Update Pinot Site from dev branch 4c466d7b53b6df0ef92b5046b86b6fdbfb1ba4e5
commit 4c466d7b53b6df0ef92b5046b86b6fdbfb1ba4e5
Author: Gio <153032991+gio-startree@users.noreply.github.com>
Date: Thu Apr 25 17:07:40 2024 +0200
WB-286 - Handle redirect both /who_uses and /who_uses/ (#124)
commit 0067e4f8feee4a6b1b25376fa8c62a05c218ab65
Author: Gio <153032991+gio-startree@users.noreply.github.com>
Date: Wed Apr 24 16:38:55 2024 +0200
Wb 286 redirect who uses (#123)
* WB-286 - Redirect from "who-uses" to "powered-by" page
* WB-286 - Fix typo
diff --git a/404.html b/404.html
index fbef360..08a34bf 100644
--- a/404.html
+++ b/404.html
@@ -1 +1 @@
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href="/static/favicons/favicon-32x32.png"/><link rel="icon" type="image/png" sizes="16x16" href="/static/favicons/favicon-16x16.png"/><link rel="manifest" href="/static/favicons/site.webmanifest"/><link rel="mask-icon" href="/static/favicons/safari-pinned-tab.svg" color="#5bbad5"/><meta name="msapplication-TileColor" content="#000000"/><meta name="theme-color" media="(prefers-color-scheme: light)" content="#fff"/><meta name="theme-color" media="(prefers-color-scheme: dark)" content="#000"/><link rel="alternate" type="application/rss+xml" href="/feed.xml"/><title>About | Apache Pinot™</title><meta name="description" content="Realtime distributed OLAP datastore"/><meta name="robots" content="index, follow"/><meta name="googlebot" content="index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1"/><link rel="canonical" href="https://pinot.apache.org/about"/><link rel="alternate" type="application/rss+xml" href="https://pinot.apache.org/feed.xml"/><meta property="og:title" content="About | Apache Pinot™"/><meta property="og:description" content="Realtime distributed OLAP datastore"/><meta property="og:url" content="https://pinot.apache.org/about"/><meta property="og:site_name" content="Apache Pinot™"/><meta property="og:locale" content="en_US"/><meta property="og:image" content="https://pinot.apache.org/static/images/twitter-card.png"/><meta property="og:type" content="website"/><meta name="twitter:card" content="summary_large_image"/><meta name="twitter:title" content="About | Apache Pinot™"/><meta name="twitter:description" content="Realtime distributed OLAP datastore"/><meta name="twitter:image" content="https://pinot.apache.org/static/images/twitter-card.png"/><meta name="next-size-adjust"/><script src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js" crossorigin="" noModule=""></script></head><body class="bg-white text-black antialiased dark:bg-gray-950 dark:text-white"><script>!function(){try{var 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leading-[1.1rem] text-stone-900">Hubert Dulay, Mayank Shrivastava, Neha Pawar</p></div><p class="text-sm text-muted-foreground leading-[1.1rem]">September 19th, 2023<!-- --> • <!-- -->13 min read</p></div></div></a><a aria-label="Segment Compaction for Upsert Enabled Tables in Apache Pinot" class="px-5 sm:px-0" href="/blog/2023/08/04/segment-compaction-for-upsert-enabled-tables-in-apache-pinot-3f30657aa077"><div class="rounded-lg border bg-card text-card-foreground shadow-sm h-full border-neutral-500 p-5"><div class="flex flex-col space-y-1.5 h-full justify-between p-0"><div><h3 class="tracking-tight pb-2 text-xl font-semibold leading-8 text-stone-900">Segment Compaction for Upsert Enabled Tables in Apache Pinot</h3><p class="text-sm leading-[1.1rem] text-stone-900">Robert Zych</p></div><p class="text-sm text-muted-foreground leading-[1.1rem]">August 4th, 2023<!-- --> • <!-- -->4 min read</p></div></div></a><a aria-label="Star-Tree Index in Apache Pinot - Part 3 - Understanding the 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-<!DOCTYPE html><html lang="en-us" class="__variable_1fc36d scroll-smooth"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="preload" href="/_next/static/media/6905431624c34d00-s.p.woff2" as="font" crossorigin="" type="font/woff2"/><link rel="preload" as="image" href="https://content.linkedin.com/content/dam/engineering/en-us/blog/migrated/data_centers_pops_0.png"/><link rel="stylesheet" href="/_next/static/css/9e925a33b1acdac1.css" crossorigin="" data-precedence="next"/><link rel="stylesheet" href="/_next/static/css/c130d1629644f070.css" crossorigin="" data-precedence="next"/><link rel="preload" as="script" fetchPriority="low" href="/_next/static/chunks/webpack-dde39ac7c1b4eb4b.js" crossorigin=""/><script src="/_next/static/chunks/fd9d1056-f172993f20f9bb67.js" async="" crossorigin=""></script><script src="/_next/static/chunks/472-928e738895d89765.js" async="" crossorigin=""></script><script src="/_next/static/chunks/main-app-344a87763a0866a6.js" async="" crossorigin=""></script><script src="/_next/static/chunks/326-3a90a6443b9c824c.js" async=""></script><script src="/_next/static/chunks/980-6e243f9cd384c7d2.js" async=""></script><script src="/_next/static/chunks/702-a2bf9fe707814b79.js" async=""></script><script src="/_next/static/chunks/app/layout-776a485845c720ef.js" async=""></script><script src="/_next/static/chunks/413-f9f40b83f7bb3f22.js" async=""></script><script src="/_next/static/chunks/app/blog/%5B...slug%5D/page-502e08b6677b55da.js" async=""></script><link rel="preload" href="https://www.googletagmanager.com/gtag/js?id=G-ZXG79NJEBY" as="script"/><title>A Brief History of Scaling LinkedIn | Apache Pinot™</title><meta name="description" content="LinkedIn started in 2003 with the goal of connecting to your network for better job opportunities. 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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.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2015-05-16-LinkedIn-Scaling","author":[{"@type":"Person","name":"LinkedIn Engineering Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">A Brief History of Scaling LinkedIn</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">May 16th, 2015<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><h2 id="a-brief-history-of-scaling-linkedin"><a href="#a-brief-history-of-scaling-linkedin" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>A Brief History of Scaling LinkedIn</h2><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin">https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin</a></p><p><img alt="A Brief History of Scaling LinkedIn" src="https://content.linkedin.com/content/dam/engineering/en-us/blog/migrated/data_centers_pops_0.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#a-brief-history-of-scaling-linkedin">A Brief History of Scaling LinkedIn</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 2.426-2.068l.002-.002m6.452 5.605c-.445-.793-1.032-1.395-1.76-1.808a4.72 4.72 0 0 0-2.362-.618c-.847 0-1.602.211-2.33.635-.728.423-1.315 1.038-1.76 1.841-.445.804-.668 1.749-.668 2.835 0 1.087.221 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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.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2015-05-16-LinkedIn-Scaling","author":[{"@type":"Person","name":"LinkedIn Engineering Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">A Brief History of Scaling LinkedIn</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">May 16th, 2015<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><h2 id="a-brief-history-of-scaling-linkedin"><a href="#a-brief-history-of-scaling-linkedin" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>A Brief History of Scaling LinkedIn</h2><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin">https://engineering.linkedin.com/architecture/brief-history-scaling-linkedin</a></p><p><img alt="A Brief History of Scaling LinkedIn" src="https://content.linkedin.com/content/dam/engineering/en-us/blog/migrated/data_centers_pops_0.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#a-brief-history-of-scaling-linkedin">A Brief History of Scaling LinkedIn</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 2.426-2.068l.002-.002m6.452 5.605c-.445-.793-1.032-1.395-1.76-1.808a4.72 4.72 0 0 0-2.362-.618c-.847 0-1.602.211-2.33.635-.728.423-1.315 1.038-1.76 1.841-.445.804-.668 1.749-.668 2.835 0 1.087.221 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w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Open Sourcing Pinot - Scaling the Wall of Real-Time Analytics</h1><p class="pt-2 text-lg">By: </p><p class="py-2 text-sm">June 10th, 2015<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics">https://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics</a></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" 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w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Open Sourcing Pinot - Scaling the Wall of Real-Time Analytics</h1><p class="pt-2 text-lg">By: </p><p class="py-2 text-sm">June 10th, 2015<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics">https://engineering.linkedin.com/pinot/open-sourcing-pinot-scaling-wall-real-time-analytics</a></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" 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In this article, we discuss how we architected this analytics platform and its robust data pipeline.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2017-09-17-Restaurant-Manager","author":[{"@type":"Person","name":"Uber Data Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Engineering Restaurant Manager - UberEATS Analytics Dashboard</h1><p class="pt-2 text-lg">By: <!-- -->Uber Data Team</p><p class="py-2 text-sm">September 17th, 2017<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://eng.uber.com/restaurant-manager/">https://eng.uber.com/restaurant-manager/</a></p><p><img alt="Engineering Restaurant Manager - UberEATS Analytics Dashboard" src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2017/09/image4-2.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none 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In this article, we discuss how we architected this analytics platform and its robust data pipeline.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2017-09-17-Restaurant-Manager","author":[{"@type":"Person","name":"Uber Data Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Engineering Restaurant Manager - UberEATS Analytics Dashboard</h1><p class="pt-2 text-lg">By: <!-- -->Uber Data Team</p><p class="py-2 text-sm">September 17th, 2017<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://eng.uber.com/restaurant-manager/">https://eng.uber.com/restaurant-manager/</a></p><p><img alt="Engineering Restaurant Manager - UberEATS Analytics Dashboard" src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2017/09/image4-2.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none 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right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">January 9th, 2019<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor">https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor</a></p><p><img alt="Star-tree index - Powering fast aggregations on Pinot" src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav 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right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">January 9th, 2019<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor">https://engineering.linkedin.com/blog/2019/01/introducing-thirdeye--linkedins-business-wide-monitoring-platfor</a></p><p><img alt="Star-tree index - Powering fast aggregations on Pinot" src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav 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efficiently.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2019-06-14-LinkedIn-StarTree","author":[{"@type":"Person","name":"LinkedIn Engineering Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Star-tree index - Powering fast aggregations on Pinot</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">June 14th, 2019<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/blog/2019/06/star-tree-index--powering-fast-aggregations-on-pinot">https://engineering.linkedin.com/blog/2019/06/star-tree-index--powering-fast-aggregations-on-pinot</a></p><p><img alt="Star-tree index - Powering fast aggregations on Pinot" src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 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efficiently.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2019-06-14-LinkedIn-StarTree","author":[{"@type":"Person","name":"LinkedIn Engineering Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Star-tree index - Powering fast aggregations on Pinot</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">June 14th, 2019<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/blog/2019/06/star-tree-index--powering-fast-aggregations-on-pinot">https://engineering.linkedin.com/blog/2019/06/star-tree-index--powering-fast-aggregations-on-pinot</a></p><p><img alt="Star-tree index - Powering fast aggregations on Pinot" src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/06/startree3.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 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audience when factoring in multiple criteria of an advertising campaign.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2019-07-11-LinkedIn-AutoTune","author":[{"@type":"Person","name":"LinkedIn Engineering Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Auto-tuning Pinot real-time consumption</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">July 11th, 2019<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing">https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing</a></p><p><img alt="Bridging batch and stream processing for the Recruiter usage statistics dashboard" src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/07/bridging-1.png"/></p></div></main></div></div><aside class="mt-10 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audience when factoring in multiple criteria of an advertising campaign.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2019-07-11-LinkedIn-AutoTune","author":[{"@type":"Person","name":"LinkedIn Engineering Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Auto-tuning Pinot real-time consumption</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">July 11th, 2019<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing">https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing</a></p><p><img alt="Bridging batch and stream processing for the Recruiter usage statistics dashboard" src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/07/bridging-1.png"/></p></div></main></div></div><aside class="mt-10 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datastore.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-01-15-Pinot-Presto-SQL","author":[{"@type":"Person","name":"Uber Data Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Engineering SQL Support on Apache Pinot at Uber</h1><p class="pt-2 text-lg">By: <!-- -->Uber Data Team</p><p class="py-2 text-sm">January 15th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://eng.uber.com/engineering-sql-support-on-apache-pinot/">Read More at https://eng.uber.com/engineering-sql-support-on-apache-pinot/</a></p><p><img alt="SQL Support on Apache Pinot at Uber" src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/01/Header-SQL-768x329.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t 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datastore.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-01-15-Pinot-Presto-SQL","author":[{"@type":"Person","name":"Uber Data Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Engineering SQL Support on Apache Pinot at Uber</h1><p class="pt-2 text-lg">By: <!-- -->Uber Data Team</p><p class="py-2 text-sm">January 15th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://eng.uber.com/engineering-sql-support-on-apache-pinot/">Read More at https://eng.uber.com/engineering-sql-support-on-apache-pinot/</a></p><p><img alt="SQL Support on Apache Pinot at Uber" src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/01/Header-SQL-768x329.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t 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when factoring in multiple criteria of an advertising campaign.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-02-20-LinkedIn-Thirdeye","author":[{"@type":"Person","name":"LinkedIn Engineering Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Analyzing anomalies with ThirdEye</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">February 20th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye">Read More at https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye</a></p><p><img alt="Analyzing anomalies with ThirdEye" src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/02/datacube-1.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 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4.725 1.157a8.5 8.5 0 0 1 3.34 3.275c.815 1.411 1.222 3.041 1.222 4.888s-.418 3.475-1.255 4.888a8.708 8.708 0 0 1-3.388 3.275c-1.424.772-3.014 1.157-4.774 1.157-1.76 0-3.301-.385-4.691-1.157m7.021-3.421c.729-.402 1.309-1.005 1.744-1.809.435-.803.651-1.781.651-2.933 0-1.715-.451-3.035-1.351-3.958-.902-.924-2.004-1.385-3.307-1.385s-2.395.461-3.275 1.385c-.88.923-1.32 2.243-1.32 3.958 0 1.715.428 3.035 1.287 3.958.858.924 1.938 1.385 3.241 1.385.825 0 1.602-.2 2.33-.603v.002M115.96 21.658v8.734c0 .608.147 1.048.44 1.32.293.271.787.406 1.482.406H120v3.845h-2.867c-3.845 0-5.766-1.868-5.766-5.605v-8.7h-2.15v-3.746h2.15V13l4.595-1v5.912h4.04v3.746h-4.042M20.03 46.757l-5.538-1.385A1.97 1.97 0 0 1 13 43.46v-5.462c0-.841.349-1.601.907-2.146a12.212 12.212 0 0 0 6.975-3.644c2.602-2.731 3.627-6.578 2.882-10.251L21 9h-4V4a1 1 0 0 0-2 0v7a1 1 0 0 1-2 0v-1a1 1 0 0 0-2 0v6.758a4.489 4.489 0 0 1 2.694-.755c2.278.095 4.156 1.934 4.297 4.21a4.501 4.501 0 0 1-6.992 4.029V29a1 1 0 0 1-2 0V7a1 1 0 0 0-2 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when factoring in multiple criteria of an advertising campaign.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-02-20-LinkedIn-Thirdeye","author":[{"@type":"Person","name":"LinkedIn Engineering Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Analyzing anomalies with ThirdEye</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">February 20th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye">Read More at https://engineering.linkedin.com/blog/2020/analyzing-anomalies-with-thirdeye</a></p><p><img alt="Analyzing anomalies with ThirdEye" src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/02/datacube-1.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 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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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7">https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7</a></p><p><img alt="Using Apache Pinot and Kafka to Analyze GitHub Events" src="https://miro.medium.com/max/4728/1*eR64jBH1ZvC3uNfPP56p5g.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of 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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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7">https://medium.com/apache-pinot-developer-blog/using-apache-pinot-and-kafka-to-analyze-github-events-93cdcb57d5f7</a></p><p><img alt="Using Apache Pinot and Kafka to Analyze GitHub Events" src="https://miro.medium.com/max/4728/1*eR64jBH1ZvC3uNfPP56p5g.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of 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alerts","datePublished":"2020-06-25T00:00:00.000Z","dateModified":"2020-06-25T00:00:00.000Z","description":"Focus on using large set intersection cardinality approximations with Apache Pinot and Theta Sketches, which allow us to efficiently figure out the unique size of a targeted audience when factoring in multiple criteria of an advertising campaign.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-06-25-LinkedIn-SmartAlerts","author":[{"@type":"Person","name":"LinkedIn Engineering Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Monitoring business performance data with ThirdEye smart alerts</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">June 25th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts">https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts</a></p><p><img alt="Monitoring business performance data with ThirdEye smart alerts" src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/06/thirdeye_business_performance-3.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 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alerts","datePublished":"2020-06-25T00:00:00.000Z","dateModified":"2020-06-25T00:00:00.000Z","description":"Focus on using large set intersection cardinality approximations with Apache Pinot and Theta Sketches, which allow us to efficiently figure out the unique size of a targeted audience when factoring in multiple criteria of an advertising campaign.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-06-25-LinkedIn-SmartAlerts","author":[{"@type":"Person","name":"LinkedIn Engineering Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Monitoring business performance data with ThirdEye smart alerts</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">June 25th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts">https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts</a></p><p><img alt="Monitoring business performance data with ThirdEye smart alerts" src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/06/thirdeye_business_performance-3.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 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making","datePublished":"2020-06-29T00:00:00.000Z","dateModified":"2020-06-29T00:00:00.000Z","description":"Focus on using large set intersection cardinality approximations with Apache Pinot and Theta Sketches, which allow us to efficiently figure out the unique size of a targeted audience when factoring in multiple criteria of an advertising campaign.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-06-29-LinkedIn-TalentInsight","author":[{"@type":"Person","name":"LinkedIn Engineering Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Building LinkedIn Talent Insights to democratize data-driven decision making</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">June 29th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" 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making","datePublished":"2020-06-29T00:00:00.000Z","dateModified":"2020-06-29T00:00:00.000Z","description":"Focus on using large set intersection cardinality approximations with Apache Pinot and Theta Sketches, which allow us to efficiently figure out the unique size of a targeted audience when factoring in multiple criteria of an advertising campaign.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-06-29-LinkedIn-TalentInsight","author":[{"@type":"Person","name":"LinkedIn Engineering Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Building LinkedIn Talent Insights to democratize data-driven decision making</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">June 29th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" 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dashboard","datePublished":"2020-07-14T00:00:00.000Z","dateModified":"2020-07-14T00:00:00.000Z","description":"Focus on using large set intersection cardinality approximations with Apache Pinot and Theta Sketches, which allow us to efficiently figure out the unique size of a targeted audience when factoring in multiple criteria of an advertising campaign.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-07-14-LinkedIn-BatchRealtime","author":[{"@type":"Person","name":"LinkedIn Engineering Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Bridging batch and stream processing for the Recruiter usage statistics dashboard</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">July 14th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" 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href="https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing">https://engineering.linkedin.com/blog/2020/bridging-batch-and-stream-processing</a></p><p><img alt="Bridging batch and stream processing for the Recruiter usage statistics dashboard" src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2020/07/bridging-1.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap 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as a system is a platform that allows you to integrate your metrics (quantitative information) with events (knowledge or qualitative information) and combine the two so you can distinguish between meaningless anomalies and those ones that matter.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-07-28-DevBlog-AnomalyDetection","author":[{"@type":"Person","name":"Hubert Dulay"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Building a culture around metrics and anomaly detection</h1><p class="pt-2 text-lg">By: <!-- -->Hubert Dulay</p><p class="py-2 text-sm">July 28th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img alt="Building a culture around metrics and anomaly detection" src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none 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as a system is a platform that allows you to integrate your metrics (quantitative information) with events (knowledge or qualitative information) and combine the two so you can distinguish between meaningless anomalies and those ones that matter.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-07-28-DevBlog-AnomalyDetection","author":[{"@type":"Person","name":"Hubert Dulay"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Building a culture around metrics and anomaly detection</h1><p class="pt-2 text-lg">By: <!-- -->Hubert Dulay</p><p class="py-2 text-sm">July 28th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2">https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2</a></p><p><img alt="Building a culture around metrics and anomaly detection" src="https://miro.medium.com/max/1400/0*xYm2ZURZVpyJ1JQ5"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none 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Over a few decades, the Silicon Valley tech giant has helped hundreds of millions of people around the world navigate their careers.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-07-28-DevBlog-DevUpStack","author":[{"@type":"Person","name":"Hubert Dulay"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Moving developers up the stack with Apache Pinot</h1><p class="pt-2 text-lg">By: <!-- -->Hubert Dulay</p><p class="py-2 text-sm">July 28th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img alt="Moving developers up the stack with Apache Pinot" src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 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Over a few decades, the Silicon Valley tech giant has helped hundreds of millions of people around the world navigate their careers.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-07-28-DevBlog-DevUpStack","author":[{"@type":"Person","name":"Hubert Dulay"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Moving developers up the stack with Apache Pinot</h1><p class="pt-2 text-lg">By: <!-- -->Hubert Dulay</p><p class="py-2 text-sm">July 28th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4">https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4</a></p><p><img alt="Moving developers up the stack with Apache Pinot" src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 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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.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-08-08-DevBlog-IngestPlugins","author":[{"@type":"Person","name":"Pinot Dev"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</h1><p class="pt-2 text-lg">By: <!-- -->Pinot Dev</p><p class="py-2 text-sm">August 8th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d">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 alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot" src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none 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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.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-08-08-DevBlog-IngestPlugins","author":[{"@type":"Person","name":"Pinot Dev"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Leverage Plugins to Ingest Parquet Files from S3 in Pinot</h1><p class="pt-2 text-lg">By: <!-- -->Pinot Dev</p><p class="py-2 text-sm">August 8th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d">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 alt="Leverage Plugins to Ingest Parquet Files from S3 in Pinot" src="https://miro.medium.com/max/6000/0*afbs7azGt-GpSVeP"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none 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flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Monitoring Apache Pinot with JMX, Prometheus and Grafana</h1><p class="pt-2 text-lg">By: <!-- -->Pinot Dev</p><p class="py-2 text-sm">August 8th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana" src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none 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4.57 0 0 0 2.362-.652c.728-.435 1.313-1.053 1.76-1.856.445-.804.668-1.76.668-2.867s-.223-2.025-.668-2.818v-.004M62.947 17.912v18.051h-4.562V17.912h4.562m.551-6.079a2.833 2.833 0 1 1-5.666 0 2.833 2.833 0 0 1 5.666 0M82.954 19.687c1.325 1.358 1.988 3.253 1.988 5.685v10.59H80.38v-9.97c0-1.434-.358-2.537-1.075-3.307-.717-.772-1.695-1.157-2.933-1.157-1.239 0-2.254.387-2.982 1.157-.728.772-1.091 1.873-1.091 3.307v9.97h-4.562V17.91h4.562v2.248a6.322 6.322 0 0 1 2.33-1.841c.944-.445 1.981-.669 3.111-.669 2.15 0 3.889.68 5.214 2.037v.002M92.892 35.098c-1.39-.77-2.482-1.861-3.275-3.275-.794-1.411-1.19-3.041-1.19-4.888s.406-3.475 1.221-4.888a8.502 8.502 0 0 1 3.34-3.275c1.412-.772 2.987-1.157 4.725-1.157 1.739 0 3.312.387 4.725 1.157a8.5 8.5 0 0 1 3.34 3.275c.815 1.411 1.222 3.041 1.222 4.888s-.418 3.475-1.255 4.888a8.708 8.708 0 0 1-3.388 3.275c-1.424.772-3.014 1.157-4.774 1.157-1.76 0-3.301-.385-4.691-1.157m7.021-3.421c.729-.402 1.309-1.005 1.744-1.809.435-.803.651-1.781.651-2.933 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flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Monitoring Apache Pinot with JMX, Prometheus and Grafana</h1><p class="pt-2 text-lg">By: <!-- -->Pinot Dev</p><p class="py-2 text-sm">August 8th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5">Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5</a></p><p><img alt="Monitoring Apache Pinot with JMX, Prometheus and Grafana" src="https://miro.medium.com/max/1400/1*5kWginewoWzzQHQoZdPAGQ.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none 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dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Achieving 99th percentile latency SLA using Apache Pinot</h1><p class="pt-2 text-lg">By: <!-- -->Pinot Dev</p><p class="py-2 text-sm">August 8th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img alt="Achieving 99th percentile latency SLA using Apache Pinot" src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl 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dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Achieving 99th percentile latency SLA using Apache Pinot</h1><p class="pt-2 text-lg">By: <!-- -->Pinot Dev</p><p class="py-2 text-sm">August 8th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff">Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff</a></p><p><img alt="Achieving 99th percentile latency SLA using Apache Pinot" src="https://miro.medium.com/max/1140/0*VCPyrmNB2PteCmnC"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl 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fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Utilize UDFs to Supercharge Queries in Apache Pinot</h1><p class="pt-2 text-lg">By: <!-- -->Pinot Dev</p><p class="py-2 text-sm">August 8th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://pinot.apache.org/">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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img alt="Utilize UDFs to Supercharge Queries in Apache Pinot" src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" 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fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Utilize UDFs to Supercharge Queries in Apache Pinot</h1><p class="pt-2 text-lg">By: <!-- -->Pinot Dev</p><p class="py-2 text-sm">August 8th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://pinot.apache.org/">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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1">Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1</a></p><p><img alt="Utilize UDFs to Supercharge Queries in Apache Pinot" src="https://miro.medium.com/max/10368/0*VtswFI-HcaXyyjhK"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" 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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 target="_blank" rel="noopener noreferrer" href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img alt="Deep Analysis of Russian Twitter Trolls" src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 2.426-2.068l.002-.002m6.452 5.605c-.445-.793-1.032-1.395-1.76-1.808a4.72 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4.888s-.418 3.475-1.255 4.888a8.708 8.708 0 0 1-3.388 3.275c-1.424.772-3.014 1.157-4.774 1.157-1.76 0-3.301-.385-4.691-1.157m7.021-3.421c.729-.402 1.309-1.005 1.744-1.809.435-.803.651-1.781.651-2.933 0-1.715-.451-3.035-1.351-3.958-.902-.924-2.004-1.385-3.307-1.385s-2.395.461-3.275 1.385c-.88.923-1.32 2.243-1.32 3.958 0 1.715.428 3.035 1.287 3.958.858.924 1.938 1.385 3.241 1.385.825 0 1.602-.2 2.33-.603v.002M115.96 21.658v8.734c0 .608.147 1.048.44 1.32.293.271.787.406 1.482.406H120v3.845h-2.867c-3.845 0-5.766-1.868-5.766-5.605v-8.7h-2.15v-3.746h2.15V13l4.595-1v5.912h4.04v3.746h-4.042M20.03 46.757l-5.538-1.385A1.97 1.97 0 0 1 13 43.46v-5.462c0-.841.349-1.601.907-2.146a12.212 12.212 0 0 0 6.975-3.644c2.602-2.731 3.627-6.578 2.882-10.251L21 9h-4V4a1 1 0 0 0-2 0v7a1 1 0 0 1-2 0v-1a1 1 0 0 0-2 0v6.758a4.489 4.489 0 0 1 2.694-.755c2.278.095 4.156 1.934 4.297 4.21a4.501 4.501 0 0 1-6.992 4.029V29a1 1 0 0 1-2 0V7a1 1 0 0 0-2 0v2h-4L.237 21.957c-.745 3.675.279 7.52 2.882 10.251a12.202 12.202 0 0 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hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Deep Analysis of Russian Twitter Trolls</h1><p class="pt-2 text-lg">By: <!-- -->Hubert Dulay</p><p class="py-2 text-sm">October 16th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843">Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843</a></p><p><img alt="Deep Analysis of Russian Twitter Trolls" src="https://miro.medium.com/max/4320/0*iqUTy0GkLFTcSYlR.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 2.426-2.068l.002-.002m6.452 5.605c-.445-.793-1.032-1.395-1.76-1.808a4.72 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Explain how uber scaled from a few use cases to a multi-cluster powering hundreds of use cases for querying terabyte-scale data with millisecond latencies.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-10-20-Uber-Operating","author":[{"@type":"Person","name":"Uber Data Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Operating Apache Pinot at Uber Scale</h1><p class="pt-2 text-lg">By: <!-- -->Uber Data Team</p><p class="py-2 text-sm">October 20th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://eng.uber.com/operating-apache-pinot/">https://eng.uber.com/operating-apache-pinot/</a></p><p><img alt="Operating Apache Pinot at Uber Scale" src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/10/1224-5.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none 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Explain how uber scaled from a few use cases to a multi-cluster powering hundreds of use cases for querying terabyte-scale data with millisecond latencies.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-10-20-Uber-Operating","author":[{"@type":"Person","name":"Uber Data Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Operating Apache Pinot at Uber Scale</h1><p class="pt-2 text-lg">By: <!-- -->Uber Data Team</p><p class="py-2 text-sm">October 20th, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://eng.uber.com/operating-apache-pinot/">https://eng.uber.com/operating-apache-pinot/</a></p><p><img alt="Operating Apache Pinot at Uber Scale" src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2020/10/1224-5.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none 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methods.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-12-01-LinkedIn-Lamda","author":[{"@type":"Person","name":"LinkedIn Engineering Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">From Lambda to Lambda-less Lessons learned</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">December 1st, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture">https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture</a></p><p><img alt="From Lambda to Lambda-less Lessons learned" src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/03/pinotincubator2.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 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methods.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2020-12-01-LinkedIn-Lamda","author":[{"@type":"Person","name":"LinkedIn Engineering Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">From Lambda to Lambda-less Lessons learned</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">December 1st, 2020<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture">https://engineering.linkedin.com/blog/2020/lambda-to-lambda-less-architecture</a></p><p><img alt="From Lambda to Lambda-less Lessons learned" src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2019/03/pinotincubator2.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 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Over a few decades, the Silicon Valley tech giant has helped hundreds of millions of people around the world navigate their careers.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2021-01-08-DevBlog-DebeziumCDC","author":[{"@type":"Person","name":"Hubert Dulay"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Change Data Analysis with Debezium and Apache Pinot</h1><p class="pt-2 text-lg">By: <!-- -->Hubert Dulay</p><p class="py-2 text-sm">January 8th, 2021<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img alt="Change Data Analysis with Debezium and Apache Pinot" src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png"/></p></div></main></div></div><aside class="mt-10 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Over a few decades, the Silicon Valley tech giant has helped hundreds of millions of people around the world navigate their careers.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2021-01-08-DevBlog-DebeziumCDC","author":[{"@type":"Person","name":"Hubert Dulay"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Change Data Analysis with Debezium and Apache Pinot</h1><p class="pt-2 text-lg">By: <!-- -->Hubert Dulay</p><p class="py-2 text-sm">January 8th, 2021<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7">Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7</a></p><p><img alt="Change Data Analysis with Debezium and Apache Pinot" src="https://miro.medium.com/max/1400/1*dnSikeGxTrfrF95niX16PA.png"/></p></div></main></div></div><aside class="mt-10 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dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Real-time Analytics with Presto and Apache Pinot</h1><p class="pt-2 text-lg">By: <!-- -->Pinot Dev</p><p class="py-2 text-sm">February 2nd, 2021<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a target="_blank" rel="noopener noreferrer" href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img alt="Real-time Analytics with Presto and Apache Pinot" src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 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dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Real-time Analytics with Presto and Apache Pinot</h1><p class="pt-2 text-lg">By: <!-- -->Pinot Dev</p><p class="py-2 text-sm">February 2nd, 2021<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/">Read Part 1 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-i/</a></p><p><a target="_blank" rel="noopener noreferrer" href="https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/">Read Part 2 at https://www.startree.ai/blogs/real-time-analytics-with-presto-and-apache-pinot-part-ii/</a></p><p><img alt="Real-time Analytics with Presto and Apache Pinot" src="https://miro.medium.com/max/1400/0*hJc6aV9aBJaKyXcx"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 2.426-2.068l.002-.002m6.452 5.605c-.445-.793-1.032-1.395-1.76-1.808a4.72 4.72 0 0 0-2.362-.618c-.847 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fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Introduction to Upserts in Apache Pinot</h1><p class="pt-2 text-lg">By: <!-- -->Hubert Dulay</p><p class="py-2 text-sm">April 8th, 2021<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93">https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93</a></p><p><img alt="Introduction to Upserts in Apache Pinot" src="https://miro.medium.com/max/1400/0*So3GjHjLY7DJAiaP"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none 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4.57 0 0 0 2.362-.652c.728-.435 1.313-1.053 1.76-1.856.445-.804.668-1.76.668-2.867s-.223-2.025-.668-2.818v-.004M62.947 17.912v18.051h-4.562V17.912h4.562m.551-6.079a2.833 2.833 0 1 1-5.666 0 2.833 2.833 0 0 1 5.666 0M82.954 19.687c1.325 1.358 1.988 3.253 1.988 5.685v10.59H80.38v-9.97c0-1.434-.358-2.537-1.075-3.307-.717-.772-1.695-1.157-2.933-1.157-1.239 0-2.254.387-2.982 1.157-.728.772-1.091 1.873-1.091 3.307v9.97h-4.562V17.91h4.562v2.248a6.322 6.322 0 0 1 2.33-1.841c.944-.445 1.981-.669 3.111-.669 2.15 0 3.889.68 5.214 2.037v.002M92.892 35.098c-1.39-.77-2.482-1.861-3.275-3.275-.794-1.411-1.19-3.041-1.19-4.888s.406-3.475 1.221-4.888a8.502 8.502 0 0 1 3.34-3.275c1.412-.772 2.987-1.157 4.725-1.157 1.739 0 3.312.387 4.725 1.157a8.5 8.5 0 0 1 3.34 3.275c.815 1.411 1.222 3.041 1.222 4.888s-.418 3.475-1.255 4.888a8.708 8.708 0 0 1-3.388 3.275c-1.424.772-3.014 1.157-4.774 1.157-1.76 0-3.301-.385-4.691-1.157m7.021-3.421c.729-.402 1.309-1.005 1.744-1.809.435-.803.651-1.781.651-2.933 0-1.715-.451-3.035-1.351-3.958-.902-.924-2.004-1.385-3.307-1.385s-2.395.461-3.275 1.385c-.88.923-1.32 2.243-1.32 3.958 0 1.715.428 3.035 1.287 3.958.858.924 1.938 1.385 3.241 1.385.825 0 1.602-.2 2.33-.603v.002M115.96 21.658v8.734c0 .608.147 1.048.44 1.32.293.271.787.406 1.482.406H120v3.845h-2.867c-3.845 0-5.766-1.868-5.766-5.605v-8.7h-2.15v-3.746h2.15V13l4.595-1v5.912h4.04v3.746h-4.042M20.03 46.757l-5.538-1.385A1.97 1.97 0 0 1 13 43.46v-5.462c0-.841.349-1.601.907-2.146a12.212 12.212 0 0 0 6.975-3.644c2.602-2.731 3.627-6.578 2.882-10.251L21 9h-4V4a1 1 0 0 0-2 0v7a1 1 0 0 1-2 0v-1a1 1 0 0 0-2 0v6.758a4.489 4.489 0 0 1 2.694-.755c2.278.095 4.156 1.934 4.297 4.21a4.501 4.501 0 0 1-6.992 4.029V29a1 1 0 0 1-2 0V7a1 1 0 0 0-2 0v2h-4L.237 21.957c-.745 3.675.279 7.52 2.882 10.251a12.202 12.202 0 0 0 6.975 3.644c.558.545.907 1.305.907 2.146V43.4c0 .938-.639 1.757-1.55 1.985l-5.48 1.37c-.57.143-.97.655-.97 1.243h18c0-.588-.4-1.1-.97-1.243v.002"></path><path d="M13.5 23a2.5 2.5 0 1 0 0-5 2.5 2.5 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fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Introduction to Upserts in Apache Pinot</h1><p class="pt-2 text-lg">By: <!-- -->Hubert Dulay</p><p class="py-2 text-sm">April 8th, 2021<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93">https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93</a></p><p><img alt="Introduction to Upserts in Apache Pinot" src="https://miro.medium.com/max/1400/0*So3GjHjLY7DJAiaP"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none 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1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">April 16th, 2021<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches">https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches</a></p><p><img alt="From Lambda to Lambda-less Lessons learned" src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/04/thetasketches2.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg 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1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">April 16th, 2021<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches">https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches</a></p><p><img alt="From Lambda to Lambda-less Lessons learned" src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/04/thetasketches2.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg 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dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Deploying Apache Pinot at a Large Retail Chain</h1><p class="pt-2 text-lg">By: <!-- -->Pinot Dev</p><p class="py-2 text-sm">April 27th, 2021<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38">Read More at https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38</a></p><p><img alt="Deploying Apache Pinot at a Large Retail Chain" src="https://miro.medium.com/max/1400/1*EtqD0vTPEe569jybXCt69w.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap 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dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Deploying Apache Pinot at a Large Retail Chain</h1><p class="pt-2 text-lg">By: <!-- -->Pinot Dev</p><p class="py-2 text-sm">April 27th, 2021<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38">Read More at https://medium.com/apache-pinot-developer-blog/deploying-apache-pinot-at-a-large-retail-chain-42aed2921a38</a></p><p><img alt="Deploying Apache Pinot at a Large Retail Chain" src="https://miro.medium.com/max/1400/1*EtqD0vTPEe569jybXCt69w.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap 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merchants.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2021-04-29-Uber-Charon","author":[{"@type":"Person","name":"Uber Data Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Automating Merchant Live Monitoring with Real-Time Analytics - Charon</h1><p class="pt-2 text-lg">By: <!-- -->Uber Data Team</p><p class="py-2 text-sm">April 29th, 2021<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://eng.uber.com/charon/">https://eng.uber.com/charon/</a></p><p><img alt="Operating Apache Pinot at Uber Scale" src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2021/04/deliverDoor@3x-768x263.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 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merchants.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2021-04-29-Uber-Charon","author":[{"@type":"Person","name":"Uber Data Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Automating Merchant Live Monitoring with Real-Time Analytics - Charon</h1><p class="pt-2 text-lg">By: <!-- -->Uber Data Team</p><p class="py-2 text-sm">April 29th, 2021<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://eng.uber.com/charon/">https://eng.uber.com/charon/</a></p><p><img alt="Operating Apache Pinot at Uber Scale" src="https://1fykyq3mdn5r21tpna3wkdyi-wpengine.netdna-ssl.com/wp-content/uploads/2021/04/deliverDoor@3x-768x263.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 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class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Introduction to Geospatial Queries in Apache Pinot</h1><p class="pt-2 text-lg">By: <!-- -->Hubert Dulay</p><p class="py-2 text-sm">June 13th, 2021<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9">https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9</a></p><p><img alt="Introduction to Geospatial Queries in Apache Pinot" src="https://miro.medium.com/max/1400/0*1xrDSs9lLZ5dD3zK"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none 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class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Introduction to Geospatial Queries in Apache Pinot</h1><p class="pt-2 text-lg">By: <!-- -->Hubert Dulay</p><p class="py-2 text-sm">June 13th, 2021<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9">https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9</a></p><p><img alt="Introduction to Geospatial Queries in Apache Pinot" src="https://miro.medium.com/max/1400/0*1xrDSs9lLZ5dD3zK"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none 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4.57 0 0 0 2.362-.652c.728-.435 1.313-1.053 1.76-1.856.445-.804.668-1.76.668-2.867s-.223-2.025-.668-2.818v-.004M62.947 17.912v18.051h-4.562V17.912h4.562m.551-6.079a2.833 2.833 0 1 1-5.666 0 2.833 2.833 0 0 1 5.666 0M82.954 19.687c1.325 1.358 1.988 3.253 1.988 5.685v10.59H80.38v-9.97c0-1.434-.358-2.537-1.075-3.307-.717-.772-1.695-1.157-2.933-1.157-1.239 0-2.254.387-2.982 1.157-.728.772-1.091 1.873-1.091 3.307v9.97h-4.562V17.91h4.562v2.248a6.322 6.322 0 0 1 2.33-1.841c.944-.445 1.981-.669 3.111-.669 2.15 0 3.889.68 5.214 2.037v.002M92.892 35.098c-1.39-.77-2.482-1.861-3.275-3.275-.794-1.411-1.19-3.041-1.19-4.888s.406-3.475 1.221-4.888a8.502 8.502 0 0 1 3.34-3.275c1.412-.772 2.987-1.157 4.725-1.157 1.739 0 3.312.387 4.725 1.157a8.5 8.5 0 0 1 3.34 3.275c.815 1.411 1.222 3.041 1.222 4.888s-.418 3.475-1.255 4.888a8.708 8.708 0 0 1-3.388 3.275c-1.424.772-3.014 1.157-4.774 1.157-1.76 0-3.301-.385-4.691-1.157m7.021-3.421c.729-.402 1.309-1.005 1.744-1.809.435-.803.651-1.781.651-2.933 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enables the long term success of their employees","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2021-06-16-LinkedIn-TextAnalytics","author":[{"@type":"Person","name":"LinkedIn Engineering Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Text analytics on LinkedIn Talent Insights using Apache Pinot</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">June 16th, 2021<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot">Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot</a></p><p><img alt="Text analytics on LinkedIn Talent Insights using Apache Pinot" src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/06/ltipinot6.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" 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enables the long term success of their employees","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2021-06-16-LinkedIn-TextAnalytics","author":[{"@type":"Person","name":"LinkedIn Engineering Team"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Text analytics on LinkedIn Talent Insights using Apache Pinot</h1><p class="pt-2 text-lg">By: <!-- -->LinkedIn Engineering Team</p><p class="py-2 text-sm">June 16th, 2021<!-- --> • <!-- -->1 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><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 target="_blank" rel="noopener noreferrer" href="https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot">Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot</a></p><p><img alt="Text analytics on LinkedIn Talent Insights using Apache Pinot" src="https://content.linkedin.com/content/dam/engineering/site-assets/images/blog/posts/2021/06/ltipinot6.png"/></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" 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diff --git a/blog/2022/04/04/Announcing-Apache-Pinot-0-10.html b/blog/2022/04/04/Announcing-Apache-Pinot-0-10.html
