WSl9kvaY0S+vLaGkcE6y4FrTBCJfQ1j+hL3pKIgKeKJAxYwpOGDVh0ciJJ0/nCHL+QUqkCHWNlibmIsgIlcq3dj9yRHy8hBvUmT3j+T+bPX7w/dUVZP0bAJAKkDFiZzKK2tpb2nBLytyiZ/j8mgUsK7aJs+GsydPUfrEdPX9BM4L4XE8Tun7D3N/+licDDDz3yDpgrjialnRLb002ezuVnflPiMfNMIuVwYNvCtuMi5cjM7EFAup//SoOZLoj1BkHV79AzDdKPlrQSHEJYAXFPKTrhoqX0jLgCZTbf/W0hVE8pUYi3HdqYLiSnSBbxyWT14kGAhAELuRqObs20X49k011KiGYNJQxYZ7e0tBwYD6x0uCLh4gZoU6CjUWlNfOmvL32k2laaCdz3ao8k96fiN3tj7AWE2N8lxy6ZiK/vnySmlj5hf9XZ2RlNmIJU5Kp3+Z44i9o5wfpUhwASOlPwwBqYfvh0/Q5jZENo/WEgrKu2ktjwnGcDVqWZlHS87Uqo2RdDBUpfSf8dkGVnCXmIN6kz2loWnUdcSVaPETCGgNMGrI6FHbNbW9quUUX5d6HEshEoNXki/bYRbbxbOwI+hjxS+zDnRjxYl8S0X2y06WpOS8uihXVhxIOtRWD7xJ0fgHCHWCtgQMGU8n4acGjNw8DroZoHWT4AISWvsVxEFs8YAiQMWBuWL19usJJyeq4x+GMg7DV5psMtY9AqVJaUPbAeAFL6fT/SLZvN/gsML4uUqSXMEEL5jebmjhdYIg6LwQg4hYCTBixdHae1pf2bBc+/ByvrNwHxcnpcMG/evAanZsRKYWW9Ffqs1GqIUA/jIfrMkP3af/qCcg6sQTyk8M+uHRgeYTsCLS0nzEKxhktslzOAfA8fcvj0WwOMCzQEXr/kPLAAxPMzmcwxgQDhQU4jgGqkFDywzBpkfFIhZU/BW5VWHr/wr0C6BqwYK1AeMuOgr0ohbg9/uiyniCqvKelfd8wxxzRaLimLxwhYh0A5w491gmqBdJgLDFcfhsfVf7D7Pvwby3V75vjGibzo1uDVseFFNvKvMnWIW/tQqe6ufdCIEdQ9sKAuvDFeqxN9j9Ccdx1HICUKV0IFcpWVEAJ1nVnvi+ETL6Ui54GlNZQqpT8S8ZYwBPByONN1lZEP1agBC2HXhAwaHD449vlOOFVEjOGj+jktffEW4E+qku/Y59Ngj5dO22//RHqgVYkPd2MESiLghAFLW6czmbaLdkzadS+0+CL+TS2pTenGD6PZCT1Lix9/K76MqPilMCcBytr+sx7qCxYsmILx0+uh4cjYgzZv3PxKR2RlMatAoLW5/c24uJdU0dW1LkWZKn4/UqF9YXSxHKkuw5m9vaOjY9zwJt6jjADmexLuC85XyfJMV9VThAwaMXrguHAt7Y7mkDrnGckN78GxPr+6buv6J0LqPk8S3LGUUuKS9pb2E8bqxscZAUbgWQRsN+x4WGC9YdrU/f+J5JLfgdgHPSt6lb9QVautue20Kntzt5IIqFoMhiUp2NwohfePeuRLpcbPrWe8S2OVFBe7JC/LWh4BXeQCHgRfL9/D3SNYfN/Y09NzX5QapPyUzuVBcTug0OefSVEx1qk0AgMDA7NKH3GrFb7jphfldJ79SpnGyq2TZ4S0jY2T9TVB1gPdk/HPf0NT6gvw+V07Avok7HoIc7lm/vzFzn80SMJksY52IGCtAat1YceJCBe8HXFL1wGqul4SlJQfsQNuR6VQ8mBHJa9KbF/INVV1LNMpJYrk81/tU13JE1F56cX79vmHqwjAdpW+UhHwsig1AdLzvlmq3WRbZ2+nzoEVTcVDk4qUoi3Vu0s1cxtVBFJ6se785kux3qQS+KBDJoQQurABq8LJ4nmk8p2N0tSGCpTIwVYQXvHtEG5glID0G2Y0pgZ0OgfeGAFGoAoErDNgwVtqPgxXq4Tn3wz5X1qFDtV0mc/umdXANLoPQgn2gxFx4ugjZFp2NjaKujywEKGaHAOWnnZf6vxzvDmMQFum7WKEBp/usAqVRP9HT0/n6kodDB1DFIbQ+RkJbhJ2645WgoqxSiUQQD4aEoaZYjFtzAsTIWUTkFyh9qiAEnjb0IScgcawskG/umXwCYWLlgDDlgqU8Jz+G4rK6FQxydukOBNRR+cmT3HWmBGoHQFrDFiodHRkpqX9V/CWug1qvLx2VSqPgHvmpZV78NFSCBQK4iWl2sm0SXHH4FefehRStL/MjYZGvqG5+YQZo9u5xQUEMpmOl2GxQvcFUYlvYB5gS4p+QyXCe6LnGg1H6fsfj4YTc4kdARqeRTt6e2951BSWTU2TdWQAvgPQ2JTy2IBVYSrhskzCqFtGxSdtqkD55NOPfxZy/r2MrNSbv42vRc+nriTrxwjUi0DsBiy9EG5tabsSlY7+gTeBs6CQ5CHRrgAAQABJREFUqReCk/TCrV7AEjfeV2F5wdkJnRI99QoG1/tkeWCh+mdKFj5dL248PnoETpx34lRP+T8HZ6plmx9LN3k/ix7Z3RyRT+/OuHib5guL4JJMZlGbaT5M3wIEFIHFuhoMHzRmyCYWUubvt9/4DRacefaKQLvStFXGy3Xr1vV7arAqYfJCCXXEi5/6mS5eZu/FwJIxAvEjEJsBCx5XkxEq+CkshBFyIc8DFGnTcEildEVC3mpCQJ1QU3fXOiuvs36RabuWl8HnLS0tHS8sc4ybLUQA4cDpXeP6foEV3REWiheWSN/Dl+RdYRGrlQ48sP5S6xiX+uMZ+kmX5GVZgyFAIreT6ZxORUrPfbVhxYoVfcHOloSMomDULTdVpq+VcnwrtHflu/6KwySLzFRQe88hNQ/Fyz4xdj/uwQgkF4HIDVjNzc3j21oWXQKPqwcAu34ZHh8d/OrM5uaOF0THz21Oeq6gwcvd1qKi9AXlDWQr9hjj4LJly1IwwM4coxvFwylP+HTD0AjOWLHP/4ZQ8pUEVdur0hbkTvz23p04/jb2NeoKSsa8PuLQaQTPxchTecaINt4lhgDc4Oe4rpISyqxXCaWQMsUJ3Ks43+dW0cfNLkqYvVYCojJ56sRP4ml6d8Dhrg/7CDyeKa+/XJ8flj9mBCIzYOmv/0gc/I6UbPg3Xiy+BL2nxaC7l/I4j0e1uKdEo17sTqi2v3P9pOjNZrPP1CP3pk2bdC6ohnpoODz2FDxgT3JY/sSI3tbS9h54VVxEWWGl1NeQAPapOHVcuXblFvD/b5wymOaNPJXfXnLsEsqFPUxDaDV95F/R72b7WS1kFcJ5pr1KVH3VsatQIboukhO4VwJbh97j+HMq9XH5GKqP3G+j/NorUHne2yFb0Ub5DMvkweP5GjgSkD3vDOPH5IkjEIkBq3Vhx4mFAX8tEgf/AHgeFiumSry+vbn9RbHK4Apzz3+nK6IGkVP54rdBxg0dI4vS+S/VQ/Wp+bdSV8I4PanmcTwgMgQymfbTlJA6sTnl7cmC3/8tKxSU4nYr5DAnxOE7Ju/6mDnyTDlOBGSBxjNN+d56ozhKSckjx0oDhtH5q4H4jqYC7fc8z14DZjbbeRvWjrF6VtdwqoTd9TBPNlwZNlGmxwhQQMCoAQtltztQEvQ2hHXcDDfQYy0BzIMnwkctkcVaMdrb2+cg3GiJtQKGIJgvvLoNWCg5QPvFZgycpRCzEZr2hTG68eGYENAfD6QSvwR7hLrS3aSUX+vt7d1qg4YwjHfZIIdRGZT4ADx1XmyUBxMPhIAOa9dG60CD9SCPxjPNl9KwUUbNCoyxZQPxTmwYK8sUrlEcKX1KxspR2vu+6WtlFMuaGnb1b9MfTJAvOXkb3rHPwjr6zcnTnDVmBCojYMSAhQTtx7Rm2q8Xvn8rFvgLKosQ/VElxLK2BW22GNSiB6AKjn5BfQTdjJwfVbA330WKu/L5zrpDfeDZMtu8sHZzwMvvBciLs8huKZMnHTxNW/Dx4DfQfBxl7fGC94QvCt+xRkev2GmNLOYEaRRF72fz5s2jG2JuDjtjlLXxavNDj/wI10RzYCZEqq1N6EsbM8p0HN9xMPAl43kMDxcrcyAFPofDHugTChcdjU1x69bHHhzdbE/L2rVrd6Aq4VshkW+PVBFKIsX32he0Py9CjsyKEbAegVANFNprp62l/adI0H4XPK6WWay9p1Le5yyWL1bRBr2vhHxrrEIYZq58Vb/3FWSEd8tsw6K6QB5ejfLHe3KnuCAveRkxFwt8KVZAUTKLrHKThg8SX6k3l1052kHaIcu/MG5jkLFOjZHi6AmNE7/mlMyEhdXGq00PP/JjfFB4C7ynAxtvkA9njuswaaP2nnx0RlTpb1DOYzQUmGIxzQasoYCM+C1Je9qrh9atW9c/QmXrdlGVMAeHiMutEywagSaplLgOH4waomHHXBgB+xEIxYCFHDgHtLa0f9EviH9iMfFGqB0KXbPwqVNbm1tPNcvDTer+gPgqJCd9o5QpdUMYs4PFwuww6BCgMVf43i93V2UkoI3DKujQbczFn6HCFIfVqFb0DTv7ttvjfbVXailW7/1J+S/uf+9CuNqZlHV0QTe9sNm88ZGf4MPhYKgJ5iWwAYtCWDw8o9ebnDcp1VyT9COmvaO395ZHI+bpFjsCRt3ygLvjfTfhmXEfRpHfe8vrQvcI1tbHj2ucwLkn6U4xa1YjAnUZmnQlIhiuPlzo9/UNBTcW0VQj/3i7S/ldDoEYPgVYjJyMF1jSCxI8CNajWtnfhmseeI/Ul9jAKOweeNLDD23+RJ00eHgdCKDa4OkI3daeV7pqEvlNCvUBHV5gm6KoHnSzbTKZkgdeqFdzSL4pdMemiypV48c3Tbphz8fDwQFKpYJ71CiPwjMtuAFvbMgRyETKgKXPFZw+vJVFgFbC/uFqKhH8XjGckvG9m+66absnpC4ulcjzFbk+P8bpOoyfZszAEQQCGbCOOeaYxtaWRe/dMWmXfkn4Iv45+qVfzhrfNPESR+bKuJjz5y/eH4uRK4wzipkBFr3XhyHCHnfeGWHQokJDP2BbWxe9ioo+LumRaW4/H54Hv4bMpHNe7ZsTpTq7c92/2rdv0Y++QuMfIE5SSn9PVin5x+bmE/heGPE5CO/3/VKyAd6Waqg3ebGvb8uDAUWRoDUr4FiLhvlGF+WkQspUHd56Fs24QVGIXBNlEKrHW7MMSZPNXbmuVbhH/dAkD4tp63Qd13K6DotniEWLDIFaDVgevvCfM23K/vfgBqJLlk+PTFJzjD7MyfEGwZVN6YFr8Oswc1BbQVl5RRnKw2/8+PGHQyPS1d0CzJgniurngwnEAwzmIYEQ8FA04wtYVH0fo5NyPhalL94XCK0IBq1Zc8sTeEbmI2BlC4sZKVm4QXsD2SIQdTmQq/LwwoC/Gnq2DdUVrgkb4JU4MLSt2t8wQh6Kvs4bwOsKoawKLDm3qm4udJLueODEAWfHgg5tmHf+miiLnYMGzIFi/wehz4ayOtE+cLhQHnlHA9pTyNqFgUDVBiwdWoZF0l/xhf9aCjkShoA3TifH015lQ9oS9xOhoB/Fi+/J5BWX6pau3q7/hKGnLEoKoRZhQDGchhQTkUD8j20L2146/ADvhY0AKr5OzrS03wCH+kvDpm01PRjrunu777JZRhgUf2+zfAZkm5+WDb+FVxDdxZ4B0IKQHCzSUBRrcN2XqqYc2PsolfJJPNM837hXEQmcBs89Bw0YQa6ZoGMG0rQS9o/EQXle4PvFSFpR7ff29m6FJ9L5UfGzjg+KpGFN/kbr5GKBGIEIERjTgIV42/kwbqxCaNn/lXlZilBcM6xguDl+vykHfNYMdfuptjYvOhtSftp+SeuXMMwQSd+Ts+uXiCyFqcqTf2pu7ngBWQ1jVkx7jqLia68U4vSYRYma/VPpBu9TUTOtlZ+S6ne1jnG9P56lSwp9/g1Lly51Kx+mQ8APPq99rxPvYweXEhvPuPtLtVfV5tNYrKuUCo7BGEDt+dipvXJobCl3knjHAzipfGejIFSq3zkDllYim129Ave6n4xSKCEN0P37eL8+IiHqspqMwCgEyhqw2he2HwWPqxtg5e7FqJePGkmsAVVl/ieTWbSUmFpjqtPa3PFKIZV+CGAdTHyTYvOOvh2hLSrxAJlNHLF61Zuekv7N8BI6pl5CPH44AljELvNT4na0HjX8SBL25KWdnZ2P264pCkX8GzLeYbucocsnxdJtW7azEStkYOHZls40L/oyntfXgXRZLzfpBTdIEMnt5E+ePPmBkOHfR27/SfvPwg6ZUG3fl8aMfftAc/gH3vPmOCx+ZdGV2J7L5R6r3Mneo039jTqNwEZ7JTQq2aSU51/Nlb+NYszELUZglAEL8d6HwePqCt8Td+EL36stlj1s0TxUjvpFkqop7a446P8GQDaEDaaV9JT4UdDcIKX0gcWP7otNKYWDtR0GL6FuGIeH5WkJRopH6RxDuD9/C4tYXYhgagIRubUnt/oH7ugttbEhcZtCOPozW7avamtro5AnM/b50+9lhf7irfjQ9iEIg0dPhU3VkcCcRnW9h1esWNFXAaG6DimPlkdOY6NYXxcg1AdLwu95u/Of4Xbt5rZy7cotQvnvclP6EKRWIrP54Uc/GgIlJsEIOIfAPgMWFkbP0V/3CilffzXWZUrTzmlTv8BTkA/r9x3Hd5R0za+fvD0UYLx6Hb4s/RYSlf2Sa4+0oUji+yId6sIXiWJnhyIZfSLTYBy+GQUgXkNfVXMaZjIdL0PFsTvB4b3muFhNeQeu4fMgoTMv3DLl/xzyJqUa4ciTp0UVZQ+HOYyEpbZ93DdPwXvZX2G3aq1mJAwsgT1qiDzTAutfDb5CenOr6udGp0fgzbrNDVFjklLRMlgORRGWcLPXylBmhn735HtuxL3xF4bIW09WKfXxlpZFzdYLygIyAiEj4M2bN29Ca3P7pVgY3bvn617CqwjJWYUG/w+6PHXIWNtCTsKD41MwXumFVTI8rzTyUvwul1v1QKiTwCGEtcDZhAIQv2hrWXTJ4GzUMjLhfXU+od3XrJ8DFM9PLhzyI7iG73VJ/+7u7k1YJNzikswhy/r8tPRva29pbw+ZLnlyCxYsmJJpWfQD3Df/AGUPqFZhKYOHEILHnGr5WNtPml2U+4RCynBvcjL/UbTnHqGKkyOBI1KBsr+QvgiqPTJSvYTspz2lrtPPi4Toy2oyAoMIeOOaJvZgcf8F7FE12ASZ6pcV+v2bUelnWpDBto5ZcuySiahYthzyfRL/8O6SmE2hKtFnwtR2T5LiQ8KkmQBaKSXUl1pbFv1ee3wmQN+6VWxvbm955untOo+SvmaTY3AegRxuVnmEDn53RLMTu0rJq50Q1JCQSoj9fRjxYIT9KOfrqA7klpb2JQ2ppr9Lod6BEbU8q5+B0TRQThsyycnrSWJfzfRIOh458Lhz3gOnmikL2mdPRVWyERl4NpGY/zVrbnkC53JSPdP1E2JOQ7rpO0HPcx7HCLiIgIcXpJtdFDwCmV8mfG/l/PmL94+Al3EWOrfXjkl9umLZWcaZ2cZAiRu78l0IwQhve+aZZ2aB2r4Q3PAoJ4GSOlWHwmnjTBK0DaKj/pqmc135UnTj5eToIDQIjemTvng79IEdxL3tqa2P/xpzuNk9yUOVWKck+Oymhzav1PmcQqVMiBhyhh3S2tJ2DR4sf4JaM2tWrQ6DxLRp02aDn/PJyU0bZfAONafmebF1gGIPrEpT09cnZuM43fc8VSTjgZfNdl2PjyW/rjSfpI8p8eY9FeVJq8nKMQJ7EYBjSsOV2MF1z1sJBF7amO7vxUulywtIhAwueqdKyTym+ZgSOpJv8qUM1ftKA+b73mzywJlV8HAYZzpR6fQLOjG5WVZOUfcQ0n0uvC/ugdT6iyLdl+eqp0X+b9dtXf+surtlHdetW9cvhQw1/55lKlYvjpQdxZR/J87xN1Q/iH5P7f2UaW7/IHKG/Quf098EjWEnCbCpOsIHi+m5AThaNwRuvqa9SkjgNDhx5rGy7vyoRSDP8+nMdQnF68mXV4Jc7E1eSr0bQgTyQI1d+DAEkOryjoUds8MgxTQYAdsR8AZziii12nZB45NPPhcvlbnW5o5XxidDMM46eS68OOBhp64AhQnBqDg+SokVudzq28PWAjnE5oRNM4H0GmA61/n3/tHW3PaKBOo/TGUkaV+I6zWLpeuPcIDDUzU6uH4ROviNYUA5uJMqSP2hqOCg6KGLjK9l++Mcvw7n+k2c4F14KKjy2mlTD7hLSvEVgD25HsBVPQYJKqFxaXMGrD2h73TSbaQ8Mh449Vw35cbC85fye57atWuXaWNvOWiNtOvwaUQVfcAIcTeITi14xWs5VN+NyWIp60Ngz9d9/jo8BoxThfRvxAv3F/fkPhqje7yHtUdLW2bRp1PS/zskWRyvNPFyV573aRMSeELOMkE3oTTnKin/1NbS/lPknas9bMZx0HR1QXik/FEqH16SYqHj6oQp/kaZVm8BQdg83N46ezsfglPNb93WInTpT9LPKDyrPqGLyYRO3W6CEoarM/FOcSc+hvwSp/iRoYjr+XUYJEjkdurv6uraGAqWJYikUilSBg14kpMyYJSYsvqakFuoPgIWj5bikbVr1+6wWMJAonXnun+a7GetbN208ZFLA4HHgxgBhxAYNGClm7wbpBBPOCR3HKLq3BAffmbr9jVYZL84DgGq4Omh5PbrPdlwty6tiv7jqhhDtgvO6Zuy2c7bTCiIPBt0X2xMAFYFTVgp3oi8c/9BWOF3M5nMoVUMcbpL28K2l8Jo9zsYrtbAI2Wp08qEL3xReN45QRNShy9O/RR9Ib5cPxVyFMbhWXXZ+KaJ/9Gh7kiarHNlkd30BzA8o98KXf8Kw5XO1/KiMJX16vLAEhTCpR4AnrjUDG3FFAWM9oIzMGPG/jCs81YWAUnimiitHuH8ZzLlXwClnyqteCJaP6U9+hOhKSuZWAQGDVidnZ27sHiE1Zq3MRFQ4lgssv+CL6dXLFiw+KAx+0fTwWttXfQqVBj8C0pu/xyGm9nRsLWai+9L76PmJFSMsRlwG+Fvc6FUqf8imfHXdFJjM2xioyqxgD0F1+otypNrcd89DZLgkuVtBAKf6enp7BzR5vSuDmXGRN/ktBLmhIfBWl1R6Ff/0B9hqBmy9H0s09x22TNbtj+IZ/RV0NXIRzDp15EDS0nnjTO4vu43d4qCMpUwS62KEA8sX768aBQv14kTuCbKTQHuQ3V4a5ajakc7PnxtwnvkB+2QJhYp0vgwep0uBhQLd2bKCESAwL6vnUoWf4BF4/si4EmBhcbtnQ2pgdch/OHLwO7ynp6eyK39J847cequpoE3CqUuFL46mlfBw06tq+F99ZdhLWHuSDHb/cCmMAEJnRYSu8sPqKJ4D7wVfucJdXlXrutWcIHNx71N505JiYY3YdVwPhQ4iq/VCnOoVOchhx382Qo93D2k1OeFlEvcVcC05OpI/RFmoN//Aj4SfX9cX+P3V65ducU0VxP0tbfVM8/sWILn85twHztDStFggs8QmmpADqwfsl/jTzWnxgH2dTftVaI9r518ApWcKrIGjJLaBmokcE2U0Rsl6O8vc4hEc0++68etmbZlQknn8heHNAFzG1KN3wStc0Oix2QYAasQGLaOwgtjFtK1WCWhG8JsQzjA1SrlfweGrH8bFhlJXxdlpD+YG+b1WBBPNMzPRfLPpAe853fe3rnZhPA6XwtCXraB9rDrxwQvpjkMgXsQbnSF8Pzrs9nsw8OOWLijE2lu2vDYy0XKfxsWPWdCxESH9FY5RY/gg8BLXZjfKvUZ1Q2ehd24dbSOOsANpRDQH4au8YV3dS7XeWepDja16WqC06ZMP0FJdRaSCb8GskWZ8PvhnlzXjCB4wENsOorVPBpkrGVjLgUGXzIlE0K+/wz7FQ0DtBRX9GS73mUKK9fpzp+/eP/G9MDjrutRXn55LgqkXFX+uPtHmptPmJGShX9Akyjvw1YBh+fQ2cgL9gurhGJhGIEQENjngaVp4UT/Ab5+sgGrdmAnISfSRQgtvAhGwDsw/Pqi8n6dz3f+t3ZSo0doN9DGVGMbXpxOxyydji+6B7LpZDROQ1o+Z8p4pXlMbJg4C0k22Hg1BPCIfr5ASvkNoVJfgxEghy9rv4Jx6DcwGj8YEf8x2egF7H77HbAI7tvLkEjz1cITBxD6Yj+m/nV26EMOnzO7cvYbJ+vRU0nvE1KpVfXQSNDYadD1fZ7w3wdPzL9J6f9EpuRvkajbGu8BvdBtaBhYjHP3NDyjT0EKpv3ieTiowB41+DCA8MF4pA7zXMZ7mOnzwvkwy31415MvbR8Ruj/S6QKduS4xTXhHCXy/KEHOyqZ8ftXGTKbtI1LJy60UMAKhsKa/vKXlhHwut+qBCNgxC0YgMgSGGbB29O24Hp4l2uVwamQS0GP0Uqj0UlRX+iKMWQ/jnTCrfJWTInWPSBXue+qpp9avW7euv5Ta2mPjsQ2PHY5a60eoVPEI3HSPQz9tUDwaL8ZeqTHcNgqB+yZPHTyHRx0Iq6GYEnPgccdbfAjgWoAHixStMBp/E9fZP5VQK6Xn3TxuZ0NXxCFH8IjMHCVVGtU+FcKFRAcMzPCKdH8xGPX0YvF5IcJEc1HzjZpfNrv6Vnhy/BG3kJOj5u02P/VipeTXVUF8Hdf8P3CJ3QhP5D8VRGFNPp/fGZVuOhzY8xoXwAi5CNf5iUIMHIfrHvar2Lf7g0qA3FlzLJA/qPj7xuH8CIzBPiJlfgx61G58ZGaZw+41K7o5kMKYDKnznVG4KMqAIRuSMf/ZbPcVeF5oD/iTykBBvXk/TxSuxf3r5ZzzjvpUJ0u/YQYsXVIVFcB+ipv2hcmCwZi2hwLLZfAaWTZYGMf3xLSp+/u4meqcHtux4O3DC/AAfuswwEnw2JgKM9WgoQrGK2NCkSas5AdXrFgBXM1tmJvZjqZiMgdKvJSRU0oehTxw79nV1K/gqfFvvHfejgXmX5Dn4e8iLf4Dj42HIGJdr6NILD2uUCgcqZR3JIxULwbPBaB5PKgiUeYe0nzZBj0Tvp3Ndv0o6GDXxqHE4ofhVfQKyK2r2/JWOwIvxCX3QiXlpcgtN9Da3H6H9ETeF+Iv8NK6J51O/wvFaXSYd+BNGyw2bnxitqf6j4J96gXIXYZrXulr/nm4/vfQ3fs3MJsQB9axIJUeFus26RIMlsZGeX+wkWOPwrlwGF7OGsfu6UiPVJG8B049MyF95Duj+zzvP+igg6xPw1DP/A0Zq9K+986C5/8dbZOGtCfpZ9umhx75EBT+YpKUZl1pIzDMgKVVlZ76f8iFoEuQ0r11xzun2kClwyLwjyEOcyqA5h+786t/EybNUrTwnj8bCXl5sxMBzIw6Ev8dicvrHCxohYBLI4zG2kPjv6gitREL38dghHocubQegzHSR76aLVJ5g12xiBuPceNgdJ4Ir67nYMyh+Hcg5nxGod+fCUdI2MT0xifAIAxh/CfVynRD6n/CIOUKjVyu8x84J38Cec91RWaL5WzA5bgA1+iCwatS4Xtzv6+v+QfRvgGhxo/hnqAXazrH0y4YnrWp5mmtj1L+BFzrTbjWp6B9Iu4Nh+CwvuYPwQclbbBowkvRnqvdcgNPHQnM4SU+l8AtbSuMlsZyFklZJOWRUywWjRn79LXl/CZhwKK7rU+SN07nbZ3r8WETVcnVt+hO6RiaSfGZ1tbWW5Fyo3eMnnyYEXACgVEGLJQfvRv5ZZDMnZPMOjGDLOReBLbKtIgkISmMV5RfbPbiSe0vqhqKF2Ex+yKtGMID8C4zuNzFelX/3bM43d2Ehe3wxeqeZj2UtzARQB6WdGPqbCw8YWZM1lZU6U8gwSy8c8XkZGkembYzcVnD6Dz8Woaxap8AMF4N/obxanfb4B83r3blyfv3KVbrDynm1jrEuv5SrDcrk84TRmbbgrDbJ8loY0KRwRBCN+8FVcAR/F5RBXEbuyBh/XczLe0oriHabZQvApnSwpfXIeXFcSiS80wE/JgFI2AUAXxgLLElOOFdCTS4yQEEYG+4BGFiG6IRVc2Ohg9zYQRII/CUTKtTTXpN2IyeTjAL+S6zWUaWzR0EfD91Xx3SPreOsXYMNZzTCd885tihaP1SwIRbz7lSvwAuUCA036PhDl7wYTQtZ1p86fnnQdrI8iXah4x8rlDpr9snF0vECNSOQEkD1lNbn/gVSFEoqVw7IjzCOQTwMtaVzXddGZXg+IJD5kU2KsyYDyMwAoFdyF90uvb4HdGeqN10o6dDGu5MlNKsrAkEdsEguikIYV05FeNmBBlr0xh8xDLrVULL85oNWBVOXp3/Dn7aMyt0cfoQHE/NXiuWooPwuX/jPvEJS8WLRCzkcXxHJtP+2kiYMRNGwCACJQ1YukoewmoSk1DXIL5M2jwCu/Z8VYEdy/wG99vJYLS/eU7MgREgi0ARL1HnoBpfN1kNq1RMh04iI9P56O5XOYS7MQKlENAL0kDn0LRp02ZjLBbsbm+e9M0uyhWBMMu9U2zYW20vG1f/6mrgkL3BVfnHlLuOfHlj0ra8Az52fx0R40iTk9wN3qTfRz4ssgba5M5ssjQvacDSEBRE6nv4058sOFhb1xBA7pJP6q8qUcmd8lOzo+LFfBgBigggaf7F3bnuX1PULYhO3fnuNcjTxB+MgoDHYwYRgFdwYOONLEoSHsXIWxgYgypPo7lV9rO+GyqRmMbKegwqCeh7Ppm5Lqmnn+j594u+9w7gsqskNslonKZ879rdnobJUJi1pIdAWQOWzs8BK+3P6anMGpFBQKnOg2cc+LUo9Sl6HD4YJd7MixYCWGR+Ipvt/i4trerXBsUw/0cJ00mo65eTKdiJAOpQBDdISI/EYl155gxYS45dMhEzf6Cds1+7VErVkfC/dnbOjYCLMAmjbjngU+PqypdXjqwz7fl85z0o4PEZZwQ2ICg+erRvfnjzBwyQZpKMQCQIlDVgae5F6X0Vf/BezRsjYB0CjynPf2PUpYARWjvbOiRYIEbABQSkuCKb7070S2O5adJVgaTnvQnHi+X6cDsjUBaBekLCdLU19ze1a9eu4Ea8MfTfNXEXBYye1TJVuO/ZHf41EgF8vKdswHoKoetPj9Q5afupBvll6PyXpOk9VF8Ysj/X1tw2f2gb/2YEXEGgogErl+v8BxS5yRVlWM7EIKCEJ9+ORd/DUWuMBJCzo+bJ/BgB5xGQ4meHHHrQhc7rYVCBnp7OHrxQftsgCyZNFAGp/MAGCeWTyO20ee3atTtMTS/CnikZsPzJkyc/YAorGnRJzfeIKZGB7xUjCDm9O5h/sqjeDiWSnCqnQUh5XUdHxySnJ5OFTyQCFQ1YGhFUioo0RCuRs8BK14QAFnnf7OlZ/YeaBoXUWXpqdkikmAwjkBAE5K/SDd5bovaWdBFcX/R/FNWv1rkoO8scHwIqVUf4nCRhwDLmfTU4qzQw2nuCblyxYkXf3h3+WwoBGnnhSmkmpGID1h5gunu778LPL5TEKSGNCLE6ojDg62gr3hgBpxAY04CFSlE3I1Y275RWLCxhBOTap7c+fmlsCtJ2LY8NVmZMFAElbkg3yrP1106iGoaqVj6f3ym84pkg+kyohJkYaQQGBgbqMeAQ8C5S95qcYJ/Qcx8L1nrOFZMw20SbwDVRBk7zxQ7KMLazOd3ofRaS3WGndBFJpcT5rc2LlkXEjdkwAqEgMKYBS3PxpfxkKNyYCCNQBwIwpD6Bxd1r161bF6fL7+w6VOChjEByEJDiNzv7t7+ejVe1TbmuqiqFemdto7h3ghF4rLe3d2sQ/RE6cgDGTQky1q4xZsOipPDm2KVvPdKYxaoeyWwYSy1h/0hM6yr4MJIYgX39fuILT4cSDhBQJ7gKUv2/TCZzaHACPJIRiBaBqgxY2gsLYq2OVjTmxggMQ2BAed5rsLiLzf35xHknToVE+w2TincYAUZgNAJKrJg8ZeLZyEuT7JfC0chU1dKd6/4FAvh/UFVn7pRsBJQI/EwsFArPpQCe8UW5opMTyaunYiWFk2UMHcgl7B+hL1JwBL5fjCBFZjeX67wTBZqSHkZ3gFSpqzGpVdkFyEw+K+IsAtWfqJ73MWe1ZMGdR0AKeSGSHHfGqciOpgKhr7BxIsm8SSOgxB+e2vrEGZxnpb5ZTjem3gsKyQ5tqA/CZIyWMnBImPSp5PoxuijH64eYTeZkqiPhPxkMKihS9EhXIBRKeWzAKjH/k/abcBnnnxQntbYsurgEPNzECFiHQNUGLF0hCRf3r6zTgAUij4AS6jvdudWxeyN4SrEBi/zZxgrWh4D8BcIGz4o5zLc+FSwZjdCGXUoWXwVxHrJEJBbDQgSUqKMCoZAkPLB8P2VsUd7W1nYwpn2ChVMfTCSfc2BVBM6n421XQk9/v/3GbyjRnvimwQ9uXlGHEhaTDYb6fEtLx0uSjQFr7wICVRuwBpXxih/GX65e4sLM0pHxlobG1AdsUEd6/mwb5GAZGAE7EVCX9+RWv5HDBsObnWw2+7D01emguCM8qkyJEgJ1hYTRSE6+K59ftcnUnMoCFS+13Qj5KT+wx54pjG2iKyWt+R6OrdrAntHDERm6hxQlvdj/5tC2BP5u8oT/s3nz5tEx2idwEpOgck0GrN35h9S3kgAM6xg/AkqIv47razzLmiTQvpwbPyosASNgJQJf6sl1XwDJfCulc1io7tu670BS9zdDBdwSeWMEhiOA3C3BvY+koPBM0wYZY/cdFDEi4aW256zZBaO4MWPf8DPT1T3C73mKve/GOisnT534UfT551j9iB8/avy4iV8mriOr5zgCNRmwtK47+3Zchtdo/oLj+MTbLr4U4r8NA97JK9eu3GKLrEgUyyGEtkwGy2ELAj6eBxf35LoutUUginIgqfuvodcnKOrGOtWJQLqe9zES4VLBDXhVQC9pPff1uzsbwivPO933PFmHsbsyZmSOag81X0gdSmjMKO4EWEpckMm0n+aErCxkIhGo2YCF8JAdypMXJhItVjoqBDamfO+kzts7N0fFsEo+FL5WV6kqd2MExkQAOZrEG3ryXeyVOyZU9XeAkfCzWHt+vX5KTIEQAgXP8wLltEGISAPymh7mOhbIkWnUgOXTCLMcnGZ8GOSPz5VPeEBEOtcpz3/l+R88msutzuO+8r0qulLuIqUSP0QOwEMoK8m6uYtAOojo2ezqFahUgITu6jVBxvMYRqACAo/LlFrSmetcX6FPHIfwYiNmx8GYeTICtiGAi+EJ5XlnZAeLe9gmHV15EKb5wdaWtmkwPLyNrpasWfUIqAeChthPTE+cDReDVPW87OxZVw6wqlQi4aU2qCk+OLABo8KcdxzfcVBB+GRz//D8V5j8EYd29e24dELTxKVwVzxixKEk7U4XRXk1FH4l/gGKaLbW1taZQnlfDZObLKpvIR1DdijN9pb2dl+Ki4a27f2N3KPf6853r967X8vf1uZFZwtPvbrUmKKffj9yNm4sdYzbakMgkAFLsxgopi9qSA0sws/ptbHk3oxAWQSehuvuybnurrvL9ojpQCaTOQS37/ExsWe2jIBNCNyjPP+Unp4uo54PNilskSwKYfznj2+aeCBkOsUiuViUWBAIHhKkPG2YgSna9U0Jo/chIETH85rTf1Q82/20T2euS2iq6smXV4Ie5SYdbdTa2nGe8P1V0JPAjTLYbMFqtaS1uf29UXrae0VvKgxLy4JJXHqUkt5vcGSYAQvrzVlCqZJ8lJDj0D+QAUtI9XGsF48qLYn/idLt3ForAl6tA/b27+295REklj1v7z7/ZQTqROApqdQr4Lp7e510jAyXsoH0i40R0JgoPQSUWJFu9Jp3F/Sgp54LGukqj0g0exbeqW90QV6W0SgC9wemLj0Sz7SiSN0bGIMxBi5durQJXQ4do5szh9kDp/JUFT3aeU6LxbRRY29ldN072tPT2QnT1ZXuSR6yxFJ8CV5RLw6Zqt3kpDi5vb398FqFbFvYlsGYMsarWqlx/0oIBDZgaaJILPs7/PlxJQZ8jBGoAoFHPCUWwV1zTRV94+lSpP1lLh5QmatDCGj38S/hK9ypCFl62iG5SYqqE80+teXxs/BZ+PckFWSlqkOgHo8aSSM0rrFRrK8OrNp7bd26dTZG1fWeXDtXcyNSPocQVkTXp3FNlNFxBxwPHi1zjJvLIDBQ6LsEhx4sczgpzU2i6P2subk5kiiUAeHtxAe6teX/iTtHAy//Vb6/XCuF/+ToMRVbUn5BvKVijxIHkSP83BLN3GQAgcAhhHtlwdf49xX6/Rbsv2BvG/9lBGpA4EGvKE7s6u36Tw1jou9KqxJR9PgxR5cR2AZv27fuqYTnsh6kZF+3bl3/Mcccs2za1AN+ifQUZ5BSjpWpCgHlBQ+fUz5C41wPjJFiMwzq26oCK0An6TfMEdIPMNLOIX7KX2+nZHZIRazi5EhQtfeV/hDFWw0I9Pb2bm1rbjtXSXkzhrl+x6xB8xFdpTg6JRq+iNb3jTgS+m4+3/lfEH1ZOcILFiyY0pBq2jL0uKfUuV35rtzQtgC/d2FMAf8mDY5VQhujPo9/VT0EkGpmMq6w1w6O3f3f4/hzwJB9/hkiAnV/WdIvD0i6jXAGYewlIkR9mZRVCKh7fZFut954BcxQjWOOVdCxMIxANAj80xdeMxuvogG7Vi7aiAVPrNfhrRpGLN6ShgBy2gQOIUQ4mfPPNDyXA+tfzbmCMlzOYzREzy0I/X5qyD7/HIkA5fc8w9fKSCgp7SM65BbY/q6mpFNAXd7T2tx6asCxLgx7BgaoZ9+l8IzMZBYtrlZwT3ivQ9/dxi89SIofVjuW+9WOQN0GLM2yu7v7brwMvaN29jwiwQjcNlBszORyqx5wAQOc33NdkJNlZATCQ0Bdu7Nv+8tyuc5/hEeTKYWNgDZidee6zsYL9pfDps307EZAqf7A+Z9g9HT+mYYkvIH1r2ZmlfBnV9PPkT5GsXIEg8piSun8NVFWQRncW7MszQQdGNfX9H6o+1CCVC6lqhTS+zGqdR5c6iCFNl/KHw3VA9UIq7Zt4IPS2/eNVWI7vHd/sW+ff4SOQCgGLC1VNtulrZZfC11CJkgPASl+htDTl+tCAA4pR+lLrEOws6gxIPAMDLbn9OS636wr8cTAn1nWjoDCfH0YXw8vxtCq3N1rZ8EjLENgaz6frzWvx6AKHR0dOqxhqmX61C6O8nVYlLlNydnmiEdLWQmz3mrRahM+t3nz5jWA6ozwKVtCkT2w6pqIlWtXbkEqhXfVRYTG4OmFxuJVUAXfQOhtudzqPFT72z7NpDijpaVFV32uuDU3d+g0Sgv3dZLqes/ztu7b5x+hIxCaAUtL1pPrugSn9G9Cl5IJUkEA71BIBJ3tOgehpzrW2IkNL/vjICiZSkROgM5CxoXAbchJNw8fJK6LSwDmGxwBXeoaL9lvBAUkQeWNOAKBjTf9/TRC4zxp1ihDKSeSaaxcv9YmpifOhg4p1/UoK39KBr5flKWZsANIpfB/sNrwu5GSr2xtbr+A6vQr5V89RLdGT6TfNGS/5M+ULJ439ICnJIcPDgXEwO9QDViQzy/6A/rl+TYDsjJJtxHow8LqzTByXgo1tCHLma2vT8yGsGFfK87oz4ImAoEBaHnZITMOanUhJ10iZiSgknjJ/gXylrXgJrs+IAke5gICKnhIUEr4z3VBxbFkRDyL0UU5rqHZY8ngyvF68qW5omM9chZS7ueEq6S/78v7Kx3nY9Uh0FdoeB8cNTZX15twLym+2rag7ViKGiK9zbXQa4iThdTGqbIeZyim04jD5wzB4h4klIcnF28mEQh9UQ6X9p1I6n4aTBR3mxScabuDwOBCyvMXYWH1U3ekflZSz6PxtfpZjfgXIzAUAfk3WVQvg3H5U8uXLy8OPcK/3UQgl+u8s1BsgDu76nFTA5Z6TATq8D6CMWP2mPQd6OAVPWMGrCXHLpkICKY7AENVIiIsnA0YFZCSlBO440EwaVsjz3+F+a/20Jo1tzwhlbqo2v6E+41TaXnt0qVLm6jpqOcYl8yQaDJ1ZGtrR6acns+Z+pxX4di+MEMU/7gS+1j68mYSgdANWFpYJHV/rCjSSzB9fMM0OXsu0FbihoZG7zhUv+l1QdxSMlJ5sUE4xDehH+c0KjXJyWzbqaT63519247v7u2+K5kQ0NVa5xh8asuTi6Hht/CPX6bITXVw7yN8Sp5FAI6+zt7Oh03psW1S/xxTtOOgyx44lVH3hKRwTZRT8tGb7rppe7mD3F4bAoNVmaVYXtsogr2VOHbr0zu+QFAzhNwMDwGURb9CMnd57hAM+jHYSWeNITo48dOIAUtrns+v2ihS/on4+ZATSLCQYSOwC4vjdyMny1nId/V02MSjpafmRsvPCLfik08/8WEvLV4I6n82woGJOoMArBldReW9NJvt/gIStevwQd4IIqArFMKz7mLhydNhtMBXRd7IIKCCG7Bw/VNYrN+PufRNzaenCrNN0Y6BLqa874EY+DrDUnmKwjVREm/c+415KpZkmIDGdIOnc0A9mgBVK6oIb6OLM5n2kyt2cvBgV67rVlw3/90rOjxYX9va2jpt7/7ev83NJ8zAzfUVe/cRSniDduJ5dp9/mULAmAFLCwyvm/uwYG7Hx997TSnAdK1E4B7h+QuxOP6+ldLVKhQB13LcYDfoxWxXV9f9WNC+EvnI3sQL2lpPBBL9H8Xcvy2b6+rI5zvvIaERKzEmAj09q/+QKnovwX2ga8zO3MEJBJQ3cH9gQaWcGXisLQMNe/hLKefYomoIcmzW6T1CoEOZxCzCyrEBK+TJxYf5x4WSuupv0jfYsMRVCxYsPogYEMqX6sdDdBov/NTZQ/YHf6Zk4a34sa/4g1Q+J28fCZKhfaMGLC2zXjCnB1KtSH/GISqGJtEisgXI8u0J28a9DMbLZ8uQWiRgIFGkpJDwdtgLjM5HVhSFo7Gg/XUgTHiQawhoL6tvj+trfD7m/mr8xtTzliQEEG71EAyXL8cXwvMx+xxO4vbk+1OmTFkfXAXlvAELH2CMfhjFDXJ2cHwtGynZA2fMGVEkvBJLqolz+f6SB7ixLgR68qt/jmfpDXURoTH4wAZv4Cqogtsyna2hP6V1ejZCQarzR2iHzCzirfva8FGlO9996759/mEUAeMGLC195+2dm/sHGk7AmqnHqDZMPEYE5N+01xW8e95HL9be/STu8LoZ9bKfy+UexYL2NfiK9FqcOOzyGuPVY5S1VCuVLB6nr82Va1duMcqLiduOgN+TW32lL9MvhqCrbReW5SuLwMYV/7+9ew2Oq7zvOP48Z1eyFWwuNcQxBlt2QgrDJQUXXEuWUW0nbZhJhyQkJWFyaVImM70lTae39E06nV7yprRNO8y0kzQkaVrqdJIMJG6AgCx5pZggIBS3JQXsQBIMhIuDLVmWznn6e2SvI8RK7Gr3nD3P2e/OaLR79uxz+Txnd8/+z3PZvXtqwWcXeeKqq3as0tMrF9kliKecten+KC9QDyxWIFz8kN60aVOX9liz+F4BP5tyb8WAZZouenkm+k0l8nzTCYWegDVv7e8fmB/gCbpWPnbhjPnGqUpozi8NI9xUfdzff/Wgnn9D9bHCd5/R/eTUY+6kKpBJAMvXwM/q7yeUVVfDW1KtEYlnLeCj05964fCPr1Kvq/GsM087v5Mn+6ennU/a6S+2ApGuIu1KzMwlztgvqhz6POZWEIH79CPvLXsrI2+uVCr7C1InqtECgdHRux9bs3b1DvX9/7iSe6kFSZJEhgL6kD6w1OyWRcfXL/W1eXqdhmq8rFdxq8vmnOltdZrtSi+ySz9e2lXmLPNd2bVyrfI7NQwoy7wzyauU3mqdmZQ/x5nMBjms8d+jHX+zzt60bcu2S4sEMX9IoI2jX6/WL3Luw9X7+j8Tu/Ln5jzmbsoCmQWwfD38HDwjY8Mf1OTen9DDOOW6kXzaAs7s1kTQl6lnxx/5tk07u3akXy7PbGxHvq3Pc/EJf0/0xtrzvsTYzcr73tbnT4rZCdhHfK86vS+vqlT23JldvuQUksCuXbvikcrITTrpukg9//8tpLJ3ellr9ait18TZqBABLF3nTjWAZY3ZUK9p3vdTMG7JAc+8160V5YttXIz3xIIYM6m+VxbMtkOeqFSGb3HGfK1DqrtYNZcn1nxpcHBw+WI7hfTcmvPW7FZ5n6yWWZ0BblD9VuzctPMMtfnbq9v1ffGN2cXrqhv4n7pApgGsam38ylcminbq8VPVbfwPSuB7xiVv0wqD1xR9Imj1UCjESayGENR1AjM6uuc76pnRp35Yv6Uj8oWgjsoOL6y+QB9Vu3243G0v8b3qxKHvV24ILC7gT7o0rPA9OhnYoUAWPfUW58rHs87etdSCOJMU4sd6qadU13faUpz8jxO97sylvDaXr4nsgVyWKyeFUu/zQrwnFuCcXrNmzQ8XeI7NrRKwsV+VMPAV11uCcUk8nfxZS1LKQSL+Qp+KccucoqycOe7edWzZ9A3a9prqdhdZJm+vYmT0vy0BLF+3vXuHhsrT0RW6e09GdSWb5gWe15Xf35mcOqofyHtvbz65/KdgE1OIHlgzM+W6T/b9B7aCk/9Q7o7eqBjIX6uVjuW/pTq4hH6BDGff+7q1qy9Uu31Wq+P4xRS4IdCQgJaNvnvN2te+SVcYfRd5fvA0pJfpzveXl0X/vvQco3VLf21uXvmMPueOpFWaiWUzhbhwVfVJkogAVhWj1n9rivCeqFUzv+3gyR/hCz3P9hYIaJqGH1lj/6AFSQWfhHp8fnzrLwz6TiqFuJWTaN7cVk7nSHOGD1pzqFy2vqcWtwwF2hbA8nX0Y4c1zGXnyXk4JjOsN1k1JuDnSPlU7KYv0Apmnx4fH/fzXnXGzboiBLAO+znoGm0wv0zw3tGR3yvH0QW6QumvLhAYaRQx3f1HTGR/ZW9l+Of8ajicpKaL3Qmp+2NIwyE+4z/r1X/vj1VnemHmp+E1abv7guYrfGszQWpdhCpCb5O6L8gspfk0t0mRAljTa9eu+sFSHDrlNUXugaWe2QQvMzqQR0b3+PPkb2aUXZ6zibSo1y0aand2ngtZb9mGvj10UO+ju+bs36f7vgPO7M05x4XjKkaG/9sawDpZz8TPw6GD/RLj3FCGdSerVxfwVzj/bjruusDPczU2NtZ5K23Y8OfBcKa5uUKG9g39oDK650a9Ry/WKhtf0jFBIOvV3ztp7aHecO6fI2eu0Hty2969e25TRmpibgi0TkCf9ZPqzfdX6oW5TkfXx5Qyw/1bx9toSt5eF5DKr9cFhff7+QobTeBl+1vT+7LHIT6wzX2nvVqVbZSEb3Sqku4JLm6cwqh5pyBB3Zp10+d3qsHe2pl27FaXmLJfiY+FUYw5V0Pt/qkoR4Lm9vK9sGrd1OGs67O1nmBbugJ5CGDN1lAr2D2+d2xkuz5sP6ANzZ2gpWvWCalXe1yt14/kj+7b962nO6HSNevobBF6YLXkBEbv0e+pt88NUdm80Trz9/KaqGnGxjQEHrfW/oktuXX6Efuh4bHhB9LIhDQRmCvgh2kpkPW3Gjb+Bl1l/F09d2oy07n7cT8VgXv0w/o6H0Q8cQHp7lYN61yXSmmzTDTlH+WJC//C1anmYAXCUxQL37HhvycWqpy1BxZ6iu2tF9AKv9/X71jfe5mbcdcO9F19YxEgXnzxua+qHq+MTVj3Lb+qcxHqGFodchPAOgnndLL8efX0uPDkD2R6emR7RP2fPng/Nh1PnXfihLkDe1zN8Vb317Ienj9nU5h365zAvd7KDQ8PH9Bqor+tH1br9Zo/1d8rP9TrTYz9FhOY1NCGLzprt+v9+IaRyp6/GBkZeXaxF/AcAmkIaNj4RGVs5G/0nt+ooMq7lMdIGvmQpvmJOlTe7Gx8id7z2zVk/z+aGS443/Mtl73lNG0LfliHjsFUfzBoOfje+XbBPmYFwldrOo0OcoUNYLEC5as1f+uf1+/Ym5UqK0ALQRe+burv77+49crZprh///7jyvEL83M9Ob3K/M08zkAgbwGs2Sqrp8cL/gey7+mhDf+ovzgDi07NIjHW3aVJoN+t1ecu8lfb9+3bp5NobkmS+OBVV/ASUdKSHljzHU7MkTX8yRcOP3e+Jn6+3hlzt/bRP25NCMQnHO1Hlk91r9HQzfdVKnvuUXq4NoHKS1sj4IMpCqp8WcGVbYmJLleq/vv5cGtS79hUJvQL+lZ9B79DAcLV6mH5G5oQOJXVICfPmFxfBOXYRKn4nLIpwNQB1brou/lA9T7/XynQ19d3jrb2vPKZgmwpxamc/xVEJ61qJMdnut6jxLG35jTrSru3bdsWfGcAXVjyQyJPnYvre/u5008/zffM4tYGAd/DJLc339NDhftIX9/gp61JPqmD5e16nMugW24RFy6YhgW6f4ld6eax0aFHF96tg5+ZKcYwAtfiHljzj4iTVyZu1fZbt27dquGF0Y266vdePT53/r48rimQaGtFX4u3TiddX+7oIbs1ediYR4HR0aEHVa6PqKfqR2em3Ns0P9779Z3yVm0r5bG8OSvTlDH2Tl2e3uWi+Ct7K5VM5kzRanTr1XspZxQNFydZcWTZfzf8qsZesL6x3fO7t+ZL9OfR3BYU6FJbB/+eWLB2cRzT/gvqpPeEXzhJwdEtkSt/Tt+N/nuxk2/nxzNmWEGs7Sd/1wdpoQtLj2zt3zaqj4t+X4HE2c/v3r1b3+Xc2iGQ6wBWFWR0dOhh3b9OP443miT6qA6eD+sDwXeF59aYwKTcbjfWfmFy8sh/dtRqgo05ze6trqGvL8aJTTnV4RZzaf08WXr8+/r7w61bB/tMkrxL939Vf6v1x60q4MxRvQ/v0cPbFET++thYy+a3qebAfwQyEVCvLC0sYHb5vy1btq8t2fidzrh36oLTVm3jgtNPW+Elmeg9775S6i59VW4v/vSpbO4lyfF9pVLp57PJLZ1ckqQc3/HQHUfTSX021Ujnme9IMf1Mk56xU/dnmmFgmXVP2ydnyubdgRW7ruJamyQdufhSXTrp73RywY1rZi/sJqUd+l7058HL08+59TlYa0q6MH36UlPWd5+JY3ue/jUdUO3p6ZmYOWbePLcs3dPl/XMf13PflpK73HTpVDpRlBx/tdclrvyBKEk2+P2irvi7i+0/PT19qOSWnUrf77tyovvJxV7Dc/UL+GMquNuWLVt+pmS7P6iCf0gng8GPrU25AfTjwmqIoPnS5PEjX/NzmaScX2GS7+8b+HNr7CcCr1CsCZh72hms9HOJxVPxDhfZt+k49FeiNgZuupTiz6ju4wogD+kX/R1Hp46OtLNNllIBXoNAIwKDVw6+bro7vlZD435ZJxo79NoVjby+APv6OTzv1Rwgd9lS6c5y2XzbD8EsQL2oAgIIIIAAAggg0DaBIANYc7UU2d5sXPRrmvT9OmfMqrnPdex9aw6ZxHxdQwVu7znac2fKVysLy7y17+p/VYD0+pArqPfEwcro8OzVgrzUQxM6/mxkoms0tPGXVCbfFbeIP2yP6cP1AQWsNDTQDSU2GVb340yGCeWlnSkHAlWBiy++uHvVGau2JsbpPW+3afsm/XVVny/Ifz8U+BG95++2zt15PD5+D/NJFqRlqQYCCCCAAAII5EYg+ABWVdL38lCXwkETxer6ba/V9jXV5zrgv+/2eJ+/0mui0m2VytC4Hit2wa0Zga192/bp9Vc1k0a7X6uD4G4FsHzvh1zeZt+3MzNvMnG0VUPqNOTI9amg5+aysAsXyo+B/x+V/QFrou9oTpt7JycnH6KH1cJgPNPZAn41vGMrjm1WxGdAJyFXaaLpK/SN9bqAVGKV9xGtEHq/ccl4Sf+n4qkHCVgF1IIUFQEEEEAAAQSCFChMAGu+/on5sko7tcLeTp1o+jGoZ87fJ+DHE7rK+4DqtddEtjI9fWwPJ86tb00FsJ5Rque0PuXsUvRLvGoluxuzy7H5nAYGBs4xM+YyZ6M3KQB3qT6kLlVwSPORtf09/Kzec48r0HZA5flf9XDcr55V/9XV1fUYQ4Oab3dS6GwB9cw81yb2Cg01vlQ9qi/S8O0L9f73KxGf0UYZxdic5qywj+o791HNYfJwKbH3Lz+6/Lv0bG5jq5A1AggggAACCHSsQGEDWHNbdHYOnmPxZp0Y/6IqfKVOiq/Q834yuRBufs6qh33ASifPD0aJub+0rHQ/P5jTbTr9mFqppV9/km4u6afurPtEpTLyl+nnlH4Os3PflUobTFzaqPfCemud72V5jubYOVtBpdfqh6YPNq7UX4/+6pkoc1r7HVFQ6kW9vzQpsHtRaT2jz4gfKTDsg1VPucgcimJ3MFoWHdB77oj254YAAhkK+DNfWmsAAAu3SURBVLm04nK8LonseTYx5ysor/d+crYCXKs0qewqTS67St/pfvoAPySxnuHIvsek/171Q3qf12ufVxrP6f4zes8fUm+wJ20c/TC25gdnntlzgFWGJMMNAQQQQAABBBDIiUBHBLBqWWt509da27VJc1VcoRPYy3UCe4FOXv3k0vWcANdKstlth5XAY/oh7VeMe8wk9qHIuQdXn7/6e7t27YqbTZzXNyYwsHngMleyi64w0ViK7dlbP8aur1SGb21P7u3NdTYIaW3NlVZXrlw5wQ/T9rYPuSOQhoAC3T1aae8VAexyuTxNEDoNcdJEAAEEEEAAAQSyE+jYANZCxJs371hdKs1sLJnk9cZGGxOnK73WrlJwy1/h9T08ztaf7+Vxlv4Wu1Wv8moVQDehq8XP6Grxj/WCZxSkekpDEp5V75indSX5oE6s/RAk/xy3nAgMbBm4VvObfCUnxVlyMTQM70rNiXbfkhPghQ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"></image></g><image 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"></image><path 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1.78-.523 2.79-.523 3.378 0 5.067 1.517 5.067 4.551 0 2.186-.567 3.947-1.702 5.285-1.135 1.35-2.686 2.105-4.654 2.267a17.76 17.76 0 003.499.349c.922 0 1.722-.18 2.402-.54.19-.105.322-.216.399-.332.378.314.567.782.567 1.403 0 .623-.3 1.115-.898 1.475-.599.361-1.481.542-2.648.542-1.166 0-2.424-.25-3.773-.752a13.464 13.464 0 01-3.764-2.145zm19.384-2.913c.24.28.36.69.36 1.232 0 .54-.224.975-.67 1.308-.45.33-1.065.496-1.845.496-.455 0-1.035-.047-1.74-.14-1.387-.197-2.28-.296-2.677-.296-.396 0-.677.009-.842.027l-.641.06 2.287-11.512h4.142l-1.89 9.593c.163.024.32.036.472.036h.473c1.058 0 1.915-.268 2.57-.804z" fill="#CCC"></path><path fill="#000" d="M682 231h6v1h-6zM689 231h6v1h-6zM682 235h4v1h-4zM686 235h4v1h-4zM691 235h4v1h-4z"></path><path fill="#000" d="M694 235h4v1h-4zM682 238h5v1h-5z"></path><path fill="#000" d="M686 238h5v1h-5zM692 238h5v1h-5zM697 238h5v1h-5zM682 242h2v1h-2zM684 242h2v1h-2zM686 242h2v1h-2zM689 242h2v1h-2zM691 242h2v1h-2zM693 242h2v1h-2zM695 242h2v1h-2zM697 242h2v1h-2zM699 242h2v1h-2zM701 242h2v1h-2zM704 242h2v1h-2zM706 242h2v1h-2zM683 245h2v1h-2zM686 245h2v1h-2zM689 245h2v1h-2zM691 245h2v1h-2zM695 245h2v1h-2zM697 245h2v1h-2zM700 245h2v1h-2zM703 245h2v1h-2zM682 251h2v1h-2zM684 251h2v1h-2zM687 251h2v1h-2zM690 251h2v1h-2zM692 251h2v1h-2zM695 251h2v1h-2z"></path><path fill="#000" d="M696 251h2v1h-2zM699 251h2v1h-2z"></path></g></g></svg></div></section><section class="topologies"><div class="container"><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="user-facing-analytics"></a>User-Facing Analytics<a class="hash-link" href="#user-facing-analytics" title="Direct link to heading">#</a></h2><p align="center"></p><h3 class="installSubTitle_sbCG">Building Latency Sensitive User Facing Analytics via Apache Pinot</h3><div><div style="border:1px solid white;width:720px;height:480px"></div></div><h3 class="installSubTitle_sbCG">Using Apache Kafka and Apache Pinot for User-Facing Analytics</h3><div><div style="border:1px solid white;width:720px;height:480px"></div></div><div class="hero--buttons"><a href="https://docs.pinot.apache.org/community-1/videos" target="_blank" rel="noopener noreferrer" class="button button--primary button--highlight">More Videos</a></div><p></p></div></section><section><div