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@@ -19,4 +19,4 @@
</span><span class="code-line"> <span class="token parameter variable">-p</span> <span class="token number">9000</span>:9000 <span class="token punctuation">\</span>
</span><span class="code-line"> apachepinot/pinot:0.10.0 QuickStart <span class="token punctuation">\</span>
</span><span class="code-line"> <span class="token parameter variable">-type</span> empty
-</span></code></pre></div><p>You can then ingest your own dataset without needing to worry about spinning up each of the Pinot components individually.</p><h2 id="data-ingestion"><a href="#data-ingestion" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Data Ingestion</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 id="other-changes"><a href="#other-changes" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Other changes</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 target="_blank" rel="noopener noreferrer" href="https://dev.startree.ai/docs/pinot/recipes/upserts-partial">partial upsert feature</a>.</li><li>Jeff Moszuti added support for the <code>TIMESTAMP</code> data type in the <a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/operators/configuration-recommendation-engine">configuration recommendation engine</a>.</li></ul><h2 id="dependency-updates"><a href="#dependency-updates" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Dependency updates</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 id="resources"><a href="#resources" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Resources</h2><p>If you want to try out Apache Pinot, the following resources will help you get started:</p><ul><li>Download page: <a target="_blank" rel="noopener noreferrer" href="https://pinot.apache.org/download/">https://pinot.apache.org/download/</a></li><li>Getting started: <a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/getting-started">https://docs.pinot.apache.org/getting-started</a></li><li>Apache Pinot Recipes: <a target="_blank" rel="noopener noreferrer" href="https://dev.startree.ai/docs/pinot/recipes/">https://dev.startree.ai/docs/pinot/recipes/</a></li><li>Join our Slack channel: <a target="_blank" rel="noopener noreferrer" href="https://communityinviter.com/apps/apache-pinot/apache-pinot">https://communityinviter.com/apps/apache-pinot/apache-pinot</a></li><li>See our upcoming events: <a target="_blank" rel="noopener noreferrer" href="https://www.meetup.com/apache-pinot">https://www.meetup.com/apache-pinot</a></li><li>Follow us on Twitter: <a target="_blank" rel="noopener noreferrer" href="https://twitter.com/startreedata">https://twitter.com/startreedata</a></li><li>Subscribe to our YouTube channel: <a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/c/StarTree">https://www.youtube.com/c/StarTree</a></li></ul></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#query-plans">Query Plans</a></li><li class="text-sm font-normal leading-tight"><a href="#filter-clauses-for-aggregates">FILTER Clauses for Aggregates</a></li><li class="text-sm font-normal leading-tight"><a href="#greatest-and-least">greatest and least</a></li><li class="text-sm font-normal leading-tight"><a href="#distinctcountsmarthll">DistinctCountSmartHLL</a></li><li class="text-sm font-normal leading-tight"><a href="#ui-updates">UI updates</a></li><li class="text-sm font-normal leading-tight"><a href="#realtimetooffline-task">RealTimeToOffline Task</a></li><li class="text-sm font-normal leading-tight"><a href="#empty-quickstart">Empty QuickStart</a></li><li class="text-sm font-normal leading-tight"><a href="#data-ingestion">Data Ingestion</a></li><li class="text-sm font-normal leading-tight"><a href="#other-changes">Other changes</a></li><li class="text-sm font-normal leading-tight"><a href="#dependency-updates">Dependency updates</a></li><li class="text-sm font-normal leading-tight"><a href="#resources">Resources</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 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Apache Pinot is a real-time distributed datastore designed to answer OLAP queries with high throughput and low latency.\"}],[\"$\",\"p\",null,{\"children\":[\"This release is cut from commit \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/apache/pinot/commit/fd9c58a11ed16d27109baefcee138eea30132ad3\",\"children\":\"fd9c58a11ed16d27109baefcee138eea30132ad3\"}],\". 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\",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"AS\"}],\" alHomeRuns\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\" baseballStats\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"If we run this query, we'll see the following output:\"}],[\"$\",\"div\",null,{\"className\":\"w-full 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keyword\",\"children\":\"FROM\"}],\" baseballStats\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\" league \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'NL'\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"AND\"}],\" teamID \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'SFN'\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"GROUP\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" playerID\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"LIMIT\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"5\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"If we run this query, we'll see the following output:\"}],[\"$\",\"div\",null,{\"className\":\"w-full overflow-x-auto\",\"children\":[\"$\",\"table\",null,{\"children\":[[\"$\",\"thead\",null,{\"children\":[\"$\",\"tr\",null,{\"children\":[[\"$\",\"th\",null,{\"children\":\"playerID\"}],[\"$\",\"th\",null,{\"children\":\"homeRuns\"}],[\"$\",\"th\",null,{\"children\":\"hits\"}]]}]}],[\"$\",\"tbody\",null,{\"children\":[[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"ramirju01\"}],[\"$\",\"td\",null,{\"children\":\"0\"}],[\"$\",\"td\",null,{\"children\":\"5\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"milneed01\"}],[\"$\",\"td\",null,{\"children\":\"4\"}],[\"$\",\"td\",null,{\"children\":\"54\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"testani01\"}],[\"$\",\"td\",null,{\"children\":\"0\"}],[\"$\",\"td\",null,{\"children\":\"5\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"shawbo01\"}],[\"$\",\"td\",null,{\"children\":\"0\"}],[\"$\",\"td\",null,{\"children\":\"8\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"vogelry01\"}],[\"$\",\"td\",null,{\"children\":\"0\"}],[\"$\",\"td\",null,{\"children\":\"12\"}]]}]]}]]}]}],[\"$\",\"h2\",null,{\"id\":\"distinctcountsmarthll\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#distinctcountsmarthll\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"DistinctCountSmartHLL\"]}],[\"$\",\"p\",null,{\"children\":[\"Xiaotian (Jackie) Jiang added the \",[\"$\",\"code\",null,{\"children\":\"DistinctCountSmartHLL\"}],\" 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:\"]}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\" DISTINCTCOUNTSMARTHLL\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"homeRuns\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'hllLog2m=8;hllConversionThreshold=10'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\" baseballStats\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"If we run this query, we'll see the following output:\"}],[\"$\",\"div\",null,{\"className\":\"w-full overflow-x-auto\",\"children\":[\"$\",\"table\",null,{\"children\":[[\"$\",\"thead\",null,{\"children\":[\"$\",\"tr\",null,{\"children\":[\"$\",\"th\",null,{\"children\":\"distinctcountsmarthll(homeRuns)\"}]}]}],[\"$\",\"tbody\",null,{\"children\":[\"$\",\"tr\",null,{\"children\":[\"$\",\"td\",null,{\"children\":\"66\"}]}]}]]}]}],[\"$\",\"h2\",null,{\"id\":\"ui-updates\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#ui-updates\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"UI updates\"]}],[\"$\",\"p\",null,{\"children\":\"There were also a bunch of updates to the Pinot Data Explorer, by Sanket Shah and Johan Adami.\"}],[\"$\",\"p\",null,{\"children\":\"The display of reported size and estimated size is now in a human readable format:\"}],[\"$\",\"div\",null,{\"children\":[\"$\",\"$L14\",null,{\"alt\":\"Human readable sizes\",\"src\":\"/static/images/blogs/announcing-apache-pinot-0-10/human-readable-sizes.png\",\"width\":\"1055\",\"height\":\"115\"}]}],[\"$\",\"p\",null,{\"children\":\"Fixes for the following issues:\"}],[\"$\",\"ul\",null,{\"children\":[\"$\",\"li\",null,{\"children\":\"Error messages weren't showing on the UI when an invalid operation is attempted:\"}]}],[\"$\",\"div\",null,{\"children\":[\"$\",\"$L14\",null,{\"alt\":\"A backwards incompatible attempted schema change\",\"src\":\"/static/images/blogs/announcing-apache-pinot-0-10/backwards-incompatible.png\",\"width\":\"615\",\"height\":\"87\"}]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Query console goes blank on syntax error.\"}],[\"$\",\"li\",null,{\"children\":\"Query console cannot show query result when multiple columns have the same name.\"}],[\"$\",\"li\",null,{\"children\":[\"Adding extra fields after \",[\"$\",\"code\",null,{\"children\":\"SELECT *\"}],\" would throw a NullPointerException.\"]}],[\"$\",\"li\",null,{\"children\":[\"Some queries were returning \",[\"$\",\"code\",null,{\"children\":\"--\"}],\" instead of \",[\"$\",\"code\",null,{\"children\":\"0\"}],\".\"]}],[\"$\",\"li\",null,{\"children\":\"Query console couldn't show the query result if multiple columns had the same name.\"}],[\"$\",\"li\",null,{\"children\":\"Pinot Dashboard tenant view showing the incorrect amount of servers and brokers.\"}]]}],[\"$\",\"h2\",null,{\"id\":\"realtimetooffline-task\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#realtimetooffline-task\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"RealTimeToOffline Task\"]}],[\"$\",\"p\",null,{\"children\":[\"Xiaotian (Jackie) Jiang made some fixes to the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://dev.startree.ai/docs/pinot/recipes/real-time-offline-job\",\"children\":\"RealTimeToOffline job\"}],\" to handle time gaps and proceed to the next time window when no segment matches the current one.\"]}],[\"$\",\"h2\",null,{\"id\":\"empty-quickstart\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#empty-quickstart\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Empty QuickStart\"]}],[\"$\",\"p\",null,{\"children\":\"bastani added an empty QuickStart command, which lets you quickly spin up an empty Pinot cluster:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight 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+</span></code></pre></div><p>You can then ingest your own dataset without needing to worry about spinning up each of the Pinot components individually.</p><h2 id="data-ingestion"><a href="#data-ingestion" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Data Ingestion</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 id="other-changes"><a href="#other-changes" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Other changes</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 target="_blank" rel="noopener noreferrer" href="https://dev.startree.ai/docs/pinot/recipes/upserts-partial">partial upsert feature</a>.</li><li>Jeff Moszuti added support for the <code>TIMESTAMP</code> data type in the <a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/operators/configuration-recommendation-engine">configuration recommendation engine</a>.</li></ul><h2 id="dependency-updates"><a href="#dependency-updates" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Dependency updates</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 id="resources"><a href="#resources" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Resources</h2><p>If you want to try out Apache Pinot, the following resources will help you get started:</p><ul><li>Download page: <a target="_blank" rel="noopener noreferrer" href="https://pinot.apache.org/download/">https://pinot.apache.org/download/</a></li><li>Getting started: <a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/getting-started">https://docs.pinot.apache.org/getting-started</a></li><li>Apache Pinot Recipes: <a target="_blank" rel="noopener noreferrer" href="https://dev.startree.ai/docs/pinot/recipes/">https://dev.startree.ai/docs/pinot/recipes/</a></li><li>Join our Slack channel: <a target="_blank" rel="noopener noreferrer" href="https://communityinviter.com/apps/apache-pinot/apache-pinot">https://communityinviter.com/apps/apache-pinot/apache-pinot</a></li><li>See our upcoming events: <a target="_blank" rel="noopener noreferrer" href="https://www.meetup.com/apache-pinot">https://www.meetup.com/apache-pinot</a></li><li>Follow us on Twitter: <a target="_blank" rel="noopener noreferrer" href="https://twitter.com/startreedata">https://twitter.com/startreedata</a></li><li>Subscribe to our YouTube channel: <a target="_blank" rel="noopener noreferrer" href="https://www.youtube.com/c/StarTree">https://www.youtube.com/c/StarTree</a></li></ul></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#query-plans">Query Plans</a></li><li class="text-sm font-normal leading-tight"><a href="#filter-clauses-for-aggregates">FILTER Clauses for Aggregates</a></li><li class="text-sm font-normal leading-tight"><a href="#greatest-and-least">greatest and least</a></li><li class="text-sm font-normal leading-tight"><a href="#distinctcountsmarthll">DistinctCountSmartHLL</a></li><li class="text-sm font-normal leading-tight"><a href="#ui-updates">UI updates</a></li><li class="text-sm font-normal leading-tight"><a href="#realtimetooffline-task">RealTimeToOffline Task</a></li><li class="text-sm font-normal leading-tight"><a href="#empty-quickstart">Empty QuickStart</a></li><li class="text-sm font-normal 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keyword\",\"children\":\"LIMIT\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"5\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"If we run this query, we'll see the following output:\"}],[\"$\",\"div\",null,{\"className\":\"w-full overflow-x-auto\",\"children\":[\"$\",\"table\",null,{\"children\":[[\"$\",\"thead\",null,{\"children\":[\"$\",\"tr\",null,{\"children\":[[\"$\",\"th\",null,{\"children\":\"playerID\"}],[\"$\",\"th\",null,{\"children\":\"homeRuns\"}],[\"$\",\"th\",null,{\"children\":\"hits\"}]]}]}],[\"$\",\"tbody\",null,{\"children\":[[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"ramirju01\"}],[\"$\",\"td\",null,{\"children\":\"0\"}],[\"$\",\"td\",null,{\"children\":\"5\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"milneed01\"}],[\"$\",\"td\",null,{\"children\":\"4\"}],[\"$\",\"td\",null,{\"children\":\"54\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"testani01\"}],[\"$\",\"td\",null,{\"children\":\"0\"}],[\"$\",\"td\",null,{\"children\":\"5\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"shawbo01\"}],[\"$\",\"td\",null,{\"children\":\"0\"}],[\"$\",\"td\",null,{\"children\":\"8\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"vogelry01\"}],[\"$\",\"td\",null,{\"children\":\"0\"}],[\"$\",\"td\",null,{\"children\":\"12\"}]]}]]}]]}]}],[\"$\",\"h2\",null,{\"id\":\"distinctcountsmarthll\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#distinctcountsmarthll\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"DistinctCountSmartHLL\"]}],[\"$\",\"p\",null,{\"children\":[\"Xiaotian (Jackie) Jiang added the \",[\"$\",\"code\",null,{\"children\":\"DistinctCountSmartHLL\"}],\" aggregation function, which automatically converts the Set to HyperLogLog if the set size grows too big to protect the servers from running out of memory:\"]}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\" DISTINCTCOUNTSMARTHLL\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"homeRuns\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'hllLog2m=8;hllConversionThreshold=10'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\" baseballStats\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"If we run this query, we'll see the following output:\"}],[\"$\",\"div\",null,{\"className\":\"w-full overflow-x-auto\",\"children\":[\"$\",\"table\",null,{\"children\":[[\"$\",\"thead\",null,{\"children\":[\"$\",\"tr\",null,{\"children\":[\"$\",\"th\",null,{\"children\":\"distinctcountsmarthll(homeRuns)\"}]}]}],[\"$\",\"tbody\",null,{\"children\":[\"$\",\"tr\",null,{\"children\":[\"$\",\"td\",null,{\"children\":\"66\"}]}]}]]}]}],[\"$\",\"h2\",null,{\"id\":\"ui-updates\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#ui-updates\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"UI updates\"]}],[\"$\",\"p\",null,{\"children\":\"There were also a bunch of updates to the Pinot Data Explorer, by Sanket Shah and Johan Adami.\"}],[\"$\",\"p\",null,{\"children\":\"The display of reported size and estimated size is now in a human readable format:\"}],[\"$\",\"div\",null,{\"children\":[\"$\",\"$L14\",null,{\"alt\":\"Human readable sizes\",\"src\":\"/static/images/blogs/announcing-apache-pinot-0-10/human-readable-sizes.png\",\"width\":\"1055\",\"height\":\"115\"}]}],[\"$\",\"p\",null,{\"children\":\"Fixes for the following issues:\"}],[\"$\",\"ul\",null,{\"children\":[\"$\",\"li\",null,{\"children\":\"Error messages weren't showing on the UI when an invalid operation is attempted:\"}]}],[\"$\",\"div\",null,{\"children\":[\"$\",\"$L14\",null,{\"alt\":\"A backwards incompatible attempted schema change\",\"src\":\"/static/images/blogs/announcing-apache-pinot-0-10/backwards-incompatible.png\",\"width\":\"615\",\"height\":\"87\"}]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Query console goes blank on syntax error.\"}],[\"$\",\"li\",null,{\"children\":\"Query console cannot show query result when multiple columns have the same name.\"}],[\"$\",\"li\",null,{\"children\":[\"Adding extra fields after \",[\"$\",\"code\",null,{\"children\":\"SELECT *\"}],\" would throw a NullPointerException.\"]}],[\"$\",\"li\",null,{\"children\":[\"Some queries were returning \",[\"$\",\"code\",null,{\"children\":\"--\"}],\" instead of \",[\"$\",\"code\",null,{\"children\":\"0\"}],\".\"]}],[\"$\",\"li\",null,{\"children\":\"Query console couldn't show the query result if multiple columns had the same name.\"}],[\"$\",\"li\",null,{\"children\":\"Pinot Dashboard tenant view showing the incorrect amount of servers and brokers.\"}]]}],[\"$\",\"h2\",null,{\"id\":\"realtimetooffline-task\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#realtimetooffline-task\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"RealTimeToOffline Task\"]}],[\"$\",\"p\",null,{\"children\":[\"Xiaotian (Jackie) Jiang made some fixes to the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://dev.startree.ai/docs/pinot/recipes/real-time-offline-job\",\"children\":\"RealTimeToOffline job\"}],\" to handle time gaps and proceed to the next time window when no segment matches the current one.\"]}],[\"$\",\"h2\",null,{\"id\":\"empty-quickstart\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#empty-quickstart\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Empty QuickStart\"]}],[\"$\",\"p\",null,{\"children\":\"bastani added an empty QuickStart command, which lets you quickly spin up an empty Pinot cluster:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-p\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"8000\"}],\":8000 \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-p\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"9000\"}],\":9000 \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.10.0 QuickStart \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-type\"}],\" empty\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"You can then ingest your own dataset without needing to worry about spinning up each of the Pinot components individually.\"}],[\"$\",\"h2\",null,{\"id\":\"data-ingestion\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#data-ingestion\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Data Ingestion\"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":[\"$\",\"p\",null,{\"children\":\"Richard Startin fixed some issues with real-time ingestion where consumption of messages would stop if a bad batch of messages was consumed from Kafka.\"}]}],[\"$\",\"li\",null,{\"children\":[\"$\",\"p\",null,{\"children\":\"Mohemmad Zaid Khan added the BoundedColumnValue partition function, which partitions segments based on column values.\"}]}],[\"$\",\"li\",null,{\"children\":[\"$\",\"p\",null,{\"children\":\"Xiaobing Li added the fixed name segment generator, which can be used when you want to replace a specific existing segment.\"}]}]]}],[\"$\",\"h2\",null,{\"id\":\"other-changes\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#other-changes\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Other changes\"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Richard Startin set LZ4 compression as the default for all metrics fields.\"}],[\"$\",\"li\",null,{\"children\":[\"Mark Needham added the \",[\"$\",\"code\",null,{\"children\":\"ST_Within\"}],\" geospatial function.\"]}],[\"$\",\"li\",null,{\"children\":\"Rong Rong fixed a bug where query stats wouldn't show if there was an error processing the query (e.g. if the query timed out).\"}],[\"$\",\"li\",null,{\"children\":[\"Prashant Pandey fixed the query engine to handle extra columns added to a \",[\"$\",\"code\",null,{\"children\":\"SELECT *\"}],\" statement.\"]}],[\"$\",\"li\",null,{\"children\":\"Richard Startin added support for forward indexes on JSON columns.\"}],[\"$\",\"li\",null,{\"children\":\"Rong Rong added the GRPC broker request handler so that data can be streamed back from the server to the broker when processing queries.\"}],[\"$\",\"li\",null,{\"children\":[\"deemoliu made it possible to add a default strategy when using the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://dev.startree.ai/docs/pinot/recipes/upserts-partial\",\"children\":\"partial upsert feature\"}],\".\"]}],[\"$\",\"li\",null,{\"children\":[\"Jeff Moszuti added support for the \",[\"$\",\"code\",null,{\"children\":\"TIMESTAMP\"}],\" data type in the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/operators/configuration-recommendation-engine\",\"children\":\"configuration recommendation engine\"}],\".\"]}]]}],[\"$\",\"h2\",null,{\"id\":\"dependency-updates\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#dependency-updates\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Dependency updates\"]}],[\"$\",\"p\",null,{\"children\":\"The following dependencies were updated:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"async-http-client because the library moved to a different organization.\"}],[\"$\",\"li\",null,{\"children\":\"RoaringBitmap to 0.9.25\"}],[\"$\",\"li\",null,{\"children\":\"JsonPath to 2.7.0\"}],[\"$\",\"li\",null,{\"children\":\"Kafka to 2.8.1\"}],[\"$\",\"li\",null,{\"children\":\"Prometheus to 0.16.1\"}]]}],[\"$\",\"h2\",null,{\"id\":\"resources\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#resources\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Resources\"]}],[\"$\",\"p\",null,{\"children\":\"If you want to try out Apache Pinot, the following resources will help you get started:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":[\"Download page: \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://pinot.apache.org/download/\",\"children\":\"https://pinot.apache.org/download/\"}]]}],[\"$\",\"li\",null,{\"children\":[\"Getting started: \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/getting-started\",\"children\":\"https://docs.pinot.apache.org/getting-started\"}]]}],[\"$\",\"li\",null,{\"children\":[\"Apache Pinot Recipes: \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://dev.startree.ai/docs/pinot/recipes/\",\"children\":\"https://dev.startree.ai/docs/pinot/recipes/\"}]]}],[\"$\",\"li\",null,{\"children\":[\"Join our Slack channel: \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://communityinviter.com/apps/apache-pinot/apache-pinot\",\"children\":\"https://communityinviter.com/apps/apache-pinot/apache-pinot\"}]]}],[\"$\",\"li\",null,{\"children\":[\"See our upcoming events: \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://www.meetup.com/apache-pinot\",\"children\":\"https://www.meetup.com/apache-pinot\"}]]}],[\"$\",\"li\",null,{\"children\":[\"Follow us on Twitter: \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://twitter.com/startreedata\",\"children\":\"https://twitter.com/startreedata\"}]]}],[\"$\",\"li\",null,{\"children\":[\"Subscribe to our YouTube channel: \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://www.youtube.com/c/StarTree\",\"children\":\"https://www.youtube.com/c/StarTree\"}]]}]]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L15\",null,{\"chapters\":[{\"value\":\"Query Plans\",\"url\":\"#query-plans\",\"depth\":2},{\"value\":\"FILTER Clauses for Aggregates\",\"url\":\"#filter-clauses-for-aggregates\",\"depth\":2},{\"value\":\"greatest and least\",\"url\":\"#greatest-and-least\",\"depth\":2},{\"value\":\"DistinctCountSmartHLL\",\"url\":\"#distinctcountsmarthll\",\"depth\":2},{\"value\":\"UI updates\",\"url\":\"#ui-updates\",\"depth\":2},{\"value\":\"RealTimeToOffline Task\",\"url\":\"#realtimetooffline-task\",\"depth\":2},{\"value\":\"Empty QuickStart\",\"url\":\"#empty-quickstart\",\"depth\":2},{\"value\":\"Data Ingestion\",\"url\":\"#data-ingestion\",\"depth\":2},{\"value\":\"Other changes\",\"url\":\"#other-changes\",\"depth\":2},{\"value\":\"Dependency updates\",\"url\":\"#dependency-updates\",\"depth\":2},{\"value\":\"Resources\",\"url\":\"#resources\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Announcing 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diff --git a/blog/2022/04/04/Announcing-Apache-Pinot-0-10.txt b/blog/2022/04/04/Announcing-Apache-Pinot-0-10.txt
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diff --git a/blog/2022/08/02/GapFill-Function-For-Time-Series-Datasets-In-Pinot.html b/blog/2022/08/02/GapFill-Function-For-Time-Series-Datasets-In-Pinot.html
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@@ -23,4 +23,4 @@
</span><span class="code-line"> <span class="token keyword">GROUP</span> <span class="token keyword">BY</span> <span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">2</span>
</span><span class="code-line"> <span class="token keyword">ORDER</span> <span class="token keyword">BY</span> <span class="token number">1</span>
</span><span class="code-line"> <span class="token keyword">LIMIT</span> <span class="token number">100</span>
-</span></code></pre></div><h3 id="execution-plan"><a href="#execution-plan" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Execution Plan</h3><p>The sample execution plan for this query is as shown in the figure below:</p><p><img alt="Sample query execution plan" src="https://www.datocms-assets.com/75153/1661700642-execution-plan.png" title="Sample query execution plan"/></p><h3 id="time-and-space-complexity"><a href="#time-and-space-complexity" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Time and Space complexity:</h3><p>Let us say there are M entities, R rows returned from servers, and N time buckets. The data is gapfilled time bucket by time bucket to limit the broker memory usage to O(M + N + R). When the data is gapfilled for a time bucket, it will be aggregated and stored in the final result (which has N slots). The previous values for each of the M entities are maintained in memory and carried forward as the gapfilling is performed in sequence. The time complexity is O(M * N) where M is the number of entities and N is the number of time buckets.</p><h3 id="challenges"><a href="#challenges" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Challenges</h3><p><img alt="Sample server challenges graph" src="https://www.datocms-assets.com/75153/1661700716-challenges.png" title="Sample server challenges graph"/></p><p>As the time-series datasets are enormous and partitioned, it's hard to get answers to the following questions:</p><ul><li>How many different entities exist within the query time frame. In the temporal partition scheme demonstrated above, a server/partition may not know the answer.</li><li>What's the previously observed value for entities especially for the first data points in a time bucket where previous time buckets don’t exist in the same server.</li></ul><p>For the scenario shown in the figure above, server2 may not know about the circle entity, as there are no events for the circle in Server2. It would also not know the last observed value for the square entity frame beginning of the time bucket till the first observed value timestamp within the partition.</p><h2 id="the-future-work"><a href="#the-future-work" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>The Future Work</h2><p>When doing the gapfill for one or a few entities, there might not be too much data. But when we deal with a large dataset that has multiple entities queried over a long date range without any filtering, this gets tricky. Since gapfill happens at the pinot broker, it will become very slow and the broker will become a bottleneck. The raw data transferred from servers to brokers would be enormous. Data explodes when interpolated. Parallelism is limited as the single broker instance is handling the query.</p><p>The next step of the gapfill project is to remove the pinot broker as a bottleneck. The gapfill logic will be pushed down to the servers and be running where the data live. This will reduce the data transmission and increase the parallelism and performance of gapfill.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#processing-time-series-data-in-pinot">Processing time series data in Pinot</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#sample-dataset">Sample Dataset:</a></li><li class="text-sm font-normal leading-tight"><a href="#interpolating-missing-data">Interpolating missing data</a></li><li class="text-sm font-normal leading-tight"><a href="#gapfill-query-with-a-use-case">Gapfill Query with a Use Case:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#query-syntax">Query Syntax:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#query-components">Query components:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#query-workflow">Query Workflow</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#other-supported-query-scenarios">Other Supported Query Scenarios:</a></li><li class="text-sm font-normal leading-tight"><a href="#how-does-it-work">How does it work?</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#request-flow">Request Flow</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#execution-plan">Execution Plan</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#time-and-space-complexity">Time and Space complexity:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#challenges">Challenges</a></li><li class="text-sm font-normal leading-tight"><a href="#the-future-work">The Future Work</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 2.426-2.068l.002-.002m6.452 5.605c-.445-.793-1.032-1.395-1.76-1.808a4.72 4.72 0 0 0-2.362-.618c-.847 0-1.602.211-2.33.635-.728.423-1.315 1.038-1.76 1.841-.445.804-.668 1.749-.668 2.835 0 1.087.221 2.032.668 2.835.445.804 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The values may be polled and recorded at constant time intervals or at random irregular intervals or only when the value/state changes. There are many real-world use cases of time series data. Here are some specific examples:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Telemetry from sensors monitoring the status of industrial equipment.\"}],[\"$\",\"li\",null,{\"children\":\"Real-time vehicle data such as speed, braking, and acceleration, to produce the driver's risk score trend.\"}],[\"$\",\"li\",null,{\"children\":\"Server performance metrics such as CPU, I/O, memory, and network usage over time.\"}],[\"$\",\"li\",null,{\"children\":\"An automated system tracking the status of a store or items in an online marketplace.\"}]]}],[\"$\",\"p\",null,{\"children\":\"Let us use an IOT dataset tracking the occupancy status of the individual parking slots in a parking garage using automated sensors in this post. The granularity of recorded data points might be sparse or the events could be missing due to network and other device issues in the IOT environment. The following figure demonstrates entities emitting values at irregular intervals as the value changes. Polling and recording values of all entities regularly at a lower granularity would consume more resources, take up more space on disk and during processing and incur high costs. But analytics applications that are operating on these datasets, might be querying for values at a lower granularity than the data recording interval (Ex: A dashboard showing the total no of occupied parking slots at 15 min granularity in the past week when the sensors are not recording status as frequent).\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Entities emitting data over time at irregular intervals\",\"src\":\"https://www.datocms-assets.com/75153/1661700264-entities-emitting-data.png\",\"title\":\"Entities emitting data over time at irregular intervals\"}]}],[\"$\",\"p\",null,{\"children\":\"It is important for Pinot to provide the on-the-fly interpolation (filling the missing data) functionality to better handle time-series data.\"}],[\"$\",\"p\",null,{\"children\":\"Starting from the 0.11.0 release, we introduced the new query syntax, gapfilling functions to interpolate data and perform powerful aggregations and data processing over time series data.\"}],[\"$\",\"p\",null,{\"children\":\"We will discuss the query syntax with an example and then the internal architecture.\"}],[\"$\",\"h2\",null,{\"id\":\"processing-time-series-data-in-pinot\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#processing-time-series-data-in-pinot\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Processing time series data in Pinot\"]}],[\"$\",\"p\",null,{\"children\":\"Let us use the following sample data set tracking the status of parking lots in the parking space to understand this feature in detail.\"}],[\"$\",\"h3\",null,{\"id\":\"sample-dataset\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#sample-dataset\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Sample Dataset:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample parking lot dataset\",\"src\":\"https://www.datocms-assets.com/75153/1661700333-parking-data-table.png\",\"title\":\"Sample parking lot dataset\"}]}],[\"$\",\"p\",null,{\"children\":\"parking_data table\"}],[\"$\",\"p\",null,{\"children\":\"Use case: We want to find out the total number of parking lots that are occupied over a period of time, which would be a common use case for a company that manages parking spaces.\"}],[\"$\",\"p\",null,{\"children\":\"Let us take 30 minutes time bucket as an example:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample parking lot dataset with 30 minute time bucket\",\"src\":\"https://www.datocms-assets.com/75153/1661700377-30-min-bucket-example.png\",\"title\":\"Sample parking lot dataset with 30 minute time bucket\"}]}],[\"$\",\"p\",null,{\"children\":\"In the 30 mins aggregation results table above, we can see a lot of missing data as many lots didn't have anything recorded in those 30-minute windows. To calculate the number of occupied parking lots per time bucket, we need to gap-fill the missing data for each of these 30-minute windows.\"}],[\"$\",\"h2\",null,{\"id\":\"interpolating-missing-data\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#interpolating-missing-data\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Interpolating missing data\"]}],[\"$\",\"p\",null,{\"children\":\"There are multiple ways to infer and fill the missing values. In the current version, we introduce the following methods, which are more common:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"FILL_PREVIOUS_VALUE can be used to fill time buckets missing values for entities with the last observed value. If no previous observed value can be found, the default value is used as an alternative.\"}],[\"$\",\"li\",null,{\"children\":\"FILL_DEFAULT_VALUE can be used to fill time buckets missing values for entities with the default value depending on the data type.\"}]]}],[\"$\",\"p\",null,{\"children\":\"More advanced gapfilling strategies such as using the next observed value, the value from the previous day or past week, or the value computed using a subquery shall be introduced in the future.\"}],[\"$\",\"h2\",null,{\"id\":\"gapfill-query-with-a-use-case\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#gapfill-query-with-a-use-case\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Gapfill Query with a Use Case:\"]}],[\"$\",\"p\",null,{\"children\":[\"Let us write a query to \",[\"$\",\"em\",null,{\"children\":\"get\"}],\" \",[\"$\",\"em\",null,{\"children\":\"the total number of occupied parking lots every 30 minutes over time on the parking lot dataset\"}],\" discussed above.\"]}],[\"$\",\"h3\",null,{\"id\":\"query-syntax\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#query-syntax\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Query Syntax:\"]}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\" time_col\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"SUM\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"status\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"AS\"}],\" occupied_slots_count\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\" GAPFILL\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"time_col\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'1:MILLISECONDS:SIMPLE_DATE_FORMAT:yyyy-MM-dd HH:mm:ss.SSS'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2021-10-01 09:00:00.000'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2021-10-01 12:00:00.000'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'30:MINUTES'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" FILL\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"status\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'FILL_PREVIOUS_VALUE'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" TIMESERIESON\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"lot_id\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" lot_id\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"status\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\" DATETIMECONVERT\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"event_time\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'1:MILLISECONDS:EPOCH'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'1:MILLISECONDS:SIMPLE_DATE_FORMAT:yyyy-MM-dd HH:mm:ss.SSS'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'30:MINUTES'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"AS\"}],\" time_col\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" lot_id\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" lastWithTime\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"is_occupied\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" event_time\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'INT'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"AS\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"status\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\" parking_data\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\" event_time \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1633078800000\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"AND\"}],\" event_time \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003c=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1633089600000\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"GROUP\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"2\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ORDER\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"LIMIT\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"100\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"LIMIT\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"100\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"GROUP\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"LIMIT\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"100\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"This query suggests three main steps:\"}],[\"$\",\"ol\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"The raw data will be aggregated;\"}],[\"$\",\"li\",null,{\"children\":\"The aggregated data will be gapfilled;\"}],[\"$\",\"li\",null,{\"children\":\"The gapfilled data will be aggregated.\"}]]}],[\"$\",\"p\",null,{\"children\":\"We make one assumption that the raw data is sorted by timestamp. The Gapfill and Post-Gapfill Aggregation will not sort the data.\"}],[\"$\",\"h3\",null,{\"id\":\"query-components\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#query-components\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Query components:\"]}],[\"$\",\"p\",null,{\"children\":\"The following concepts were added to interpolate and handle time-series data.\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":[\"LastWithTime(dataColumn, timeColumn, 'dataType') - To get the last value of dataColumn where the timeColumn is used to define the time of dataColumn. This is useful to pick the latest value when there are multiple values found within a time bucket. Please see \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/users/user-guide-query/supported-aggregations\",\"children\":\"https://docs.pinot.apache.org/users/user-guide-query/supported-aggregations\"}],\" for more details.\"]}],[\"$\",\"li\",null,{\"children\":\"Fill(colum, FILL_TYPE) - To fill the missing data of the column with the FILL_TYPE.\"}],[\"$\",\"li\",null,{\"children\":\"TimeSeriesOn - To specify the columns to uniquely identify entities whose data will be interpolated.\"}],[\"$\",\"li\",null,{\"children\":\"Gapfill - Specify the time range, the time bucket size, how to fill the missing data, and entity definition.\"}]]}],[\"$\",\"h3\",null,{\"id\":\"query-workflow\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#query-workflow\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Query Workflow\"]}],[\"$\",\"p\",null,{\"children\":\"The innermost sql will convert the raw event table to the following table.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample parking lot query workflow innermost SQL\",\"src\":\"https://www.datocms-assets.com/75153/1661700439-innermost-sql.png\",\"title\":\"Sample parking lot query workflow innermost SQL\"}]}],[\"$\",\"p\",null,{\"children\":\"The second most nested sql will gap fill the returned data as below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample parking lot query workflow second most SQL\",\"src\":\"https://www.datocms-assets.com/75153/1661700473-second-most.png\",\"title\":\"Sample parking lot query workflow second most SQL\"}]}],[\"$\",\"p\",null,{\"children\":\"The outermost query will aggregate the gapfilled data as follows:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample parking lot query workflow outermost SQL\",\"src\":\"https://www.datocms-assets.com/75153/1661700517-outermost.png\",\"title\":\"Sample parking lot query workflow outermost SQL\"}]}],[\"$\",\"h3\",null,{\"id\":\"other-supported-query-scenarios\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#other-supported-query-scenarios\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Other Supported Query Scenarios:\"]}],[\"$\",\"p\",null,{\"children\":\"The above example demonstrates the support to aggregate before and post gapfilling. Pre and/or post aggregations can be skipped if they are not needed. The gapfilling query syntax is flexible to support the following use cases:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Select/Gapfill - Gapfill the missing data for the time bucket. Just the raw events are fetched, gapfilled, and returned. No aggregation is needed.\"}],[\"$\",\"li\",null,{\"children\":\"Aggregate/Gapfill - If there are multiple entries within the time bucket we can pick a representative value by applying an aggregate function. Then the missing data for the time buckets will be gap filled.\"}],[\"$\",\"li\",null,{\"children\":\"Gapfill/Aggregate - Gapfill the data and perform some form of aggregation on the interpolated data.\"}]]}],[\"$\",\"p\",null,{\"children\":[\"For detailed query syntax and how it works, please refer to the documentation here: \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/users/user-guide-query/gap-fill-functions\",\"children\":\"https://docs.pinot.apache.org/users/user-guide-query/gap-fill-functions\"}],\".\"]}],[\"$\",\"h2\",null,{\"id\":\"how-does-it-work\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#how-does-it-work\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"How does it work?\"]}],[\"$\",\"p\",null,{\"children\":\"Let us use the sample query given above as an example to understand what's going on behind the scenes and how Pinot executes the gapfill queries.\"}],[\"$\",\"h3\",null,{\"id\":\"request-flow\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#request-flow\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Request Flow\"]}],[\"$\",\"p\",null,{\"children\":\"Here is the list of steps in executing the query at a high level:\"}],[\"$\",\"ol\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Pinot Broker receives the gapfill query. It will strip off the gapfill part and send out the stripped SQL query to the pinot server.\"}],[\"$\",\"li\",null,{\"children\":\"The pinot server will process the query as a normal query and return the result back to the pinot broker.\"}],[\"$\",\"li\",null,{\"children\":\"The pinot broker will run the DataTableReducer to merge the results from pinot servers. The result will be sent to GapfillProcessor.\"}],[\"$\",\"li\",null,{\"children\":\"The GapfillProcessor will gapfill the received result and apply the filter against the gap-filled result.\"}],[\"$\",\"li\",null,{\"children\":\"Post-Gapfill aggregation and filtering will be applied to the result from the last step.\"}]]}],[\"$\",\"p\",null,{\"children\":\"There are two gapfill-specific steps:\"}],[\"$\",\"ol\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"When Pinot Broker Server receives the gapfill SQL query, it will strip out gapfill related information and send out the stripped SQL query to the pinot server\"}],[\"$\",\"li\",null,{\"children\":\"GapfillProcessor will process the result from BrokerReducerService. The gapfill logic will be applied to the reduced result.\"}]]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Gapfill steps\",\"src\":\"https://www.datocms-assets.com/75153/1661700601-gapfill-steps.png\",\"title\":\"Gapfill steps\"}]}],[\"$\",\"p\",null,{\"children\":\"Here is the stripped version of the sql query sent to servers for the query shared above:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\" DATETIMECONVERT\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"event_time\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'1:MILLISECONDS:EPOCH'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'1:MILLISECONDS:SIMPLE_DATE_FORMAT:yyyy-MM-dd HH:mm:ss.SSS'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'30:MINUTES'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"AS\"}],\" time_col\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" lot_id\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" lastWithTime\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"is_occupied\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" event_time\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'INT'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"AS\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"status\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\" parking_data\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\" event_time \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1633078800000\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"AND\"}],\" event_time \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003c=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1633089600000\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"GROUP\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"2\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ORDER\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"LIMIT\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"100\"}],\"\\n\"]}]]}]}],[\"$\",\"h3\",null,{\"id\":\"execution-plan\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#execution-plan\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Execution Plan\"]}],[\"$\",\"p\",null,{\"children\":\"The sample execution plan for this query is as shown in the figure below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample query execution plan\",\"src\":\"https://www.datocms-assets.com/75153/1661700642-execution-plan.png\",\"title\":\"Sample query execution plan\"}]}],[\"$\",\"h3\",null,{\"id\":\"time-and-space-complexity\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#time-and-space-complexity\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Time and Space complexity:\"]}],[\"$\",\"p\",null,{\"children\":\"Let us say there are M entities, R rows returned from servers, and N time buckets. The data is gapfilled time bucket by time bucket to limit the broker memory usage to O(M + N + R). When the data is gapfilled for a time bucket, it will be aggregated and stored in the final result (which has N slots). The previous values for each of the M entities are maintained in memory and carried forward as the gapfilling is performed in sequence. The time complexity is O(M * N) where M is the number of entities and N is the number of time buckets.\"}],[\"$\",\"h3\",null,{\"id\":\"challenges\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#challenges\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Challenges\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample server challenges graph\",\"src\":\"https://www.datocms-assets.com/75153/1661700716-challenges.png\",\"title\":\"Sample server challenges graph\"}]}],[\"$\",\"p\",null,{\"children\":\"As the time-series datasets are enormous and partitioned, it's hard to get answers to the following questions:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"How many different entities exist within the query time frame. In the temporal partition scheme demonstrated above, a server/partition may not know the answer.\"}],[\"$\",\"li\",null,{\"children\":\"What's the previously observed value for entities especially for the first data points in a time bucket where previous time buckets don’t exist in the same server.\"}]]}],[\"$\",\"p\",null,{\"children\":\"For the scenario shown in the figure above, server2 may not know about the circle entity, as there are no events for the circle in Server2. It would also not know the last observed value for the square entity frame beginning of the time bucket till the first observed value timestamp within the partition.\"}],[\"$\",\"h2\",null,{\"id\":\"the-future-work\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#the-future-work\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"The Future Work\"]}],[\"$\",\"p\",null,{\"children\":\"When doing the gapfill for one or a few entities, there might not be too much data. But when we deal with a large dataset that has multiple entities queried over a long date range without any filtering, this gets tricky. Since gapfill happens at the pinot broker, it will become very slow and the broker will become a bottleneck. The raw data transferred from servers to brokers would be enormous. Data explodes when interpolated. Parallelism is limited as the single broker instance is handling the query.\"}],[\"$\",\"p\",null,{\"children\":\"The next step of the gapfill project is to remove the pinot broker as a bottleneck. The gapfill logic will be pushed down to the servers and be running where the data live. This will reduce the data transmission and increase the parallelism and performance of gapfill.\"}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"Processing time series data in Pinot\",\"url\":\"#processing-time-series-data-in-pinot\",\"depth\":2},{\"value\":\"Sample Dataset:\",\"url\":\"#sample-dataset\",\"depth\":3},{\"value\":\"Interpolating missing data\",\"url\":\"#interpolating-missing-data\",\"depth\":2},{\"value\":\"Gapfill Query with a Use Case:\",\"url\":\"#gapfill-query-with-a-use-case\",\"depth\":2},{\"value\":\"Query Syntax:\",\"url\":\"#query-syntax\",\"depth\":3},{\"value\":\"Query components:\",\"url\":\"#query-components\",\"depth\":3},{\"value\":\"Query Workflow\",\"url\":\"#query-workflow\",\"depth\":3},{\"value\":\"Other Supported Query Scenarios:\",\"url\":\"#other-supported-query-scenarios\",\"depth\":3},{\"value\":\"How does it work?\",\"url\":\"#how-does-it-work\",\"depth\":2},{\"value\":\"Request Flow\",\"url\":\"#request-flow\",\"depth\":3},{\"value\":\"Execution Plan\",\"url\":\"#execution-plan\",\"depth\":3},{\"value\":\"Time and Space complexity:\",\"url\":\"#time-and-space-complexity\",\"depth\":3},{\"value\":\"Challenges\",\"url\":\"#challenges\",\"depth\":3},{\"value\":\"The Future Work\",\"url\":\"#the-future-work\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"GapFill Function For Time-Series Datasets In Pinot | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"Gapfilling functions in Pinot to provide the on-the-fly interpolation (filling the missing data) functionality to better handle time-series data.\"}],[\"$\",\"meta\",\"4\",{\"name\":\"robots\",\"content\":\"index, follow\"}],[\"$\",\"meta\",\"5\",{\"name\":\"googlebot\",\"content\":\"index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1\"}],[\"$\",\"link\",\"6\",{\"rel\":\"canonical\",\"href\":\"https://pinot.apache.org/blog/2022/08/02/GapFill-Function-For-Time-Series-Datasets-In-Pinot\"}],[\"$\",\"link\",\"7\",{\"rel\":\"alternate\",\"type\":\"application/rss+xml\",\"href\":\"https://pinot.apache.org/feed.xml\"}],[\"$\",\"meta\",\"8\",{\"property\":\"og:title\",\"content\":\"GapFill Function For Time-Series Datasets In Pinot\"}],[\"$\",\"meta\",\"9\",{\"property\":\"og:description\",\"content\":\"Gapfilling functions in Pinot to provide the on-the-fly interpolation (filling the missing data) functionality to better handle time-series data.\"}],[\"$\",\"meta\",\"10\",{\"property\":\"og:url\",\"content\":\"https://pinot.apache.org/blog/2022/08/02/GapFill-Function-For-Time-Series-Datasets-In-Pinot\"}],[\"$\",\"meta\",\"11\",{\"property\":\"og:site_name\",\"content\":\"Apache Pinot™\"}],[\"$\",\"meta\",\"12\",{\"property\":\"og:locale\",\"content\":\"en_US\"}],[\"$\",\"meta\",\"13\",{\"property\":\"og:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"14\",{\"property\":\"og:type\",\"content\":\"article\"}],[\"$\",\"meta\",\"15\",{\"property\":\"article:published_time\",\"content\":\"2022-08-02T00:00:00.000Z\"}],[\"$\",\"meta\",\"16\",{\"property\":\"article:modified_time\",\"content\":\"2022-08-02T00:00:00.000Z\"}],[\"$\",\"meta\",\"17\",{\"property\":\"article:author\",\"content\":\"Weixiang Sun\"}],[\"$\",\"meta\",\"18\",{\"property\":\"article:author\",\"content\":\"Lakshmanan Velusamy\"}],[\"$\",\"meta\",\"19\",{\"name\":\"twitter:card\",\"content\":\"summary_large_image\"}],[\"$\",\"meta\",\"20\",{\"name\":\"twitter:title\",\"content\":\"GapFill Function For Time-Series Datasets In Pinot\"}],[\"$\",\"meta\",\"21\",{\"name\":\"twitter:description\",\"content\":\"Gapfilling functions in Pinot to provide the on-the-fly interpolation (filling the missing data) functionality to better handle time-series data.\"}],[\"$\",\"meta\",\"22\",{\"name\":\"twitter:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"23\",{\"name\":\"next-size-adjust\"}]]\n"])</script><script>self.__next_f.push([1,"f:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>
\ No newline at end of file
+</span></code></pre></div><h3 id="execution-plan"><a href="#execution-plan" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Execution Plan</h3><p>The sample execution plan for this query is as shown in the figure below:</p><p><img alt="Sample query execution plan" src="https://www.datocms-assets.com/75153/1661700642-execution-plan.png" title="Sample query execution plan"/></p><h3 id="time-and-space-complexity"><a href="#time-and-space-complexity" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Time and Space complexity:</h3><p>Let us say there are M entities, R rows returned from servers, and N time buckets. The data is gapfilled time bucket by time bucket to limit the broker memory usage to O(M + N + R). When the data is gapfilled for a time bucket, it will be aggregated and stored in the final result (which has N slots). The previous values for each of the M entities are maintained in memory and carried forward as the gapfilling is performed in sequence. The time complexity is O(M * N) where M is the number of entities and N is the number of time buckets.</p><h3 id="challenges"><a href="#challenges" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Challenges</h3><p><img alt="Sample server challenges graph" src="https://www.datocms-assets.com/75153/1661700716-challenges.png" title="Sample server challenges graph"/></p><p>As the time-series datasets are enormous and partitioned, it's hard to get answers to the following questions:</p><ul><li>How many different entities exist within the query time frame. In the temporal partition scheme demonstrated above, a server/partition may not know the answer.</li><li>What's the previously observed value for entities especially for the first data points in a time bucket where previous time buckets don’t exist in the same server.</li></ul><p>For the scenario shown in the figure above, server2 may not know about the circle entity, as there are no events for the circle in Server2. It would also not know the last observed value for the square entity frame beginning of the time bucket till the first observed value timestamp within the partition.</p><h2 id="the-future-work"><a href="#the-future-work" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>The Future Work</h2><p>When doing the gapfill for one or a few entities, there might not be too much data. But when we deal with a large dataset that has multiple entities queried over a long date range without any filtering, this gets tricky. Since gapfill happens at the pinot broker, it will become very slow and the broker will become a bottleneck. The raw data transferred from servers to brokers would be enormous. Data explodes when interpolated. Parallelism is limited as the single broker instance is handling the query.</p><p>The next step of the gapfill project is to remove the pinot broker as a bottleneck. The gapfill logic will be pushed down to the servers and be running where the data live. This will reduce the data transmission and increase the parallelism and performance of gapfill.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#processing-time-series-data-in-pinot">Processing time series data in Pinot</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#sample-dataset">Sample Dataset:</a></li><li class="text-sm font-normal leading-tight"><a href="#interpolating-missing-data">Interpolating missing data</a></li><li class="text-sm font-normal leading-tight"><a href="#gapfill-query-with-a-use-case">Gapfill Query with a Use Case:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#query-syntax">Query Syntax:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#query-components">Query components:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#query-workflow">Query Workflow</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#other-supported-query-scenarios">Other Supported Query Scenarios:</a></li><li class="text-sm font-normal leading-tight"><a href="#how-does-it-work">How does it work?</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#request-flow">Request Flow</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#execution-plan">Execution Plan</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#time-and-space-complexity">Time and Space complexity:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#challenges">Challenges</a></li><li class="text-sm font-normal leading-tight"><a href="#the-future-work">The Future Work</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 2.426-2.068l.002-.002m6.452 5.605c-.445-.793-1.032-1.395-1.76-1.808a4.72 4.72 0 0 0-2.362-.618c-.847 0-1.602.211-2.33.635-.728.423-1.315 1.038-1.76 1.841-.445.804-.668 1.749-.668 2.835 0 1.087.221 2.032.668 2.835.445.804 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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.\"]}]]}]]}]}]]}]}]]}],null]}]]\n"])</script><script>self.__next_f.push([1,"12:I[1514,[\"326\",\"static/chunks/326-3a90a6443b9c824c.js\",\"413\",\"static/chunks/413-f9f40b83f7bb3f22.js\",\"980\",\"static/chunks/980-6e243f9cd384c7d2.js\",\"797\",\"static/chunks/app/blog/%5B...slug%5D/page-502e08b6677b55da.js\"],\"\"]\n13:I[2529,[\"326\",\"static/chunks/326-3a90a6443b9c824c.js\",\"413\",\"static/chunks/413-f9f40b83f7bb3f22.js\",\"980\",\"static/chunks/980-6e243f9cd384c7d2.js\",\"797\",\"static/chunks/app/blog/%5B...slug%5D/page-502e08b6677b55da.js\"],\"\"]\n14:I[5185,[\"326\",\"static/chunks/326-3a90a6443b9c824c.js\",\"413\",\""])</script><script>self.__next_f.push([1,"static/chunks/413-f9f40b83f7bb3f22.js\",\"980\",\"static/chunks/980-6e243f9cd384c7d2.js\",\"797\",\"static/chunks/app/blog/%5B...slug%5D/page-502e08b6677b55da.js\"],\"\"]\n"])</script><script>self.__next_f.push([1,"10:[[\"$\",\"script\",null,{\"type\":\"application/ld+json\",\"dangerouslySetInnerHTML\":{\"__html\":\"{\\\"@context\\\":\\\"https://schema.org\\\",\\\"@type\\\":\\\"BlogPosting\\\",\\\"headline\\\":\\\"GapFill Function For Time-Series Datasets In Pinot\\\",\\\"datePublished\\\":\\\"2022-08-02T00:00:00.000Z\\\",\\\"dateModified\\\":\\\"2022-08-02T00:00:00.000Z\\\",\\\"description\\\":\\\"Gapfilling functions in Pinot to provide the on-the-fly interpolation (filling the missing data) functionality to better handle time-series data.\\\",\\\"image\\\":\\\"/static/images/twitter-card.png\\\",\\\"url\\\":\\\"https://pinot.apache.org/blog/2022-08-02-GapFill-Function-For-Time-Series-Datasets-In-Pinot\\\",\\\"author\\\":[{\\\"@type\\\":\\\"Person\\\",\\\"name\\\":\\\"Weixiang Sun\\\"},{\\\"@type\\\":\\\"Person\\\",\\\"name\\\":\\\"Lakshmanan Velusamy\\\"}]}\"}}],[\"$\",\"section\",null,{\"className\":\" px-5 pt-10 md:px-[13.313rem] md:py-16\",\"children\":[[\"$\",\"$L12\",null,{}],[\"$\",\"article\",null,{\"className\":\"\",\"children\":[\"$\",\"div\",null,{\"className\":\"mx-auto lg:flex\",\"children\":[[\"$\",\"div\",null,{\"className\":\"lg:pr-12\",\"children\":[[\"$\",\"header\",null,{\"className\":\"pt-6 md:pr-10\",\"children\":[[\"$\",\"h1\",null,{\"className\":\"text-4xl font-semibold\",\"children\":\"GapFill Function For Time-Series Datasets In Pinot\"}],[\"$\",\"p\",null,{\"className\":\"pt-2 text-lg\",\"children\":[\"By: \",\"Weixiang Sun, Lakshmanan Velusamy\"]}],[\"$\",\"p\",null,{\"className\":\"py-2 text-sm\",\"children\":[\"August 2nd, 2022\",\" • \",\"9 min read\"]}]]}],[\"$\",\"div\",null,{\"className\":\"flex flex-col lg:flex-row\",\"children\":[\"$\",\"main\",null,{\"className\":\"\",\"children\":[\"$\",\"div\",null,{\"className\":\"prose max-w-[45rem] pb-8 pt-10 dark:prose-invert\",\"children\":[[\"$\",\"p\",null,{\"children\":\"Many real-world datasets are time-series in nature, tracking the value or state changes of entities over time. The values may be polled and recorded at constant time intervals or at random irregular intervals or only when the value/state changes. There are many real-world use cases of time series data. Here are some specific examples:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Telemetry from sensors monitoring the status of industrial equipment.\"}],[\"$\",\"li\",null,{\"children\":\"Real-time vehicle data such as speed, braking, and acceleration, to produce the driver's risk score trend.\"}],[\"$\",\"li\",null,{\"children\":\"Server performance metrics such as CPU, I/O, memory, and network usage over time.\"}],[\"$\",\"li\",null,{\"children\":\"An automated system tracking the status of a store or items in an online marketplace.\"}]]}],[\"$\",\"p\",null,{\"children\":\"Let us use an IOT dataset tracking the occupancy status of the individual parking slots in a parking garage using automated sensors in this post. The granularity of recorded data points might be sparse or the events could be missing due to network and other device issues in the IOT environment. The following figure demonstrates entities emitting values at irregular intervals as the value changes. Polling and recording values of all entities regularly at a lower granularity would consume more resources, take up more space on disk and during processing and incur high costs. But analytics applications that are operating on these datasets, might be querying for values at a lower granularity than the data recording interval (Ex: A dashboard showing the total no of occupied parking slots at 15 min granularity in the past week when the sensors are not recording status as frequent).\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Entities emitting data over time at irregular intervals\",\"src\":\"https://www.datocms-assets.com/75153/1661700264-entities-emitting-data.png\",\"title\":\"Entities emitting data over time at irregular intervals\"}]}],[\"$\",\"p\",null,{\"children\":\"It is important for Pinot to provide the on-the-fly interpolation (filling the missing data) functionality to better handle time-series data.\"}],[\"$\",\"p\",null,{\"children\":\"Starting from the 0.11.0 release, we introduced the new query syntax, gapfilling functions to interpolate data and perform powerful aggregations and data processing over time series data.\"}],[\"$\",\"p\",null,{\"children\":\"We will discuss the query syntax with an example and then the internal architecture.\"}],[\"$\",\"h2\",null,{\"id\":\"processing-time-series-data-in-pinot\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#processing-time-series-data-in-pinot\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Processing time series data in Pinot\"]}],[\"$\",\"p\",null,{\"children\":\"Let us use the following sample data set tracking the status of parking lots in the parking space to understand this feature in detail.\"}],[\"$\",\"h3\",null,{\"id\":\"sample-dataset\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#sample-dataset\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Sample Dataset:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample parking lot dataset\",\"src\":\"https://www.datocms-assets.com/75153/1661700333-parking-data-table.png\",\"title\":\"Sample parking lot dataset\"}]}],[\"$\",\"p\",null,{\"children\":\"parking_data table\"}],[\"$\",\"p\",null,{\"children\":\"Use case: We want to find out the total number of parking lots that are occupied over a period of time, which would be a common use case for a company that manages parking spaces.\"}],[\"$\",\"p\",null,{\"children\":\"Let us take 30 minutes time bucket as an example:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample parking lot dataset with 30 minute time bucket\",\"src\":\"https://www.datocms-assets.com/75153/1661700377-30-min-bucket-example.png\",\"title\":\"Sample parking lot dataset with 30 minute time bucket\"}]}],[\"$\",\"p\",null,{\"children\":\"In the 30 mins aggregation results table above, we can see a lot of missing data as many lots didn't have anything recorded in those 30-minute windows. To calculate the number of occupied parking lots per time bucket, we need to gap-fill the missing data for each of these 30-minute windows.\"}],[\"$\",\"h2\",null,{\"id\":\"interpolating-missing-data\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#interpolating-missing-data\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Interpolating missing data\"]}],[\"$\",\"p\",null,{\"children\":\"There are multiple ways to infer and fill the missing values. In the current version, we introduce the following methods, which are more common:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"FILL_PREVIOUS_VALUE can be used to fill time buckets missing values for entities with the last observed value. If no previous observed value can be found, the default value is used as an alternative.\"}],[\"$\",\"li\",null,{\"children\":\"FILL_DEFAULT_VALUE can be used to fill time buckets missing values for entities with the default value depending on the data type.\"}]]}],[\"$\",\"p\",null,{\"children\":\"More advanced gapfilling strategies such as using the next observed value, the value from the previous day or past week, or the value computed using a subquery shall be introduced in the future.\"}],[\"$\",\"h2\",null,{\"id\":\"gapfill-query-with-a-use-case\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#gapfill-query-with-a-use-case\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Gapfill Query with a Use Case:\"]}],[\"$\",\"p\",null,{\"children\":[\"Let us write a query to \",[\"$\",\"em\",null,{\"children\":\"get\"}],\" \",[\"$\",\"em\",null,{\"children\":\"the total number of occupied parking lots every 30 minutes over time on the parking lot dataset\"}],\" discussed above.\"]}],[\"$\",\"h3\",null,{\"id\":\"query-syntax\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#query-syntax\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Query Syntax:\"]}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\" time_col\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"SUM\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"status\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"AS\"}],\" occupied_slots_count\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\" GAPFILL\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"time_col\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'1:MILLISECONDS:SIMPLE_DATE_FORMAT:yyyy-MM-dd HH:mm:ss.SSS'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2021-10-01 09:00:00.000'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2021-10-01 12:00:00.000'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'30:MINUTES'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" FILL\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"status\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'FILL_PREVIOUS_VALUE'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" TIMESERIESON\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"lot_id\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" lot_id\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"status\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\" DATETIMECONVERT\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"event_time\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'1:MILLISECONDS:EPOCH'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'1:MILLISECONDS:SIMPLE_DATE_FORMAT:yyyy-MM-dd HH:mm:ss.SSS'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'30:MINUTES'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"AS\"}],\" time_col\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" lot_id\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" lastWithTime\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"is_occupied\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" event_time\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'INT'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"AS\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"status\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\" parking_data\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\" event_time \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1633078800000\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"AND\"}],\" event_time \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003c=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1633089600000\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"GROUP\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"2\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ORDER\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"LIMIT\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"100\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"LIMIT\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"100\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"GROUP\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"LIMIT\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"100\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"This query suggests three main steps:\"}],[\"$\",\"ol\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"The raw data will be aggregated;\"}],[\"$\",\"li\",null,{\"children\":\"The aggregated data will be gapfilled;\"}],[\"$\",\"li\",null,{\"children\":\"The gapfilled data will be aggregated.\"}]]}],[\"$\",\"p\",null,{\"children\":\"We make one assumption that the raw data is sorted by timestamp. The Gapfill and Post-Gapfill Aggregation will not sort the data.\"}],[\"$\",\"h3\",null,{\"id\":\"query-components\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#query-components\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Query components:\"]}],[\"$\",\"p\",null,{\"children\":\"The following concepts were added to interpolate and handle time-series data.\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":[\"LastWithTime(dataColumn, timeColumn, 'dataType') - To get the last value of dataColumn where the timeColumn is used to define the time of dataColumn. This is useful to pick the latest value when there are multiple values found within a time bucket. Please see \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/users/user-guide-query/supported-aggregations\",\"children\":\"https://docs.pinot.apache.org/users/user-guide-query/supported-aggregations\"}],\" for more details.\"]}],[\"$\",\"li\",null,{\"children\":\"Fill(colum, FILL_TYPE) - To fill the missing data of the column with the FILL_TYPE.\"}],[\"$\",\"li\",null,{\"children\":\"TimeSeriesOn - To specify the columns to uniquely identify entities whose data will be interpolated.\"}],[\"$\",\"li\",null,{\"children\":\"Gapfill - Specify the time range, the time bucket size, how to fill the missing data, and entity definition.\"}]]}],[\"$\",\"h3\",null,{\"id\":\"query-workflow\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#query-workflow\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Query Workflow\"]}],[\"$\",\"p\",null,{\"children\":\"The innermost sql will convert the raw event table to the following table.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample parking lot query workflow innermost SQL\",\"src\":\"https://www.datocms-assets.com/75153/1661700439-innermost-sql.png\",\"title\":\"Sample parking lot query workflow innermost SQL\"}]}],[\"$\",\"p\",null,{\"children\":\"The second most nested sql will gap fill the returned data as below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample parking lot query workflow second most SQL\",\"src\":\"https://www.datocms-assets.com/75153/1661700473-second-most.png\",\"title\":\"Sample parking lot query workflow second most SQL\"}]}],[\"$\",\"p\",null,{\"children\":\"The outermost query will aggregate the gapfilled data as follows:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample parking lot query workflow outermost SQL\",\"src\":\"https://www.datocms-assets.com/75153/1661700517-outermost.png\",\"title\":\"Sample parking lot query workflow outermost SQL\"}]}],[\"$\",\"h3\",null,{\"id\":\"other-supported-query-scenarios\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#other-supported-query-scenarios\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Other Supported Query Scenarios:\"]}],[\"$\",\"p\",null,{\"children\":\"The above example demonstrates the support to aggregate before and post gapfilling. Pre and/or post aggregations can be skipped if they are not needed. The gapfilling query syntax is flexible to support the following use cases:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Select/Gapfill - Gapfill the missing data for the time bucket. Just the raw events are fetched, gapfilled, and returned. No aggregation is needed.\"}],[\"$\",\"li\",null,{\"children\":\"Aggregate/Gapfill - If there are multiple entries within the time bucket we can pick a representative value by applying an aggregate function. Then the missing data for the time buckets will be gap filled.\"}],[\"$\",\"li\",null,{\"children\":\"Gapfill/Aggregate - Gapfill the data and perform some form of aggregation on the interpolated data.\"}]]}],[\"$\",\"p\",null,{\"children\":[\"For detailed query syntax and how it works, please refer to the documentation here: \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/users/user-guide-query/gap-fill-functions\",\"children\":\"https://docs.pinot.apache.org/users/user-guide-query/gap-fill-functions\"}],\".\"]}],[\"$\",\"h2\",null,{\"id\":\"how-does-it-work\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#how-does-it-work\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"How does it work?\"]}],[\"$\",\"p\",null,{\"children\":\"Let us use the sample query given above as an example to understand what's going on behind the scenes and how Pinot executes the gapfill queries.\"}],[\"$\",\"h3\",null,{\"id\":\"request-flow\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#request-flow\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Request Flow\"]}],[\"$\",\"p\",null,{\"children\":\"Here is the list of steps in executing the query at a high level:\"}],[\"$\",\"ol\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Pinot Broker receives the gapfill query. It will strip off the gapfill part and send out the stripped SQL query to the pinot server.\"}],[\"$\",\"li\",null,{\"children\":\"The pinot server will process the query as a normal query and return the result back to the pinot broker.\"}],[\"$\",\"li\",null,{\"children\":\"The pinot broker will run the DataTableReducer to merge the results from pinot servers. The result will be sent to GapfillProcessor.\"}],[\"$\",\"li\",null,{\"children\":\"The GapfillProcessor will gapfill the received result and apply the filter against the gap-filled result.\"}],[\"$\",\"li\",null,{\"children\":\"Post-Gapfill aggregation and filtering will be applied to the result from the last step.\"}]]}],[\"$\",\"p\",null,{\"children\":\"There are two gapfill-specific steps:\"}],[\"$\",\"ol\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"When Pinot Broker Server receives the gapfill SQL query, it will strip out gapfill related information and send out the stripped SQL query to the pinot server\"}],[\"$\",\"li\",null,{\"children\":\"GapfillProcessor will process the result from BrokerReducerService. The gapfill logic will be applied to the reduced result.\"}]]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Gapfill steps\",\"src\":\"https://www.datocms-assets.com/75153/1661700601-gapfill-steps.png\",\"title\":\"Gapfill steps\"}]}],[\"$\",\"p\",null,{\"children\":\"Here is the stripped version of the sql query sent to servers for the query shared above:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\" DATETIMECONVERT\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"event_time\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'1:MILLISECONDS:EPOCH'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'1:MILLISECONDS:SIMPLE_DATE_FORMAT:yyyy-MM-dd HH:mm:ss.SSS'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'30:MINUTES'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"AS\"}],\" time_col\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" lot_id\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" lastWithTime\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"is_occupied\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" event_time\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'INT'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"AS\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"status\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\" parking_data\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\" event_time \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1633078800000\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"AND\"}],\" event_time \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003c=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1633089600000\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"GROUP\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"2\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ORDER\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"LIMIT\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"100\"}],\"\\n\"]}]]}]}],[\"$\",\"h3\",null,{\"id\":\"execution-plan\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#execution-plan\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Execution Plan\"]}],[\"$\",\"p\",null,{\"children\":\"The sample execution plan for this query is as shown in the figure below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample query execution plan\",\"src\":\"https://www.datocms-assets.com/75153/1661700642-execution-plan.png\",\"title\":\"Sample query execution plan\"}]}],[\"$\",\"h3\",null,{\"id\":\"time-and-space-complexity\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#time-and-space-complexity\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Time and Space complexity:\"]}],[\"$\",\"p\",null,{\"children\":\"Let us say there are M entities, R rows returned from servers, and N time buckets. The data is gapfilled time bucket by time bucket to limit the broker memory usage to O(M + N + R). When the data is gapfilled for a time bucket, it will be aggregated and stored in the final result (which has N slots). The previous values for each of the M entities are maintained in memory and carried forward as the gapfilling is performed in sequence. The time complexity is O(M * N) where M is the number of entities and N is the number of time buckets.\"}],[\"$\",\"h3\",null,{\"id\":\"challenges\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#challenges\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Challenges\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample server challenges graph\",\"src\":\"https://www.datocms-assets.com/75153/1661700716-challenges.png\",\"title\":\"Sample server challenges graph\"}]}],[\"$\",\"p\",null,{\"children\":\"As the time-series datasets are enormous and partitioned, it's hard to get answers to the following questions:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"How many different entities exist within the query time frame. In the temporal partition scheme demonstrated above, a server/partition may not know the answer.\"}],[\"$\",\"li\",null,{\"children\":\"What's the previously observed value for entities especially for the first data points in a time bucket where previous time buckets don’t exist in the same server.\"}]]}],[\"$\",\"p\",null,{\"children\":\"For the scenario shown in the figure above, server2 may not know about the circle entity, as there are no events for the circle in Server2. It would also not know the last observed value for the square entity frame beginning of the time bucket till the first observed value timestamp within the partition.\"}],[\"$\",\"h2\",null,{\"id\":\"the-future-work\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#the-future-work\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"The Future Work\"]}],[\"$\",\"p\",null,{\"children\":\"When doing the gapfill for one or a few entities, there might not be too much data. But when we deal with a large dataset that has multiple entities queried over a long date range without any filtering, this gets tricky. Since gapfill happens at the pinot broker, it will become very slow and the broker will become a bottleneck. The raw data transferred from servers to brokers would be enormous. Data explodes when interpolated. Parallelism is limited as the single broker instance is handling the query.\"}],[\"$\",\"p\",null,{\"children\":\"The next step of the gapfill project is to remove the pinot broker as a bottleneck. The gapfill logic will be pushed down to the servers and be running where the data live. This will reduce the data transmission and increase the parallelism and performance of gapfill.\"}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"Processing time series data in Pinot\",\"url\":\"#processing-time-series-data-in-pinot\",\"depth\":2},{\"value\":\"Sample Dataset:\",\"url\":\"#sample-dataset\",\"depth\":3},{\"value\":\"Interpolating missing data\",\"url\":\"#interpolating-missing-data\",\"depth\":2},{\"value\":\"Gapfill Query with a Use Case:\",\"url\":\"#gapfill-query-with-a-use-case\",\"depth\":2},{\"value\":\"Query Syntax:\",\"url\":\"#query-syntax\",\"depth\":3},{\"value\":\"Query components:\",\"url\":\"#query-components\",\"depth\":3},{\"value\":\"Query Workflow\",\"url\":\"#query-workflow\",\"depth\":3},{\"value\":\"Other Supported Query Scenarios:\",\"url\":\"#other-supported-query-scenarios\",\"depth\":3},{\"value\":\"How does it work?\",\"url\":\"#how-does-it-work\",\"depth\":2},{\"value\":\"Request Flow\",\"url\":\"#request-flow\",\"depth\":3},{\"value\":\"Execution Plan\",\"url\":\"#execution-plan\",\"depth\":3},{\"value\":\"Time and Space complexity:\",\"url\":\"#time-and-space-complexity\",\"depth\":3},{\"value\":\"Challenges\",\"url\":\"#challenges\",\"depth\":3},{\"value\":\"The Future Work\",\"url\":\"#the-future-work\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"GapFill Function For Time-Series Datasets In Pinot | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"Gapfilling functions in Pinot to provide the on-the-fly interpolation (filling the missing data) functionality to better handle time-series data.\"}],[\"$\",\"meta\",\"4\",{\"name\":\"robots\",\"content\":\"index, follow\"}],[\"$\",\"meta\",\"5\",{\"name\":\"googlebot\",\"content\":\"index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1\"}],[\"$\",\"link\",\"6\",{\"rel\":\"canonical\",\"href\":\"https://pinot.apache.org/blog/2022/08/02/GapFill-Function-For-Time-Series-Datasets-In-Pinot\"}],[\"$\",\"link\",\"7\",{\"rel\":\"alternate\",\"type\":\"application/rss+xml\",\"href\":\"https://pinot.apache.org/feed.xml\"}],[\"$\",\"meta\",\"8\",{\"property\":\"og:title\",\"content\":\"GapFill Function For Time-Series Datasets In Pinot\"}],[\"$\",\"meta\",\"9\",{\"property\":\"og:description\",\"content\":\"Gapfilling functions in Pinot to provide the on-the-fly interpolation (filling the missing data) functionality to better handle time-series data.\"}],[\"$\",\"meta\",\"10\",{\"property\":\"og:url\",\"content\":\"https://pinot.apache.org/blog/2022/08/02/GapFill-Function-For-Time-Series-Datasets-In-Pinot\"}],[\"$\",\"meta\",\"11\",{\"property\":\"og:site_name\",\"content\":\"Apache Pinot™\"}],[\"$\",\"meta\",\"12\",{\"property\":\"og:locale\",\"content\":\"en_US\"}],[\"$\",\"meta\",\"13\",{\"property\":\"og:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"14\",{\"property\":\"og:type\",\"content\":\"article\"}],[\"$\",\"meta\",\"15\",{\"property\":\"article:published_time\",\"content\":\"2022-08-02T00:00:00.000Z\"}],[\"$\",\"meta\",\"16\",{\"property\":\"article:modified_time\",\"content\":\"2022-08-02T00:00:00.000Z\"}],[\"$\",\"meta\",\"17\",{\"property\":\"article:author\",\"content\":\"Weixiang Sun\"}],[\"$\",\"meta\",\"18\",{\"property\":\"article:author\",\"content\":\"Lakshmanan Velusamy\"}],[\"$\",\"meta\",\"19\",{\"name\":\"twitter:card\",\"content\":\"summary_large_image\"}],[\"$\",\"meta\",\"20\",{\"name\":\"twitter:title\",\"content\":\"GapFill Function For Time-Series Datasets In Pinot\"}],[\"$\",\"meta\",\"21\",{\"name\":\"twitter:description\",\"content\":\"Gapfilling functions in Pinot to provide the on-the-fly interpolation (filling the missing data) functionality to better handle time-series data.\"}],[\"$\",\"meta\",\"22\",{\"name\":\"twitter:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"23\",{\"name\":\"next-size-adjust\"}]]\n"])</script><script>self.__next_f.push([1,"f:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>
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</span><span class="code-line"> <span class="token punctuation">}</span>
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-</span></code></pre></div><p>From reading this config we learn that a timestamp index is being applied to the <em>ts</em> column. It is applied at DAY, WEEK, and MONTH granularities, which means that the derived columns <span class="math math-inline"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>t</mi><mi>s</mi></mrow><annotation encoding="application/x-tex">ts</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:.6151em"></span><span class="mathnormal mord">t</span><span class="mathnormal mord">s</span></span></span></span></span>DAY, <span class="math math-inline"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>t</mi><mi>s</mi></mrow><annotation encoding="application/x-tex">ts</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:.6151em"></span><span class="mathnormal mord">t</span><span class="mathnormal mord">s</span></span></span></span></span>WEEK, and <span class="math math-inline"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>t</mi><mi>s</mi></mrow><annotation encoding="application/x-tex">ts</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:.6151em"></span><span class="mathnormal mord">t</span><span class="mathnormal mord">s</span></span></span></span></span>MONTH will be created for the segments in this table.</p><h2 id="viewing-indexes"><a href="#viewing-indexes" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Viewing Indexes</h2><p>Now, close the table config modal, and under the segments section, open airlineStats_OFFLINE_16071_16071_0 and airlineStats_OFFLINE_16073_16073_0 in new tabs.</p><p>If you look at one of those segments, you’ll see the following grid that lists columns/field names against the indexes defined on those fields.</p><p><img alt="Segment grid that lists columns/field names against the indexes defined on those fields" src="https://www.datocms-assets.com/75153/1667915996-image7.png" title="Segment grid that lists columns/field names against the indexes defined on those fields"/></p><p>All the fields on display are persisting their values using the dictionary/forward <a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/basics/indexing/forward-index">index format</a> ). Still, we can also see that the Quarter column is sorted and has an inverted index, neither of which we explicitly defined.</p><p>This is because Pinot will automatically create sorted and inverted indexes for columns whose data is sorted when the segment is created.</p><p>So the data for the Quarter column was sorted, and hence it has a sorted index.</p><p>I’ve written a couple of blog posts explaining how sorted indexes work on offline and real-time tables:</p><h2 id="adding-an-index"><a href="#adding-an-index" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Adding an Index</h2><p>Next, let’s see what happens if we add an explicit index. We’re going to add an inverted index to the FlightNum column. Go to Edit Table config again and update tableIndexConfig to have the following value:</p><p><img alt="Inverted index addition" src="https://www.datocms-assets.com/75153/1667916147-image6.png" title="Inverted index addition"/></p><p>If you go back to the page for segment airlineStats_OFFLINE_16073_16073_0, notice that it does not have an inverted index for this field.</p><p><img alt="page for segment airlineStats_OFFLINE_16073_16073_0 without an inverted index" src="https://www.datocms-assets.com/75153/1667916232-image2.png" title="page for segment airlineStats_OFFLINE_16073_16073_0 without an inverted index"/></p><p>This is because indexes are applied on a per segment basis. If we want the inverted index on the FlightNum column in this segment, we can click <em>Reload Segment</em> on this page, or we can go back to the table page and click <em>Reload All Segments</em>.</p><p>If we do that, all the segments in the airlineStats_OFFLINE table will eventually have an inverted index on FlightNum.</p><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>As I mentioned in the introduction, information about the indexes on each segment has always been available via the REST API, but this feature democratizes that information.</p><p>If you have any questions about this feature, feel free to join us on <a target="_blank" rel="noopener noreferrer" href="https://stree.ai/slack">Slack</a>, where we’ll be happy to help you out.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#spinning-up-pinot">Spinning up Pinot</a></li><li class="text-sm font-normal leading-tight"><a href="#indexing-config">Indexing Config</a></li><li class="text-sm font-normal leading-tight"><a href="#viewing-indexes">Viewing Indexes</a></li><li class="text-sm font-normal leading-tight"><a href="#adding-an-index">Adding an Index</a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" 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property\",\"children\":\"\\\"enableDefaultStarTree\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"enableDynamicStarTreeCreation\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"aggregateMetrics\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"nullHandlingEnabled\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"optimizeDictionaryForMetrics\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"noDictionarySizeRatioThreshold\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"0\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"From reading this config we learn that no indexes have been explicitly defined.\"}],[\"$\",\"p\",null,{\"children\":\"Now for fieldConfigList, which is defined below:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"fieldConfigList\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"encodingType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"DICTIONARY\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"indexType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"TIMESTAMP\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"indexTypes\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"TIMESTAMP\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"timestampConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"granularities\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"DAY\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"WEEK\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MONTH\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"From reading this config we learn that a timestamp index is being applied to the \",[\"$\",\"em\",null,{\"children\":\"ts\"}],\" column. It is applied at DAY, WEEK, and MONTH granularities, which means that the derived columns \",[\"$\",\"span\",null,{\"className\":\"math math-inline\",\"children\":[\"$\",\"span\",null,{\"className\":\"katex\",\"children\":[[\"$\",\"span\",null,{\"className\":\"katex-mathml\",\"children\":[\"$\",\"math\",null,{\"xmlns\":\"http://www.w3.org/1998/Math/MathML\",\"children\":[\"$\",\"semantics\",null,{\"children\":[[\"$\",\"mrow\",null,{\"children\":[[\"$\",\"mi\",null,{\"children\":\"t\"}],[\"$\",\"mi\",null,{\"children\":\"s\"}]]}],[\"$\",\"annotation\",null,{\"encoding\":\"application/x-tex\",\"children\":\"ts\"}]]}]}]}],[\"$\",\"span\",null,{\"className\":\"katex-html\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"span\",null,{\"className\":\"base\",\"children\":[[\"$\",\"span\",null,{\"className\":\"strut\",\"style\":{\"height\":\".6151em\"}}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"t\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"s\"}]]}]}]]}]}],\"DAY, \",[\"$\",\"span\",null,{\"className\":\"math math-inline\",\"children\":[\"$\",\"span\",null,{\"className\":\"katex\",\"children\":[[\"$\",\"span\",null,{\"className\":\"katex-mathml\",\"children\":[\"$\",\"math\",null,{\"xmlns\":\"http://www.w3.org/1998/Math/MathML\",\"children\":[\"$\",\"semantics\",null,{\"children\":[[\"$\",\"mrow\",null,{\"children\":[[\"$\",\"mi\",null,{\"children\":\"t\"}],[\"$\",\"mi\",null,{\"children\":\"s\"}]]}],[\"$\",\"annotation\",null,{\"encoding\":\"application/x-tex\",\"children\":\"ts\"}]]}]}]}],[\"$\",\"span\",null,{\"className\":\"katex-html\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"span\",null,{\"className\":\"base\",\"children\":[[\"$\",\"span\",null,{\"className\":\"strut\",\"style\":{\"height\":\".6151em\"}}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"t\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"s\"}]]}]}]]}]}],\"WEEK, and \",[\"$\",\"span\",null,{\"className\":\"math math-inline\",\"children\":[\"$\",\"span\",null,{\"className\":\"katex\",\"children\":[[\"$\",\"span\",null,{\"className\":\"katex-mathml\",\"children\":[\"$\",\"math\",null,{\"xmlns\":\"http://www.w3.org/1998/Math/MathML\",\"children\":[\"$\",\"semantics\",null,{\"children\":[[\"$\",\"mrow\",null,{\"children\":[[\"$\",\"mi\",null,{\"children\":\"t\"}],[\"$\",\"mi\",null,{\"children\":\"s\"}]]}],[\"$\",\"annotation\",null,{\"encoding\":\"application/x-tex\",\"children\":\"ts\"}]]}]}]}],[\"$\",\"span\",null,{\"className\":\"katex-html\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"span\",null,{\"className\":\"base\",\"children\":[[\"$\",\"span\",null,{\"className\":\"strut\",\"style\":{\"height\":\".6151em\"}}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"t\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"s\"}]]}]}]]}]}],\"MONTH will be created for the segments in this table.\"]}],[\"$\",\"h2\",null,{\"id\":\"viewing-indexes\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#viewing-indexes\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Viewing Indexes\"]}],[\"$\",\"p\",null,{\"children\":\"Now, close the table config modal, and under the segments section, open airlineStats_OFFLINE_16071_16071_0 and airlineStats_OFFLINE_16073_16073_0 in new tabs.\"}],[\"$\",\"p\",null,{\"children\":\"If you look at one of those segments, you’ll see the following grid that lists columns/field names against the indexes defined on those fields.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Segment grid that lists columns/field names against the indexes defined on those fields\",\"src\":\"https://www.datocms-assets.com/75153/1667915996-image7.png\",\"title\":\"Segment grid that lists columns/field names against the indexes defined on those fields\"}]}],[\"$\",\"p\",null,{\"children\":[\"All the fields on display are persisting their values using the dictionary/forward \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/basics/indexing/forward-index\",\"children\":\"index format\"}],\" ). Still, we can also see that the Quarter column is sorted and has an inverted index, neither of which we explicitly defined.\"]}],[\"$\",\"p\",null,{\"children\":\"This is because Pinot will automatically create sorted and inverted indexes for columns whose data is sorted when the segment is created.\"}],[\"$\",\"p\",null,{\"children\":\"So the data for the Quarter column was sorted, and hence it has a sorted index.\"}],[\"$\",\"p\",null,{\"children\":\"I’ve written a couple of blog posts explaining how sorted indexes work on offline and real-time tables:\"}],[\"$\",\"h2\",null,{\"id\":\"adding-an-index\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#adding-an-index\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Adding an Index\"]}],[\"$\",\"p\",null,{\"children\":\"Next, let’s see what happens if we add an explicit index. We’re going to add an inverted index to the FlightNum column. Go to Edit Table config again and update tableIndexConfig to have the following value:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Inverted index addition\",\"src\":\"https://www.datocms-assets.com/75153/1667916147-image6.png\",\"title\":\"Inverted index addition\"}]}],[\"$\",\"p\",null,{\"children\":\"If you go back to the page for segment airlineStats_OFFLINE_16073_16073_0, notice that it does not have an inverted index for this field.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"page for segment airlineStats_OFFLINE_16073_16073_0 without an inverted index\",\"src\":\"https://www.datocms-assets.com/75153/1667916232-image2.png\",\"title\":\"page for segment airlineStats_OFFLINE_16073_16073_0 without an inverted index\"}]}],[\"$\",\"p\",null,{\"children\":[\"This is because indexes are applied on a per segment basis. If we want the inverted index on the FlightNum column in this segment, we can click \",[\"$\",\"em\",null,{\"children\":\"Reload Segment\"}],\" on this page, or we can go back to the table page and click \",[\"$\",\"em\",null,{\"children\":\"Reload All Segments\"}],\".\"]}],[\"$\",\"p\",null,{\"children\":\"If we do that, all the segments in the airlineStats_OFFLINE table will eventually have an inverted index on FlightNum.\"}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":\"As I mentioned in the introduction, information about the indexes on each segment has always been available via the REST API, but this feature democratizes that information.\"}],[\"$\",\"p\",null,{\"children\":[\"If you have any questions about this feature, feel free to join us on \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://stree.ai/slack\",\"children\":\"Slack\"}],\", where we’ll be happy to help you out.\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"Spinning up Pinot\",\"url\":\"#spinning-up-pinot\",\"depth\":2},{\"value\":\"Indexing Config\",\"url\":\"#indexing-config\",\"depth\":2},{\"value\":\"Viewing Indexes\",\"url\":\"#viewing-indexes\",\"depth\":2},{\"value\":\"Adding an Index\",\"url\":\"#adding-an-index\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Apache Pinot™ 0.11 - How do I see my indexes? | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"How you can work out which indexes are currently defined on a Pinot table\"}],[\"$\",\"meta\",\"4\",{\"name\":\"robots\",\"content\":\"index, follow\"}],[\"$\",\"meta\",\"5\",{\"name\":\"googlebot\",\"content\":\"index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1\"}],[\"$\",\"link\",\"6\",{\"rel\":\"canonical\",\"href\":\"https://pinot.apache.org/blog/2022/11/08/Apache%20Pinot-How-do-I-see-my-indexes\"}],[\"$\",\"link\",\"7\",{\"rel\":\"alternate\",\"type\":\"application/rss+xml\",\"href\":\"https://pinot.apache.org/feed.xml\"}],[\"$\",\"meta\",\"8\",{\"property\":\"og:title\",\"content\":\"Apache Pinot™ 0.11 - How do I see my indexes?\"}],[\"$\",\"meta\",\"9\",{\"property\":\"og:description\",\"content\":\"How you can work out which indexes are currently defined on a Pinot table\"}],[\"$\",\"meta\",\"10\",{\"property\":\"og:url\",\"content\":\"https://pinot.apache.org/blog/2022/11/08/Apache%20Pinot-How-do-I-see-my-indexes\"}],[\"$\",\"meta\",\"11\",{\"property\":\"og:site_name\",\"content\":\"Apache Pinot™\"}],[\"$\",\"meta\",\"12\",{\"property\":\"og:locale\",\"content\":\"en_US\"}],[\"$\",\"meta\",\"13\",{\"property\":\"og:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"14\",{\"property\":\"og:type\",\"content\":\"article\"}],[\"$\",\"meta\",\"15\",{\"property\":\"article:published_time\",\"content\":\"2022-11-08T00:00:00.000Z\"}],[\"$\",\"meta\",\"16\",{\"property\":\"article:modified_time\",\"content\":\"2022-11-08T00:00:00.000Z\"}],[\"$\",\"meta\",\"17\",{\"property\":\"article:author\",\"content\":\"Mark Needham\"}],[\"$\",\"meta\",\"18\",{\"name\":\"twitter:card\",\"content\":\"summary_large_image\"}],[\"$\",\"meta\",\"19\",{\"name\":\"twitter:title\",\"content\":\"Apache Pinot™ 0.11 - How do I see my indexes?\"}],[\"$\",\"meta\",\"20\",{\"name\":\"twitter:description\",\"content\":\"How you can work out which indexes are currently defined on a Pinot table\"}],[\"$\",\"meta\",\"21\",{\"name\":\"twitter:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"22\",{\"name\":\"next-size-adjust\"}]]\n"])</script><script>self.__next_f.push([1,"f:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>
\ No newline at end of file
+</span></code></pre></div><p>From reading this config we learn that a timestamp index is being applied to the <em>ts</em> column. It is applied at DAY, WEEK, and MONTH granularities, which means that the derived columns <span class="math math-inline"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>t</mi><mi>s</mi></mrow><annotation encoding="application/x-tex">ts</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:.6151em"></span><span class="mathnormal mord">t</span><span class="mathnormal mord">s</span></span></span></span></span>DAY, <span class="math math-inline"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>t</mi><mi>s</mi></mrow><annotation encoding="application/x-tex">ts</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:.6151em"></span><span class="mathnormal mord">t</span><span class="mathnormal mord">s</span></span></span></span></span>WEEK, and <span class="math math-inline"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>t</mi><mi>s</mi></mrow><annotation encoding="application/x-tex">ts</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:.6151em"></span><span class="mathnormal mord">t</span><span class="mathnormal mord">s</span></span></span></span></span>MONTH will be created for the segments in this table.</p><h2 id="viewing-indexes"><a href="#viewing-indexes" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Viewing Indexes</h2><p>Now, close the table config modal, and under the segments section, open airlineStats_OFFLINE_16071_16071_0 and airlineStats_OFFLINE_16073_16073_0 in new tabs.</p><p>If you look at one of those segments, you’ll see the following grid that lists columns/field names against the indexes defined on those fields.</p><p><img alt="Segment grid that lists columns/field names against the indexes defined on those fields" src="https://www.datocms-assets.com/75153/1667915996-image7.png" title="Segment grid that lists columns/field names against the indexes defined on those fields"/></p><p>All the fields on display are persisting their values using the dictionary/forward <a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/basics/indexing/forward-index">index format</a> ). Still, we can also see that the Quarter column is sorted and has an inverted index, neither of which we explicitly defined.</p><p>This is because Pinot will automatically create sorted and inverted indexes for columns whose data is sorted when the segment is created.</p><p>So the data for the Quarter column was sorted, and hence it has a sorted index.</p><p>I’ve written a couple of blog posts explaining how sorted indexes work on offline and real-time tables:</p><h2 id="adding-an-index"><a href="#adding-an-index" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Adding an Index</h2><p>Next, let’s see what happens if we add an explicit index. We’re going to add an inverted index to the FlightNum column. Go to Edit Table config again and update tableIndexConfig to have the following value:</p><p><img alt="Inverted index addition" src="https://www.datocms-assets.com/75153/1667916147-image6.png" title="Inverted index addition"/></p><p>If you go back to the page for segment airlineStats_OFFLINE_16073_16073_0, notice that it does not have an inverted index for this field.</p><p><img alt="page for segment airlineStats_OFFLINE_16073_16073_0 without an inverted index" src="https://www.datocms-assets.com/75153/1667916232-image2.png" title="page for segment airlineStats_OFFLINE_16073_16073_0 without an inverted index"/></p><p>This is because indexes are applied on a per segment basis. If we want the inverted index on the FlightNum column in this segment, we can click <em>Reload Segment</em> on this page, or we can go back to the table page and click <em>Reload All Segments</em>.</p><p>If we do that, all the segments in the airlineStats_OFFLINE table will eventually have an inverted index on FlightNum.</p><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>As I mentioned in the introduction, information about the indexes on each segment has always been available via the REST API, but this feature democratizes that information.</p><p>If you have any questions about this feature, feel free to join us on <a target="_blank" rel="noopener noreferrer" href="https://stree.ai/slack">Slack</a>, where we’ll be happy to help you out.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#spinning-up-pinot">Spinning up Pinot</a></li><li class="text-sm font-normal leading-tight"><a href="#indexing-config">Indexing Config</a></li><li class="text-sm font-normal leading-tight"><a href="#viewing-indexes">Viewing Indexes</a></li><li class="text-sm font-normal leading-tight"><a href="#adding-an-index">Adding an Index</a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" 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This will show a window with the config for this table.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Window with configuration for airlineStats_OFFLINE table\",\"src\":\"https://www.datocms-assets.com/75153/1667915654-image3.png\",\"title\":\"Window with configuration for airlineStats_OFFLINE table\"}]}],[\"$\",\"h2\",null,{\"id\":\"indexing-config\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#indexing-config\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Indexing Config\"]}],[\"$\",\"p\",null,{\"children\":\"We’re interested in the tableIndexConfig and fieldConfigList sections. 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punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"rangeIndexVersion\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"2\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"autoGeneratedInvertedIndex\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"createInvertedIndexDuringSegmentGeneration\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"loadMode\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MMAP\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"enableDefaultStarTree\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"enableDynamicStarTreeCreation\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"aggregateMetrics\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"nullHandlingEnabled\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"optimizeDictionaryForMetrics\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"noDictionarySizeRatioThreshold\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"0\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"From reading this config we learn that no indexes have been explicitly defined.\"}],[\"$\",\"p\",null,{\"children\":\"Now for fieldConfigList, which is defined below:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"fieldConfigList\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"encodingType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"DICTIONARY\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"indexType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"TIMESTAMP\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"indexTypes\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"TIMESTAMP\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"timestampConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"granularities\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"DAY\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"WEEK\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MONTH\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"From reading this config we learn that a timestamp index is being applied to the \",[\"$\",\"em\",null,{\"children\":\"ts\"}],\" column. It is applied at DAY, WEEK, and MONTH granularities, which means that the derived columns \",[\"$\",\"span\",null,{\"className\":\"math math-inline\",\"children\":[\"$\",\"span\",null,{\"className\":\"katex\",\"children\":[[\"$\",\"span\",null,{\"className\":\"katex-mathml\",\"children\":[\"$\",\"math\",null,{\"xmlns\":\"http://www.w3.org/1998/Math/MathML\",\"children\":[\"$\",\"semantics\",null,{\"children\":[[\"$\",\"mrow\",null,{\"children\":[[\"$\",\"mi\",null,{\"children\":\"t\"}],[\"$\",\"mi\",null,{\"children\":\"s\"}]]}],[\"$\",\"annotation\",null,{\"encoding\":\"application/x-tex\",\"children\":\"ts\"}]]}]}]}],[\"$\",\"span\",null,{\"className\":\"katex-html\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"span\",null,{\"className\":\"base\",\"children\":[[\"$\",\"span\",null,{\"className\":\"strut\",\"style\":{\"height\":\".6151em\"}}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"t\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"s\"}]]}]}]]}]}],\"DAY, \",[\"$\",\"span\",null,{\"className\":\"math math-inline\",\"children\":[\"$\",\"span\",null,{\"className\":\"katex\",\"children\":[[\"$\",\"span\",null,{\"className\":\"katex-mathml\",\"children\":[\"$\",\"math\",null,{\"xmlns\":\"http://www.w3.org/1998/Math/MathML\",\"children\":[\"$\",\"semantics\",null,{\"children\":[[\"$\",\"mrow\",null,{\"children\":[[\"$\",\"mi\",null,{\"children\":\"t\"}],[\"$\",\"mi\",null,{\"children\":\"s\"}]]}],[\"$\",\"annotation\",null,{\"encoding\":\"application/x-tex\",\"children\":\"ts\"}]]}]}]}],[\"$\",\"span\",null,{\"className\":\"katex-html\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"span\",null,{\"className\":\"base\",\"children\":[[\"$\",\"span\",null,{\"className\":\"strut\",\"style\":{\"height\":\".6151em\"}}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"t\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"s\"}]]}]}]]}]}],\"WEEK, and \",[\"$\",\"span\",null,{\"className\":\"math math-inline\",\"children\":[\"$\",\"span\",null,{\"className\":\"katex\",\"children\":[[\"$\",\"span\",null,{\"className\":\"katex-mathml\",\"children\":[\"$\",\"math\",null,{\"xmlns\":\"http://www.w3.org/1998/Math/MathML\",\"children\":[\"$\",\"semantics\",null,{\"children\":[[\"$\",\"mrow\",null,{\"children\":[[\"$\",\"mi\",null,{\"children\":\"t\"}],[\"$\",\"mi\",null,{\"children\":\"s\"}]]}],[\"$\",\"annotation\",null,{\"encoding\":\"application/x-tex\",\"children\":\"ts\"}]]}]}]}],[\"$\",\"span\",null,{\"className\":\"katex-html\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"span\",null,{\"className\":\"base\",\"children\":[[\"$\",\"span\",null,{\"className\":\"strut\",\"style\":{\"height\":\".6151em\"}}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"t\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"s\"}]]}]}]]}]}],\"MONTH will be created for the segments in this table.\"]}],[\"$\",\"h2\",null,{\"id\":\"viewing-indexes\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#viewing-indexes\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Viewing Indexes\"]}],[\"$\",\"p\",null,{\"children\":\"Now, close the table config modal, and under the segments section, open airlineStats_OFFLINE_16071_16071_0 and airlineStats_OFFLINE_16073_16073_0 in new tabs.\"}],[\"$\",\"p\",null,{\"children\":\"If you look at one of those segments, you’ll see the following grid that lists columns/field names against the indexes defined on those fields.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Segment grid that lists columns/field names against the indexes defined on those fields\",\"src\":\"https://www.datocms-assets.com/75153/1667915996-image7.png\",\"title\":\"Segment grid that lists columns/field names against the indexes defined on those fields\"}]}],[\"$\",\"p\",null,{\"children\":[\"All the fields on display are persisting their values using the dictionary/forward \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/basics/indexing/forward-index\",\"children\":\"index format\"}],\" ). Still, we can also see that the Quarter column is sorted and has an inverted index, neither of which we explicitly defined.\"]}],[\"$\",\"p\",null,{\"children\":\"This is because Pinot will automatically create sorted and inverted indexes for columns whose data is sorted when the segment is created.\"}],[\"$\",\"p\",null,{\"children\":\"So the data for the Quarter column was sorted, and hence it has a sorted index.\"}],[\"$\",\"p\",null,{\"children\":\"I’ve written a couple of blog posts explaining how sorted indexes work on offline and real-time tables:\"}],[\"$\",\"h2\",null,{\"id\":\"adding-an-index\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#adding-an-index\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Adding an Index\"]}],[\"$\",\"p\",null,{\"children\":\"Next, let’s see what happens if we add an explicit index. We’re going to add an inverted index to the FlightNum column. Go to Edit Table config again and update tableIndexConfig to have the following value:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Inverted index addition\",\"src\":\"https://www.datocms-assets.com/75153/1667916147-image6.png\",\"title\":\"Inverted index addition\"}]}],[\"$\",\"p\",null,{\"children\":\"If you go back to the page for segment airlineStats_OFFLINE_16073_16073_0, notice that it does not have an inverted index for this field.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"page for segment airlineStats_OFFLINE_16073_16073_0 without an inverted index\",\"src\":\"https://www.datocms-assets.com/75153/1667916232-image2.png\",\"title\":\"page for segment airlineStats_OFFLINE_16073_16073_0 without an inverted index\"}]}],[\"$\",\"p\",null,{\"children\":[\"This is because indexes are applied on a per segment basis. If we want the inverted index on the FlightNum column in this segment, we can click \",[\"$\",\"em\",null,{\"children\":\"Reload Segment\"}],\" on this page, or we can go back to the table page and click \",[\"$\",\"em\",null,{\"children\":\"Reload All Segments\"}],\".\"]}],[\"$\",\"p\",null,{\"children\":\"If we do that, all the segments in the airlineStats_OFFLINE table will eventually have an inverted index on FlightNum.\"}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":\"As I mentioned in the introduction, information about the indexes on each segment has always been available via the REST API, but this feature democratizes that information.\"}],[\"$\",\"p\",null,{\"children\":[\"If you have any questions about this feature, feel free to join us on \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://stree.ai/slack\",\"children\":\"Slack\"}],\", where we’ll be happy to help you out.\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"Spinning up Pinot\",\"url\":\"#spinning-up-pinot\",\"depth\":2},{\"value\":\"Indexing Config\",\"url\":\"#indexing-config\",\"depth\":2},{\"value\":\"Viewing Indexes\",\"url\":\"#viewing-indexes\",\"depth\":2},{\"value\":\"Adding an Index\",\"url\":\"#adding-an-index\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Apache Pinot™ 0.11 - How do I see my indexes? | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"How you can work out which indexes are currently defined on a Pinot table\"}],[\"$\",\"meta\",\"4\",{\"name\":\"robots\",\"content\":\"index, follow\"}],[\"$\",\"meta\",\"5\",{\"name\":\"googlebot\",\"content\":\"index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1\"}],[\"$\",\"link\",\"6\",{\"rel\":\"canonical\",\"href\":\"https://pinot.apache.org/blog/2022/11/08/Apache%20Pinot-How-do-I-see-my-indexes\"}],[\"$\",\"link\",\"7\",{\"rel\":\"alternate\",\"type\":\"application/rss+xml\",\"href\":\"https://pinot.apache.org/feed.xml\"}],[\"$\",\"meta\",\"8\",{\"property\":\"og:title\",\"content\":\"Apache Pinot™ 0.11 - How do I see my indexes?\"}],[\"$\",\"meta\",\"9\",{\"property\":\"og:description\",\"content\":\"How you can work out which indexes are currently defined on a Pinot table\"}],[\"$\",\"meta\",\"10\",{\"property\":\"og:url\",\"content\":\"https://pinot.apache.org/blog/2022/11/08/Apache%20Pinot-How-do-I-see-my-indexes\"}],[\"$\",\"meta\",\"11\",{\"property\":\"og:site_name\",\"content\":\"Apache Pinot™\"}],[\"$\",\"meta\",\"12\",{\"property\":\"og:locale\",\"content\":\"en_US\"}],[\"$\",\"meta\",\"13\",{\"property\":\"og:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"14\",{\"property\":\"og:type\",\"content\":\"article\"}],[\"$\",\"meta\",\"15\",{\"property\":\"article:published_time\",\"content\":\"2022-11-08T00:00:00.000Z\"}],[\"$\",\"meta\",\"16\",{\"property\":\"article:modified_time\",\"content\":\"2022-11-08T00:00:00.000Z\"}],[\"$\",\"meta\",\"17\",{\"property\":\"article:author\",\"content\":\"Mark Needham\"}],[\"$\",\"meta\",\"18\",{\"name\":\"twitter:card\",\"content\":\"summary_large_image\"}],[\"$\",\"meta\",\"19\",{\"name\":\"twitter:title\",\"content\":\"Apache Pinot™ 0.11 - How do I see my indexes?\"}],[\"$\",\"meta\",\"20\",{\"name\":\"twitter:description\",\"content\":\"How you can work out which indexes are currently defined on a Pinot table\"}],[\"$\",\"meta\",\"21\",{\"name\":\"twitter:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"22\",{\"name\":\"next-size-adjust\"}]]\n"])</script><script>self.__next_f.push([1,"f:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>
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index fcdffa1..9c4b0ce 100644
--- a/blog/2022/11/17/Apache Pinot-Inserts-from-SQL.html
+++ b/blog/2022/11/17/Apache Pinot-Inserts-from-SQL.html
@@ -43,4 +43,4 @@
</span><span class="code-line"> input<span class="token punctuation">.</span>fs<span class="token punctuation">.</span>prop<span class="token punctuation">.</span>secretKey<span class="token operator">=</span>gfz71RX<span class="token operator">+</span>Tj4udve43YePCBqMsIeN1PvHXrVFyxJS<span class="token punctuation">,</span>
</span><span class="code-line"> input<span class="token punctuation">.</span>fs<span class="token punctuation">.</span>prop<span class="token punctuation">.</span>region<span class="token operator">=</span>eu<span class="token operator">-</span>west<span class="token operator">-</span><span class="token number">2</span>
</span><span class="code-line"><span class="token punctuation">)</span><span class="token punctuation">;</span>
-</span></code></pre></div><p>If we run this query, we’ll see the following output:</p><p><img alt="Sample events_OFFLINE query result" src="https://www.datocms-assets.com/75153/1668701654-image5.png" title="Sample events_OFFLINE query result"/></p><p>We can check on the state of the ingestion job via the Swagger REST API. If we navigate to <a target="_blank" rel="noopener noreferrer" href="http://localhost:9000/help#/Task/getTaskState">http://localhost:9000/help#/Task/getTaskState</a>, paste Task_SegmentGenerationAndPushTask_events-task as our task name, and then click Execute, we’ll see the following:</p><p><img alt="Checking the state of an ingestion job screen" src="https://www.datocms-assets.com/75153/1668701727-image2.png" title="Checking the state of an ingestion job screen"/></p><p>If we see the state COMPLETED, this means the data has been ingested, which we can check by going back to the Query console and clicking on the events table.</p><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>I have to say that batch ingestion of data into Apache Pinot has always felt a bit clunky, but with this new clause, it’s super easy, and it’s gonna save us all a bunch of time.</p><p>Also, anything that means I’m not writing YAML files has got to be a good thing!</p><p>So give it a try and let us know how you get on. If you have any questions about this feature, feel free to join us on <a target="_blank" rel="noopener noreferrer" href="https://stree.ai/slack">Slack</a>, where we’ll be happy to help you out.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#batch-importing-the-job-specification">Batch importing: The Job Specification</a></li><li class="text-sm font-normal leading-tight"><a href="#batch-importing-with-sql">Batch Importing with SQL</a></li><li class="text-sm font-normal leading-tight"><a 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atrule key\",\"children\":\"recordReaderSpec\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token atrule key\",\"children\":\"dataFormat\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'json'\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token atrule key\",\"children\":\"className\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'org.apache.pinot.plugin.inputformat.json.JSONRecordReader'\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token atrule key\",\"children\":\"tableSpec\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token atrule key\",\"children\":\"tableName\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'events'\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token atrule key\",\"children\":\"pinotClusterSpecs\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"-\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token atrule key\",\"children\":\"controllerURI\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'http://${PINOT_CONTROLLER}:9000'\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"At a high level, this file describes a batch import job that will ingest files from the S3 bucket at s3://marks-st-cloud-bucket/events/ where the files match the glob:**/*.json pattern.\"}],[\"$\",\"p\",null,{\"children\":\"We can import the data by running the following command from the terminal:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--network\"}],\" ingest-json-files-s3 \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/config:/config \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-e\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable assign-left\",\"children\":\"AWS_ACCESS_KEY_ID\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"AKIARCOCT6DWLUB7F77Z \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-e\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable assign-left\",\"children\":\"AWS_SECRET_ACCESS_KEY\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"gfz71RX+Tj4udve43YePCBqMsIeN1PvHXrVFyxJS \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.11.0 LaunchDataIngestionJob \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-jobSpecFile\"}],\" /config/job-spec.yml \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-values\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable assign-left\",\"children\":\"PINOT_CONTROLLER\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"pinot-controller\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"And don’t worry, those credentials have already been deleted; I find it easier to understand what values go where if we use real values.\"}],[\"$\",\"p\",null,{\"children\":[\"Once we’ve run this command, if we go to the Pinot UI at \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/\",\"children\":\"http://localhost:9000\"}],\" and click through to the events table from the Query Console menu, we’ll see that the records have been imported, as shown in the screenshot below:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample imported records shown in the Apache Pinot Query Console menu\",\"src\":\"https://www.datocms-assets.com/75153/1668701512-image3.png\",\"title\":\"Sample imported records shown in the Apache Pinot Query Console menu\"}]}],[\"$\",\"p\",null,{\"children\":\"This approach works, and we may still prefer to use it when we need fine-grained control over the ingestion parameters, but it is a bit heavyweight for your everyday data import!\"}],[\"$\",\"h2\",null,{\"id\":\"batch-importing-with-sql\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#batch-importing-with-sql\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Batch Importing with SQL\"]}],[\"$\",\"p\",null,{\"children\":\"Now let’s do the same thing in SQL.\"}],[\"$\",\"p\",null,{\"children\":\"There are some prerequisites to using the SQL approach, so let’s go through those now, so you don’t end up with a bunch of exceptions when you try this out!\"}],[\"$\",\"p\",null,{\"children\":[\"First of all, you must have a \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/basics/components/minion\",\"children\":\"Minion\"}],\" in the Pinot cluster, as this is the component that will do the data import.\"]}],[\"$\",\"p\",null,{\"children\":\"You’ll also need to include the following in your table config:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"task\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"taskTypeConfigsMap\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"SegmentGenerationAndPushTask\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"As long as you’ve done those two things, we’re ready to write our import query! A query that imports JSON files from my S3 bucket is shown below:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"INSERT\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"INTO\"}],\" events\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FILE\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'s3://marks-st-cloud-bucket/events/'\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"OPTION\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" taskName\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"events\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"task\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" includeFileNamePattern\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"glob:\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"/\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"json\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" input\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"fs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"className\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"org\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"apache\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"pinot\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"plugin\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"filesystem\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"S3PinotFS\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" input\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"fs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"prop\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"accessKey\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"AKIARCOCT6DWLUB7F77Z\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" input\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"fs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"prop\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"secretKey\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"gfz71RX\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"+\"}],\"Tj4udve43YePCBqMsIeN1PvHXrVFyxJS\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" input\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"fs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"prop\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"region\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"eu\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"west\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"2\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"If we run this query, we’ll see the following output:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample events_OFFLINE query result\",\"src\":\"https://www.datocms-assets.com/75153/1668701654-image5.png\",\"title\":\"Sample events_OFFLINE query result\"}]}],[\"$\",\"p\",null,{\"children\":[\"We can check on the state of the ingestion job via the Swagger REST API. If we navigate to \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/help#/Task/getTaskState\",\"children\":\"http://localhost:9000/help#/Task/getTaskState\"}],\", paste Task_SegmentGenerationAndPushTask_events-task as our task name, and then click Execute, we’ll see the following:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Checking the state of an ingestion job screen\",\"src\":\"https://www.datocms-assets.com/75153/1668701727-image2.png\",\"title\":\"Checking the state of an ingestion job screen\"}]}],[\"$\",\"p\",null,{\"children\":\"If we see the state COMPLETED, this means the data has been ingested, which we can check by going back to the Query console and clicking on the events table.\"}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":\"I have to say that batch ingestion of data into Apache Pinot has always felt a bit clunky, but with this new clause, it’s super easy, and it’s gonna save us all a bunch of time.\"}],[\"$\",\"p\",null,{\"children\":\"Also, anything that means I’m not writing YAML files has got to be a good thing!\"}],[\"$\",\"p\",null,{\"children\":[\"So give it a try and let us know how you get on. If you have any questions about this feature, feel free to join us on \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://stree.ai/slack\",\"children\":\"Slack\"}],\", where we’ll be happy to help you out.\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"Batch importing: The Job Specification\",\"url\":\"#batch-importing-the-job-specification\",\"depth\":2},{\"value\":\"Batch Importing with SQL\",\"url\":\"#batch-importing-with-sql\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Apache Pinot™ 0.11 - Inserts from SQL | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"Explore the INSERT INTO clause, which makes ingesting batch data into Pinot as easy as writing a SQL query.\"}],[\"$\",\"meta\",\"4\",{\"name\":\"robots\",\"content\":\"index, follow\"}],[\"$\",\"meta\",\"5\",{\"name\":\"googlebot\",\"content\":\"index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1\"}],[\"$\",\"link\",\"6\",{\"rel\":\"canonical\",\"href\":\"https://pinot.apache.org/blog/2022/11/17/Apache%20Pinot-Inserts-from-SQL\"}],[\"$\",\"link\",\"7\",{\"rel\":\"alternate\",\"type\":\"application/rss+xml\",\"href\":\"https://pinot.apache.org/feed.xml\"}],[\"$\",\"meta\",\"8\",{\"property\":\"og:title\",\"content\":\"Apache Pinot™ 0.11 - Inserts from SQL\"}],[\"$\",\"meta\",\"9\",{\"property\":\"og:description\",\"content\":\"Explore the INSERT INTO clause, which makes ingesting batch data into Pinot as easy as writing a SQL query.\"}],[\"$\",\"meta\",\"10\",{\"property\":\"og:url\",\"content\":\"https://pinot.apache.org/blog/2022/11/17/Apache%20Pinot-Inserts-from-SQL\"}],[\"$\",\"meta\",\"11\",{\"property\":\"og:site_name\",\"content\":\"Apache Pinot™\"}],[\"$\",\"meta\",\"12\",{\"property\":\"og:locale\",\"content\":\"en_US\"}],[\"$\",\"meta\",\"13\",{\"property\":\"og:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"14\",{\"property\":\"og:type\",\"content\":\"article\"}],[\"$\",\"meta\",\"15\",{\"property\":\"article:published_time\",\"content\":\"2022-11-17T00:00:00.000Z\"}],[\"$\",\"meta\",\"16\",{\"property\":\"article:modified_time\",\"content\":\"2022-11-17T00:00:00.000Z\"}],[\"$\",\"meta\",\"17\",{\"property\":\"article:author\",\"content\":\"Mark Needham\"}],[\"$\",\"meta\",\"18\",{\"name\":\"twitter:card\",\"content\":\"summary_large_image\"}],[\"$\",\"meta\",\"19\",{\"name\":\"twitter:title\",\"content\":\"Apache Pinot™ 0.11 - Inserts from SQL\"}],[\"$\",\"meta\",\"20\",{\"name\":\"twitter:description\",\"content\":\"Explore the INSERT INTO clause, which makes ingesting batch data into Pinot as easy as writing a SQL query.\"}],[\"$\",\"meta\",\"21\",{\"name\":\"twitter:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"22\",{\"name\":\"next-size-adjust\"}]]\n"])</script><script>self.__next_f.push([1,"f:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>