class="container"><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="installation"></a>Installs Everywhere<a class="hash-link" href="#installation" title="Direct link to heading">#</a></h2><div class="sub-title">Pinot can be installed using docker with Trino/Presto</div><div class="installationChecks_XhS6"><div><i class="feather icon-package"></i> Helm or K8s crds</div><div><i class="feather icon-cpu"></i> On-Premise</div><div><i class="feather icon-zap"></i> Public Cloud</div><div><i class="feather icon-feather"></i> Locally</div></div><h3 class="installSubTitle_sbCG">Install:</h3><div class="tabs-container"><ul role="tablist" aria-orientation="horizontal" class="tabs mini"><li role="tab" tabindex="0" aria-selected="true" class="tabs__item tabItem_vU9c tabs__item--active"><i class="feather icon-download-cloud"></i> Using Helm</li><li role="tab" tabindex="-1" aria-selected="false" class="tabs__item tabItem_vU9c"><i class="feather icon-download"></i> Using Binary</li><li role="tab" tabindex="-1" aria-selected="false" class="tabs__item tabItem_vU9c"><i class="feather icon-github"></i> Build From Source</li></ul><div class="margin-vert--md"><div role="tabpanel"><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">helm repo </span><span class="token function" style="color:rgb(80, 250, 123)">add</span><span class="token plain"> pinot https://raw.githubusercontent.com/apache/pinot/master/kubernetes/helm</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">kubectl create ns pinot</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">helm </span><span class="token function" style="color:rgb(80, 250, 123)">install</span><span class="token plain"> pinot pinot/pinot -n pinot --set cluster.name</span><span class="token operator">=</span><span class="token plain">pinot</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></div><div role="tabpanel" hidden=""><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">VERSION</span><span class="token operator">=</span><span class="token number">0.10</span><span class="token plain">.0</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token function" style="color:rgb(80, 250, 123)">wget</span><span class="token plain"> https://downloads.apache.org/pinot/apache-pinot-</span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">$VERSION</span><span class="token plain">/apache-pinot-</span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">$VERSION</span><span class="token plain">-bin.tar.gz</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token function" style="color:rgb(80, 250, 123)">tar</span><span class="token plain"> vxf apache-pinot-*-bin.tar.gz</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token builtin class-name" style="color:rgb(189, 147, 249)">cd</span><span class="token plain"> apache-pinot-*-bin</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">bin/quick-start-batch.sh</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></div><div role="tabpanel" hidden=""><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token comment" style="color:rgb(98, 114, 164)"># Clone a repo</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token function" style="color:rgb(80, 250, 123)">git</span><span class="token plain"> clone https://github.com/apache/pinot.git</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token builtin class-name" style="color:rgb(189, 147, 249)">cd</span><span class="token plain"> pinot</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain" style="display:inline-block">
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Check the release notes</a><h1 class="hero__title">Apache Pinot™</h1><p class="hero__subtitle">Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency<svg width="724" height="341" viewBox="0 0 724 341" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" class="svg image-overview figure"><title>PinotOverview</title><defs><circle id="pinot-overview-user_svg__a" cx="32.5" cy="32.5" r="32.5"></circle><circle id="pinot-overview-user_svg__c" cx="32.5" cy="32.5" r="32.5"></circle><circle id="pinot-overview-user_svg__e" cx="32.5" cy="32.5" r="32.5"></circle><circle id="pinot-overview-user_svg__g" cx="32.5" cy="32.5" r="32.5"></circle><circle id="pinot-overview-user_svg__i" cx="32.5" cy="32.5" r="32.5"></circle><circle id="pinot-overview-user_svg__k" cx="32.5" cy="32.5" r="32.5"></circle><circle id="pinot-overview-user_svg__n" cx="32.5" cy="32.5" r="32.5"></circle><circle id="pinot-overview-user_svg__p" cx="32.5" cy="32.5" 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font-weight="bold"><tspan x="0" y="19">EVENTS</tspan></text><g fill-rule="nonzero"><circle cx="32.5" cy="32.5" r="32.5" fill="#000" transform="translate(549 104)"></circle><circle cx="32.5" cy="32.5" r="32.5" fill="#FFF" transform="translate(549 104)"></circle></g><g transform="translate(549 104)"><mask id="pinot-overview-user_svg__h" fill="#fff"><use xlink:href="#pinot-overview-user_svg__g"></use></mask><image mask="url(#pinot-overview-user_svg__h)" x="10.432" y="12.84" width="43.333" height="43.333" 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jMP+7fePEre+Snk9hqEAAktukoOAII+CHgHvWvGNz1R3JV/z/LBX5ydT+39Pnh2iKNyySquenApoErW2zH6gAFCAACxCVpBBCIt8Cy259cWBmt3XjiqH9JvEvbc6U7XznOWoKA6NqVACA6e3JGAIGoBOQK/0vW7b7crtu3yon+66QYTOQTTVu4QcDX929cfh23CYbfAAQA4ZuTIwIIRCgwMLg1d8ng7j/WxnxXinFZhEUh6+MCC2Ua4S8+vWTZHxEEhPuTIAAI15vcEEAgQgF37v6MnvU3clHd56QY50RYFLI+WaDfGPUZgoCTUYL+iwAgaGHSRwCBWAisuH3XEpVVN8vjef9MCtQ7V9XHQteXQvTLRZifOLB42VW+pEYiLQUIAFoSsQECCCRdQG7xe6Wq63+Ui/1eLXVJ9KQ6SW+L6crvThgk915+6cD9yxmamQ7Kp3UEAD5BkgwCCMRTYOXgzivlFr+bpHR0KvFsolNLdb4y5jMyWdCSU1fwt78CBAD+epIaAgjESODSW/ZcaJT1BSnS+TEqFkVpLfBKozOf4tkBraG62YIAoBs99kUAgdgKuGP+jmXWSAFXxLaQFKy5gFHX1HP6hq1bB7hFs7lSV2sIALriY2cEEIijwPgFf3Xrq3Lq/8o4lo8yeRLIKK3fOWfEvNnT1mzUtgABQNtk7IAAAnEWoPOPc+u0XbY+mSPgYzw3oG03TzsQAHhiYiMEEEiCAJ1/Elqp7TLKREH6MzxGuG23ljsQALQkYgMEEEiCwEXfP9SvHP0pTvsnobXaLuNlVt18bN/9q5i/oW265jsQADS3YQ0CCCREwJ3eN18p3SATyVyTkCJTzLYF9JstNfLatndjh6YCBABNaViBAAJJEbB0/5tlhr93SnmZ5CcpjdZ+Od27AT74zM8vnNf+ruwxlQABwFQqfIcAAokRWHnb7su00R+TAnN6ODGt1mFBjV5Zz2bey0ODOvQ7ZTf7lL/5E4HYCKy8fc98p+Z8WKYGLcamUI2CaLN7aMT51M7rFpcaX/EevoD7cB/HMR+X38jC8HMnxwgEpKnVuw4suvhHSj1yVwT591SWBAA91Zy9VRnHmDlyC9Afyvzt/fGrmd6cnzPzs1IuAoCIGucNgybziNrzQa3M70RUBLKNQMB9XoBS1oefvWfpA2e8+IljERShZ7JkCKBnmpKKIJAugYfNU3LBn+M+2Y9x/3Q1vcwPZK6oZexrU1Zt36tLAOA7KQkigEDQApcO7p4n94Z/JJ5nh4KuPemLQM4o/V4uCOzut0AA0J0feyOAQNgCxmhHqXdLtivDzpr8YiWwvGbb7zYSCcSqVAkqDAFAghqLoiKAgFIDt+6Vjt+9NoR/+FP+e9DKMn944L7ly1Pu0HH1CQA6pmNHBBAIW8Cd8MeynA9KvtwLHjZ+HPMzSoaCzDs4C9BZ4xAAdObGXgggEIFAxsy6WrJlNrgI7GObpVarOQvQWesQAHTmxl4IIBCygHvPv9HGHftnwp+Q7WOdHWcBOm4eAoCO6dgRAQTCFNA59TbJ7+Vh5kleiRF4/aFfDJyfmNLGpKAEADFpCIqBAALNBdzb/uTif+b6b06U9jXzZUbIV6Qdod36EwC0K8b2CCAQukDdOO5T/rjaO3T5xGQotwKa/7p164D7wCAWjwIEAB6h2AwBBKIRWHrz47Nk6rc3SO7c7x1NEyQiV/lxvGL2mHNJIgobk0ISAMSkISgGAghMLVDI5X9f/nF/0dRr+RaB4wLuMwK0Ub+Nh3cBAgDvVmyJAAIhC7hH/9L5/4lky6ndkO2TmJ38Vl697/5V3CXisfEIADxCsRkCCIQvUMzm3CN/jv7Dp09qjiu0M7Y4qYUPu9wEAGGLkx8CCHgTkMv+ZXGf+MYRnTcxtlJKHiGuLgPCmwABgDcntkIAgZAFLrll33wZ131lyNmSXbIFtNLmNWb95XayqxFO6QkAwnEmFwQQaFfAqv+u7MLkLu26pX17o174TP/BM5PAYGrR3tlClJSEXwllRCBlAqvu2NdXGau9Saot13WxINCWwFl1rRbKHvvb2ivMja831qpzNxWq1eosk89H9hsnAAiz0ckLAQQ8CVRHKhcry1rlaWM2QuBkgRlyO+CvyVf3nPx19H9dfv16+/CSOfnKsY2FsmVsI68oT8MTAET/m6AECCBwioCxMjLxj5l9ytf8iYAnAXk88G942jCMjeRi1lVrN9nDdqmwZ6SvUCiPWXY+jIxb5xFl8NG6dGyBAAKpE3Cf+ied/6tSV3Eq7J+AUcsPPLhyhn8JdpCSdPxLv/R4fsnaTbOqTnl21rH7CsVKrPpczgB00K7sggACwQlYWesCRzkXMPwfnHHPp6zVC1SpfJbUc0fodZXx/fmzNuRnr7m7oLNW1rEyWl6hF8NLhgQAXpTYBgEEQhM4fuuf7g8tQzLqRYGz5PkR7nMBwgsABgczq44syY9UNxbtvGUbZSn5Lcd6IQCIdfNQOATSJXD5emM/c2j3yyO7LDpd3L1cW7cDnht4BSeN75sjhXzZqmXiMr7vpe4EAF6U2AYBBEIROHh415m2sniiWyjavZ2JtpyzA6uhdPwD67Zlx9ZuKh4tW7mCJRfzW7XAsgsqYQKAoGRJFwEE2hbIVu2FxnLcsVsWBLoTMOrC7hKYYm8Z31+08M5c/5q7i1pb2T47o1Vxiu0S8hUBQEIaimIikAqBTP1CZXS0V2+nAppKtiUweXy/VrRNVu5TaSuBeG5MABDPdqFUCKRSwBhrWW/805rK5otXpY1auGP9osLiK3aWOi2YO3HP/gXZYhLH973UmQDAixLbIIBAKALGmPPkaW4sCHQvoPXcmdm8HKur9gKAxvj+kbHiHnd835F79xM4vu8FkADAixLbIIBA4AIrv3VghtEV/8dtAy85GcRTQOfKWZ3zXLYeG9/3Um8CAC9KbIMAAoELVGbWCtmaPp0hgMCpU5KBOc+29LlS2WenrfCJ8f1SZkMhU8rZTjYjjxJIx0IAkI52ppYIxF4gV3HONZY6L/YFpYBJEag6ut703rxTx/e1/PiclPWIKatuUn63lBOBNApYM5Vy4jlnahqbI/l13p+vOftOqsaJ8f3ygaHCHpXL9/L4/kn1bvIHAUATGL5GAIGQBbgFMGTwns/OcWqZ42fzpeNf9M0786d/4ZcFpzCaKxTl/n1V6XmAVhUkAGglxHoEEAhFwCjN0X8o0unJZFdhgbXqK9/pK90k4/vVnF2bWZaOn59Z4xdAANCQ4B0BBKIVMFruAEjL5VfdU2ccc3fd0m/vPqXeS6FsbOvZ6unD/+fh9xRkfv5CGsf3vbQqAYAXJbZBAIHgBYw5WzEHgGfns1788OOysfticQUmje8rd3y/KPfvs0wrQADQhOeSW596u/xb9LImqyP92tLm6w+uXnh3pIUgcwQQQCAOAtLxL/3yEzln7aZCxilLx8/4vtdmIQBoIjXe+Wt1XZPVkX7taP2fUgACgEhbgcwRCE2gph3zTGi5JSUjmbhn/qwN+dlr7i7orJV1rIyWV1JKH4tyEgDEohkoBAIIINBMwIw5GWtXs7Vp+969f//wkjn5yrGNBTtv2UZZXDnS4Y+AAKBDOHZDAAEEwhHQWe2o165Y99SqcPKLXy7GUdoyjozpm+wzpp5VchrUni3TRpxYjOPs0KVn7238zbs3AQIAb05shQACCEQlUJCO7+PpPcyVG0RF/vj9IVpJ1//8O/m0dYtsQgDQ5i+UAKBNMDZHAAEEEAhBwLhdvrw83RlqtodQop7LggCg55qUCiGAAAIJFmir4z9RT2MOJbjGkRWdACAyejJGAAEEEJgQ6KTjn9iZD50IEAB0osY+CCCAAAI+CJw4xT/e+XeRnJbLBFnaFiAAaJuMHRBAIBABbR5TTAUYCG3sEvX3aH9Um/p2T5cKxA4i2gIRAETrT+4IINAQ0Pqgtwu+GjvwnjgBfzv+RvUdR5kx904BlvYECADa82JrBBAISEArUz9+w1dAGZBsdALBdPyN+lQlcOTZvg2NNt55WEIbWGyKAAIBCtQzMgSgRgLMgaRDFXDH9+Ulx+fj74GdozcHVal+MNSq9UhmBAA90pBUA4GkCxi7+pzUoZT0eqS+/G6n716T55wIAAIGkWcBPaPqY5wB6MCZAKADNHZBAAH/BcZKtX2S6n7/UybFUAQaHf/4eyg5jmeilbNTW7VyeDn2Tk4EAL3TltQEgUQL1Kq2HMXppxNdiTQWPqKOf4LaMQ9NfOZDWwIEAG1xsTECCAQlsPO6xSUZK+apd0EB+5ruidP7gY/vtyz0qFK1LS23YoMpBbgLYEoWvkQAgUgEmAsgEnbPmbpH+57n5/ecahcbmmdMzezpIoFU78oZgFQ3P5VHIF4CchvgDimR28uwxEkg6tP8TSzkAsCnTGnYvXiUpQMBAoAO0NgFAQSCEZBHvj8iKQ8Fkzqpti0Q046/UQ+tnQe4ALCh0f47QwDtm7EHAm0LrPjOrtnGtv6Ltky27Z2D3+HwGXMX/PDOK3Qt+Kymz6GeU3usmtotWw1MvyVrAxUY7/hjfyLGqLrDBYBd/BAIALrAY1cEvAqYvLVAO87fKaP7ve4T4nab9x975meSX+RH3o+8bt7hFYNP/VJpTQAQ4g9gPKvx8X351HgPO//285NT/457xoilQwGGADqEYzcEEAhAQGvpfrQbjMT+8DOA2keT5PjRfmO2viSxm52mWt4bDVpv5EoA0BvtSC0Q6BmBTMbcK5U53DMVimtFTur441rI5uUyymxQ1ZHh5luwppUAAUArIdYjgECoAnY+u0My5N7uoNTdjj/6+/e7rV1N1ev3dJtI2vcnAEj7L4D6IxAzgU1Xnzeqlf5RzIqV8OJIpz+54094baQy23Wl9svEVyPiChAARNwAZI8AAlMIOPV/l2+PTLGGr9oRaJzmD+nBPO0UrZtt5fT/v5vaUaaN7gZR9iUA6BKQ3RFAwH+BujXi3t71H/6nnJIUGx3/+HvP1XnIVOs/6blaRVAhAoAI0MkSAQSmF9i6eqAifdcPZKskXZY+faXCWNvbHf8JQfNAvTa8OQzOXs+DAKDXW5j6IZBUASfzUyn69qQWP7xynzK+39shk3Ec53t2rTwWnm/v5kQA0LttS80QSLTAQ9eet0crdVeiKxFk4RtH+z02vj89mdmfccpc/T89kue1BACeqdgQAQRCFXAnBTLmFslTHvnKMiHQ6PjH3ye+TccHo+6sHx3emY7KBl9LAoDgjckBAQQ6FBirVjbKru6LJc0d//HWH1Kmvk7n3FMeLH4IEAD4oUgaCCAQiMATb7ngmPxr//eSeCWQDGKfaKrG91u0hvmZHhm5v8VGrG5DgACgDSw2RQCB8AVKlfKPJdefh59zhDk2jvZTNb4/rfeQcurfdNRYddqtWNmWAAFAW1xsjAACYQuk6ixAo+NP4/j+tD8sjv6n5elwJQFAh3DshgAC4Qn0/FkAOv7pfkwc/U+n08U6AoAu8NgVAQTCEejNswCM73v79XD0782p/a0IANo3Yw8EEIhAoGfOAowf7Uvnz/i+l1/RUcb+vTB1tg0BQGdu7IUAAiELuGcBtNE3SrbJnBfgpNP8EgCwtBYwzq2VsWPcBtpaqqMtCAA6YmMnBBCIQqCuj90h+f5zFHl3nOdJHX/HqaRuR63MLl2r3px1KvXUVT6kChMAhARNNggg0L2A+5AgpfSnJaUnu08t4BTcjt9x5HFG7lh/wHn1XvJVx5gbndKRx3qvavGpkR2folASPwV2rF9U6Ouf8WFj9AI/0/UjLZnhdcyuZT51xos37/EjPdJIl8CW1Qu2rFy368vy2/6s1DwTq9q7nb27NN6P/8V/2xXQ5se1saO30UG1C9fe9vi255WYrWdm81m5xui1chptZewKbdRQLet8RcpFABC7xklGgUxF/YPJqt+ThwVdFYsSj3f4HOn70RbuqX+nUvo8T/zzQ3P6NBgCmN6HtQggEEOBLW9eeMSy9Me00rsiLd746X1O8/vYBmNO3fm8Kh192Mc0SaqJAAFAExi+RgCBeAtsfv2Ce402H5dShn9XAB1/ED8OaU7nH63R4duDSJw0ny9AAPB8E75BAIGECDhm6DtyUeDN4RTXPcUvLy7sC4jbbDCl8heZ7z8g3imSJQCYAoWvEEAgGQLuXQG1TPWTUtp7Aytx42ifiXsCI5bIaqeqVW4wtaNPB5gJSZ8iQABwCgh/IoBAsgQevmbJLqP1h6TUB3wteaPjH3/3NWUSO1ngqKo5/8uMHfnFyV/zV9ACBABBC5M+AggELvDQ6+ffJUMBn5CMZJ6ALhc6/i4B29p9WNXrn6qUj97Z1l5s7IsAAYAvjCSCAAKRCsjkErli5uvSd39NytHBzHGM70fQfnXjOF+slI5+m9n+ItCXLAkAonEnVwQQ8Flg09Xnjeqa+aiMJ//Ec9KNo33G9z2T+bRhXZn6t1Vl5Bt0/j6JdpAMAUAHaOyCAALxFHDnB5C5Ad8jwwE/m7aEjY5//H3aLVnpv8B452/Kozeo6siw/8mTolcBAgCvUmyHAAKJENhyzcInVcZ515RBAB1/1G1I5x91C0zKnwBgEgYfEUCgNwRODgIY349Jq9L5x6QhGsUgAGhI8I4AAr0jIE8KGjr25D6nOvYBUzd383CeyJuWzj/yJnh+AXgY0PNN+AYBBJIqcL2xVp27qVC6aUMhU83ZTu3wfpOb/QGVy39Oa/WypFYr4eWm849pAxIAxLRhKBYCCHgXuPz69fbhJXPylWMbC2XL2NpYyjnxr5uuHNmlzWnvM7nC/9SWfoOkGq9HCHuvZhK3HHZv9VOV0W9wwV/8mo8AIH5tQokQQMCLgJzmX7V2kz1slwp7RvoKhfKYZeen3tFUj+5RqvZRVZhxSO5+vk62mjn1lnzrm4BWe0yt/tlq6eg6bvXzTdXXhAgAfOUkMQQQCFxAOv6lX34i56zdVKg65XzWsXW26GECQLnlrFKvfjpXOG2vymQ+LOU8LfCypjMD96rLDapauUHJ9L7ZdBokotYEAIloJgqJAAJKxvfnz9qQn73m7oLOWlnHymh5tQXjHom6M8/l8qftVrYlUwfrRW0lwMatBMbcR/q6T/XjwT6tqKJfTwAQfRtQAgQQmEZg8vi+nbdsIxOYyiFmx4sbBJixQ/+uZ8wdVVbmf0sQ8OsdJ8aOkwTMdsfUP5MZHvmxUWPVSSv4GFMBAoCYNgzFQiDVAm2M73fqZEae2aCzp/3RiYsDr5F0OFvdGaY8e8G501TLN+jS0YedztJgrwgECAAiQCdLBBBoIiAd/8C6bdmxtZuKR8tWrmDZlqfx/SbJtfravTiwZkoftgunbdJav1POBlwo++hW+7H+hIBWTzuO85V65djNtoyt4JIsAQKAZLUXpUWgNwVkfH/Rwjtz/WvuLmptZfvsjFbFcKpq18pjavjpbzt2/79ZxeLblbbeKGMMZ4WTe2JzqWtl/sOpVD+njj23yc65T1NiSZoAAUDSWozyItBLAoODmVVHluRHqhuLdq1oGzkJH1VPomtD+51nRz6pZ53+E53NvktOBLxKqEMKQ5LTqNLx7zKmflOlMnyre9Svc8kpOyU9WYAA4GQP/kIAgaAFJo3vmyOFfNmqZZrdvx90UU5NX7tHsqXD9+tS8UGnr+8qnbHfLoHAb8p2XB+g1FFlnFtVrXqzKR15jM7j1F9P8v6mDZPXZpQYgWQKTDG+r6xaLOviuFexj479i6P777KK+Stl3oC3pTgQkLF9c6dy6t+sjB3byKQ+sfzJdlQoAoCO2NgJAQQ8C0Q4vu+5jE021GZoyIwOfd/o/p+lLhDQZr8c8f9A1eu36dGxR9ygiNMgTX4oCf2aACChDUexEYi9wInx/VJGHsxTkgfzZDM6qvH9bq1SFAhIE5nH5PV/Va12m/Pcc4+5wyJJbbdu273X9ycA6PUWpn4IhCzgTtyzf0G22Bjfn/xgnpCL4nt2kwMBVci/1MpkXiU3Db5EhgcWSWbJvX1QbueTRybfL7f0/VSVS3e6F0RKfRQX+LkKvbsQAPRu21IzBMITaIzvHxkr7nHv33cqVlzH9/1AcQMBNTb0r1LNn5iZM86VyQpeLU8afLV0mQOSflKeMTAmR/pPjI/vy9F++djo9pxdiudFGX40Gmk8T4AA4HkkfIEAAp4FpONf9M0786d/4ZcFpzCaC/P+fc9lDHBD9/S4rgztU5Whr1Xt/HeyuRlLHZ15uWVZl0q2S5TWZ8doToEh6ez3SpM9o7XzgKk562v14a2NCXxy9AYB/lLimTRNHs92oVQIxFtALuxbde6mQukmGd+v5uzazLKc/m7vwTzxrmD7pXMnFDK18haB2CJnBrSeUZyhs/l5EhD8tgQEL5YUL5YzBGfLe5OHFrefZ4s9ytLhH5QO/ym5mO8uU69vVOXKNlUfq8jZGVmnFB1AC8EeX03793gDUz0E/BRwx/cPnakKdbNR7t83di+N7/vpND6fgDx+2FRHHpWA4NFyrbA211c4w2StC7TJzJczA7bW5hKZdXCF5OtONjRTgoM5k8pQkM/N/n12r8kb+dW25rB8HpbXmHT0W6TDf0jG82tG1/foqvO4KQ0/p6XDn7hAwfrVnnxKt0CzH1i6Vag9Agj8SkB6FHd+/vKBocIelcsXchXLjmy+vl8VK0mfxsfWK6WDuqIONsrt9uKWKvY5WctSOjvD2OqMxjqtJEhQ1uSAoLFKrjQ0MnbvPOnIjfnul7qmnlUSaVhVuYRPjY02Nmx0+JoOv0HC+ykCBACngPAnAgicEJCOf/L4fqEo8/OrCjw+Cox32McfnDs8OTiQLLY1y8YNHNyl0cEf/0tCgsYH3hHwKEAA4BGKzRBIjYCM78+ftSE/e83dBa1zWcb3U9PyVDRlAgQAKWtwqotAMwF3fP/wkjn5yrGNBTtv2UZOUDeONpvtw/cIIJBcAQKA5LYdJUfAJwGjrWOHZu6Zc1qhUB6z4vJgHp8qRzIIINBEgMtDmsDwNQI9L+BO8Oq+HCeTqdf7CkWZvIcFAQRSI8AZgNQ0NRVF4ITA+MzubuePCAIIpFmAACDNrU/d0yVAx5+u9qa2CLQQIABoAcRqBBIvQMef+CakAggEIUAAEIQqaSIQucCJU/zjnX/khaEACCAQQwECgBg2CkVCoGMBjvY7pmNHBNImQACQthanvr0pQMffm+1KrRAIUIAAIEBckkYgcAE6/sCJyQCBXhUgAOjVlqVePSzA+H4PNy5VQyA0AQKA0KjJCIEuBTja7xKQ3RFAYLIAAcBkDT4jEEcBOv44tgplQiDxAgQAiW9CKtCzAinp+M2MM9+klXVZz7YjFYtEwLLM3vrRw1/SVq0cSQESkCkBQAIaiSKmSSB94/tu568tfW2aWpm6Bi8g/ydt0/n8GlUlAGimTQDQTIbvEQhTICVH+2GSkhcCCEwvQAAwvQ9rEQhWgI4/WF9SRwCBpgIEAE1pWIFAgAJ0/AHikjQCCHgR4PnfXpTYBoEuBUytqseTcDt+x5FH8Z4Y6+8yXXZHAAEEOhXgDECncuyHgBcBY/TSLz+R08aZoRzreBDgZT+2QQABBAIWIAAIGJjkUypwvbHmz9qQn73m7oLOWtm6c0ZeZ/jfLaW/BqqNQCwF+Bcpls1CoZIqcPn16+3DS+bkK8c2Fuy8ZRtlKTnZz4IAAgjEToAAIHZNQoESJyCn+Vet3WQP26XCnpG+QqE8Ztn5xNWCAiOAQMoECABS1uBU1z+B+tBzemBwa25s7abi0bKVK1i2lS1W/MuAlBBAAIEABQgAAsQl6R4WkKP+QqV0mjV0rNBnZ7Qq9nBdqRoCCPSkALcB9mSzUqnABMZv33Nv43MyMryfc9zOnwUBBBBIoABnABLYaBQ5AgEm7okAnSwRQCBIAQKAIHVJO+ECJybrGe/8E14Vio8AAgicIkAAcAoIfyIwPkufe/Me9+/xY0AAgR4WIADo4calam0KcJq/TTA2RwCBJAsQACS59Si7PwJ0/P44kgoCCCRKgAAgUc1FYf0TYHzfP0tSQgCBJAoQACSx1Shz5wIc7Xdux54IINBTAgQAPdWcVKapAB1/UxpWIIBAOgUIANLZ7umpNR1/7NvaKOdeeVRy7MtJAZMlYFlmr1MuVzU/raYNRwDQlIYVyRVgfD9JbadHDn1Xyuu+WBDwTUDm61R0/tNzEgBM78PaJAlwtJ+k1qKsCCAQsQABQMQNQPY+CNDx+4BIEgggkDYBAoC0tXgv1ZeOv5dak7oggEDIAgQAIYOTnQ8Cbsc/3vn7kBZJRC5QqRXsXDFTiLwgFMBXATN+AV6t7GuiJOarAAGAr5wkFphAo8NvvAeWEQmHLZDtn/k72s58MOx8yS9YAasw48fO0NN/E2wupN6NAAFAN3rsG6iAcWqWNu49PHI9Lw/mCdQ6ysS11qfL9drLoywDefsv4Dhqs/+pkqKfAgQAfmqSlj8Cg4OZVUeW5CvV6iyVyWl/EiUVBBBAAIHJAgQAkzX4HJ2AMXrV2k32sF0qmCOFfNmqZWSCmAxzeETXJOSMAAK9LUAA0NvtG//aScc/sG5bdmztpuLRspUrWLalrFr8y00JEUAAgYQLEAAkvAGTWvzRum0t+sb6Qv+au4taW9k+O6NVMam1odwIIIBA8gQIAJLXZokusSMX9VVMVv/iuYtnzawVTzNZru9LdINSeAQQSKwAAUBimy5ZBa9Lx192cqpqbFU3GVUyhUyyakBpEUAAgd4SIADorfaMVW2M0uOdfdnJjnf8Mtwfq/JRGAQQQCDNAgQAaW79gOrudvxVx1ZlkxsPAJi7JyBokkUAAQS6ECAA6AKPXU8WcMf33VP8FTnir6sMs/WezMNfCCCAQKwECABi1RzJLMzk8X03CGBBAAEEEIi/AAFA/NsoliVsjO+7R/sVOepnfD+WzUShEEAAgaYCBABNaVgxlUBjfF9u5VO18Y5/qq34DgEEEEAg7gIEAHFvoZiUz+34x4/2Gd+PSYtQDAQQQKA7AQKA7vx6fm93fN892q+6HT/j+z3f3lQQAQTSI0AAkJ629lzT4+P70vGPj+9nGd/3LMeGCCCAQHIECACS01aBl9Tt+GsyS5/b8VflqJ/79wMnJwMEEEAgMgECgMjo45PxxPi+dPruNL10/PFpG0qCAAIIBCVAABCUbALSdcf03SN994if8f0ENBhFRAABBHwUIADwETMJSTG+n4RWoowIIIBA8AIEAMEbxyKH4x1/Rp7Ix/h+LBqEQkwI6Fplo1G59018wYeeEDCOs4PHf8W7KQkA4t0+XZfO7fh5ME/XjCQQoICpHH1SVdSTAWZB0hEI0PlHgN5mlkzc3iZYUjZ/qLosM+bkraHaDDVSL6qaw8V9SWk7yokAAgiEIcAZgDCUw8pDJuRftXaTPWyXCt8/cN/p71x4u2XLc/lYEEAAAQQQOFWAAOBUkST+LR3/wLpt2bG1m4pHy1auYNlWXWcySawKZUYAAQQQCEeAACAc52Byud5Yixbemetfc3dRayvbZ2e0KgaTFakigAACCPSWAAFAAtvTGMda9ZX7+0qZDYVMKWc72Yw2CawHRUYAAQQQiE6AACA6+85ydqfpq9f6ylatX8tEPg4t2Jkje8VGwBTOuExn7DfFpkApLoip176rS8/em2KCVFWd7iMRzS2dvnuI35ij12ju3khEu1FILwLashZrS1/rZVu2CVjAWPdIDgQAATPHJXkCgLi0xFTlGO/wT3T+U63nOwQQQAABBDoUIADoEC7Q3ej4A+UlcQQQQAABpQgA4vQroOOPU2tQFgQQQKCnBQgAIm/eU8b3Iy8PBUAAAQQQSIMAAUBUrczRflTy5IsAAgggIAIEAGH/DOj4wxYnPwQQQACBKQQIAKZACeQrOv5AWEkUAQQQQKAzAQKAzty87+V2/OOdv/dd2BIBBBBAAIGgBQgAghBudPiN9yDyIE0EEEAAAQS6ECAA6ALvebuOd/hM3PM8F75AAAEEEIidAAGAH01Cx++HImkggAACCIQoQADQDTYdfzd67IsAAgggEKEAAUDb+Ezc0zYZOyCAAAIIxE6AAMBrk3C071WK7RBAAAEEEiBAANCqkej4WwmxHgEEEEAggQIEAFM02uXXr7efdZya0mp4itWRf2UsXdMtSjFmFZ2ak9murBYbRrDaGD1SrhXKEWQdWZbaMWWVMQ9LAWZEVogmGcug1nar6jhOk/V8jQACvSlAANBoV+mVBtZty44dGSvuKVu5fGnk71Qu893G6ji912ulva0a7p6jK0aVesufF52xGIYASt3z7IoRlWuhWqkfUYX6bcZYhRZbhr7assxep1yuao+65tjQk2pG7U2hF9RDhsc7/zH5vbAggECaBFr1I71vcb2xFi28M9e/5u6i1la2z85oVZRq1yr7VU3tjyOA10Y7HgTEsQZSpladv2yia0P7zfDQh+JYA/do2Wvn75Zf5xyjqiOxPKPEkX8cf2GUCYHgBbz2JcGXJOwcBgczq44syZcyGwqZUs52shktp0JZEEAAAQQQSIVA6gIAd3x//4Js0Rwp5MtWTXp9SzmpU0jFb5tKJkTAOM4OOZ1yS0KK29PFdNui1fVFPQ2Qssqlo+s7Mb5fPjBU2KNy+YJTsZRVS1lTU10E4imgS8/eKyVzXywRC9D5R9wAIWff2wGAdPyLvnln/vQv/LLgFEZzhaKM76tKyMRkhwACCCCAQPwEejMAkAv7Vp27qVC6Scb3qzm7NrMsHX8mfvqUCAEEEEAAgYgEeioAcMf3D50pN46ZjTK+b2zG9yP6VZEtAggggEDsBZIfAJw6vp+rWLbiev7Y//IoIAIIIIBApAJRBwAVrXRng/LS8S/98hM5Z+2mQsYp5xnfj/R3ROYIIIAAAvESMFbNnnaaj6gDgKqpZaptmcn4/vxZG/Kz19xd0Fkr61gZLa+2kmBjBBBAAAEEohbQVma+lCGomU73VXLWtJPZRR0AzFB2zdPc6O74/uElc/KVYxsLdt6yjUxyz4n+qH++5I8AAggg0KmApa050o8F1Q9XTGZ42jPsQWXcqcfJ+8lp/lVrN9nDdqmwZ6SvUCiPWXb+5E34CwEEEEAAAQTaF4g6AMhajjX3ecU+cWHf2NpNxaqM72cdW2eL0wYyz0uCLxBAAAEEEIizgKP0mVFOvhR1AFAw2iycaKApHszD+P6EDh8QQAABBHpIQGt9QZTVaRkAuFcRGkueZBbUYqwL1YkH84xUNxbtWtE2WcX4flDepIsAAgggELmApYp9RqvzAyzIaP7o6dPeBdDyaeYnriLcF1QhjapfsHR46Rx5ME+/nTctA5KgykG6CCCAAAIIhCXg5Kx+eVD4mUHlZ4x5bPNbzxmZLv2WAcCJqwgDG4DXyrqkqO0zpisk6xBAAAEEEOglAW3n3FsAn38NnE+V1JYut0qqZQBw7qyLxowyu1ol1MX6JU6+77Iu9mdXBBBAAAEEkiVgZ39PCtwXWKGN2twq7ZYBwJ1X6JrM1hdkAJC17MzrZDxERv5ZEEAAAQQQ6G0BY/efq7T1qgBrWdOmdb/dMgBwC2iMeiDAgkrS+sp634yXBZsHqSOAAAIIIBCtgKlYWheLr5dSXBhgSUaV0Ttape8pAJDJdh+ShIZaJdbF+n4ra39A26ed1UUa7IoAAggggECsBfSc01dpy3qXFDLIKQB2t5oG2EXyFACU7dx22fYpd4cAl1V6RvGTZsbpA26EFGA+JI0AAggggECoAlUrlzF9c6/QmewnJONgD3aN+uUjr5t3uFUFPd125ya0YnDXLyVeGWiVYBfrtUw28BrLLqxSc7M/ULX6bc5YdacqjwzrXIDzEHRRYHZFAAEEEECgmUClJo+uKVj9ptC3LGdlXqO0vka2Pa3Z9n59r7X5f5JXy/l7PAUAbkL61h0/NEq/UQrobZ/OayIXR2T+WGUz11rZ3G65SHK7jGUMyXwB21TdDGtT3+4oM9Z58uyJAAIIIIBAAALycJ+MPOHPneJX+s3z81rNkTP9S+SU9jzJrRhAjlMluVeGGP5jqhWnfue5M69rtV7GCx6VBII8CzC5fG6UJC99iTtSopU88ne8tPaIlGPa2Y0mJ8JnBBBAAAEEQhLIymF3IcoxbMn7Xx+85gXbvNTXcwCwdfXiAysGd9wsXfGnvCQc4DaeHh8cYP4kjQACCCCAQBwFRuVZerd4Of3vFt5zAOBubFnWHY5j/lw+nuP+zYIAAvEScC+fvfSMvLr49Jy68LTceOEefq6iNh0qqe3HqvEqLKVBAAFfBeTswy9K1cpGr4m2FQDMPuMFjz57aNcGSfx1XjNgOwQQCEdghm2pa5f2q+sumqVm5WSgbNKya6iqbtz6nPrpnlHFJbWTYPiIQA8JyP/1P3jiLRcc81qlk/+VaLGXOyugTAt8q2wW2LMBWhSB1QggMIWAe+Tvdv7vueT053X+7uYL+7Pqf//6Geql54R1HdIUheQrBBAIUkAu/tM/aieDtgIAN+FSpfZjeft5O5mwLQIIBCvgnvZ3j/ynm0HDPSvwXgkQ5hbkgloWBBDoLQGtBr1e/NeoeNsBgHt6Qe4uvFESGG0kwjsCCEQr4I75n3raf6oSLZXrAgbmHL82YKr1fIcAAokU2CbR/41eL/5r1LDtAMDdsa5H7pC3f24kwjsCCEQr0Ljgr1UpMjJU8EI5W8CCAAI9I1CXqXq+uuWahU+2W6OOAoCtqwfkGoDMpyWztjNst4BsjwAC/goMV1tOEOZvhqSGAAKBCcgEff8m0+f/QycZdBQAuBltWb1gi8wJ8Bn5yAWBncizDwI+Cri3+nlZSnV5SPizZS+bsg0CCMRf4IBW5uNb3rzwSCdF7TgAcDPLFbM3K20YCuhEnn0Q8FHAvc/fvdWv1XLf0yXlNVholRbrEUAgUoG6jPl/fssbFt7TaSm6CgA2XX3eqMwO9GHJ/GedFoD9EECgewF3kh/3Pv9jleazZD8p23xhyxE1XG2+TfclIQUEEAhBoG6M+k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"></image></g></g><image 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2.816-.627-1.184 2.373-2.816z" fill="#979797" fill-rule="nonzero"></path></g></svg></p><div class="hero--buttons"><a href="https://docs.pinot.apache.org/getting-started" target="_blank" rel="noopener noreferrer" class="button button--primary button--highlight">Getting Started</a><a href="https://join.slack.com/t/apache-pinot/shared_invite/zt-5z7pav2f-yYtjZdVA~EDmrGkho87Vzw" target="_blank" rel="noopener noreferrer" class="button button--primary button--highlight">Join our Slack</a></div><p class="hero--subsubtitle">Pinot is proven at <strong>scale in LinkedIn</strong> powers 50+ user-facing apps and serving <strong>100k+ queries</strong></p><div class="container"><p align="center"></p><h3 class="installSubTitle_sbCG">What is Apache Pinot?</h3><div><div style="border:1px solid white;width:720px;height:480px"></div></div><p></p></div></div></header><main><section class="features_eXGx"><div class="container"><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="features"></a>Features<a class="hash-link" href="#features" title="Direct link to heading">#</a></h2><div class="row"><div class="col col--4 feature_Fxce"><div class="featureIcon_NFNa"><i class="feather icon-zap"></i></div><h3>Blazing Fast</h3><p>Pinot is designed to answer OLAP queries with low latency on immutable data and mutable data(Upsert Support)</p></div><div class="col col--4 feature_Fxce"><div class="featureIcon_NFNa"><i class="feather icon-unlock"></i></div><h3>Pluggable indexing</h3><p>Pluggable indexing technologies - Sorted Index, Bitmap Index, Inverted Index, StarTree Index, Bloom Filter, Range Index, Text Search Index(Lucence/FST), Json Index, Geospatial Index</p></div><div class="col col--4 feature_Fxce"><div class="featureIcon_NFNa"><i class="feather icon-rss"></i></div><h3>Near Real time ingestion</h3><p>Near Realtime ingestion with <a href="https://kafka.apache.org/" target="_blank" rel="noopener noreferrer">Apache Kafka</a>, <a href="https://pulsar.apache.org/" target="_blank" rel="noopener noreferrer">Apache Pulsar</a>, <a href="https://aws.amazon.com/kinesis/" target="_blank" rel="noopener noreferrer">Kinesis</a>supports <a href="https://www.json.org/" target="_blank" rel="noopener noreferrer">JSON</a>, <a href="https://avro.apache.org/" target="_blank" rel="noopener noreferrer">Avro</a>, <a href="https://developers.google.com/protocol-buffers/" target="_blank" rel="noopener noreferrer">ProtoBuf</a>, <a href="https://thrift.apache.org/" target="_blank" rel="noopener noreferrer">Thrift</a> formats</p></div></div><div class="row"><div class="col col--4 feature_Fxce"><div class="featureIcon_NFNa"><i class="feather icon-code"></i></div><h3>Horizontally scalable</h3><p>Horizontally scalable and fault tolerant</p></div><div class="col col--4 feature_Fxce"><div class="featureIcon_NFNa"><i class="feather icon-shuffle"></i></div><h3>Joins using Trino or PrestoDB</h3><p>Joins are currently not supported, but this problem can be overcome by using <a href="https://trino.io/" target="_blank" rel="noopener noreferrer">Trino</a> or <a href="https://prestodb.io/" target="_blank" rel="noopener noreferrer">PrestoDB</a> for querying</p></div><div class="col col--4 feature_Fxce"><div class="featureIcon_NFNa"><i class="feather icon-search"></i></div><h3>SQL-like Query Interface (PQL)</h3><p>SQL like language that supports selection, aggregation, filtering, group by, order by, distinct queries on data</p></div></div><div class="row"><div class="col col--4 feature_Fxce"><div class="featureIcon_NFNa"><i class="feather icon-list"></i></div><h3>Hybrid tables</h3><p>Consist of of <a href="http://pinot.apache.org/img/dynamic-table.png" target="_blank" rel="noopener noreferrer">both offline and realtime table</a>. Use realtime table only to cover segments for which offline data may not be available yet</p></div><div class="col col--4 feature_Fxce"><div class="featureIcon_NFNa"><i class="feather icon-bar-chart"></i></div><h3>Anomaly Detection</h3><p>Run ML Algorithms to detect Anomalies on the data stored in Pinot. Use <a href="https://docs.pinot.apache.org/integrations/thirdeye" target="_blank" rel="noopener noreferrer">ThirdEye</a> with Pinot for Anomaly Detection and Root Cause Analysis</p></div><div class="col col--4 feature_Fxce"><div class="featureIcon_NFNa"><i class="feather icon-bell"></i></div><h3>Smart Alerts in ThirdEye</h3><p>Detect the right anomalies by customizing anomaly detect flow and notification flow</p></div></div></div></section><section class="topologies"><div class="container"><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="ingest-query"></a>Ingest and Query Options<a class="hash-link" href="#ingest-query" title="Direct link to heading">#</a></h2><div class="sub-title">Ingest with Kafka, Spark, HDFS or Cloud Storages</div><div class="sub-title">Query using PQL(Pinot Query Language ), SQL or Trino/Presto(supports Joins)</div><svg width="800" height="400" viewBox="0 0 800 400" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" class="svg 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vFT16Dz7vUVNxKly1+jAL+WSWh08sTXd5+592S2NbdqE2HYJ2D/ZprrkmN97axsZF27txJ9fX1409b+j6ZStB7H/0jdQ09Ry5XHvNH2TtpZfSGgtQfGcBgt6XRsr8xmV7cUFFPswN1PDiff+tEhGPTokeouqK5ZJWM8tjcr156adLMq3gsuvSBBx44VTLHNDEMATEYyIkCIsVdccUV9M1vfrOkIiJ+JHh67P4zP2Eh2clCMnmgXQa/+8LD1Dl8DtNoBRi2GQnI+MmcQCPNqaqd8kn8ZMpLjVWfo4sX3F1S4ZCKiHj82yuvUGdn16R6QUAmISnqBASkKGwXMk0lIHLVLiIivkiL5PS5vXS0+2l+BuMYi8YAdY8MYDxD4GArmoC0TOZWN6VbJz5PEy1q/GNa0byd/N4LT90XXbjBjNOJhxQNATEIeDQ7BMQgx1wCIsXaSUTGV7O1+yCLyT56++zfUjT5zvhLeA8CeRP4vXnP0uqmzbS8aQNPDbbPcOpM4iEVhIDkHeZpE0JApsUz88XpBERy21VExLdEMk4fnTsCMREY2PIikBGNJXPWchfW+WVX8spoUaJ8xENcgYCYExAIiEGOMwmIFL948WJ64oknaPny5QatqcueEZPWc/vpQPcvqDf8E3XGULJjCNR4b6K1TbelWxp2FY0MzIGBAXr9jTfp3LlzmVM5XyEgOdEUdAECUhCuyYnzEZBMrm984xu0ffv2zKGtXwdDvdw6OUzHe9+ig9070dVl62iZ69xFjU/Q6jnbaMnsdTSndr65hSsqTVbF3rV7D8Xj8bwsQEDywjRjIgjIjIimT1CIgEhJn/zkJ+nRRx+1ZPXe6T0v7Gpb33HqGGil1r536GjvTyAoheGzdeoNc/6GFjdspLl1y2hB4wpbjWfMBE66rN7hxUyPHDk6U9Ks6xCQLBxFH0BAikZ3PmOhAiK5pEtLWiNWrp1lsJqTsvcNd1Hn4ClqH/iQWvv30umh70xKgxP2IzC36n5a1ngVLahbTXPrlzqmhTEVSXlAcNeu3Xl1WU3MDwGZSKS4YwhIcdzGchUjIJnMn/3sZ9NLn8yaVZondDN+mPEqYyjdQ23Uw3vvSDt1DZ+gAz1/bkbRKKNIAsvr/4Jaai6iuTXLqIXFYlbNXFsOfBdavXA4TAcOHKSDhw4VmnUsPQRkDIWhNxAQQ/iIjAhIxrS0RuT3RKxciDFjW/WrtFT6R7qpd/gsdQSP86KPAxAWk6HLmEWVv4EW1q2jusAcfpCv2dEti+nwHOFVr997bz+JiBjZICBG6F3ICwG5wKKod2YIiBhet24dPfLIIyVZR6uoihvMJD8i1RvsoKFwHw2GesbEZSDShu6wCWyl22lO1Wpetr8yLRLVvKxIA4tEPYtFwK/+lzAnuFOSw86uLtq3762iuqumchgCMhWVws9BQApnlpXDLAHJFHrjjTfSHXfc4ejxkUxdjLxmusSkDOkWi/APUkUSI9Q+eHisWKd3kUnLQURBtubq5fxbHfKTtr50d5Oc06XLSepS7NbT00O/+90B01fShYAUG5HsfBCQbB4FH5ktIBkHrr766rSQlGJl34wPTnqVaccj0aFJLmfEZ9KF0RMTRSlXuvHnM1/248+Nfz9eBMafr/AGqLG6dAsLjvfF7u9VCUem3hCQDAljrxAQY/xMGQOZzgV5kv3ee+8tm66t6Vjgmv4E5DfLDxw8SO3tZ5VWFgJiDl77LGBjTn20K0V+qEp2ERLp2tqwYYPjniHRLiiokKkEEokEtbW10aEPPphy5VxTjaEwUwlAQEzFqa6wjJCIhYceeoi2bt1q66VR1JFAyboQkOc4Thw/QcdPnDA8q0oXJk6rB7qwDEZM1RhIPm5Jq+TWW/lHezZtKvlvj+TjL9KAgDw5Lq2Ng4c+MG1GVTFU0YVVDLXJedACmczEMWfGt0ruvPPOdKtEnm7X8XkSxwQFjk4iIF1UZ8920Jm2M3ScWxz5rlc1qSCcsB0BCIjtQlKcQz/+8Y9JdtlETKR1smbNGoyXFIcTuQwSgGgYBOiQ7BAQhwSqEDfHi4ksl7Jt2zaISSEAkbYoAtI91dHRQafPtNHJkyfR0iiKorMyQUCcFa+CvX3++edJdtlkJeBLL700/ZCiLOiIDQSMEpCB8O7ubmpvazf9YT+jviG/egIQEPWMbWPhxRdfJNlla2xspJtvvjk9AL9s2TJqbsYDbrYJlI0dGRoKUk8PC4aMaZw5g9lTNo6VFa5hFpZByqWchWXQ9azsshaXLOgo4yaLFi2CoGTRKd8DEYy+vl7q6uqmtvY26u8f0AIGZmGZE0a0QMzh6PhSDvHS2LJnNmmh3HDDDbR27dr075csWLAAA/IZOJq+yhhGX38/9fX2pmdNdXHXlNFVbzVFhWqNEoCA4FaYkkAf921nxk4yCWRm12WXXUZLlixJi4p0e2HKcIaOs15llpTEuJ9/R7y/r586OjtL+lyGs+jB2wwBCEiGBF5nJDD+uZNMYun6kl1aKiIoDQ0NaXHJXMdr6QkMsEhIV1QwOJTuiurrZ+HQpCuq9HTL2wMISHnH33DtJ3Z9ZQqU1YRlLEXGVKQ7TIRl9uzZ6AbLADL5VbqfBgcHucspkhaKdMuCu6M6O7tMtoTiQOACAQjIBRZ4ZyKB119/PWdpIi4tLS20YsWKtKCI0Hi9XrRcchI7f0GmzCa560mWOpcuKBnYjifiyleuncEtXC5jAhCQMg5+qao+nbiIT5luMXk/b948ampqkrfp15qamvR7HcZfMuMQUqFYLEbS1STbSChEA6NdTINDg+huSlPBHzsSgIDYMSpl7lOubrHpsMhDkuMH9McLj+STa3Iuny0QCJDLVfgMd+k2irMQZLbxQpA599Hp05m3eAUBxxOAgDg+hKiAEMg8IGkGjTvvujtLjMwoE2WAgI4E3DpWCnUCARAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcMCCIAACGhJAAKiZVhRKRAAARBQTwACop4xLIAACICAlgQgIFqGFZUCARAAAfUEICDqGcPCKIHUgg1gYSKBQV+9iaWhKBAonAAEpHBmyFEEgVTLaopd/QVKXLyjiNzIMpFAv7+R9taspqC3duIlHIOAZQQgIJahLmND/gDFN/87cnk8lFh/NaVmLStjGMarnnD56P2q5ZR0u2l/7UqSY2wgUAoCEJBSUC8zm4kttxPVNqRrnRaRKz9LxKKCrTgCR2uWUsjjTWcOuv0kx9hAoBQEICCloF5GNpPLNlNy0bqsGqfqZlPy8k9lncNBfgTO+ZvoI/+srMRy3FXRnHUOByBgBQEIiBWUy9SGdFUlrph6zCOxfBMlV15VpmSKq3bEU0nv1yyeMvP+qiU04q2Z8hpOgoAqAhAQVWTLvdzqRopf9blpu6riV9xMmJmV340i4xxv1a2lqOt819XEXDIe8lbNRSQigw0ErCIAAbGKdDnZkUHz6/5obNwjV9VlPCR25ecgIrkAjZ5Putz0dt0akvGO6TYZFxGRgYhMRwnXzCQAATFKk//TxpZNIH7Dg5RqyK9P3uXzUXzbZzAzKxth1tFBbln0eauyzuU6EJF5t3YNZmblACTiivGiHHCKOA0BKQLa+CyxGx4mgoiMIUn8/kMsBvPHjvN6wy2WxPV3Q0SmgHW0dhW1++umuJL71ICnIt1iwfTebEbyzMyu+vUUcaObL5tM8UcQkOLZnc/J01MhIoxCRIDFIzlveVFEU5XV50WEHzjERiTdVh+weLROmHGVLxtpsUi3F7qzzhPr9c+m3dwyyzWGlC9XpMsmAAHJ5lHcUbmLiAyYc7dVseKRgS4iErv2HpKpv+W8Scvhvbr1k6brFspERGRv7fqyn53VUTGX9vEDlzLRAJu5BEDULJ4sIvEdf1J2A8IyVVdaYAV3W+Xgnh4T2Xxr2S55EvZU0Z6GDdSd55hHDoxjp2Vg/c3atSTPj5TjdqJ6Ge2vWVKOVbekzhAQEzGn/4Pm9Z6S67ebWKp9i5IpuDJ2kXnK3CxPZXZWcuN1lPwYP7FeRpusb7W7fu2Ms60KRZKe4lu7nNoqFxaa1bHpM12AH1bmN5nDsRUtseNTTyovsVNONp9eqmPTdkrNnk+e//dDJ1dlWt/lyz2+/NL0+lbTJjRwMbHqCkoKxzefJ9dgu4GS7J1VuqyO83IkxY535Fu7A9ULqNdXS6tGTlFlYiTfbI5LF+OZaAdrVlGnDw9Wqg4eBEQRYVm+I3XLn5Hntz8nV+dRRVasLzb9dDmvZSXLkbgsMC9dY7Edj5D36F5yv/eiBRatNSGtDlkYMbO2lWrrMqOrw7ue1oXO0oLwGdXmLC9fWllHq1owWG4ReQiIQtDyJRvffh+5Ww+Q562fEkVDCq2pL1qWYk+svZJknMLKTewl1l/Fa2qt1UaQrWp1TBUn6dKS1khnRSOtGW6lqnhwqmSOOidjR4dqVpg2duSoypfQWQiIBfCTyzZQsnkJefb/K4vJXgssmmsiPdax8ffTA+VWtDpyeS+CHLv+XvKe+h2533+ZaLgvV1Lbnpe+eZkVdKxynmWtjlwwZKC+m5+LWBXuokWhNvIlo7mS2va8dFedDiyg4/45mGVVgihBQKyCXl1LiW23UXLdleQ+9KYjhCQtHOuvo1STfQZf02NMvBBjfNF68p4+6BghkRZHR0ULHa9sKblwTLzlZaBZvoBXRHu4W+ssVSTCE5PY7lh4trMIHws0obuqhNGBgFgMX5b4sLuQ2FE4JoYp3a3lACGR/5DTLQ6bf9FJt5YIiezLor20ZOS0LYUkvRSJvxnCMfEDUaJjCEiJwGeEJHHJdvK0HyHX4V2lnWnEDwMmV11JyQWr817HqkTossyOFxJPz2lytR8l9+FXs9JYfSD/HZ/jwfFOfvq50GVIrPZ1KnsyG0z2hdEBamIxmR3tI08qNlVSy87JcyztFXMcydMySCUwBAEpAfQsk9K1xdNViXdXbzu5z54g19mj1szcYtFILN9MqXkrbdVNlcUnzwMRkvST8LyUSnzjDWS1mMjYRp9vNi/U10BnvA1a9Mef8deT7ETLLBcTEeF+XwOdY54dnjrbdfvleVtqnwwCYqMQy5TVhCxEyDOOXOFhcvV1spCwoPR3kKvtgGFPpWsqOYvHMxqbKVU7x1EtjUIqP1FM3DzY7mZxpj7m2HWKhbq1kOKmTCsL8w3yHvRVU6+nmmQBQ5238WLSFB+hOn6OpCY2TDUJ3uNDhqsuXVMRdwUNeWrTs8PMehLfsGMoYFoCEJBp8ZTuojzVnpKFCccvTjjUz8ISJPdQzwXHus8QJS50L6RqGslVc/73xyVRkoWCfH5txeICiKnfiZikuwvHLS+fisVIRIUiofOvmayDzHVkiGIuP8V8dfzqo2EWB9mC3gqK8HlpaZT7l1t69pYstcJdSplNRMWdStLs2AUx8aTiaYHJpInzj2FleMo5YRrilXHLnWeGjxNfISBOihqvt5XiPTF+VhQPJGMrjEBGVCRXgiavkzTo76bOqoX8xYYnmfMlmxEBPP2dLzE90mEtLD3iiFqAAAiAgOUEICCWI4dBEAABENCDAAREjziiFiAAAiBgOQEIiOXIYRAEQAAE9CAAAdEjjqgFCIAACFhOAAJiOXIYBAEQAAE9CEBA9IgjagECIAAClhOAgFiOHAZBAARAQA8CEBA94ohagAAIgIDlBCAgliOHQRAAARDQgwAERI84ohYgAAIgYDkBCIjlyGEQBEAABPQgAAHRI46oBQiAAAhYTgACYjlyGAQBEAABPQhAQPSII2oBAiAAApYTgIBYjhwGQQAEQEAPAhAQPeKIWoAACICA5QQgIJYjh0EQAAEQ0IMABESPOKIWIAACIGA5AQiI5chhEARAAAT0IAAB0SOOqAUIgAAIWE4AAmI5chi0O4GEy2t3F+EfCNiCAATEFmGAE3YiEPd47OQOfAEB2xKAgNg2NHAMBEAABOxNAAJi7/jAOxAAARCwLQEIiG1DA8dAAARAwN4EICD2jg+8AwEQAAHbEoCA2DY0cAwEQAAE7E0AAmLv+MA7EAABELAtAQiIbUMDx0AABEDA3gQgIPaOD7wDARAAAdsSgIDYNjRwDARAAATsTQACYu/4wDsQAAEQsC0BCIhtQwPHQAAEQMDeBCAg9o4PvAMBEAAB2xKAgNg2NHAMBEAABOxNAAJi7/jAOxAAARCwLQEIiG1DA8dAAARAwN4EICD2jg+8AwEQAAHbEoCA2DY0cAwEQAAE7E0AAmLv+MA7EAABELAtAQiIbUMDx0AABEDA3gQgIPaOD7wDARAAAdsSgIDYNjRwDARAAATsTQACYu/4wLsSEIh6KktgFSZBwHkEICDOixk8BgEQAAFbEICA2CIMcAIEQAAEnEcAAuK8mMFjEAABELAFAQiILcIAJ0AABEDAeQQgIM6LGTwGARAAAVsQgIDYIgxwAgRAAAScRwAC4ryYwWMQAAEQsAUBCIgtwgAnQAAEQMB5BCAgzosZPAYBEAABWxCAgNgiDHACBEAABJxHAALivJjBYxAAARCwBQEIiC3CACdAAARAwHkEICDOixk8BgEQAAFbEICA2CIMcAIEQAAEnEcAAuK8mMFjEAABELAFAQiILcIAJ0AABEDAeQQgIM6LGTwGARAAAVsQgIDYIgxwAgRAAAScRwAC4ryYwWMQAAEQsAUBCIgtwgAnQAAEQMB5BCAgzosZPAYBEAABWxCAgNgiDHDCTgRSbped3IEvIGBbAhAQ24YGjpWKQMztL5Vp2AUBRxGAgDgqXHAWBEAABOxDAAJiMBapRDJlsAhkBwEQAAFHEoCAGAybKxIKGiwC2UEABCwk4EtGaWRkpMNCk9qagoAYDW080mO0COQHARCwjkBlMnL2S1/6UsQ6i/pagoAYjG0qGj5psAhkBwEQsJBAIBk6ZKE5rU1BQAyG1+V2vWGwCGQHARCwiIA/FaeKZPh9i8xpbwYCYjDEDdWBb/FAetJgMcgOAiBgAYElkV5KxGI7LTBVFiYgIAbD/KsvfTziDva8YrAYZAcBELCAQEuo5937779/vwWmysIEBMSMMFfXfM2MYlAGCICAOgILowMUiA0+oc5C+ZUMATEh5ru+uH2fayR4yoSiUAQIgIAiAnMjPb3B4MBLioovy2IhICaFPeV13YKxEJNgohgQMJmAtD4aQ933YfquuWAhICbx3P3Yjb9zh4N/bVJxKAYEQMAkAjX84OBFw63fve++e39uUpEoZpQABMTEW2HXV3f8JwoPYYqgiUxRFAgYJbAuePLUSF/nV42Wg/yTCUBAJjMxdCaZqv8DikfDhgpBZhAAAVMIrB3piNWFum5C15UpOCcVAgGZhMTYib1f3tLpovg1qXg8Zqwk5AYBEDBCYGm0L74weOoWnrZ7xEg5yJubAAQkN5uir8isLDdFr4SIFI0QGUHAEIHlkXORVf1HP3nfff/+ZUMFIfO0BCAg0+Ip/iJEpHh2yAkCRgisCXWElvd/sBniYYRifnkhIPlxKipVRkR4TCRUVAHIBAIgUBABEY+Fg8e24mnzgrAVnRgCUjS6/DKKiFR7Y/NckaGD+eVAKhAAgUIJyCKJlwVbD8/vP7oM4lEoveLTu4rPipyFEtj27V8+lapueLzQfEhvLYGts3gahM9P3d4aaw3DWlEEmuIjtCp88ruPff62LxZVADIVTQAtkKLRFZ5x11c//uV4cGA7urQKZ4ccIDAVgVXhruCG4LFbIB5T0VF/DgKinnGWhX1/evNvpEuLhod2ZV3AgW0IxFwe2/gCR6YmEEjE6fLgiX2L+g6vfOju21+cOhXOqiaALizVhKcp/2NP/voTbq93p8tfMXuaZLhkMYFLmyrIy/9aoQvLYvB5mHPzT++siPX0Lwx13APhyAOY4iQQEMWA8yl+21P/978lfZVfcnm9vnzSI41aAhAQtXyLLX1edCixOHL2GXd363/Ak+XFUjQ3HwTEXJ5Fl7b5qT0tLhr6n65A9XVFF4KMphCAgJiC0bRCpLtqTejM27OCpz/zwAMPnDKtYBRkmAAExDBCcwu46tnXL0mGQn+fqqq53NySUVq+BCAg+ZJSm06EY0mk+8OWSNcX//ieO36t1hpKL4YABKQYahbk2fb0m0tS0aG/p+q6GywwBxPjCEBAxsEowVtZfn1h9NwHLaGuxyEcJQhAASYhIAXAKkVSCIn11CEg1jMXiyIcS8Ndb88dOsnPAuJ3y0sThcKsQkAK41Wy1OeFhH/PuSJwO3n9VSVzpAwMQ0CsDXJzfDg2OzawZ8HgiccgHNayN2oNAmKUYAnyb/vb3/wRpeJ/mqqoXV8C89qbhICoD7GMb8yP97fNivQ96e058TRmValnrsICBEQFVYvKTLdKEqGvk897N3nQKjELOwTELJKTy1kY7Q81xIP/h7up/jO3NvA7HZMROeoMBMRR4crt7JZn3/icKxT6YqoysNXldntzp8SVmQhAQGYiVNj1xvhIsik69N6s2LnnHr/rtu8Vlhup7UwAAmLn6BTh283f+WVFn7/u0xCTIuCNZoGAFM8ukzMjGvXJge97u1r/Dl1UGTJ6vUJA9IpnVm3GxCQSeTDlr7gWLZMsPDkPICA50Ux7AaIxLR4tL0JAtAzr1JXa+t1d11Js5E5yuf6AZ3MtnToVzkJA8rsHZF2qlkRwuC4+8lZtfPgffd3HfoiWRn7sdEkFAdElkgXWY/tzv64PpgI7XOHIHeRzX0/eisYCi9A2OQQkd2jnxEfitcnQ8dpI8IWm4Y+ewdIiuVmVwxUISDlEOY86pmd0eRK3UDh8C/l9v1fOggIBuXDD1CciVJ8ItVUnQrtnRYae/ZO7b/nNhat4V+4EICDlfgfkqL8s7uipTGxJRUIfJ7fravIH1uZIqt3pchUQ+VnYxng4WJ2IfBiIj7zSEA/9EoKh3e1taoUgIKbi1LswGUNxuVNbUtHoJ1Iu10Uuv79ZxxqXi4BId1RNInw6kIy87Y0nfjF78NgvHn744QEdY4o6qSEAAVHDtWxK3fL065uTbtcadyz6MfK4ruSZXsvJ46t1MgDdBES6oSqTse5AMnoyEI984E0m/hVi4eQ71D6+Q0DsEwttPJEB+uFE9aZ0ayU0sprHVC5NJVML+ZcXm5xQSacKiAiFN5Xoq4+PnHFT7Lg3nnp1QfD4CxjodsJd50wfISDOjJtjvU4P1rtdS5Nu2uROxOYSpW7ivYYH7VfZpVJ2FhBeeDCSSlGwNhH+yJVKtPlSyT2BRPhYZfeRFzCF1i53UPn4AQEpn1g7oqbSJeZyeypT7uRcfl5ljTjtSkRvpJSrKkXuWfyzv0tUV6QUApJpPUjd6pKhrhh5z/oT0XBFIv7bqNvTOzfafwAD2qojj/ILJQABKZQY0tuCQKYlM+aMK7km5XZxi2Z0S7kucaWSzZSMjy19n68AXdZcGfe4Ut5ub02mtBlf5bcsKpPxgRRRMpOYWwn9cZfnVOZYXuujg++E3RUfyfsKSgbnDhx98cEHH+yUY2wgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAKmEvj/pGLSjhTZ+HkAAAAASUVORK5CYII="></image></g><image 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"></image><path 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1.78-.523 2.79-.523 3.378 0 5.067 1.517 5.067 4.551 0 2.186-.567 3.947-1.702 5.285-1.135 1.35-2.686 2.105-4.654 2.267a17.76 17.76 0 003.499.349c.922 0 1.722-.18 2.402-.54.19-.105.322-.216.399-.332.378.314.567.782.567 1.403 0 .623-.3 1.115-.898 1.475-.599.361-1.481.542-2.648.542-1.166 0-2.424-.25-3.773-.752a13.464 13.464 0 01-3.764-2.145zm19.384-2.913c.24.28.36.69.36 1.232 0 .54-.224.975-.67 1.308-.45.33-1.065.496-1.845.496-.455 0-1.035-.047-1.74-.14-1.387-.197-2.28-.296-2.677-.296-.396 0-.677.009-.842.027l-.641.06 2.287-11.512h4.142l-1.89 9.593c.163.024.32.036.472.036h.473c1.058 0 1.915-.268 2.57-.804z" fill="#CCC"></path><path fill="#000" d="M682 231h6v1h-6zM689 231h6v1h-6zM682 235h4v1h-4zM686 235h4v1h-4zM691 235h4v1h-4z"></path><path fill="#000" d="M694 235h4v1h-4zM682 238h5v1h-5z"></path><path fill="#000" d="M686 238h5v1h-5zM692 238h5v1h-5zM697 238h5v1h-5zM682 242h2v1h-2zM684 242h2v1h-2zM686 242h2v1h-2zM689 242h2v1h-2zM691 242h2v1h-2zM693 242h2v1h-2zM695 242h2v1h-2zM697 242h2v1h-2zM699 242h2v1h-2zM701 242h2v1h-2zM704 242h2v1h-2zM706 242h2v1h-2zM683 245h2v1h-2zM686 245h2v1h-2zM689 245h2v1h-2zM691 245h2v1h-2zM695 245h2v1h-2zM697 245h2v1h-2zM700 245h2v1h-2zM703 245h2v1h-2zM682 251h2v1h-2zM684 251h2v1h-2zM687 251h2v1h-2zM690 251h2v1h-2zM692 251h2v1h-2zM695 251h2v1h-2z"></path><path fill="#000" d="M696 251h2v1h-2zM699 251h2v1h-2z"></path></g></g></svg></div></section><section class="topologies"><div class="container"><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="user-facing-analytics"></a>User-Facing Analytics<a class="hash-link" href="#user-facing-analytics" title="Direct link to heading">#</a></h2><p align="center"></p><h3 class="installSubTitle_sbCG">Building Latency Sensitive User Facing Analytics via Apache Pinot</h3><div><div style="border:1px solid white;width:720px;height:480px"></div></div><h3 class="installSubTitle_sbCG">Using Apache Kafka and Apache Pinot for User-Facing Analytics</h3><div><div style="border:1px solid white;width:720px;height:480px"></div></div><div class="hero--buttons"><a href="https://docs.pinot.apache.org/community-1/videos" target="_blank" rel="noopener noreferrer" class="button button--primary button--highlight">More Videos</a></div><p></p></div></section><section><div class="container"><h2><a aria-hidden="true" tabindex="-1" class="anchor" id="installation"></a>Installs Everywhere<a class="hash-link" href="#installation" title="Direct link to heading">#</a></h2><div class="sub-title">Pinot can be installed using docker with Trino/Presto</div><div class="installationChecks_XhS6"><div><i class="feather icon-package"></i> Helm or K8s crds</div><div><i class="feather icon-cpu"></i> On-Premise</div><div><i class="feather icon-zap"></i> Public Cloud</div><div><i class="feather icon-feather"></i> Locally</div></div><h3 class="installSubTitle_sbCG">Install:</h3><div class="tabs-container"><ul role="tablist" aria-orientation="horizontal" class="tabs mini"><li role="tab" tabindex="0" aria-selected="true" class="tabs__item tabItem_vU9c tabs__item--active"><i class="feather icon-download-cloud"></i> Using Helm</li><li role="tab" tabindex="-1" aria-selected="false" class="tabs__item tabItem_vU9c"><i class="feather icon-download"></i> Using Binary</li><li role="tab" tabindex="-1" aria-selected="false" class="tabs__item tabItem_vU9c"><i class="feather icon-github"></i> Build From Source</li></ul><div class="margin-vert--md"><div role="tabpanel"><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token plain">helm repo </span><span class="token function" style="color:rgb(80, 250, 123)">add</span><span class="token plain"> pinot https://raw.githubusercontent.com/apache/pinot/master/kubernetes/helm</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">kubectl create ns pinot</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">helm </span><span class="token function" style="color:rgb(80, 250, 123)">install</span><span class="token plain"> pinot pinot/pinot -n pinot --set cluster.name</span><span class="token operator">=</span><span class="token plain">pinot</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></div><div role="tabpanel" hidden=""><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token assign-left variable" style="color:rgb(189, 147, 249);font-style:italic">VERSION</span><span class="token operator">=</span><span class="token number">0.10</span><span class="token plain">.0</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token function" style="color:rgb(80, 250, 123)">wget</span><span class="token plain"> https://downloads.apache.org/pinot/apache-pinot-</span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">$VERSION</span><span class="token plain">/apache-pinot-</span><span class="token variable" style="color:rgb(189, 147, 249);font-style:italic">$VERSION</span><span class="token plain">-bin.tar.gz</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token function" style="color:rgb(80, 250, 123)">tar</span><span class="token plain"> vxf apache-pinot-*-bin.tar.gz</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token builtin class-name" style="color:rgb(189, 147, 249)">cd</span><span class="token plain"> apache-pinot-*-bin</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">bin/quick-start-batch.sh</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></div><div role="tabpanel" hidden=""><div class="codeBlockContainer_J+bg"><div class="codeBlockContent_csEI bash"><pre tabindex="0" class="prism-code language-bash codeBlock_rtdJ thin-scrollbar" style="color:#F8F8F2;background-color:#282A36"><code class="codeBlockLines_1zSZ"><span class="token-line" style="color:#F8F8F2"><span class="token comment" style="color:rgb(98, 114, 164)"># Clone a repo</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token function" style="color:rgb(80, 250, 123)">git</span><span class="token plain"> clone https://github.com/apache/pinot.git</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token builtin class-name" style="color:rgb(189, 147, 249)">cd</span><span class="token plain"> pinot</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain" style="display:inline-block">
</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token comment" style="color:rgb(98, 114, 164)"># Build Pinot</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">mvn clean </span><span class="token function" style="color:rgb(80, 250, 123)">install</span><span class="token plain"> -DskipTests -Pbin-dist</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain" style="display:inline-block">
</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token comment" style="color:rgb(98, 114, 164)"># Run the Quick Demo</span><span class="token plain"></span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain"></span><span class="token builtin class-name" style="color:rgb(189, 147, 249)">cd</span><span class="token plain"> pinot-distribution/target/apache-pinot-*-SNAPSHOT-bin/apache-pinot-*-SNAPSHOT-bin</span></span><span class="token-line" style="color:#F8F8F2"><span class="token plain">bin/quick-start-batch.sh</span></span></code></pre><button type="button" aria-label="Copy code to clipboard" class="copyButton_M3SB clean-btn">Copy</button></div></div></div></div></div><h3 class="installSubTitle_sbCG">Or choose your preferred method:</h3><div class="row"><div class="col"><a href="https://docs.pinot.apache.org/getting-started/running-pinot-in-docker" target="_blank" rel="noopener noreferrer" class="jump-to jump-to--undefined"><div class="jump-to--inner"><div class="jump-to--inner-2"><div class="jump-to--main">Containers</div><div class="jump-to--right"><i class="feather icon-chevron-right arrow"></i></div></div></div></a></div><div class="col"><a href="https://docs.pinot.apache.org/getting-started/kubernetes-quickstart" target="_blank" rel="noopener noreferrer" class="jump-to jump-to--undefined"><div class="jump-to--inner"><div class="jump-to--inner-2"><div class="jump-to--main">Helm</div><div class="jump-to--right"><i class="feather icon-chevron-right arrow"></i></div></div></div></a></div><div class="col"><a href="https://docs.pinot.apache.org/basics/getting-started/public-cloud-examples" target="_blank" rel="noopener noreferrer" class="jump-to jump-to--undefined"><div class="jump-to--inner"><div class="jump-to--inner-2"><div class="jump-to--main">Cloud</div><div class="jump-to--right"><i class="feather icon-chevron-right arrow"></i></div></div></div></a></div><div class="col"><a href="https://docs.pinot.apache.org/getting-started/running-pinot-locally" target="_blank" rel="noopener noreferrer" class="jump-to jump-to--undefined"><div class="jump-to--inner"><div class="jump-to--inner-2"><div class="jump-to--main">Manual/Local</div><div class="jump-to--right"><i class="feather icon-chevron-right arrow"></i></div></div></div></a></div></div></div></section></main></div><footer class="footer"><div class="container"><div class="row footer__links"><div class="col footer__col"><h4 class="footer__title">About</h4><ul class="footer__items"><li class="footer__item"><a href="https://docs.pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footer__link-item">What is Apache Pinot?</a></li><li class="footer__item"><a class="footer__link-item" href="/who_uses">Who uses Apache Pinot?</a></li><li class="footer__item"><a href="https://docs.pinot.apache.org/pinot-components" target="_blank" rel="noopener noreferrer" 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class="margin-bottom--sm"><a href="https://pinot.apache.org/" target="_blank" rel="noopener noreferrer" class="footerLogoLink_94kH"><img src="/img/logo.svg" alt="Apache Pinot™" class="themedImage_TMUO themedImage--light_4Vu1 footer__logo"><img src="/img/logo.svg" alt="Apache Pinot™" class="themedImage_TMUO themedImage--dark_uzRr footer__logo"></a></div><div class="footerCopyright_-piB">Copyright © 2022 The Apache Software Foundation.<br>Apache Pinot, Pinot, Apache, the Apache feather logo, and the Apache Pinot project logo are registered trademarks of The Apache Software Foundation.<br><br>This page has references to third party software - Presto, PrestoDB, ThirdEye, Trino, TrinoDB, that are not part of the Apache Software Foundation and are not covered under the Apache License.</div></div></div></footer></div>
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