\ No newline at end of file
+</span></code></pre></div><p>If we run this query, we’ll see the following output:</p><p><img alt="Sample events_OFFLINE query result" src="https://www.datocms-assets.com/75153/1668701654-image5.png" title="Sample events_OFFLINE query result"/></p><p>We can check on the state of the ingestion job via the Swagger REST API. If we navigate to <a target="_blank" rel="noopener noreferrer" href="http://localhost:9000/help#/Task/getTaskState">http://localhost:9000/help#/Task/getTaskState</a>, paste Task_SegmentGenerationAndPushTask_events-task as our task name, and then click Execute, we’ll see the following:</p><p><img alt="Checking the state of an ingestion job screen" src="https://www.datocms-assets.com/75153/1668701727-image2.png" title="Checking the state of an ingestion job screen"/></p><p>If we see the state COMPLETED, this means the data has been ingested, which we can check by going back to the Query console and clicking on the events table.</p><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>I have to say that batch ingestion of data into Apache Pinot has always felt a bit clunky, but with this new clause, it’s super easy, and it’s gonna save us all a bunch of time.</p><p>Also, anything that means I’m not writing YAML files has got to be a good thing!</p><p>So give it a try and let us know how you get on. If you have any questions about this feature, feel free to join us on <a target="_blank" rel="noopener noreferrer" href="https://stree.ai/slack">Slack</a>, where we’ll be happy to help you out.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#batch-importing-the-job-specification">Batch importing: The Job Specification</a></li><li class="text-sm font-normal leading-tight"><a href="#batch-importing-with-sql">Batch Importing with SQL</a></li><li class="text-sm font-normal leading-tight"><a 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atrule key\",\"children\":\"recordReaderSpec\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token atrule key\",\"children\":\"dataFormat\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'json'\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token atrule key\",\"children\":\"className\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'org.apache.pinot.plugin.inputformat.json.JSONRecordReader'\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token atrule key\",\"children\":\"tableSpec\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token atrule key\",\"children\":\"tableName\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'events'\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token atrule key\",\"children\":\"pinotClusterSpecs\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"-\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token atrule key\",\"children\":\"controllerURI\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'http://${PINOT_CONTROLLER}:9000'\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"At a high level, this file describes a batch import job that will ingest files from the S3 bucket at s3://marks-st-cloud-bucket/events/ where the files match the glob:**/*.json pattern.\"}],[\"$\",\"p\",null,{\"children\":\"We can import the data by running the following command from the terminal:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--network\"}],\" ingest-json-files-s3 \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/config:/config \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-e\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable assign-left\",\"children\":\"AWS_ACCESS_KEY_ID\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"AKIARCOCT6DWLUB7F77Z \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-e\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable assign-left\",\"children\":\"AWS_SECRET_ACCESS_KEY\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"gfz71RX+Tj4udve43YePCBqMsIeN1PvHXrVFyxJS \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.11.0 LaunchDataIngestionJob \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-jobSpecFile\"}],\" /config/job-spec.yml \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-values\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable assign-left\",\"children\":\"PINOT_CONTROLLER\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"pinot-controller\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"And don’t worry, those credentials have already been deleted; I find it easier to understand what values go where if we use real values.\"}],[\"$\",\"p\",null,{\"children\":[\"Once we’ve run this command, if we go to the Pinot UI at \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/\",\"children\":\"http://localhost:9000\"}],\" and click through to the events table from the Query Console menu, we’ll see that the records have been imported, as shown in the screenshot below:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample imported records shown in the Apache Pinot Query Console menu\",\"src\":\"https://www.datocms-assets.com/75153/1668701512-image3.png\",\"title\":\"Sample imported records shown in the Apache Pinot Query Console menu\"}]}],[\"$\",\"p\",null,{\"children\":\"This approach works, and we may still prefer to use it when we need fine-grained control over the ingestion parameters, but it is a bit heavyweight for your everyday data import!\"}],[\"$\",\"h2\",null,{\"id\":\"batch-importing-with-sql\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#batch-importing-with-sql\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Batch Importing with SQL\"]}],[\"$\",\"p\",null,{\"children\":\"Now let’s do the same thing in SQL.\"}],[\"$\",\"p\",null,{\"children\":\"There are some prerequisites to using the SQL approach, so let’s go through those now, so you don’t end up with a bunch of exceptions when you try this out!\"}],[\"$\",\"p\",null,{\"children\":[\"First of all, you must have a \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/basics/components/minion\",\"children\":\"Minion\"}],\" in the Pinot cluster, as this is the component that will do the data import.\"]}],[\"$\",\"p\",null,{\"children\":\"You’ll also need to include the following in your table config:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"task\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"taskTypeConfigsMap\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"SegmentGenerationAndPushTask\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"As long as you’ve done those two things, we’re ready to write our import query! A query that imports JSON files from my S3 bucket is shown below:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"INSERT\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"INTO\"}],\" events\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FILE\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'s3://marks-st-cloud-bucket/events/'\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"OPTION\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" taskName\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"events\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"task\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" includeFileNamePattern\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"glob:\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"/\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"json\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" input\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"fs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"className\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"org\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"apache\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"pinot\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"plugin\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"filesystem\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"S3PinotFS\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" input\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"fs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"prop\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"accessKey\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"AKIARCOCT6DWLUB7F77Z\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" input\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"fs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"prop\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"secretKey\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"gfz71RX\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"+\"}],\"Tj4udve43YePCBqMsIeN1PvHXrVFyxJS\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" input\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"fs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"prop\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"region\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"eu\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"west\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"2\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"If we run this query, we’ll see the following output:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample events_OFFLINE query result\",\"src\":\"https://www.datocms-assets.com/75153/1668701654-image5.png\",\"title\":\"Sample events_OFFLINE query result\"}]}],[\"$\",\"p\",null,{\"children\":[\"We can check on the state of the ingestion job via the Swagger REST API. If we navigate to \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/help#/Task/getTaskState\",\"children\":\"http://localhost:9000/help#/Task/getTaskState\"}],\", paste Task_SegmentGenerationAndPushTask_events-task as our task name, and then click Execute, we’ll see the following:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Checking the state of an ingestion job screen\",\"src\":\"https://www.datocms-assets.com/75153/1668701727-image2.png\",\"title\":\"Checking the state of an ingestion job screen\"}]}],[\"$\",\"p\",null,{\"children\":\"If we see the state COMPLETED, this means the data has been ingested, which we can check by going back to the Query console and clicking on the events table.\"}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":\"I have to say that batch ingestion of data into Apache Pinot has always felt a bit clunky, but with this new clause, it’s super easy, and it’s gonna save us all a bunch of time.\"}],[\"$\",\"p\",null,{\"children\":\"Also, anything that means I’m not writing YAML files has got to be a good thing!\"}],[\"$\",\"p\",null,{\"children\":[\"So give it a try and let us know how you get on. If you have any questions about this feature, feel free to join us on \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://stree.ai/slack\",\"children\":\"Slack\"}],\", where we’ll be happy to help you out.\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"Batch importing: The Job Specification\",\"url\":\"#batch-importing-the-job-specification\",\"depth\":2},{\"value\":\"Batch Importing with SQL\",\"url\":\"#batch-importing-with-sql\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Apache Pinot™ 0.11 - Inserts from SQL | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"Explore the INSERT INTO clause, which makes ingesting batch data into Pinot as easy as writing a SQL query.\"}],[\"$\",\"meta\",\"4\",{\"name\":\"robots\",\"content\":\"index, follow\"}],[\"$\",\"meta\",\"5\",{\"name\":\"googlebot\",\"content\":\"index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1\"}],[\"$\",\"link\",\"6\",{\"rel\":\"canonical\",\"href\":\"https://pinot.apache.org/blog/2022/11/17/Apache%20Pinot-Inserts-from-SQL\"}],[\"$\",\"link\",\"7\",{\"rel\":\"alternate\",\"type\":\"application/rss+xml\",\"href\":\"https://pinot.apache.org/feed.xml\"}],[\"$\",\"meta\",\"8\",{\"property\":\"og:title\",\"content\":\"Apache Pinot™ 0.11 - Inserts from SQL\"}],[\"$\",\"meta\",\"9\",{\"property\":\"og:description\",\"content\":\"Explore the INSERT INTO clause, which makes ingesting batch data into Pinot as easy as writing a SQL query.\"}],[\"$\",\"meta\",\"10\",{\"property\":\"og:url\",\"content\":\"https://pinot.apache.org/blog/2022/11/17/Apache%20Pinot-Inserts-from-SQL\"}],[\"$\",\"meta\",\"11\",{\"property\":\"og:site_name\",\"content\":\"Apache Pinot™\"}],[\"$\",\"meta\",\"12\",{\"property\":\"og:locale\",\"content\":\"en_US\"}],[\"$\",\"meta\",\"13\",{\"property\":\"og:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"14\",{\"property\":\"og:type\",\"content\":\"article\"}],[\"$\",\"meta\",\"15\",{\"property\":\"article:published_time\",\"content\":\"2022-11-17T00:00:00.000Z\"}],[\"$\",\"meta\",\"16\",{\"property\":\"article:modified_time\",\"content\":\"2022-11-17T00:00:00.000Z\"}],[\"$\",\"meta\",\"17\",{\"property\":\"article:author\",\"content\":\"Mark Needham\"}],[\"$\",\"meta\",\"18\",{\"name\":\"twitter:card\",\"content\":\"summary_large_image\"}],[\"$\",\"meta\",\"19\",{\"name\":\"twitter:title\",\"content\":\"Apache Pinot™ 0.11 - Inserts from SQL\"}],[\"$\",\"meta\",\"20\",{\"name\":\"twitter:description\",\"content\":\"Explore the INSERT INTO clause, which makes ingesting batch data into Pinot as easy as writing a SQL query.\"}],[\"$\",\"meta\",\"21\",{\"name\":\"twitter:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"22\",{\"name\":\"next-size-adjust\"}]]\n"])</script><script>self.__next_f.push([1,"f:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>
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diff --git a/blog/2022/11/17/Apache Pinot-Inserts-from-SQL.txt b/blog/2022/11/17/Apache Pinot-Inserts-from-SQL.txt
index 898f3e7..5bbcfeb 100644
--- a/blog/2022/11/17/Apache Pinot-Inserts-from-SQL.txt
+++ b/blog/2022/11/17/Apache Pinot-Inserts-from-SQL.txt
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diff --git a/blog/2022/11/22/Apache-Pinot-Timestamp-Indexes.html b/blog/2022/11/22/Apache-Pinot-Timestamp-Indexes.html
index c388db0..2bd5c47 100644
--- a/blog/2022/11/22/Apache-Pinot-Timestamp-Indexes.html
+++ b/blog/2022/11/22/Apache-Pinot-Timestamp-Indexes.html
@@ -99,4 +99,4 @@
</span><span class="code-line"><span class="token keyword">group</span> <span class="token keyword">by</span> tsWeek
</span><span class="code-line"><span class="token keyword">order</span> <span class="token keyword">by</span> <span class="token function">count</span><span class="token punctuation">(</span><span class="token operator">*</span><span class="token punctuation">)</span> <span class="token keyword">DESC</span>
</span><span class="code-line"><span class="token keyword">limit</span> <span class="token number">10</span>
-</span></code></pre></div><h2 id="re-running-the-query"><a href="#re-running-the-query" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Re-running the query</h2><p>Let’s now run our initial query against the <em>crimes_indexed</em> table. We’ll get exactly the same results as before, but let’s take a look at the query stats:</p><p><img alt="Chicago crime dataset updated query stats" src="https://www.datocms-assets.com/75153/1669133083-image4.png" title="Chicago crime dataset updated query stats"/></p><p>This time the query takes 36 milliseconds rather than 140 milliseconds. That’s an almost 5x improvement, thanks to the timestamp index.</p><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>Hopefully, you’ll agree that timestamp indexes are pretty cool, and achieving a 5x query improvement without much work is always welcome!</p><p>If you’re using timestamps in your Pinot tables, be sure to try out this index and let us know how it goes on the <a target="_blank" rel="noopener noreferrer" href="https://stree.ai/slack">StarTree Community Slack</a> . We’re always happy to help out with any questions or problems you encounter.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#spinning-up-pinot">Spinning up Pinot</a></li><li class="text-sm font-normal leading-tight"><a href="#importing-chicago-crime-dataset">Importing Chicago Crime Dataset</a></li><li class="text-sm font-normal leading-tight"><a href="#querying-crimes-by-date">Querying crimes by date</a></li><li class="text-sm font-normal leading-tight"><a href="#adding-the-timestamp-index">Adding the timestamp index</a></li><li class="text-sm font-normal leading-tight"><a href="#what-does-a-timestamp-index-do">What does a timestamp index do?</a></li><li class="text-sm font-normal leading-tight"><a href="#re-running-the-query">Re-running the query</a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 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punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.11.0 \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" QuickStart \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-type\"}],\" EMPTY\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Or if you’re on a Mac M1, change the name of the image to have the arm-64 suffix, like this:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-p\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"8000\"}],\":8000 \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-p\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"9000\"}],\":9000 \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--network\"}],\" timestamp_blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--name\"}],\" pinot-timestamp-blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.11.0-arm64 \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" QuickStart \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-type\"}],\" EMPTY\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"Once that’s up and running, we’ll be able to access the Pinot Data Explorer at \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/\",\"children\":\"http://localhost:9000\"}],\", but at the moment, we don’t have any data to play with.\"]}],[\"$\",\"h2\",null,{\"id\":\"importing-chicago-crime-dataset\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#importing-chicago-crime-dataset\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Importing Chicago Crime Dataset\"]}],[\"$\",\"p\",null,{\"children\":[\"The \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/blog/analyzing-chicago-crimes-with-apache-pinot-and-streamlit\",\"children\":\"Chicago Crime dataset\"}],\" is a small to medium-sized dataset with 7 million records representing reported crimes in the City of Chicago from 2001 until today.\"]}],[\"$\",\"p\",null,{\"children\":\"It contains details of the type of crime, where it was committed, whether an arrest was recorded, which beat it occurred on, and more.\"}],[\"$\",\"p\",null,{\"children\":\"Each of the crimes has an associated timestamp, which makes it a good dataset to demonstrate timestamp indexes.\"}],[\"$\",\"p\",null,{\"children\":[\"You can find the code used in this blog post in the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/startreedata/pinot-recipes/tree/main/recipes/analyzing-chicago-crimes\",\"children\":\"Analyzing Chicago Crimes\"}],\" recipe section of \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/startreedata/pinot-recipes\",\"children\":\"Pinot Recipes GitHub repository\"}],\". From here on, I’m assuming that you’ve downloaded this repository and are in the recipes/analyzing-chicago-crimes directory.\"]}],[\"$\",\"p\",null,{\"children\":\"We’re going to create a schema and table named crimes by running the following command:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--network\"}],\" timestamp_blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/config:/config \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.11.0-arm64 AddTable \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-schemaFile\"}],\" /config/schema.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-tableConfigFile\"}],\" /config/table.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-controllerHost\"}],\" pinot-timestamp-blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-exec\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}]]}]}],[\"$\",\"p\",null,{\"children\":\"We should see the following output:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"2022\"}],\"/11/03 \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"13\"}],\":07:57.169 INFO \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"AddTableCommand\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"main\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"unrecognizedProperties\\\"\"}],\":\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\",\",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"status\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token builtin class-name\",\"children\":\":\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"TableConfigs crimes successfully added\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"A screenshot of the schema is shown below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Chicago crime dataset table schema\",\"src\":\"https://www.datocms-assets.com/75153/1669132979-image3.png\",\"title\":\"Chicago crime dataset table schema\"}]}],[\"$\",\"p\",null,{\"children\":[\"We won’t go through the table config and schema files in this blog post because we did that in the last post, but you can find them in the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/startreedata/pinot-recipes/tree/main/recipes/analyzing-chicago-crimes/config\",\"children\":\"config\"}],\" directory on GitHub.\"]}],[\"$\",\"p\",null,{\"children\":\"Now, let’s import the dataset.\"}],[\"$\",\"$L14\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--network\"}],\" timestamp_blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/config:/config \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/data:/data \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.11.0-arm64 LaunchDataIngestionJob \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-jobSpecFile\"}],\" /config/job-spec.yml \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-values\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable assign-left\",\"children\":\"controllerHost\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"pinot-timestamp-blog\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"It will take a few minutes to load, but once that command has finished, we’re ready to query the crimes table.\"}],[\"$\",\"h2\",null,{\"id\":\"querying-crimes-by-date\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#querying-crimes-by-date\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Querying crimes by date\"]}],[\"$\",\"p\",null,{\"children\":\"The following query finds the number of crimes that happened after 16th January 2017, grouped by week of the year, with the most crime-filled weeks shown first:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" datetrunc\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'WEEK'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" tsWeek\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" crimes\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\" tsWeek \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e\"}],\" fromDateTime\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2017-01-16'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'yyyy-MM-dd'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"group\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" tsWeek\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"order\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"DESC\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"If we run that query, we’ll see the following results:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Chicago crime dataset query result\",\"src\":\"https://www.datocms-assets.com/75153/1669133027-image6.png\",\"title\":\"Chicago crime dataset query result\"}]}],[\"$\",\"p\",null,{\"children\":\"And, if we look above the query result, there’s metadata about the query, including the time that it took to run.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Chicago crime dataset metadata about the query, including the time that it took to run\",\"src\":\"https://www.datocms-assets.com/75153/1669133059-image5.png\",\"title\":\"Chicago crime dataset metadata about the query, including the time that it took to run\"}]}],[\"$\",\"p\",null,{\"children\":\"The query took 141 ms to execute, so that’s our baseline.\"}],[\"$\",\"h2\",null,{\"id\":\"adding-the-timestamp-index\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#adding-the-timestamp-index\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Adding the timestamp index\"]}],[\"$\",\"p\",null,{\"children\":\"We could add a timestamp index directly to this table and then compare query performance, but to make it easier to do comparisons, we’re going to create an identical table with the timestamp index applied.\"}],[\"$\",\"p\",null,{\"children\":[\"The full table config is available in the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/startreedata/pinot-recipes/blob/main/recipes/analyzing-chicago-crimes/config/table-index.json\",\"children\":\"config/table-index.json\"}],\" file, and the main change is that we’ve added the following section to add a timestamp index on the DateEpoch column:\"]}],[\"$\",\"$L14\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"fieldConfigList\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"DateEpoch\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"encodingType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"DICTIONARY\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"indexTypes\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"TIMESTAMP\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"timestampConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"granularities\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"DAY\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"WEEK\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MONTH\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"em\",null,{\"children\":\"encodingType\"}],\" will always be ‘DICTIONARY’ and \",[\"$\",\"em\",null,{\"children\":\"indexTypes\"}],\" must contain ‘TIMESTAMP’. We should specify granularities based on our query patterns.\"]}],[\"$\",\"p\",null,{\"children\":[\"As a rule of thumb, work out which values you most commonly pass as the first argument to the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/configuration-reference/functions/datetrunc\",\"children\":\"datetrunc function\"}],\" in your queries and include those values.\"]}],[\"$\",\"p\",null,{\"children\":[\"The full list of valid granularities is: \",[\"$\",\"em\",null,{\"children\":\"millisecond\"}],\", \",[\"$\",\"em\",null,{\"children\":\"second\"}],\", \",[\"$\",\"em\",null,{\"children\":\"minute\"}],\", \",[\"$\",\"em\",null,{\"children\":\"hour\"}],\", \",[\"$\",\"em\",null,{\"children\":\"day\"}],\", \",[\"$\",\"em\",null,{\"children\":\"week\"}],\", \",[\"$\",\"em\",null,{\"children\":\"month\"}],\", \",[\"$\",\"em\",null,{\"children\":\"quarter\"}],\", and \",[\"$\",\"em\",null,{\"children\":\"year\"}],\".\"]}],[\"$\",\"p\",null,{\"children\":\"Our new table is called crimes_indexed, and we’re also going to create a new schema with all the same columns called crimes_indexed, as Pinot requires the table and schema names to match.\"}],[\"$\",\"p\",null,{\"children\":\"We can create the schema and table by running the following command:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--network\"}],\" timestamp_blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/config:/config \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.11.0-arm64 AddTable \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-schemaFile\"}],\" /config/schema-index.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-tableConfigFile\"}],\" /config/table-index.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-controllerHost\"}],\" pinot-timestamp-blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-exec\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"We’ll populate that table by copying the segment that we created earlier for the crimes table.\"}],[\"$\",\"$L14\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--network\"}],\" timestamp_blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/config:/config \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/data:/data \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.11.0-arm64 LaunchDataIngestionJob \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-jobSpecFile\"}],\" /config/job-spec-download.yml \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-values\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable assign-left\",\"children\":\"controllerHost\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"pinot-timestamp-blog\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"If you’re curious how that job spec works, I \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://www.markhneedham.com/blog/2021/12/06/apache-pinot-copy-segment-new-table/\",\"children\":\"wrote a blog post explaining it in a bit more detail\"}],\".\"]}],[\"$\",\"p\",null,{\"children\":\"Once the Pinot Server has downloaded this segment, it will apply the timestamp index to the DateEpoch column.\"}],[\"$\",\"p\",null,{\"children\":\"For the curious, we can see this happening in the log files by connecting to the Pinot container and running the following grep command:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"​​docker \",[\"$\",\"span\",null,{\"className\":\"token builtin class-name\",\"children\":\"exec\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-iti\"}],\" pinot-timestamp-blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"grep\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-rni\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-A10\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Successfully downloaded segment:.*crimes_indexed_OFFLINE.*\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" logs/pinot-all.log\\n\"}]]}]}],[\"$\",\"p\",null,{\"children\":\"We’ll see something like the following (tidied up for brevity):\"}],[\"$\",\"$L14\",null,{\"className\":\"language-log\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-log\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"BaseTableDataManager\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Successfully downloaded segment:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"crimes_OFFLINE_0 of table:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"crimes_indexed_OFFLINE to index dir:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string file-path\",\"children\":\"/tmp/1667490598253/quickstart/PinotServerDataDir0/crimes_indexed_OFFLINE/crimes_OFFLINE_0\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"V3DefaultColumnHandler\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Starting default column action:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"ADD_DATE_TIME on column:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DAY\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"SegmentDictionaryCreator\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Created dictionary for LONG column:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DAY with cardinality\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"7969\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" range\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"978307200000\"}],\" to \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1666742400000\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"V3DefaultColumnHandler\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Starting default column action:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"ADD_DATE_TIME on column:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"WEEK\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"SegmentDictionaryCreator\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Created dictionary for LONG column:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"WEEK with cardinality\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1139\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" range\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"978307200000\"}],\" to \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1666569600000\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"V3DefaultColumnHandler\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Starting default column action:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"ADD_DATE_TIME on column:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"MONTH\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"SegmentDictionaryCreator\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Created dictionary for LONG column:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"MONTH with cardinality\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"262\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" range\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"978307200000\"}],\" to \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1664582400000\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"RangeIndexHandler\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Creating new range index for segment:\"}],\" crimes_OFFLINE_0\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" column\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DAY\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"RangeIndexHandler\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Created range index for segment:\"}],\" crimes_OFFLINE_0\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" column\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DAY\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"RangeIndexHandler\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Creating new range index for segment:\"}],\" crimes_OFFLINE_0\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" column\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"WEEK\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"RangeIndexHandler\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Created range index for segment:\"}],\" crimes_OFFLINE_0\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" column\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"WEEK\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}]]}]}],[\"$\",\"h2\",null,{\"id\":\"what-does-a-timestamp-index-do\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#what-does-a-timestamp-index-do\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"What does a timestamp index do?\"]}],[\"$\",\"p\",null,{\"children\":\"So, the timestamp index has now been created, but what does it actually do?\"}],[\"$\",\"p\",null,{\"children\":\"When we add a timestamp index on a column, Pinot creates a derived column for each granularity and adds a range index for each new column.\"}],[\"$\",\"p\",null,{\"children\":[\"In our case, that means we’ll have these extra columns: \",[\"$\",\"span\",null,{\"className\":\"math math-inline\",\"children\":[\"$\",\"span\",null,{\"className\":\"katex\",\"children\":[[\"$\",\"span\",null,{\"className\":\"katex-mathml\",\"children\":[\"$\",\"math\",null,{\"xmlns\":\"http://www.w3.org/1998/Math/MathML\",\"children\":[\"$\",\"semantics\",null,{\"children\":[[\"$\",\"mrow\",null,{\"children\":[[\"$\",\"mi\",null,{\"children\":\"D\"}],[\"$\",\"mi\",null,{\"children\":\"a\"}],[\"$\",\"mi\",null,{\"children\":\"t\"}],[\"$\",\"mi\",null,{\"children\":\"e\"}],[\"$\",\"mi\",null,{\"children\":\"E\"}],[\"$\",\"mi\",null,{\"children\":\"p\"}],[\"$\",\"mi\",null,{\"children\":\"o\"}],[\"$\",\"mi\",null,{\"children\":\"c\"}],[\"$\",\"mi\",null,{\"children\":\"h\"}]]}],[\"$\",\"annotation\",null,{\"encoding\":\"application/x-tex\",\"children\":\"DateEpoch\"}]]}]}]}],[\"$\",\"span\",null,{\"className\":\"katex-html\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"span\",null,{\"className\":\"base\",\"children\":[[\"$\",\"span\",null,{\"className\":\"strut\",\"style\":{\"height\":\".8889em\",\"verticalAlign\":\"-.1944em\"}}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"style\":{\"marginRight\":\".02778em\"},\"children\":\"D\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"a\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"t\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"e\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"Ep\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"oc\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"h\"}]]}]}]]}]}],\"DAY, \",[\"$\",\"span\",null,{\"className\":\"math math-inline\",\"children\":[\"$\",\"span\",null,{\"className\":\"katex\",\"children\":[[\"$\",\"span\",null,{\"className\":\"katex-mathml\",\"children\":[\"$\",\"math\",null,{\"xmlns\":\"http://www.w3.org/1998/Math/MathML\",\"children\":[\"$\",\"semantics\",null,{\"children\":[[\"$\",\"mrow\",null,{\"children\":[[\"$\",\"mi\",null,{\"children\":\"D\"}],[\"$\",\"mi\",null,{\"children\":\"a\"}],[\"$\",\"mi\",null,{\"children\":\"t\"}],[\"$\",\"mi\",null,{\"children\":\"e\"}],[\"$\",\"mi\",null,{\"children\":\"E\"}],[\"$\",\"mi\",null,{\"children\":\"p\"}],[\"$\",\"mi\",null,{\"children\":\"o\"}],[\"$\",\"mi\",null,{\"children\":\"c\"}],[\"$\",\"mi\",null,{\"children\":\"h\"}]]}],[\"$\",\"annotation\",null,{\"encoding\":\"application/x-tex\",\"children\":\"DateEpoch\"}]]}]}]}],[\"$\",\"span\",null,{\"className\":\"katex-html\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"span\",null,{\"className\":\"base\",\"children\":[[\"$\",\"span\",null,{\"className\":\"strut\",\"style\":{\"height\":\".8889em\",\"verticalAlign\":\"-.1944em\"}}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"style\":{\"marginRight\":\".02778em\"},\"children\":\"D\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"a\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"t\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"e\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"Ep\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"oc\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"h\"}]]}]}]]}]}],\"WEEK, and \",[\"$\",\"span\",null,{\"className\":\"math math-inline\",\"children\":[\"$\",\"span\",null,{\"className\":\"katex\",\"children\":[[\"$\",\"span\",null,{\"className\":\"katex-mathml\",\"children\":[\"$\",\"math\",null,{\"xmlns\":\"http://www.w3.org/1998/Math/MathML\",\"children\":[\"$\",\"semantics\",null,{\"children\":[[\"$\",\"mrow\",null,{\"children\":[[\"$\",\"mi\",null,{\"children\":\"D\"}],[\"$\",\"mi\",null,{\"children\":\"a\"}],[\"$\",\"mi\",null,{\"children\":\"t\"}],[\"$\",\"mi\",null,{\"children\":\"e\"}],[\"$\",\"mi\",null,{\"children\":\"E\"}],[\"$\",\"mi\",null,{\"children\":\"p\"}],[\"$\",\"mi\",null,{\"children\":\"o\"}],[\"$\",\"mi\",null,{\"children\":\"c\"}],[\"$\",\"mi\",null,{\"children\":\"h\"}]]}],[\"$\",\"annotation\",null,{\"encoding\":\"application/x-tex\",\"children\":\"DateEpoch\"}]]}]}]}],[\"$\",\"span\",null,{\"className\":\"katex-html\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"span\",null,{\"className\":\"base\",\"children\":[[\"$\",\"span\",null,{\"className\":\"strut\",\"style\":{\"height\":\".8889em\",\"verticalAlign\":\"-.1944em\"}}],[\"$\",\"span\",null,{\"className\":\"mathnormal 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mord\",\"children\":\"a\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"t\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"e\"}]]}]}]]}]}],\"Epoch in the search box. You should see the following:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot timestamp index on a column\",\"src\":\"https://www.datocms-assets.com/75153/1669133112-image2.png\",\"title\":\"Apache Pinot timestamp index on a column\"}]}],[\"$\",\"p\",null,{\"children\":\"These columns will be assigned the following values:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":[[\"$\",\"span\",null,{\"className\":\"math math-inline\",\"children\":[\"$\",\"span\",null,{\"className\":\"katex\",\"children\":[[\"$\",\"span\",null,{\"className\":\"katex-mathml\",\"children\":[\"$\",\"math\",null,{\"xmlns\":\"http://www.w3.org/1998/Math/MathML\",\"children\":[\"$\",\"semantics\",null,{\"children\":[[\"$\",\"mrow\",null,{\"children\":[[\"$\",\"mi\",null,{\"children\":\"D\"}],[\"$\",\"mi\",null,{\"children\":\"a\"}],[\"$\",\"mi\",null,{\"children\":\"t\"}],[\"$\",\"mi\",null,{\"children\":\"e\"}],[\"$\",\"mi\",null,{\"children\":\"E\"}],[\"$\",\"mi\",null,{\"children\":\"p\"}],[\"$\",\"mi\",null,{\"children\":\"o\"}],[\"$\",\"mi\",null,{\"children\":\"c\"}],[\"$\",\"mi\",null,{\"children\":\"h\"}]]}],[\"$\",\"annotation\",null,{\"encoding\":\"application/x-tex\",\"children\":\"DateEpoch\"}]]}]}]}],[\"$\",\"span\",null,{\"className\":\"katex-html\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"span\",null,{\"className\":\"base\",\"children\":[[\"$\",\"span\",null,{\"className\":\"strut\",\"style\":{\"height\":\".8889em\",\"verticalAlign\":\"-.1944em\"}}],[\"$\",\"span\",null,{\"className\":\"mathnormal 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mord\",\"style\":{\"marginRight\":\".02778em\"},\"children\":\"D\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"a\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"t\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"e\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"Ep\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"oc\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"h\"}]]}]}]]}]}],\"WEEK = dateTrunc(‘WEEK’, DateEpoch)\"]}],[\"$\",\"li\",null,{\"children\":[[\"$\",\"span\",null,{\"className\":\"math math-inline\",\"children\":[\"$\",\"span\",null,{\"className\":\"katex\",\"children\":[[\"$\",\"span\",null,{\"className\":\"katex-mathml\",\"children\":[\"$\",\"math\",null,{\"xmlns\":\"http://www.w3.org/1998/Math/MathML\",\"children\":[\"$\",\"semantics\",null,{\"children\":[[\"$\",\"mrow\",null,{\"children\":[[\"$\",\"mi\",null,{\"children\":\"D\"}],[\"$\",\"mi\",null,{\"children\":\"a\"}],[\"$\",\"mi\",null,{\"children\":\"t\"}],[\"$\",\"mi\",null,{\"children\":\"e\"}],[\"$\",\"mi\",null,{\"children\":\"E\"}],[\"$\",\"mi\",null,{\"children\":\"p\"}],[\"$\",\"mi\",null,{\"children\":\"o\"}],[\"$\",\"mi\",null,{\"children\":\"c\"}],[\"$\",\"mi\",null,{\"children\":\"h\"}]]}],[\"$\",\"annotation\",null,{\"encoding\":\"application/x-tex\",\"children\":\"DateEpoch\"}]]}]}]}],[\"$\",\"span\",null,{\"className\":\"katex-html\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"span\",null,{\"className\":\"base\",\"children\":[[\"$\",\"span\",null,{\"className\":\"strut\",\"style\":{\"height\":\".8889em\",\"verticalAlign\":\"-.1944em\"}}],[\"$\",\"span\",null,{\"className\":\"mathnormal 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language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" datetrunc\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'WEEK'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" tsWeek\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" crimes_indexed\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"GROUP\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" tsWeek\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Would be rewritten as:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" $DateEpoch$WEEK \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" tsWeek\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" crimes_indexed\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"GROUP\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" tsWeek\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"And our query:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" datetrunc\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'WEEK'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" tsWeek\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" crimes\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\" tsWeek \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e\"}],\" fromDateTime\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2017-01-16'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'yyyy-MM-dd'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"group\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" tsWeek\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"order\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"DESC\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Would be rewritten as:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" $DateEpoch$WEEK \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" tsWeek\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" crimes\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\" tsWeek \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e\"}],\" fromDateTime\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2017-01-16'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'yyyy-MM-dd'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"group\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" tsWeek\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"order\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"DESC\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}]]}]}],[\"$\",\"h2\",null,{\"id\":\"re-running-the-query\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#re-running-the-query\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Re-running the query\"]}],[\"$\",\"p\",null,{\"children\":[\"Let’s now run our initial query against the \",[\"$\",\"em\",null,{\"children\":\"crimes_indexed\"}],\" table. We’ll get exactly the same results as before, but let’s take a look at the query stats:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Chicago crime dataset updated query stats\",\"src\":\"https://www.datocms-assets.com/75153/1669133083-image4.png\",\"title\":\"Chicago crime dataset updated query stats\"}]}],[\"$\",\"p\",null,{\"children\":\"This time the query takes 36 milliseconds rather than 140 milliseconds. That’s an almost 5x improvement, thanks to the timestamp index.\"}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":\"Hopefully, you’ll agree that timestamp indexes are pretty cool, and achieving a 5x query improvement without much work is always welcome!\"}],[\"$\",\"p\",null,{\"children\":[\"If you’re using timestamps in your Pinot tables, be sure to try out this index and let us know how it goes on the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://stree.ai/slack\",\"children\":\"StarTree Community Slack\"}],\" . We’re always happy to help out with any questions or problems you encounter.\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L15\",null,{\"chapters\":[{\"value\":\"Spinning up Pinot\",\"url\":\"#spinning-up-pinot\",\"depth\":2},{\"value\":\"Importing Chicago Crime Dataset\",\"url\":\"#importing-chicago-crime-dataset\",\"depth\":2},{\"value\":\"Querying crimes by date\",\"url\":\"#querying-crimes-by-date\",\"depth\":2},{\"value\":\"Adding the timestamp index\",\"url\":\"#adding-the-timestamp-index\",\"depth\":2},{\"value\":\"What does a timestamp index do?\",\"url\":\"#what-does-a-timestamp-index-do\",\"depth\":2},{\"value\":\"Re-running the query\",\"url\":\"#re-running-the-query\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Apache Pinot™ 0.11 - Timestamp Indexes | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"Users write queries that use the datetrunc function to filter at a coarser grain of functionality. 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\ No newline at end of file
+</span></code></pre></div><h2 id="re-running-the-query"><a href="#re-running-the-query" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Re-running the query</h2><p>Let’s now run our initial query against the <em>crimes_indexed</em> table. We’ll get exactly the same results as before, but let’s take a look at the query stats:</p><p><img alt="Chicago crime dataset updated query stats" src="https://www.datocms-assets.com/75153/1669133083-image4.png" title="Chicago crime dataset updated query stats"/></p><p>This time the query takes 36 milliseconds rather than 140 milliseconds. That’s an almost 5x improvement, thanks to the timestamp index.</p><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>Hopefully, you’ll agree that timestamp indexes are pretty cool, and achieving a 5x query improvement without much work is always welcome!</p><p>If you’re using timestamps in your Pinot tables, be sure to try out this index and let us know how it goes on the <a target="_blank" rel="noopener noreferrer" href="https://stree.ai/slack">StarTree Community Slack</a> . We’re always happy to help out with any questions or problems you encounter.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#spinning-up-pinot">Spinning up Pinot</a></li><li class="text-sm font-normal leading-tight"><a href="#importing-chicago-crime-dataset">Importing Chicago Crime Dataset</a></li><li class="text-sm font-normal leading-tight"><a href="#querying-crimes-by-date">Querying crimes by date</a></li><li class="text-sm font-normal leading-tight"><a href="#adding-the-timestamp-index">Adding the timestamp index</a></li><li class="text-sm font-normal leading-tight"><a href="#what-does-a-timestamp-index-do">What does a timestamp index do?</a></li><li class="text-sm font-normal leading-tight"><a href="#re-running-the-query">Re-running the query</a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 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moment, we don’t have any data to play with.\"]}],[\"$\",\"h2\",null,{\"id\":\"importing-chicago-crime-dataset\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#importing-chicago-crime-dataset\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Importing Chicago Crime Dataset\"]}],[\"$\",\"p\",null,{\"children\":[\"The \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/blog/analyzing-chicago-crimes-with-apache-pinot-and-streamlit\",\"children\":\"Chicago Crime dataset\"}],\" is a small to medium-sized dataset with 7 million records representing reported crimes in the City of Chicago from 2001 until today.\"]}],[\"$\",\"p\",null,{\"children\":\"It contains details of the type of crime, where it was committed, whether an arrest was recorded, which beat it occurred on, and more.\"}],[\"$\",\"p\",null,{\"children\":\"Each of the crimes has an associated timestamp, which makes it a good dataset to demonstrate timestamp indexes.\"}],[\"$\",\"p\",null,{\"children\":[\"You can find the code used in this blog post in the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/startreedata/pinot-recipes/tree/main/recipes/analyzing-chicago-crimes\",\"children\":\"Analyzing Chicago Crimes\"}],\" recipe section of \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/startreedata/pinot-recipes\",\"children\":\"Pinot Recipes GitHub repository\"}],\". From here on, I’m assuming that you’ve downloaded this repository and are in the recipes/analyzing-chicago-crimes directory.\"]}],[\"$\",\"p\",null,{\"children\":\"We’re going to create a schema and table named crimes by running the following command:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--network\"}],\" timestamp_blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/config:/config \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.11.0-arm64 AddTable \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-schemaFile\"}],\" /config/schema.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-tableConfigFile\"}],\" /config/table.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-controllerHost\"}],\" pinot-timestamp-blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-exec\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}]]}]}],[\"$\",\"p\",null,{\"children\":\"We should see the following output:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"2022\"}],\"/11/03 \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"13\"}],\":07:57.169 INFO \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"AddTableCommand\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"main\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"unrecognizedProperties\\\"\"}],\":\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\",\",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"status\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token builtin class-name\",\"children\":\":\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"TableConfigs crimes successfully added\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"A screenshot of the schema is shown below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Chicago crime dataset table schema\",\"src\":\"https://www.datocms-assets.com/75153/1669132979-image3.png\",\"title\":\"Chicago crime dataset table schema\"}]}],[\"$\",\"p\",null,{\"children\":[\"We won’t go through the table config and schema files in this blog post because we did that in the last post, but you can find them in the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/startreedata/pinot-recipes/tree/main/recipes/analyzing-chicago-crimes/config\",\"children\":\"config\"}],\" directory on GitHub.\"]}],[\"$\",\"p\",null,{\"children\":\"Now, let’s import the dataset.\"}],[\"$\",\"$L14\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--network\"}],\" timestamp_blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/config:/config \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/data:/data \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.11.0-arm64 LaunchDataIngestionJob \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-jobSpecFile\"}],\" /config/job-spec.yml \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-values\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable assign-left\",\"children\":\"controllerHost\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"pinot-timestamp-blog\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"It will take a few minutes to load, but once that command has finished, we’re ready to query the crimes table.\"}],[\"$\",\"h2\",null,{\"id\":\"querying-crimes-by-date\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#querying-crimes-by-date\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Querying crimes by date\"]}],[\"$\",\"p\",null,{\"children\":\"The following query finds the number of crimes that happened after 16th January 2017, grouped by week of the year, with the most crime-filled weeks shown first:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" datetrunc\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'WEEK'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" tsWeek\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" crimes\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\" tsWeek \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e\"}],\" fromDateTime\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2017-01-16'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'yyyy-MM-dd'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"group\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" tsWeek\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"order\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"DESC\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"If we run that query, we’ll see the following results:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Chicago crime dataset query result\",\"src\":\"https://www.datocms-assets.com/75153/1669133027-image6.png\",\"title\":\"Chicago crime dataset query result\"}]}],[\"$\",\"p\",null,{\"children\":\"And, if we look above the query result, there’s metadata about the query, including the time that it took to run.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Chicago crime dataset metadata about the query, including the time that it took to run\",\"src\":\"https://www.datocms-assets.com/75153/1669133059-image5.png\",\"title\":\"Chicago crime dataset metadata about the query, including the time that it took to run\"}]}],[\"$\",\"p\",null,{\"children\":\"The query took 141 ms to execute, so that’s our baseline.\"}],[\"$\",\"h2\",null,{\"id\":\"adding-the-timestamp-index\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#adding-the-timestamp-index\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Adding the timestamp index\"]}],[\"$\",\"p\",null,{\"children\":\"We could add a timestamp index directly to this table and then compare query performance, but to make it easier to do comparisons, we’re going to create an identical table with the timestamp index applied.\"}],[\"$\",\"p\",null,{\"children\":[\"The full table config is available in the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/startreedata/pinot-recipes/blob/main/recipes/analyzing-chicago-crimes/config/table-index.json\",\"children\":\"config/table-index.json\"}],\" file, and the main change is that we’ve added the following section to add a timestamp index on the DateEpoch column:\"]}],[\"$\",\"$L14\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"fieldConfigList\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"DateEpoch\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"encodingType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"DICTIONARY\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"indexTypes\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"TIMESTAMP\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"timestampConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"granularities\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"DAY\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"WEEK\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MONTH\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"em\",null,{\"children\":\"encodingType\"}],\" will always be ‘DICTIONARY’ and \",[\"$\",\"em\",null,{\"children\":\"indexTypes\"}],\" must contain ‘TIMESTAMP’. We should specify granularities based on our query patterns.\"]}],[\"$\",\"p\",null,{\"children\":[\"As a rule of thumb, work out which values you most commonly pass as the first argument to the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/configuration-reference/functions/datetrunc\",\"children\":\"datetrunc function\"}],\" in your queries and include those values.\"]}],[\"$\",\"p\",null,{\"children\":[\"The full list of valid granularities is: \",[\"$\",\"em\",null,{\"children\":\"millisecond\"}],\", \",[\"$\",\"em\",null,{\"children\":\"second\"}],\", \",[\"$\",\"em\",null,{\"children\":\"minute\"}],\", \",[\"$\",\"em\",null,{\"children\":\"hour\"}],\", \",[\"$\",\"em\",null,{\"children\":\"day\"}],\", \",[\"$\",\"em\",null,{\"children\":\"week\"}],\", \",[\"$\",\"em\",null,{\"children\":\"month\"}],\", \",[\"$\",\"em\",null,{\"children\":\"quarter\"}],\", and \",[\"$\",\"em\",null,{\"children\":\"year\"}],\".\"]}],[\"$\",\"p\",null,{\"children\":\"Our new table is called crimes_indexed, and we’re also going to create a new schema with all the same columns called crimes_indexed, as Pinot requires the table and schema names to match.\"}],[\"$\",\"p\",null,{\"children\":\"We can create the schema and table by running the following command:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--network\"}],\" timestamp_blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/config:/config \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.11.0-arm64 AddTable \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-schemaFile\"}],\" /config/schema-index.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-tableConfigFile\"}],\" /config/table-index.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-controllerHost\"}],\" pinot-timestamp-blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-exec\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"We’ll populate that table by copying the segment that we created earlier for the crimes table.\"}],[\"$\",\"$L14\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--network\"}],\" timestamp_blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/config:/config \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/data:/data \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.11.0-arm64 LaunchDataIngestionJob \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-jobSpecFile\"}],\" /config/job-spec-download.yml \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-values\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable assign-left\",\"children\":\"controllerHost\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"pinot-timestamp-blog\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"If you’re curious how that job spec works, I \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://www.markhneedham.com/blog/2021/12/06/apache-pinot-copy-segment-new-table/\",\"children\":\"wrote a blog post explaining it in a bit more detail\"}],\".\"]}],[\"$\",\"p\",null,{\"children\":\"Once the Pinot Server has downloaded this segment, it will apply the timestamp index to the DateEpoch column.\"}],[\"$\",\"p\",null,{\"children\":\"For the curious, we can see this happening in the log files by connecting to the Pinot container and running the following grep command:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"​​docker \",[\"$\",\"span\",null,{\"className\":\"token builtin class-name\",\"children\":\"exec\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-iti\"}],\" pinot-timestamp-blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"grep\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-rni\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-A10\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Successfully downloaded segment:.*crimes_indexed_OFFLINE.*\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" logs/pinot-all.log\\n\"}]]}]}],[\"$\",\"p\",null,{\"children\":\"We’ll see something like the following (tidied up for brevity):\"}],[\"$\",\"$L14\",null,{\"className\":\"language-log\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-log\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"BaseTableDataManager\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Successfully downloaded segment:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"crimes_OFFLINE_0 of table:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"crimes_indexed_OFFLINE to index dir:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string file-path\",\"children\":\"/tmp/1667490598253/quickstart/PinotServerDataDir0/crimes_indexed_OFFLINE/crimes_OFFLINE_0\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"V3DefaultColumnHandler\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Starting default column action:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"ADD_DATE_TIME on column:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DAY\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"SegmentDictionaryCreator\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Created dictionary for LONG column:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DAY with cardinality\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"7969\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" range\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"978307200000\"}],\" to \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1666742400000\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"V3DefaultColumnHandler\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Starting default column action:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"ADD_DATE_TIME on column:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"WEEK\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"SegmentDictionaryCreator\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Created dictionary for LONG column:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"WEEK with cardinality\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1139\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" range\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"978307200000\"}],\" to \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1666569600000\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"V3DefaultColumnHandler\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Starting default column action:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"ADD_DATE_TIME on column:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"MONTH\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"SegmentDictionaryCreator\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Created dictionary for LONG column:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"MONTH with cardinality\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"262\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" range\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"978307200000\"}],\" to \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1664582400000\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"RangeIndexHandler\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Creating new range index for segment:\"}],\" crimes_OFFLINE_0\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" column\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DAY\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"RangeIndexHandler\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Created range index for segment:\"}],\" crimes_OFFLINE_0\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" column\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DAY\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"RangeIndexHandler\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Creating new range index for segment:\"}],\" crimes_OFFLINE_0\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" column\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"WEEK\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"RangeIndexHandler\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Created range index for segment:\"}],\" crimes_OFFLINE_0\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" column\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"$$\"}],\"WEEK\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}]]}]}],[\"$\",\"h2\",null,{\"id\":\"what-does-a-timestamp-index-do\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#what-does-a-timestamp-index-do\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"What does a timestamp index do?\"]}],[\"$\",\"p\",null,{\"children\":\"So, the timestamp index has now been created, but what does it actually do?\"}],[\"$\",\"p\",null,{\"children\":\"When we add a timestamp index on a column, Pinot creates a derived column for each granularity and adds a range index for each new column.\"}],[\"$\",\"p\",null,{\"children\":[\"In our case, that means we’ll have these extra columns: \",[\"$\",\"span\",null,{\"className\":\"math math-inline\",\"children\":[\"$\",\"span\",null,{\"className\":\"katex\",\"children\":[[\"$\",\"span\",null,{\"className\":\"katex-mathml\",\"children\":[\"$\",\"math\",null,{\"xmlns\":\"http://www.w3.org/1998/Math/MathML\",\"children\":[\"$\",\"semantics\",null,{\"children\":[[\"$\",\"mrow\",null,{\"children\":[[\"$\",\"mi\",null,{\"children\":\"D\"}],[\"$\",\"mi\",null,{\"children\":\"a\"}],[\"$\",\"mi\",null,{\"children\":\"t\"}],[\"$\",\"mi\",null,{\"children\":\"e\"}],[\"$\",\"mi\",null,{\"children\":\"E\"}],[\"$\",\"mi\",null,{\"children\":\"p\"}],[\"$\",\"mi\",null,{\"children\":\"o\"}],[\"$\",\"mi\",null,{\"children\":\"c\"}],[\"$\",\"mi\",null,{\"children\":\"h\"}]]}],[\"$\",\"annotation\",null,{\"encoding\":\"application/x-tex\",\"children\":\"DateEpoch\"}]]}]}]}],[\"$\",\"span\",null,{\"className\":\"katex-html\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"span\",null,{\"className\":\"base\",\"children\":[[\"$\",\"span\",null,{\"className\":\"strut\",\"style\":{\"height\":\".8889em\",\"verticalAlign\":\"-.1944em\"}}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"style\":{\"marginRight\":\".02778em\"},\"children\":\"D\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"a\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"t\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"e\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"Ep\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"oc\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"h\"}]]}]}]]}]}],\"DAY, \",[\"$\",\"span\",null,{\"className\":\"math math-inline\",\"children\":[\"$\",\"span\",null,{\"className\":\"katex\",\"children\":[[\"$\",\"span\",null,{\"className\":\"katex-mathml\",\"children\":[\"$\",\"math\",null,{\"xmlns\":\"http://www.w3.org/1998/Math/MathML\",\"children\":[\"$\",\"semantics\",null,{\"children\":[[\"$\",\"mrow\",null,{\"children\":[[\"$\",\"mi\",null,{\"children\":\"D\"}],[\"$\",\"mi\",null,{\"children\":\"a\"}],[\"$\",\"mi\",null,{\"children\":\"t\"}],[\"$\",\"mi\",null,{\"children\":\"e\"}],[\"$\",\"mi\",null,{\"children\":\"E\"}],[\"$\",\"mi\",null,{\"children\":\"p\"}],[\"$\",\"mi\",null,{\"children\":\"o\"}],[\"$\",\"mi\",null,{\"children\":\"c\"}],[\"$\",\"mi\",null,{\"children\":\"h\"}]]}],[\"$\",\"annotation\",null,{\"encoding\":\"application/x-tex\",\"children\":\"DateEpoch\"}]]}]}]}],[\"$\",\"span\",null,{\"className\":\"katex-html\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"span\",null,{\"className\":\"base\",\"children\":[[\"$\",\"span\",null,{\"className\":\"strut\",\"style\":{\"height\":\".8889em\",\"verticalAlign\":\"-.1944em\"}}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"style\":{\"marginRight\":\".02778em\"},\"children\":\"D\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"a\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"t\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"e\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"Ep\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"oc\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"h\"}]]}]}]]}]}],\"WEEK, and \",[\"$\",\"span\",null,{\"className\":\"math math-inline\",\"children\":[\"$\",\"span\",null,{\"className\":\"katex\",\"children\":[[\"$\",\"span\",null,{\"className\":\"katex-mathml\",\"children\":[\"$\",\"math\",null,{\"xmlns\":\"http://www.w3.org/1998/Math/MathML\",\"children\":[\"$\",\"semantics\",null,{\"children\":[[\"$\",\"mrow\",null,{\"children\":[[\"$\",\"mi\",null,{\"children\":\"D\"}],[\"$\",\"mi\",null,{\"children\":\"a\"}],[\"$\",\"mi\",null,{\"children\":\"t\"}],[\"$\",\"mi\",null,{\"children\":\"e\"}],[\"$\",\"mi\",null,{\"children\":\"E\"}],[\"$\",\"mi\",null,{\"children\":\"p\"}],[\"$\",\"mi\",null,{\"children\":\"o\"}],[\"$\",\"mi\",null,{\"children\":\"c\"}],[\"$\",\"mi\",null,{\"children\":\"h\"}]]}],[\"$\",\"annotation\",null,{\"encoding\":\"application/x-tex\",\"children\":\"DateEpoch\"}]]}]}]}],[\"$\",\"span\",null,{\"className\":\"katex-html\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"span\",null,{\"className\":\"base\",\"children\":[[\"$\",\"span\",null,{\"className\":\"strut\",\"style\":{\"height\":\".8889em\",\"verticalAlign\":\"-.1944em\"}}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"style\":{\"marginRight\":\".02778em\"},\"children\":\"D\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"a\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"t\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"e\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"Ep\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"oc\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"h\"}]]}]}]]}]}],\"MONTH.\"]}],[\"$\",\"p\",null,{\"children\":[\"We can check if the extra columns and indexes have been added by navigating to the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/#/tenants/table/crimes_indexed_OFFLINE/crimes_OFFLINE_0\",\"children\":\"segment_page\"}],\" and typing \",[\"$\",\"span\",null,{\"className\":\"math math-inline\",\"children\":[\"$\",\"span\",null,{\"className\":\"katex\",\"children\":[[\"$\",\"span\",null,{\"className\":\"katex-mathml\",\"children\":[\"$\",\"math\",null,{\"xmlns\":\"http://www.w3.org/1998/Math/MathML\",\"children\":[\"$\",\"semantics\",null,{\"children\":[[\"$\",\"mrow\",null,{\"children\":[[\"$\",\"mi\",null,{\"children\":\"D\"}],[\"$\",\"mi\",null,{\"children\":\"a\"}],[\"$\",\"mi\",null,{\"children\":\"t\"}],[\"$\",\"mi\",null,{\"children\":\"e\"}]]}],[\"$\",\"annotation\",null,{\"encoding\":\"application/x-tex\",\"children\":\"Date\"}]]}]}]}],[\"$\",\"span\",null,{\"className\":\"katex-html\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"span\",null,{\"className\":\"base\",\"children\":[[\"$\",\"span\",null,{\"className\":\"strut\",\"style\":{\"height\":\".6833em\"}}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"style\":{\"marginRight\":\".02778em\"},\"children\":\"D\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"a\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"t\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"e\"}]]}]}]]}]}],\"Epoch in the search box. You should see the following:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot timestamp index on a column\",\"src\":\"https://www.datocms-assets.com/75153/1669133112-image2.png\",\"title\":\"Apache Pinot timestamp index on a column\"}]}],[\"$\",\"p\",null,{\"children\":\"These columns will be assigned the following values:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":[[\"$\",\"span\",null,{\"className\":\"math math-inline\",\"children\":[\"$\",\"span\",null,{\"className\":\"katex\",\"children\":[[\"$\",\"span\",null,{\"className\":\"katex-mathml\",\"children\":[\"$\",\"math\",null,{\"xmlns\":\"http://www.w3.org/1998/Math/MathML\",\"children\":[\"$\",\"semantics\",null,{\"children\":[[\"$\",\"mrow\",null,{\"children\":[[\"$\",\"mi\",null,{\"children\":\"D\"}],[\"$\",\"mi\",null,{\"children\":\"a\"}],[\"$\",\"mi\",null,{\"children\":\"t\"}],[\"$\",\"mi\",null,{\"children\":\"e\"}],[\"$\",\"mi\",null,{\"children\":\"E\"}],[\"$\",\"mi\",null,{\"children\":\"p\"}],[\"$\",\"mi\",null,{\"children\":\"o\"}],[\"$\",\"mi\",null,{\"children\":\"c\"}],[\"$\",\"mi\",null,{\"children\":\"h\"}]]}],[\"$\",\"annotation\",null,{\"encoding\":\"application/x-tex\",\"children\":\"DateEpoch\"}]]}]}]}],[\"$\",\"span\",null,{\"className\":\"katex-html\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"span\",null,{\"className\":\"base\",\"children\":[[\"$\",\"span\",null,{\"className\":\"strut\",\"style\":{\"height\":\".8889em\",\"verticalAlign\":\"-.1944em\"}}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"style\":{\"marginRight\":\".02778em\"},\"children\":\"D\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"a\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"t\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"e\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"Ep\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"oc\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"h\"}]]}]}]]}]}],\"DAY = dateTrunc(‘DAY’, DateEpoch)\"]}],[\"$\",\"li\",null,{\"children\":[[\"$\",\"span\",null,{\"className\":\"math math-inline\",\"children\":[\"$\",\"span\",null,{\"className\":\"katex\",\"children\":[[\"$\",\"span\",null,{\"className\":\"katex-mathml\",\"children\":[\"$\",\"math\",null,{\"xmlns\":\"http://www.w3.org/1998/Math/MathML\",\"children\":[\"$\",\"semantics\",null,{\"children\":[[\"$\",\"mrow\",null,{\"children\":[[\"$\",\"mi\",null,{\"children\":\"D\"}],[\"$\",\"mi\",null,{\"children\":\"a\"}],[\"$\",\"mi\",null,{\"children\":\"t\"}],[\"$\",\"mi\",null,{\"children\":\"e\"}],[\"$\",\"mi\",null,{\"children\":\"E\"}],[\"$\",\"mi\",null,{\"children\":\"p\"}],[\"$\",\"mi\",null,{\"children\":\"o\"}],[\"$\",\"mi\",null,{\"children\":\"c\"}],[\"$\",\"mi\",null,{\"children\":\"h\"}]]}],[\"$\",\"annotation\",null,{\"encoding\":\"application/x-tex\",\"children\":\"DateEpoch\"}]]}]}]}],[\"$\",\"span\",null,{\"className\":\"katex-html\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"span\",null,{\"className\":\"base\",\"children\":[[\"$\",\"span\",null,{\"className\":\"strut\",\"style\":{\"height\":\".8889em\",\"verticalAlign\":\"-.1944em\"}}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"style\":{\"marginRight\":\".02778em\"},\"children\":\"D\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"a\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"t\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"e\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"Ep\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"oc\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"h\"}]]}]}]]}]}],\"WEEK = dateTrunc(‘WEEK’, DateEpoch)\"]}],[\"$\",\"li\",null,{\"children\":[[\"$\",\"span\",null,{\"className\":\"math math-inline\",\"children\":[\"$\",\"span\",null,{\"className\":\"katex\",\"children\":[[\"$\",\"span\",null,{\"className\":\"katex-mathml\",\"children\":[\"$\",\"math\",null,{\"xmlns\":\"http://www.w3.org/1998/Math/MathML\",\"children\":[\"$\",\"semantics\",null,{\"children\":[[\"$\",\"mrow\",null,{\"children\":[[\"$\",\"mi\",null,{\"children\":\"D\"}],[\"$\",\"mi\",null,{\"children\":\"a\"}],[\"$\",\"mi\",null,{\"children\":\"t\"}],[\"$\",\"mi\",null,{\"children\":\"e\"}],[\"$\",\"mi\",null,{\"children\":\"E\"}],[\"$\",\"mi\",null,{\"children\":\"p\"}],[\"$\",\"mi\",null,{\"children\":\"o\"}],[\"$\",\"mi\",null,{\"children\":\"c\"}],[\"$\",\"mi\",null,{\"children\":\"h\"}]]}],[\"$\",\"annotation\",null,{\"encoding\":\"application/x-tex\",\"children\":\"DateEpoch\"}]]}]}]}],[\"$\",\"span\",null,{\"className\":\"katex-html\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"span\",null,{\"className\":\"base\",\"children\":[[\"$\",\"span\",null,{\"className\":\"strut\",\"style\":{\"height\":\".8889em\",\"verticalAlign\":\"-.1944em\"}}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"style\":{\"marginRight\":\".02778em\"},\"children\":\"D\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"a\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"t\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"e\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"Ep\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"oc\"}],[\"$\",\"span\",null,{\"className\":\"mathnormal mord\",\"children\":\"h\"}]]}]}]]}]}],\"MONTH = dateTrunc(‘MONTH’, DateEpoch)\"]}]]}],[\"$\",\"p\",null,{\"children\":\"Pinot will also rewrite any queries that use the dateTrunc function with DAY, WEEK, or MONTH and the DateEpoch field to use those new columns.\"}],[\"$\",\"p\",null,{\"children\":\"This means that this query:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" datetrunc\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'WEEK'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" tsWeek\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" crimes_indexed\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"GROUP\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" tsWeek\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Would be rewritten as:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" $DateEpoch$WEEK \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" tsWeek\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" crimes_indexed\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"GROUP\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" tsWeek\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"And our query:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" datetrunc\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'WEEK'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" DateEpoch\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" tsWeek\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" crimes\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\" tsWeek \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e\"}],\" fromDateTime\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2017-01-16'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'yyyy-MM-dd'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"group\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" tsWeek\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"order\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"DESC\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Would be rewritten as:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" $DateEpoch$WEEK \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" tsWeek\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" crimes\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\" tsWeek \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e\"}],\" fromDateTime\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2017-01-16'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'yyyy-MM-dd'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"group\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" tsWeek\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"order\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"DESC\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}]]}]}],[\"$\",\"h2\",null,{\"id\":\"re-running-the-query\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#re-running-the-query\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Re-running the query\"]}],[\"$\",\"p\",null,{\"children\":[\"Let’s now run our initial query against the \",[\"$\",\"em\",null,{\"children\":\"crimes_indexed\"}],\" table. We’ll get exactly the same results as before, but let’s take a look at the query stats:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Chicago crime dataset updated query stats\",\"src\":\"https://www.datocms-assets.com/75153/1669133083-image4.png\",\"title\":\"Chicago crime dataset updated query stats\"}]}],[\"$\",\"p\",null,{\"children\":\"This time the query takes 36 milliseconds rather than 140 milliseconds. That’s an almost 5x improvement, thanks to the timestamp index.\"}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":\"Hopefully, you’ll agree that timestamp indexes are pretty cool, and achieving a 5x query improvement without much work is always welcome!\"}],[\"$\",\"p\",null,{\"children\":[\"If you’re using timestamps in your Pinot tables, be sure to try out this index and let us know how it goes on the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://stree.ai/slack\",\"children\":\"StarTree Community Slack\"}],\" . We’re always happy to help out with any questions or problems you encounter.\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L15\",null,{\"chapters\":[{\"value\":\"Spinning up Pinot\",\"url\":\"#spinning-up-pinot\",\"depth\":2},{\"value\":\"Importing Chicago Crime Dataset\",\"url\":\"#importing-chicago-crime-dataset\",\"depth\":2},{\"value\":\"Querying crimes by date\",\"url\":\"#querying-crimes-by-date\",\"depth\":2},{\"value\":\"Adding the timestamp index\",\"url\":\"#adding-the-timestamp-index\",\"depth\":2},{\"value\":\"What does a timestamp index do?\",\"url\":\"#what-does-a-timestamp-index-do\",\"depth\":2},{\"value\":\"Re-running the query\",\"url\":\"#re-running-the-query\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Apache Pinot™ 0.11 - Timestamp Indexes | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"Users write queries that use the datetrunc function to filter at a coarser grain of functionality. 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@@ -126,4 +126,4 @@
</span><span class="code-line"> <span class="token property">"pauseFlag"</span><span class="token operator">:</span> <span class="token boolean">false</span><span class="token punctuation">,</span>
</span><span class="code-line"> <span class="token property">"consumingSegments"</span><span class="token operator">:</span> <span class="token punctuation">[</span><span class="token string">"events__0__22__20221123T1124Z"</span><span class="token punctuation">]</span>
</span><span class="code-line"><span class="token punctuation">}</span>
-</span></code></pre></div><p>Navigate to <a target="_blank" rel="noopener noreferrer" href="http://localhost:9000/#/query">http://localhost:9000/#/query</a> and click on the events table. You should see the following:</p><p><img alt="Sample events table containing records" src="https://www.datocms-assets.com/75153/1669668611-image2.png" title="Sample events table containing records"/></p><p>We have records! We can also run our data generator again, and more events will be ingested.</p><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>This feature makes real-time data ingestion a bit more forgiving when things go wrong, which has got to be a good thing in my book.</p><p>When you look at the name of this feature, it can seem a bit esoteric and perhaps not something that you’d want to use, but I think you’ll find it to be extremely useful.</p><p>So give it a try and let us know how you get on. If you have any questions about this feature, feel free to join us on <a target="_blank" rel="noopener noreferrer" href="https://stree.ai/slack">Slack</a>, where we’ll be happy to help you out.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#how-does-real-time-ingestion-work">How does real-time ingestion work?</a></li><li class="text-sm font-normal leading-tight"><a href="#why-do-we-need-to-pause-and-resume-ingestion">Why do we need to pause and resume ingestion?</a></li><li class="text-sm font-normal leading-tight"><a href="#data-generation">Data Generation</a></li><li class="text-sm font-normal leading-tight"><a href="#pinot-schematable-config">Pinot Schema/Table Config</a></li><li class="text-sm font-normal leading-tight"><a href="#the-pauseresume-flow">The Pause/Resume Flow</a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 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Sajjad Moradi has \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://medium.com/apache-pinot-developer-blog/pause-stream-consumption-on-apache-pinot-772a971ef403\",\"children\":\"also written a blog post about this feature\"}],\", so you can treat this post as a complement to that one.\"]}],[\"$\",\"h2\",null,{\"id\":\"how-does-real-time-ingestion-work\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#how-does-real-time-ingestion-work\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"How does real-time ingestion work?\"]}],[\"$\",\"p\",null,{\"children\":\"Before we get into this feature, let’s first recap how real-time ingestion works.\"}],[\"$\",\"p\",null,{\"children\":\"This only applies to tables that have the REALTIME type. 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operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token builtin\",\"children\":\"str\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"uuid\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"uuid4\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" count \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" random\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"randint\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"0\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1000\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"print\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" json\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"dumps\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"tsString\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" ts\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token builtin\",\"children\":\"id\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" count\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"We can view the data generated by this script by pasting the above code into a file called datagen.py and then running the following command:\"}],[\"$\",\"p\",null,{\"children\":\"python datagen.py 2\u003e/dev/null | head -n3 | jq\"}],[\"$\",\"p\",null,{\"children\":\"We’ll see the following output:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tsString\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"2022-11-23T12:08:44.127481Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"e1c58795-a009-4e21-ae76-cdd66e090797\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"203\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tsString\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"2022-11-23T12:08:44.127531Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"4eedce04-d995-4e99-82ab-6f836b35c580\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"216\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tsString\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"2022-11-23T12:08:44.127550Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"6d72411b-55f5-4f9f-84e4-7c8c5c4581ff\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"721\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"We’re going to pipe this data into a Kafka stream called ‘events’ like this:\"}],[\"$\",\"p\",null,{\"children\":\"python datagen.py | kcat -P -b localhost:9092 -t events\"}],[\"$\",\"p\",null,{\"children\":[\"We’re not setting a key for these messages in Kafka for simplicity’s sake, but Robin Moffat has an \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://rmoff.net/2020/09/30/setting-key-value-when-piping-from-jq-to-kafkacat/\",\"children\":\"excellent blog post that explains how to do it\"}],\".\"]}],[\"$\",\"h2\",null,{\"id\":\"pinot-schematable-config\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#pinot-schematable-config\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Pinot Schema/Table Config\"]}],[\"$\",\"p\",null,{\"children\":\"We want to ingest this data into a Pinot table with the same name. Let’s first define a schema:\"}],[\"$\",\"p\",null,{\"children\":\"Schema:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schemaName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dimensionFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"STRING\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metricFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"INT\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dateTimeFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"TIMESTAMP\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"format\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS:EPOCH\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"granularity\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Note that the timestamp field is called ts and not tsString, as it is in the Kafka stream. We will transform the DateTime string value held in that field into a proper timestamp using a transformation function.\"}],[\"$\",\"p\",null,{\"children\":\"Our table config is described below:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"REALTIME\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"segmentsConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"timeColumnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schemaName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"replication\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"replicasPerPartition\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableIndexConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"loadMode\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MMAP\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"streamConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"streamType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"kafka\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.topic.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.broker.list\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"kafka-pause-resume:9093\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.type\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"lowlevel\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.prop.auto.offset.reset\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"smallest\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.factory.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"ingestionConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"columnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformFunction\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"FromDateTime(tsString, 'YYYY-MM-dd''T''HH:mm:ss.SS''Z''')\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tenants\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metadata\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Our transformation has a subtle error. The second parameter passed to the FromDateTime function describes the format of the DateTime string, which we defined as:\"}],[\"$\",\"p\",null,{\"children\":\"YYYY-MM-dd''T''HH:mm:ss.SS''Z''\"}],[\"$\",\"p\",null,{\"children\":\"But tsString has values in the following format:\"}],[\"$\",\"p\",null,{\"children\":\"2022-11-23T12:08:44.127550Z\"}],[\"$\",\"p\",null,{\"children\":\"i.e., we don’t have enough S values - there should be 5 rather than 2.\"}],[\"$\",\"p\",null,{\"children\":\"If we create the table using the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"--network\"}],\" pause-resume \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/pinot/config:/config \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.11.0-arm64 AddTable \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-schemaFile\"}],\" /config/schema.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-tableConfigFile\"}],\" /config/table.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-controllerHost\"}],\" pinot-controller-pause-resume \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-exec\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Pinot will immediately start trying to ingest data from Kafka, and it will throw a lot of exceptions that look like this:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-log\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-log\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"java.lang.RuntimeException:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Caught exception while executing function:\"}],\" fromDateTime\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"(\"}],\"tsString\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'YYYY-MM-dd'\"}],\"T\",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'HH:mm:ss.SS'\"}],\"Z\",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"''\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"…\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Caused by:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"java.lang.IllegalStateException:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Caught exception while invoking method:\"}],\" public static long org\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"apache\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"pinot\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"common\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"function\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"scalar\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"DateTimeFunctions\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"fromDateTime\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"(\"}],\"java\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"lang\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"String\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"java\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"lang\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"String\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\")\"}],\" with arguments\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token number date\",\"children\":\"2022-11-23T\"}],[\"$\",\"span\",null,{\"className\":\"token number time\",\"children\":\"11:12:34.682504Z\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" YYYY\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"MM\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"dd\",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'T'\"}],\"HH\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\"mm\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\"ss\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"SS\",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'Z'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}]]}]}],[\"$\",\"p\",null,{\"children\":\"At this point, we’d usually be stuck and would need to fix the transformation function and then restart the Pinot server. With the pause/resume feature, we can fix this problem without resorting to such drastic measures.\"}],[\"$\",\"h2\",null,{\"id\":\"the-pauseresume-flow\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#the-pauseresume-flow\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"The Pause/Resume Flow\"]}],[\"$\",\"p\",null,{\"children\":\"Instead, we can follow these steps:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Pause ingestion for the table\"}],[\"$\",\"li\",null,{\"children\":\"Fix the transformation function\"}],[\"$\",\"li\",null,{\"children\":\"Resume ingestion\"}],[\"$\",\"li\",null,{\"children\":\"Profit $$$\"}]]}],[\"$\",\"p\",null,{\"children\":\"We can pause ingestion by running the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"curl\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-X\"}],\" POST \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://localhost:9000/tables/events/pauseConsumption\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-H\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"accept: application/json\\\"\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The response should be something like this:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"pauseFlag\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"true\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"consumingSegments\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events__0__0__20221123T1106Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"description\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Pause flag is set. Consuming segments are being committed. Use /pauseStatus endpoint in a few moments to check if all consuming segments have been committed.\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Let’s follow the response’s advice and check the consuming segments status:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"curl\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-X\"}],\" GET \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://localhost:9000/tables/events/pauseStatus\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-H\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"accept: application/json\\\"\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"We’ll see the following response:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"pauseFlag\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"true\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"consumingSegments\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"So far, so good. Now we need to fix the table. We have a config, table-fixed.json, that contains a working transformation config. These are the lines of interest:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"ingestionConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"columnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformFunction\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"FromDateTime(tsString, 'YYYY-MM-dd''T''HH:mm:ss.SSSSSS''Z''')\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"We now have five S values rather than two, which should sort out our ingestion.\"}],[\"$\",\"p\",null,{\"children\":\"Update the table config:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"curl\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-X\"}],\" PUT \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://localhost:9000/tables/events\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-H\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"accept: application/json\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-H\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Content-Type: application/json\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-d\"}],\" @pinot/config/table-fixed.json\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"And then resume ingestion. You can pass in the query string parameter consumeFrom, which takes a value of smallest or largest. We’ll pass in smallest since no data has been consumed yet:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"curl\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-X\"}],\" POST \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://localhost:9000/tables/events/resumeConsumption?consumeFrom=smallest\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-H\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"accept: application/json\\\"\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The response will be like this:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"pauseFlag\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"consumingSegments\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"description\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Pause flag is cleared. Consuming segments are being created. Use /pauseStatus endpoint in a few moments to double check.\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Again, let’s check the consuming segments status:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"curl\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-X\"}],\" GET \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://localhost:9000/tables/events/pauseStatus\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-H\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"accept: application/json\\\"\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"This time we will see some consuming segments:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"pauseFlag\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"consumingSegments\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events__0__22__20221123T1124Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"Navigate to \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/#/query\",\"children\":\"http://localhost:9000/#/query\"}],\" and click on the events table. You should see the following:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample events table containing records\",\"src\":\"https://www.datocms-assets.com/75153/1669668611-image2.png\",\"title\":\"Sample events table containing records\"}]}],[\"$\",\"p\",null,{\"children\":\"We have records! We can also run our data generator again, and more events will be ingested.\"}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":\"This feature makes real-time data ingestion a bit more forgiving when things go wrong, which has got to be a good thing in my book.\"}],[\"$\",\"p\",null,{\"children\":\"When you look at the name of this feature, it can seem a bit esoteric and perhaps not something that you’d want to use, but I think you’ll find it to be extremely useful.\"}],[\"$\",\"p\",null,{\"children\":[\"So give it a try and let us know how you get on. If you have any questions about this feature, feel free to join us on \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://stree.ai/slack\",\"children\":\"Slack\"}],\", where we’ll be happy to help you out.\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"How does real-time ingestion work?\",\"url\":\"#how-does-real-time-ingestion-work\",\"depth\":2},{\"value\":\"Why do we need to pause and resume ingestion?\",\"url\":\"#why-do-we-need-to-pause-and-resume-ingestion\",\"depth\":2},{\"value\":\"Data Generation\",\"url\":\"#data-generation\",\"depth\":2},{\"value\":\"Pinot Schema/Table Config\",\"url\":\"#pinot-schematable-config\",\"depth\":2},{\"value\":\"The Pause/Resume Flow\",\"url\":\"#the-pauseresume-flow\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Apache Pinot™ 0.11 - Pausing Real-Time Ingestion | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"Learn about a feature that lets you pause and resume real-time data ingestion in Apache Pinot\"}],[\"$\",\"meta\",\"4\",{\"name\":\"robots\",\"content\":\"index, follow\"}],[\"$\",\"meta\",\"5\",{\"name\":\"googlebot\",\"content\":\"index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1\"}],[\"$\",\"link\",\"6\",{\"rel\":\"canonical\",\"href\":\"https://pinot.apache.org/blog/2022/11/28/Apache-Pinot-Pausing-Real-Time-Ingestion\"}],[\"$\",\"link\",\"7\",{\"rel\":\"alternate\",\"type\":\"application/rss+xml\",\"href\":\"https://pinot.apache.org/feed.xml\"}],[\"$\",\"meta\",\"8\",{\"property\":\"og:title\",\"content\":\"Apache Pinot™ 0.11 - Pausing Real-Time Ingestion\"}],[\"$\",\"meta\",\"9\",{\"property\":\"og:description\",\"content\":\"Learn about a feature that lets you pause and resume real-time data ingestion in Apache Pinot\"}],[\"$\",\"meta\",\"10\",{\"property\":\"og:url\",\"content\":\"https://pinot.apache.org/blog/2022/11/28/Apache-Pinot-Pausing-Real-Time-Ingestion\"}],[\"$\",\"meta\",\"11\",{\"property\":\"og:site_name\",\"content\":\"Apache Pinot™\"}],[\"$\",\"meta\",\"12\",{\"property\":\"og:locale\",\"content\":\"en_US\"}],[\"$\",\"meta\",\"13\",{\"property\":\"og:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"14\",{\"property\":\"og:type\",\"content\":\"article\"}],[\"$\",\"meta\",\"15\",{\"property\":\"article:published_time\",\"content\":\"2022-11-28T00:00:00.000Z\"}],[\"$\",\"meta\",\"16\",{\"property\":\"article:modified_time\",\"content\":\"2022-11-28T00:00:00.000Z\"}],[\"$\",\"meta\",\"17\",{\"property\":\"article:author\",\"content\":\"Mark Needham\"}],[\"$\",\"meta\",\"18\",{\"name\":\"twitter:card\",\"content\":\"summary_large_image\"}],[\"$\",\"meta\",\"19\",{\"name\":\"twitter:title\",\"content\":\"Apache Pinot™ 0.11 - Pausing Real-Time Ingestion\"}],[\"$\",\"meta\",\"20\",{\"name\":\"twitter:description\",\"content\":\"Learn about a feature that lets you pause and resume real-time data ingestion in Apache Pinot\"}],[\"$\",\"meta\",\"21\",{\"name\":\"twitter:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"22\",{\"name\":\"next-size-adjust\"}]]\n"])</script><script>self.__next_f.push([1,"f:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>
\ No newline at end of file
+</span></code></pre></div><p>Navigate to <a target="_blank" rel="noopener noreferrer" href="http://localhost:9000/#/query">http://localhost:9000/#/query</a> and click on the events table. You should see the following:</p><p><img alt="Sample events table containing records" src="https://www.datocms-assets.com/75153/1669668611-image2.png" title="Sample events table containing records"/></p><p>We have records! We can also run our data generator again, and more events will be ingested.</p><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>This feature makes real-time data ingestion a bit more forgiving when things go wrong, which has got to be a good thing in my book.</p><p>When you look at the name of this feature, it can seem a bit esoteric and perhaps not something that you’d want to use, but I think you’ll find it to be extremely useful.</p><p>So give it a try and let us know how you get on. If you have any questions about this feature, feel free to join us on <a target="_blank" rel="noopener noreferrer" href="https://stree.ai/slack">Slack</a>, where we’ll be happy to help you out.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#how-does-real-time-ingestion-work">How does real-time ingestion work?</a></li><li class="text-sm font-normal leading-tight"><a href="#why-do-we-need-to-pause-and-resume-ingestion">Why do we need to pause and resume ingestion?</a></li><li class="text-sm font-normal leading-tight"><a href="#data-generation">Data Generation</a></li><li class="text-sm font-normal leading-tight"><a href="#pinot-schematable-config">Pinot Schema/Table Config</a></li><li class="text-sm font-normal leading-tight"><a href="#the-pauseresume-flow">The Pause/Resume Flow</a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 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Apache Pinot, Pinot, Apache, the Apache feather logo, and the Apache Pinot project logo are registered trademarks of The Apache Software Foundation. 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.\"]}]]}]]}]}]]}]}]]}],null]}]]\n"])</script><script>self.__next_f.push([1,"12:I[1514,[\"326\",\"static/chunks/326-3a90a6443b9c824c.js\",\"413\",\"static/chunks/413-f9f40b83f7bb3f22.js\",\"980\",\"static/chunks/980-6e243f9cd384c7d2.js\",\"797\",\"static/chunks/app/blog/%5B...slug%5D/page-502e08b6677b55da.js\"],\"\"]\n13:I[2529,[\"326\",\"static/chunks/326-3a90a6443b9c824c.js\",\"413\",\"static/chunks/413-f9f40b83f7bb3f22.js\",\"980\",\"static/chunks/980-6e243f9cd384c7d2.js\",\"797\",\"static/chunks/app/blog/%5B...slug%5D/page-502e08b6677b55da.js\"],\"\"]\n14:I[5185,[\"326\",\"static/chunks/326-3a90a6443b9c824c.js\",\"413\",\""])</script><script>self.__next_f.push([1,"static/chunks/413-f9f40b83f7bb3f22.js\",\"980\",\"static/chunks/980-6e243f9cd384c7d2.js\",\"797\",\"static/chunks/app/blog/%5B...slug%5D/page-502e08b6677b55da.js\"],\"\"]\n"])</script><script>self.__next_f.push([1,"10:[[\"$\",\"script\",null,{\"type\":\"application/ld+json\",\"dangerouslySetInnerHTML\":{\"__html\":\"{\\\"@context\\\":\\\"https://schema.org\\\",\\\"@type\\\":\\\"BlogPosting\\\",\\\"headline\\\":\\\"Apache Pinot™ 0.11 - Pausing Real-Time Ingestion\\\",\\\"datePublished\\\":\\\"2022-11-28T00:00:00.000Z\\\",\\\"dateModified\\\":\\\"2022-11-28T00:00:00.000Z\\\",\\\"description\\\":\\\"Learn about a feature that lets you pause and resume real-time data ingestion in Apache Pinot\\\",\\\"image\\\":\\\"/static/images/twitter-card.png\\\",\\\"url\\\":\\\"https://pinot.apache.org/blog/2022-11-28-Apache-Pinot-Pausing-Real-Time-Ingestion\\\",\\\"author\\\":[{\\\"@type\\\":\\\"Person\\\",\\\"name\\\":\\\"Mark Needham\\\"}]}\"}}],[\"$\",\"section\",null,{\"className\":\" px-5 pt-10 md:px-[13.313rem] md:py-16\",\"children\":[[\"$\",\"$L12\",null,{}],[\"$\",\"article\",null,{\"className\":\"\",\"children\":[\"$\",\"div\",null,{\"className\":\"mx-auto lg:flex\",\"children\":[[\"$\",\"div\",null,{\"className\":\"lg:pr-12\",\"children\":[[\"$\",\"header\",null,{\"className\":\"pt-6 md:pr-10\",\"children\":[[\"$\",\"h1\",null,{\"className\":\"text-4xl font-semibold\",\"children\":\"Apache Pinot™ 0.11 - Pausing Real-Time Ingestion\"}],[\"$\",\"p\",null,{\"className\":\"pt-2 text-lg\",\"children\":[\"By: \",\"Mark Needham\"]}],[\"$\",\"p\",null,{\"className\":\"py-2 text-sm\",\"children\":[\"November 28th, 2022\",\" • \",\"7 min read\"]}]]}],[\"$\",\"div\",null,{\"className\":\"flex flex-col lg:flex-row\",\"children\":[\"$\",\"main\",null,{\"className\":\"\",\"children\":[\"$\",\"div\",null,{\"className\":\"prose max-w-[45rem] pb-8 pt-10 dark:prose-invert\",\"children\":[[\"$\",\"p\",null,{\"children\":[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://youtu.be/u9CwDpMZRog\",\"children\":[\"$\",\"img\",null,{\"alt\":\"Watch the video\",\"src\":\"https://i3.ytimg.com/vi/u9CwDpMZRog/maxresdefault.jpg\"}]}]}],[\"$\",\"p\",null,{\"children\":[\"The Apache Pinot community recently released version \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://medium.com/apache-pinot-developer-blog/apache-pinot-0-11-released-d564684df5d4\",\"children\":\"0.11.0\"}],\", which has lots of goodies for you to play with.\"]}],[\"$\",\"p\",null,{\"children\":[\"In this post, we will learn about a feature that lets you pause and resume real-time data ingestion. Sajjad Moradi has \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://medium.com/apache-pinot-developer-blog/pause-stream-consumption-on-apache-pinot-772a971ef403\",\"children\":\"also written a blog post about this feature\"}],\", so you can treat this post as a complement to that one.\"]}],[\"$\",\"h2\",null,{\"id\":\"how-does-real-time-ingestion-work\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#how-does-real-time-ingestion-work\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"How does real-time ingestion work?\"]}],[\"$\",\"p\",null,{\"children\":\"Before we get into this feature, let’s first recap how real-time ingestion works.\"}],[\"$\",\"p\",null,{\"children\":\"This only applies to tables that have the REALTIME type. These tables ingest data that comes in from a streaming platform (e.g., Kafka).\"}],[\"$\",\"p\",null,{\"children\":\"Pinot servers ingest rows into consuming segments that reside in volatile memory.\"}],[\"$\",\"p\",null,{\"children\":[\"Once a segment reaches the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://dev.startree.ai/docs/pinot/recipes/configuring-segment-threshold\",\"children\":\"segment threshold,\"}],\" it will be persisted to disk as a completed segment, and a new consuming segment will be created. 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Some of the common ones are described below:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"There’s a problem with the underlying stream, and we need to restart the server, reset offsets, or recreate a topic\"}],[\"$\",\"li\",null,{\"children\":\"We want to ingest data from different streams into the same table.\"}],[\"$\",\"li\",null,{\"children\":\"We made a mistake in our ingestion config in Pinot, and it’s now throwing exceptions and isn’t able to ingest any more data.\"}]]}],[\"$\",\"p\",null,{\"children\":\"The 0.11 release adds the following REST API endpoints:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"/tables/{tableName}/pauseCompletion\"}]}],[\"$\",\"li\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"/tables/{tableName}/resumeCompletion\"}]}]]}],[\"$\",\"p\",null,{\"children\":[\"As the names suggest, these endpoints can be used to pause and resume streaming ingestion for a specific table. This release also adds the \",[\"$\",\"code\",null,{\"children\":\"/tables/{tableName}/pauseStatus\"}],\" endpoint, which returns the pause status for a table.\"]}],[\"$\",\"p\",null,{\"children\":\"Let’s see how to use this functionality with help from a worked example.\"}],[\"$\",\"h2\",null,{\"id\":\"data-generation\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#data-generation\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Data Generation\"]}],[\"$\",\"p\",null,{\"children\":\"Let’s imagine that we want to ingest events generated by the following Python script:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-python\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-python\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"import\"}],\" 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string\",\"children\":\"\\\"4eedce04-d995-4e99-82ab-6f836b35c580\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"216\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tsString\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"2022-11-23T12:08:44.127550Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"6d72411b-55f5-4f9f-84e4-7c8c5c4581ff\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"721\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"We’re going to pipe this data into a Kafka stream called ‘events’ like this:\"}],[\"$\",\"p\",null,{\"children\":\"python datagen.py | kcat -P -b localhost:9092 -t events\"}],[\"$\",\"p\",null,{\"children\":[\"We’re not setting a key for these messages in Kafka for simplicity’s sake, but Robin Moffat has an \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://rmoff.net/2020/09/30/setting-key-value-when-piping-from-jq-to-kafkacat/\",\"children\":\"excellent blog post that explains how to do it\"}],\".\"]}],[\"$\",\"h2\",null,{\"id\":\"pinot-schematable-config\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#pinot-schematable-config\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Pinot Schema/Table Config\"]}],[\"$\",\"p\",null,{\"children\":\"We want to ingest this data into a Pinot table with the same name. Let’s first define a schema:\"}],[\"$\",\"p\",null,{\"children\":\"Schema:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schemaName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dimensionFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"STRING\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metricFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"INT\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dateTimeFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"TIMESTAMP\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"format\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS:EPOCH\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"granularity\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Note that the timestamp field is called ts and not tsString, as it is in the Kafka stream. We will transform the DateTime string value held in that field into a proper timestamp using a transformation function.\"}],[\"$\",\"p\",null,{\"children\":\"Our table config is described below:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"REALTIME\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"segmentsConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"timeColumnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schemaName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"replication\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"replicasPerPartition\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableIndexConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token 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property\",\"children\":\"\\\"streamType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"kafka\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.topic.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.broker.list\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" 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string\",\"children\":\"\\\"smallest\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.factory.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"ingestionConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"columnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformFunction\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"FromDateTime(tsString, 'YYYY-MM-dd''T''HH:mm:ss.SS''Z''')\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tenants\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metadata\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Our transformation has a subtle error. The second parameter passed to the FromDateTime function describes the format of the DateTime string, which we defined as:\"}],[\"$\",\"p\",null,{\"children\":\"YYYY-MM-dd''T''HH:mm:ss.SS''Z''\"}],[\"$\",\"p\",null,{\"children\":\"But tsString has values in the following format:\"}],[\"$\",\"p\",null,{\"children\":\"2022-11-23T12:08:44.127550Z\"}],[\"$\",\"p\",null,{\"children\":\"i.e., we don’t have enough S values - there should be 5 rather than 2.\"}],[\"$\",\"p\",null,{\"children\":\"If we create the table using the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"--network\"}],\" pause-resume \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/pinot/config:/config \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.11.0-arm64 AddTable \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-schemaFile\"}],\" /config/schema.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-tableConfigFile\"}],\" /config/table.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-controllerHost\"}],\" pinot-controller-pause-resume \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-exec\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Pinot will immediately start trying to ingest data from Kafka, and it will throw a lot of exceptions that look like this:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-log\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-log\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"java.lang.RuntimeException:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Caught exception while executing function:\"}],\" fromDateTime\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"(\"}],\"tsString\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'YYYY-MM-dd'\"}],\"T\",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'HH:mm:ss.SS'\"}],\"Z\",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"''\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"…\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Caused by:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"java.lang.IllegalStateException:\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"Caught exception while invoking method:\"}],\" public static long org\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"apache\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"pinot\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"common\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"function\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"scalar\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"DateTimeFunctions\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"fromDateTime\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"(\"}],\"java\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"lang\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"String\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"java\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"lang\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"String\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\")\"}],\" with arguments\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token number date\",\"children\":\"2022-11-23T\"}],[\"$\",\"span\",null,{\"className\":\"token number time\",\"children\":\"11:12:34.682504Z\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" YYYY\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"MM\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"dd\",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'T'\"}],\"HH\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\"mm\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\"ss\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"SS\",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'Z'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}]]}]}],[\"$\",\"p\",null,{\"children\":\"At this point, we’d usually be stuck and would need to fix the transformation function and then restart the Pinot server. With the pause/resume feature, we can fix this problem without resorting to such drastic measures.\"}],[\"$\",\"h2\",null,{\"id\":\"the-pauseresume-flow\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#the-pauseresume-flow\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"The Pause/Resume Flow\"]}],[\"$\",\"p\",null,{\"children\":\"Instead, we can follow these steps:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Pause ingestion for the table\"}],[\"$\",\"li\",null,{\"children\":\"Fix the transformation function\"}],[\"$\",\"li\",null,{\"children\":\"Resume ingestion\"}],[\"$\",\"li\",null,{\"children\":\"Profit $$$\"}]]}],[\"$\",\"p\",null,{\"children\":\"We can pause ingestion by running the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"curl\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-X\"}],\" POST \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://localhost:9000/tables/events/pauseConsumption\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-H\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"accept: application/json\\\"\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The response should be something like this:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"pauseFlag\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"true\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"consumingSegments\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events__0__0__20221123T1106Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"description\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Pause flag is set. Consuming segments are being committed. Use /pauseStatus endpoint in a few moments to check if all consuming segments have been committed.\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Let’s follow the response’s advice and check the consuming segments status:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"curl\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-X\"}],\" GET \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://localhost:9000/tables/events/pauseStatus\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-H\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"accept: application/json\\\"\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"We’ll see the following response:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"pauseFlag\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"true\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"consumingSegments\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"So far, so good. Now we need to fix the table. We have a config, table-fixed.json, that contains a working transformation config. These are the lines of interest:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"ingestionConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"columnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformFunction\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"FromDateTime(tsString, 'YYYY-MM-dd''T''HH:mm:ss.SSSSSS''Z''')\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"We now have five S values rather than two, which should sort out our ingestion.\"}],[\"$\",\"p\",null,{\"children\":\"Update the table config:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"curl\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-X\"}],\" PUT \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://localhost:9000/tables/events\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-H\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"accept: application/json\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-H\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Content-Type: application/json\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-d\"}],\" @pinot/config/table-fixed.json\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"And then resume ingestion. You can pass in the query string parameter consumeFrom, which takes a value of smallest or largest. We’ll pass in smallest since no data has been consumed yet:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"curl\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-X\"}],\" POST \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://localhost:9000/tables/events/resumeConsumption?consumeFrom=smallest\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-H\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"accept: application/json\\\"\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The response will be like this:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"pauseFlag\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"consumingSegments\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"description\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Pause flag is cleared. Consuming segments are being created. Use /pauseStatus endpoint in a few moments to double check.\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Again, let’s check the consuming segments status:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"curl\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-X\"}],\" GET \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://localhost:9000/tables/events/pauseStatus\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-H\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"accept: application/json\\\"\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"This time we will see some consuming segments:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"pauseFlag\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"consumingSegments\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events__0__22__20221123T1124Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"Navigate to \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/#/query\",\"children\":\"http://localhost:9000/#/query\"}],\" and click on the events table. You should see the following:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample events table containing records\",\"src\":\"https://www.datocms-assets.com/75153/1669668611-image2.png\",\"title\":\"Sample events table containing records\"}]}],[\"$\",\"p\",null,{\"children\":\"We have records! We can also run our data generator again, and more events will be ingested.\"}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":\"This feature makes real-time data ingestion a bit more forgiving when things go wrong, which has got to be a good thing in my book.\"}],[\"$\",\"p\",null,{\"children\":\"When you look at the name of this feature, it can seem a bit esoteric and perhaps not something that you’d want to use, but I think you’ll find it to be extremely useful.\"}],[\"$\",\"p\",null,{\"children\":[\"So give it a try and let us know how you get on. If you have any questions about this feature, feel free to join us on \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://stree.ai/slack\",\"children\":\"Slack\"}],\", where we’ll be happy to help you out.\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"How does real-time ingestion work?\",\"url\":\"#how-does-real-time-ingestion-work\",\"depth\":2},{\"value\":\"Why do we need to pause and resume ingestion?\",\"url\":\"#why-do-we-need-to-pause-and-resume-ingestion\",\"depth\":2},{\"value\":\"Data Generation\",\"url\":\"#data-generation\",\"depth\":2},{\"value\":\"Pinot Schema/Table Config\",\"url\":\"#pinot-schematable-config\",\"depth\":2},{\"value\":\"The Pause/Resume Flow\",\"url\":\"#the-pauseresume-flow\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Apache Pinot™ 0.11 - Pausing Real-Time Ingestion | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"Learn about a feature that lets you pause and resume real-time data ingestion in Apache Pinot\"}],[\"$\",\"meta\",\"4\",{\"name\":\"robots\",\"content\":\"index, follow\"}],[\"$\",\"meta\",\"5\",{\"name\":\"googlebot\",\"content\":\"index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1\"}],[\"$\",\"link\",\"6\",{\"rel\":\"canonical\",\"href\":\"https://pinot.apache.org/blog/2022/11/28/Apache-Pinot-Pausing-Real-Time-Ingestion\"}],[\"$\",\"link\",\"7\",{\"rel\":\"alternate\",\"type\":\"application/rss+xml\",\"href\":\"https://pinot.apache.org/feed.xml\"}],[\"$\",\"meta\",\"8\",{\"property\":\"og:title\",\"content\":\"Apache Pinot™ 0.11 - Pausing Real-Time Ingestion\"}],[\"$\",\"meta\",\"9\",{\"property\":\"og:description\",\"content\":\"Learn about a feature that lets you pause and resume real-time data ingestion in Apache Pinot\"}],[\"$\",\"meta\",\"10\",{\"property\":\"og:url\",\"content\":\"https://pinot.apache.org/blog/2022/11/28/Apache-Pinot-Pausing-Real-Time-Ingestion\"}],[\"$\",\"meta\",\"11\",{\"property\":\"og:site_name\",\"content\":\"Apache Pinot™\"}],[\"$\",\"meta\",\"12\",{\"property\":\"og:locale\",\"content\":\"en_US\"}],[\"$\",\"meta\",\"13\",{\"property\":\"og:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"14\",{\"property\":\"og:type\",\"content\":\"article\"}],[\"$\",\"meta\",\"15\",{\"property\":\"article:published_time\",\"content\":\"2022-11-28T00:00:00.000Z\"}],[\"$\",\"meta\",\"16\",{\"property\":\"article:modified_time\",\"content\":\"2022-11-28T00:00:00.000Z\"}],[\"$\",\"meta\",\"17\",{\"property\":\"article:author\",\"content\":\"Mark Needham\"}],[\"$\",\"meta\",\"18\",{\"name\":\"twitter:card\",\"content\":\"summary_large_image\"}],[\"$\",\"meta\",\"19\",{\"name\":\"twitter:title\",\"content\":\"Apache Pinot™ 0.11 - Pausing Real-Time Ingestion\"}],[\"$\",\"meta\",\"20\",{\"name\":\"twitter:description\",\"content\":\"Learn about a feature that lets you pause and resume real-time data ingestion in Apache Pinot\"}],[\"$\",\"meta\",\"21\",{\"name\":\"twitter:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"22\",{\"name\":\"next-size-adjust\"}]]\n"])</script><script>self.__next_f.push([1,"f:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>
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diff --git a/blog/2023/01/29/Apache-Pinot-Deduplication-on-Real-Time-Tables.html b/blog/2023/01/29/Apache-Pinot-Deduplication-on-Real-Time-Tables.html
index 939b868..53b61b6 100644
--- a/blog/2023/01/29/Apache-Pinot-Deduplication-on-Real-Time-Tables.html
+++ b/blog/2023/01/29/Apache-Pinot-Deduplication-on-Real-Time-Tables.html
@@ -203,4 +203,4 @@
</span><span class="code-line"> group by uuid
</span><span class="code-line"> order by count<span class="token punctuation">(</span>*<span class="token punctuation">)</span>
</span><span class="code-line"> limit <span class="token number">10</span>
-</span></code></pre></div><p><img alt="Sample Apache Pinot real-time query response stats deduplicated" src="https://www.datocms-assets.com/75153/1673273248-image3.png" title="Sample Apache Pinot real-time query response stats deduplicated"/></p><p>We have every combination of hex values (16^3=4096) and no duplicates! Pinot’s de-duplication feature has done its job.</p><h2 id="how-does-it-work"><a href="#how-does-it-work" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>How does it work? </h2><p>When we’re not using the deduplication feature, events are ingested from our streaming platform into Pinot, as shown in the diagram below:</p><p><img alt="Events ingested from a streaming platform into Apache Pinot without using the deduplication feature" src="https://www.datocms-assets.com/75153/1673273272-pinot_0-11-de-duplication-diagram_1-v2.png" title="Events ingested from a streaming platform into Apache Pinot without using the deduplication feature"/></p><p>When de-dup is enabled, we have to check whether records can be ingested, as shown in the diagram below:</p><p><img alt="Events ingested from a streaming platform into Apache Pinot using the deduplication feature" src="https://www.datocms-assets.com/75153/1673273289-pinot_0-11-de-duplication-diagram_2-v3.png" title="Events ingested from a streaming platform into Apache Pinot using the deduplication feature"/></p><p>De-dup works out whether a primary key has already been ingested by using an in memory map of (primary key -> corresponding segment reference).</p><p>We need to take that into account when using this feature, otherwise, we’ll end up using all the available memory on the Pinot Server. Below are some tips for using this feature:</p><ul><li>Try to use a simple primary key type and avoid composite keys. If you don’t have a simple primary key, consider using one of the available hash functions to reduce the space taken up.</li><li>Create more partitions in the streaming platform than you might otherwise create. The number of partitions determines the partition numbers of the Pinot table. The more partitions you have in the streaming platform, the more Pinot servers you can distribute the Pinot table to, and the more horizontally scalable the table will be.</li></ul><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>This feature makes it easier to ensure that we don’t end up with duplicate data in our Apache Pinot estate.</p><p>So give it a try and let us know how you get on. If you have any questions about this feature, feel free to join us on <a target="_blank" rel="noopener noreferrer" href="https://stree.ai/slack">Slack</a>, where we’ll be happy to help you out.</p><p>And if you’re interested in how this feature was implemented, you can look at the <a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/pull/8708">pull request on GitHub</a>.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#why-do-we-need-deduplication-on-real-time-tables">Why do we need deduplication on real-time tables?</a></li><li class="text-sm font-normal leading-tight"><a href="#how-does-dedup-differ-from-upserts">How does dedup differ from upserts?</a></li><li class="text-sm font-normal leading-tight"><a href="#setting-up-apache-kafka-and-apache-pinot">Setting up Apache Kafka and Apache Pinot</a></li><li class="text-sm font-normal leading-tight"><a href="#data-generation">Data Generation</a></li><li class="text-sm font-normal leading-tight"><a href="#pinot-schematable-config">Pinot Schema/Table Config</a></li><li class="text-sm font-normal leading-tight"><a href="#how-does-it-work">How does it work? </a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" 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language-python\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"import\"}],\" datetime\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"import\"}],\" uuid\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"import\"}],\" random\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"import\"}],\" json\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"while\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token 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punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token builtin\",\"children\":\"id\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token builtin\",\"children\":\"str\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"uuid\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"uuid4\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" count \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" random\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"randint\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"0\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1000\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"print\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" json\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"dumps\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"tsString\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" ts\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token builtin\",\"children\":\"id\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"3\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" count\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"We can view the data generated by this script by pasting the above code into a file called datagen.py and then running the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"python datagen.py \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token file-descriptor important\",\"children\":\"2\"}],\"\u003e\"]}],\"/dev/null \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"head\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-n3\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" jq\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"We’ll see the following output:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tsString\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"2023-01-03T10:59:17.355081Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"f94\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"541\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tsString\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"2023-01-03T10:59:17.355125Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"057\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"96\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tsString\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"2023-01-03T10:59:17.355141Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"d7b\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"288\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"If we generate only 25,000 events, we’ll get some duplicates, which we can see by running the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"python datagen.py \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token file-descriptor important\",\"children\":\"2\"}],\"\u003e\"]}],\"/dev/null \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"jq \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-r\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'.uuid'\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"head\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-n25000\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"uniq\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-cd\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The results of running that command are shown below:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-text\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-text\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"2 3a2\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"2 a04\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"2 433\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"2 291\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"2 d73\\n\"}]]}]}],[\"$\",\"p\",null,{\"children\":\"We’re going to pipe this data into a Kafka stream called events, like this:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"python datagen.py \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token file-descriptor important\",\"children\":\"2\"}],\"\u003e\"]}],\"/dev/null \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"jq \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-cr\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"--arg\"}],\" sep 😊 \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'[.uuid, tostring] | join($sep)'\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"kcat \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-P\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-b\"}],\" localhost:9092 \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-t\"}],\" events -K😊\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"The construction of the key/value structure comes from Robin Moffatt’s \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://rmoff.net/2020/09/30/setting-key-value-when-piping-from-jq-to-kafkacat/\",\"children\":\"excellent blog post\"}],\". Since that blog post was written, kcat has started supporting multi byte separators, which is why we can use a smiley face to separate our key and value.\"]}],[\"$\",\"h2\",null,{\"id\":\"pinot-schematable-config\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#pinot-schematable-config\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Pinot Schema/Table Config\"]}],[\"$\",\"p\",null,{\"children\":\"Next, we’re going to create a Pinot table and schema with the same name. Let’s first define a schema:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schemaName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dimensionFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"STRING\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metricFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"INT\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dateTimeFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"TIMESTAMP\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"format\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS:EPOCH\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"granularity\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Note that the timestamp field is called ts and not tsString, as it is in the Kafka stream. We’re going to transform the DateTime string value held in that field into a proper timestamp using a transformation function.\"}],[\"$\",\"p\",null,{\"children\":\"Our table config is described below:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"REALTIME\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"segmentsConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"timeColumnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schemaName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"replication\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"replicasPerPartition\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableIndexConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"loadMode\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MMAP\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"streamConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"streamType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"kafka\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.topic.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.broker.list\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"kafka-dedup-blog:9093\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.type\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"lowlevel\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.prop.auto.offset.reset\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"smallest\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.factory.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"ingestionConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"columnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformFunction\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"FromDateTime(tsString, 'YYYY-MM-dd''T''HH:mm:ss.SSSSSS''Z''')\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tenants\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metadata\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Let’s create the table using the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"--network\"}],\" dedup_blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/pinot/config:/config \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.11.0-arm64 AddTable \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-schemaFile\"}],\" /config/schema.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-tableConfigFile\"}],\" /config/table.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-controllerHost\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"pinot-controller-dedup-blog\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-exec\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"Now we can navigate to \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/\",\"children\":\"http://localhost:9000\"}],\" and run a query that will return a count of the number of each uuid:\"]}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" uuid\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" events\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"group\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" uuid\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"order\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The results of this query are shown below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample Apache Pinot real-time query response stats including duplicates\",\"src\":\"https://www.datocms-assets.com/75153/1673273173-image4.png\",\"title\":\"Sample Apache Pinot real-time query response stats including duplicates\"}]}],[\"$\",\"p\",null,{\"children\":\"We can see loads of duplicates!\"}],[\"$\",\"p\",null,{\"children\":\"Now let’s add a table and schema that uses the de-duplicate feature, starting with the schema:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schemaName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events_dedup\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"primaryKeyColumns\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dimensionFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"STRING\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metricFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"INT\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dateTimeFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"TIMESTAMP\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"format\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS:EPOCH\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"granularity\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The main difference between this schema and the events schema is that we need to specify a primary key. This key can be any number of fields, but in this case, we’re only using the uuid field.\"}],[\"$\",\"p\",null,{\"children\":\"Next, the table config:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events_dedup\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"REALTIME\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"segmentsConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"timeColumnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token 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operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"columnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformFunction\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" 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punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metadata\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The changes to notice here are:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":[[\"$\",\"code\",null,{\"children\":\"\\\"dedupConfig\\\": {\\\"dedupEnabled\\\": true, \\\"hashFunction\\\": \\\"NONE\\\"}\"}],\" - This enables the feature and indicates that we won’t use a hash function on our primary key.\"]}],[\"$\",\"li\",null,{\"children\":[[\"$\",\"code\",null,{\"children\":\"\\\"routing\\\": {\\\"instanceSelectorType\\\": \\\"strictReplicaGroup\\\"}\"}],\" - This makes sure that all segments of the same partition are served from the same server to ensure data consistency across the segments.\"]}]]}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter 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punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-tableConfigFile\"}],\" /config/table-dedup.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-controllerHost\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"pinot-controller-dedup-blog\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-exec\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" uuid, count\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"*\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" from events_dedup\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" group by uuid\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" order by count\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"*\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" limit \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample Apache Pinot real-time query response stats deduplicated\",\"src\":\"https://www.datocms-assets.com/75153/1673273248-image3.png\",\"title\":\"Sample Apache Pinot real-time query response stats deduplicated\"}]}],[\"$\",\"p\",null,{\"children\":\"We have every combination of hex values (16^3=4096) and no duplicates! Pinot’s de-duplication feature has done its job.\"}],[\"$\",\"h2\",null,{\"id\":\"how-does-it-work\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#how-does-it-work\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"How does it work? \"]}],[\"$\",\"p\",null,{\"children\":\"When we’re not using the deduplication feature, events are ingested from our streaming platform into Pinot, as shown in the diagram below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Events ingested from a streaming platform into Apache Pinot without using the deduplication feature\",\"src\":\"https://www.datocms-assets.com/75153/1673273272-pinot_0-11-de-duplication-diagram_1-v2.png\",\"title\":\"Events ingested from a streaming platform into Apache Pinot without using the deduplication feature\"}]}],[\"$\",\"p\",null,{\"children\":\"When de-dup is enabled, we have to check whether records can be ingested, as shown in the diagram below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Events ingested from a streaming platform into Apache Pinot using the deduplication feature\",\"src\":\"https://www.datocms-assets.com/75153/1673273289-pinot_0-11-de-duplication-diagram_2-v3.png\",\"title\":\"Events ingested from a streaming platform into Apache Pinot using the deduplication feature\"}]}],[\"$\",\"p\",null,{\"children\":\"De-dup works out whether a primary key has already been ingested by using an in memory map of (primary key -\u003e corresponding segment reference).\"}],[\"$\",\"p\",null,{\"children\":\"We need to take that into account when using this feature, otherwise, we’ll end up using all the available memory on the Pinot Server. Below are some tips for using this feature:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Try to use a simple primary key type and avoid composite keys. If you don’t have a simple primary key, consider using one of the available hash functions to reduce the space taken up.\"}],[\"$\",\"li\",null,{\"children\":\"Create more partitions in the streaming platform than you might otherwise create. The number of partitions determines the partition numbers of the Pinot table. The more partitions you have in the streaming platform, the more Pinot servers you can distribute the Pinot table to, and the more horizontally scalable the table will be.\"}]]}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":\"This feature makes it easier to ensure that we don’t end up with duplicate data in our Apache Pinot estate.\"}],[\"$\",\"p\",null,{\"children\":[\"So give it a try and let us know how you get on. If you have any questions about this feature, feel free to join us on \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://stree.ai/slack\",\"children\":\"Slack\"}],\", where we’ll be happy to help you out.\"]}],[\"$\",\"p\",null,{\"children\":[\"And if you’re interested in how this feature was implemented, you can look at the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/apache/pinot/pull/8708\",\"children\":\"pull request on GitHub\"}],\".\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"Why do we need deduplication on real-time tables?\",\"url\":\"#why-do-we-need-deduplication-on-real-time-tables\",\"depth\":2},{\"value\":\"How does dedup differ from upserts?\",\"url\":\"#how-does-dedup-differ-from-upserts\",\"depth\":2},{\"value\":\"Setting up Apache Kafka and Apache Pinot\",\"url\":\"#setting-up-apache-kafka-and-apache-pinot\",\"depth\":2},{\"value\":\"Data Generation\",\"url\":\"#data-generation\",\"depth\":2},{\"value\":\"Pinot Schema/Table Config\",\"url\":\"#pinot-schematable-config\",\"depth\":2},{\"value\":\"How does it work? \",\"url\":\"#how-does-it-work\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Apache Pinot™ 0.11 - Deduplication on Real-Time Tables | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"Learn about the deduplication for the real-time tables feature in Apache Pinot\"}],[\"$\",\"meta\",\"4\",{\"name\":\"robots\",\"content\":\"index, follow\"}],[\"$\",\"meta\",\"5\",{\"name\":\"googlebot\",\"content\":\"index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1\"}],[\"$\",\"link\",\"6\",{\"rel\":\"canonical\",\"href\":\"https://pinot.apache.org/blog/2023/01/29/Apache-Pinot-Deduplication-on-Real-Time-Tables\"}],[\"$\",\"link\",\"7\",{\"rel\":\"alternate\",\"type\":\"application/rss+xml\",\"href\":\"https://pinot.apache.org/feed.xml\"}],[\"$\",\"meta\",\"8\",{\"property\":\"og:title\",\"content\":\"Apache Pinot™ 0.11 - Deduplication on Real-Time Tables\"}],[\"$\",\"meta\",\"9\",{\"property\":\"og:description\",\"content\":\"Learn about the deduplication for the real-time tables feature in Apache Pinot\"}],[\"$\",\"meta\",\"10\",{\"property\":\"og:url\",\"content\":\"https://pinot.apache.org/blog/2023/01/29/Apache-Pinot-Deduplication-on-Real-Time-Tables\"}],[\"$\",\"meta\",\"11\",{\"property\":\"og:site_name\",\"content\":\"Apache Pinot™\"}],[\"$\",\"meta\",\"12\",{\"property\":\"og:locale\",\"content\":\"en_US\"}],[\"$\",\"meta\",\"13\",{\"property\":\"og:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"14\",{\"property\":\"og:type\",\"content\":\"article\"}],[\"$\",\"meta\",\"15\",{\"property\":\"article:published_time\",\"content\":\"2023-01-29T00:00:00.000Z\"}],[\"$\",\"meta\",\"16\",{\"property\":\"article:modified_time\",\"content\":\"2023-01-29T00:00:00.000Z\"}],[\"$\",\"meta\",\"17\",{\"property\":\"article:author\",\"content\":\"Mark Needham\"}],[\"$\",\"meta\",\"18\",{\"name\":\"twitter:card\",\"content\":\"summary_large_image\"}],[\"$\",\"meta\",\"19\",{\"name\":\"twitter:title\",\"content\":\"Apache Pinot™ 0.11 - Deduplication on Real-Time Tables\"}],[\"$\",\"meta\",\"20\",{\"name\":\"twitter:description\",\"content\":\"Learn about the deduplication for the real-time tables feature in Apache Pinot\"}],[\"$\",\"meta\",\"21\",{\"name\":\"twitter:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"22\",{\"name\":\"next-size-adjust\"}]]\n"])</script><script>self.__next_f.push([1,"f:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>
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+</span></code></pre></div><p><img alt="Sample Apache Pinot real-time query response stats deduplicated" src="https://www.datocms-assets.com/75153/1673273248-image3.png" title="Sample Apache Pinot real-time query response stats deduplicated"/></p><p>We have every combination of hex values (16^3=4096) and no duplicates! Pinot’s de-duplication feature has done its job.</p><h2 id="how-does-it-work"><a href="#how-does-it-work" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>How does it work? </h2><p>When we’re not using the deduplication feature, events are ingested from our streaming platform into Pinot, as shown in the diagram below:</p><p><img alt="Events ingested from a streaming platform into Apache Pinot without using the deduplication feature" src="https://www.datocms-assets.com/75153/1673273272-pinot_0-11-de-duplication-diagram_1-v2.png" title="Events ingested from a streaming platform into Apache Pinot without using the deduplication feature"/></p><p>When de-dup is enabled, we have to check whether records can be ingested, as shown in the diagram below:</p><p><img alt="Events ingested from a streaming platform into Apache Pinot using the deduplication feature" src="https://www.datocms-assets.com/75153/1673273289-pinot_0-11-de-duplication-diagram_2-v3.png" title="Events ingested from a streaming platform into Apache Pinot using the deduplication feature"/></p><p>De-dup works out whether a primary key has already been ingested by using an in memory map of (primary key -> corresponding segment reference).</p><p>We need to take that into account when using this feature, otherwise, we’ll end up using all the available memory on the Pinot Server. Below are some tips for using this feature:</p><ul><li>Try to use a simple primary key type and avoid composite keys. If you don’t have a simple primary key, consider using one of the available hash functions to reduce the space taken up.</li><li>Create more partitions in the streaming platform than you might otherwise create. The number of partitions determines the partition numbers of the Pinot table. The more partitions you have in the streaming platform, the more Pinot servers you can distribute the Pinot table to, and the more horizontally scalable the table will be.</li></ul><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>This feature makes it easier to ensure that we don’t end up with duplicate data in our Apache Pinot estate.</p><p>So give it a try and let us know how you get on. If you have any questions about this feature, feel free to join us on <a target="_blank" rel="noopener noreferrer" href="https://stree.ai/slack">Slack</a>, where we’ll be happy to help you out.</p><p>And if you’re interested in how this feature was implemented, you can look at the <a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/pull/8708">pull request on GitHub</a>.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#why-do-we-need-deduplication-on-real-time-tables">Why do we need deduplication on real-time tables?</a></li><li class="text-sm font-normal leading-tight"><a href="#how-does-dedup-differ-from-upserts">How does dedup differ from upserts?</a></li><li class="text-sm font-normal leading-tight"><a href="#setting-up-apache-kafka-and-apache-pinot">Setting up Apache Kafka and Apache Pinot</a></li><li class="text-sm font-normal leading-tight"><a href="#data-generation">Data Generation</a></li><li class="text-sm font-normal leading-tight"><a href="#pinot-schematable-config">Pinot Schema/Table Config</a></li><li class="text-sm font-normal leading-tight"><a href="#how-does-it-work">How does it work? </a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" 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language-python\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"import\"}],\" datetime\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"import\"}],\" uuid\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"import\"}],\" random\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"import\"}],\" json\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"while\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token 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punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token builtin\",\"children\":\"id\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token builtin\",\"children\":\"str\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"uuid\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"uuid4\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" count \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" random\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"randint\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"0\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1000\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"print\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" json\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"dumps\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"tsString\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" ts\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token builtin\",\"children\":\"id\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"3\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" count\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"We can view the data generated by this script by pasting the above code into a file called datagen.py and then running the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"python datagen.py \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token file-descriptor important\",\"children\":\"2\"}],\"\u003e\"]}],\"/dev/null \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"head\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-n3\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" jq\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"We’ll see the following output:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tsString\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"2023-01-03T10:59:17.355081Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"f94\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"541\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tsString\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"2023-01-03T10:59:17.355125Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"057\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"96\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tsString\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"2023-01-03T10:59:17.355141Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"d7b\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"288\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"If we generate only 25,000 events, we’ll get some duplicates, which we can see by running the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"python datagen.py \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token file-descriptor important\",\"children\":\"2\"}],\"\u003e\"]}],\"/dev/null \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"jq \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-r\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'.uuid'\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"head\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-n25000\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"uniq\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-cd\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The results of running that command are shown below:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-text\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-text\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"2 3a2\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"2 a04\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"2 433\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"2 291\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"2 d73\\n\"}]]}]}],[\"$\",\"p\",null,{\"children\":\"We’re going to pipe this data into a Kafka stream called events, like this:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"python datagen.py \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token file-descriptor important\",\"children\":\"2\"}],\"\u003e\"]}],\"/dev/null \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"jq \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-cr\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"--arg\"}],\" sep 😊 \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'[.uuid, tostring] | join($sep)'\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"kcat \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-P\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-b\"}],\" localhost:9092 \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-t\"}],\" events -K😊\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"The construction of the key/value structure comes from Robin Moffatt’s \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://rmoff.net/2020/09/30/setting-key-value-when-piping-from-jq-to-kafkacat/\",\"children\":\"excellent blog post\"}],\". Since that blog post was written, kcat has started supporting multi byte separators, which is why we can use a smiley face to separate our key and value.\"]}],[\"$\",\"h2\",null,{\"id\":\"pinot-schematable-config\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#pinot-schematable-config\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Pinot Schema/Table Config\"]}],[\"$\",\"p\",null,{\"children\":\"Next, we’re going to create a Pinot table and schema with the same name. Let’s first define a schema:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schemaName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dimensionFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"STRING\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metricFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"INT\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dateTimeFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"TIMESTAMP\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"format\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS:EPOCH\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"granularity\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Note that the timestamp field is called ts and not tsString, as it is in the Kafka stream. We’re going to transform the DateTime string value held in that field into a proper timestamp using a transformation function.\"}],[\"$\",\"p\",null,{\"children\":\"Our table config is described below:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"REALTIME\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"segmentsConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"timeColumnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schemaName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"replication\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"replicasPerPartition\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableIndexConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"loadMode\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MMAP\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"streamConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"streamType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"kafka\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.topic.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.broker.list\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"kafka-dedup-blog:9093\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.type\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"lowlevel\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.prop.auto.offset.reset\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"smallest\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.factory.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"ingestionConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"columnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformFunction\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"FromDateTime(tsString, 'YYYY-MM-dd''T''HH:mm:ss.SSSSSS''Z''')\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tenants\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metadata\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Let’s create the table using the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"--network\"}],\" dedup_blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/pinot/config:/config \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.11.0-arm64 AddTable \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-schemaFile\"}],\" /config/schema.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-tableConfigFile\"}],\" /config/table.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-controllerHost\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"pinot-controller-dedup-blog\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-exec\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"Now we can navigate to \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/\",\"children\":\"http://localhost:9000\"}],\" and run a query that will return a count of the number of each uuid:\"]}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" uuid\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" events\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"group\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" uuid\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"order\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The results of this query are shown below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample Apache Pinot real-time query response stats including duplicates\",\"src\":\"https://www.datocms-assets.com/75153/1673273173-image4.png\",\"title\":\"Sample Apache Pinot real-time query response stats including duplicates\"}]}],[\"$\",\"p\",null,{\"children\":\"We can see loads of duplicates!\"}],[\"$\",\"p\",null,{\"children\":\"Now let’s add a table and schema that uses the de-duplicate feature, starting with the schema:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schemaName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events_dedup\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"primaryKeyColumns\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dimensionFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"STRING\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metricFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"INT\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dateTimeFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"TIMESTAMP\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"format\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS:EPOCH\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"granularity\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The main difference between this schema and the events schema is that we need to specify a primary key. This key can be any number of fields, but in this case, we’re only using the uuid field.\"}],[\"$\",\"p\",null,{\"children\":\"Next, the table config:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"events_dedup\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"REALTIME\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"segmentsConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"timeColumnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token 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operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"columnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformFunction\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" 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punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metadata\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The changes to notice here are:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":[[\"$\",\"code\",null,{\"children\":\"\\\"dedupConfig\\\": {\\\"dedupEnabled\\\": true, \\\"hashFunction\\\": \\\"NONE\\\"}\"}],\" - This enables the feature and indicates that we won’t use a hash function on our primary key.\"]}],[\"$\",\"li\",null,{\"children\":[[\"$\",\"code\",null,{\"children\":\"\\\"routing\\\": {\\\"instanceSelectorType\\\": \\\"strictReplicaGroup\\\"}\"}],\" - This makes sure that all segments of the same partition are served from the same server to ensure data consistency across the segments.\"]}]]}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter 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punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-tableConfigFile\"}],\" /config/table-dedup.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-controllerHost\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"pinot-controller-dedup-blog\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-exec\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" uuid, count\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"*\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" from events_dedup\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" group by uuid\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" order by count\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"*\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" limit \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample Apache Pinot real-time query response stats deduplicated\",\"src\":\"https://www.datocms-assets.com/75153/1673273248-image3.png\",\"title\":\"Sample Apache Pinot real-time query response stats deduplicated\"}]}],[\"$\",\"p\",null,{\"children\":\"We have every combination of hex values (16^3=4096) and no duplicates! Pinot’s de-duplication feature has done its job.\"}],[\"$\",\"h2\",null,{\"id\":\"how-does-it-work\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#how-does-it-work\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"How does it work? \"]}],[\"$\",\"p\",null,{\"children\":\"When we’re not using the deduplication feature, events are ingested from our streaming platform into Pinot, as shown in the diagram below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Events ingested from a streaming platform into Apache Pinot without using the deduplication feature\",\"src\":\"https://www.datocms-assets.com/75153/1673273272-pinot_0-11-de-duplication-diagram_1-v2.png\",\"title\":\"Events ingested from a streaming platform into Apache Pinot without using the deduplication feature\"}]}],[\"$\",\"p\",null,{\"children\":\"When de-dup is enabled, we have to check whether records can be ingested, as shown in the diagram below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Events ingested from a streaming platform into Apache Pinot using the deduplication feature\",\"src\":\"https://www.datocms-assets.com/75153/1673273289-pinot_0-11-de-duplication-diagram_2-v3.png\",\"title\":\"Events ingested from a streaming platform into Apache Pinot using the deduplication feature\"}]}],[\"$\",\"p\",null,{\"children\":\"De-dup works out whether a primary key has already been ingested by using an in memory map of (primary key -\u003e corresponding segment reference).\"}],[\"$\",\"p\",null,{\"children\":\"We need to take that into account when using this feature, otherwise, we’ll end up using all the available memory on the Pinot Server. Below are some tips for using this feature:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Try to use a simple primary key type and avoid composite keys. If you don’t have a simple primary key, consider using one of the available hash functions to reduce the space taken up.\"}],[\"$\",\"li\",null,{\"children\":\"Create more partitions in the streaming platform than you might otherwise create. The number of partitions determines the partition numbers of the Pinot table. The more partitions you have in the streaming platform, the more Pinot servers you can distribute the Pinot table to, and the more horizontally scalable the table will be.\"}]]}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":\"This feature makes it easier to ensure that we don’t end up with duplicate data in our Apache Pinot estate.\"}],[\"$\",\"p\",null,{\"children\":[\"So give it a try and let us know how you get on. If you have any questions about this feature, feel free to join us on \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://stree.ai/slack\",\"children\":\"Slack\"}],\", where we’ll be happy to help you out.\"]}],[\"$\",\"p\",null,{\"children\":[\"And if you’re interested in how this feature was implemented, you can look at the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/apache/pinot/pull/8708\",\"children\":\"pull request on GitHub\"}],\".\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"Why do we need deduplication on real-time tables?\",\"url\":\"#why-do-we-need-deduplication-on-real-time-tables\",\"depth\":2},{\"value\":\"How does dedup differ from upserts?\",\"url\":\"#how-does-dedup-differ-from-upserts\",\"depth\":2},{\"value\":\"Setting up Apache Kafka and Apache Pinot\",\"url\":\"#setting-up-apache-kafka-and-apache-pinot\",\"depth\":2},{\"value\":\"Data Generation\",\"url\":\"#data-generation\",\"depth\":2},{\"value\":\"Pinot Schema/Table Config\",\"url\":\"#pinot-schematable-config\",\"depth\":2},{\"value\":\"How does it work? \",\"url\":\"#how-does-it-work\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Apache Pinot™ 0.11 - Deduplication on Real-Time Tables | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"Learn about the deduplication for the real-time tables feature in Apache Pinot\"}],[\"$\",\"meta\",\"4\",{\"name\":\"robots\",\"content\":\"index, follow\"}],[\"$\",\"meta\",\"5\",{\"name\":\"googlebot\",\"content\":\"index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1\"}],[\"$\",\"link\",\"6\",{\"rel\":\"canonical\",\"href\":\"https://pinot.apache.org/blog/2023/01/29/Apache-Pinot-Deduplication-on-Real-Time-Tables\"}],[\"$\",\"link\",\"7\",{\"rel\":\"alternate\",\"type\":\"application/rss+xml\",\"href\":\"https://pinot.apache.org/feed.xml\"}],[\"$\",\"meta\",\"8\",{\"property\":\"og:title\",\"content\":\"Apache Pinot™ 0.11 - Deduplication on Real-Time Tables\"}],[\"$\",\"meta\",\"9\",{\"property\":\"og:description\",\"content\":\"Learn about the deduplication for the real-time tables feature in Apache Pinot\"}],[\"$\",\"meta\",\"10\",{\"property\":\"og:url\",\"content\":\"https://pinot.apache.org/blog/2023/01/29/Apache-Pinot-Deduplication-on-Real-Time-Tables\"}],[\"$\",\"meta\",\"11\",{\"property\":\"og:site_name\",\"content\":\"Apache Pinot™\"}],[\"$\",\"meta\",\"12\",{\"property\":\"og:locale\",\"content\":\"en_US\"}],[\"$\",\"meta\",\"13\",{\"property\":\"og:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"14\",{\"property\":\"og:type\",\"content\":\"article\"}],[\"$\",\"meta\",\"15\",{\"property\":\"article:published_time\",\"content\":\"2023-01-29T00:00:00.000Z\"}],[\"$\",\"meta\",\"16\",{\"property\":\"article:modified_time\",\"content\":\"2023-01-29T00:00:00.000Z\"}],[\"$\",\"meta\",\"17\",{\"property\":\"article:author\",\"content\":\"Mark Needham\"}],[\"$\",\"meta\",\"18\",{\"name\":\"twitter:card\",\"content\":\"summary_large_image\"}],[\"$\",\"meta\",\"19\",{\"name\":\"twitter:title\",\"content\":\"Apache Pinot™ 0.11 - Deduplication on Real-Time Tables\"}],[\"$\",\"meta\",\"20\",{\"name\":\"twitter:description\",\"content\":\"Learn about the deduplication for the real-time tables feature in Apache Pinot\"}],[\"$\",\"meta\",\"21\",{\"name\":\"twitter:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"22\",{\"name\":\"next-size-adjust\"}]]\n"])</script><script>self.__next_f.push([1,"f:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>
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</span></code></pre></div><p>In this case, that will result in a new boundary time of 2023-01-09T18:41:17, which is exactly what we need.</p><p>We’ll then be able to query the events table and have it read the offline table to get all records on or before 2023-01-09T18:41:17 and the real-time table for everything else.</p><h2 id="neat-anything-else-i-should-know"><a href="#neat-anything-else-i-should-know" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Neat, anything else I should know?</h2><p>Something to keep in mind when updating the time boundary is that it’s a one-off operation. It won’t be automatically updated if you add a new, more recent segment to the offline table.</p><p>In this scenario, you need to call the <code>tables/{tableName}/timeBoundary</code> API again.</p><p>And if you want to revert to the previous behavior where the time boundary is computed by subtracting the ingestion frequency from the latest end time, you can do that too:</p><div class="relative"><pre><code class="code-highlight language-bash"><span class="code-line"><span class="token function">curl</span> <span class="token parameter variable">-X</span> DELETE <span class="token punctuation">\</span>
</span><span class="code-line"> <span class="token string">"http://localhost:9000/tables/{tableName}/timeBoundary"</span> <span class="token punctuation">\</span>
</span><span class="code-line"> <span class="token parameter variable">-H</span> <span class="token string">"accept: application/json"</span>
-</span></code></pre></div><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>I love this feature, and it solves a problem I’ve struggled with when using my datasets. I hope you’ll find it just as useful.</p><p>Give it a try, and let us know how you get on. If you have any questions about this feature, feel free to join us on <a target="_blank" rel="noopener noreferrer" href="https://stree.ai/slack">Slack</a>, where we’ll be happy to help you out.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#what-is-a-hybrid-table">What is a hybrid table?</a></li><li class="text-sm font-normal leading-tight"><a href="#how-do-you-query-a-hybrid-table">How do you query a hybrid table?</a></li><li class="text-sm font-normal leading-tight"><a href="#so-whats-the-problem">So, what’s the problem?</a></li><li class="text-sm font-normal leading-tight"><a href="#and-the-solution">And the solution?</a></li><li class="text-sm font-normal leading-tight"><a href="#neat-anything-else-i-should-know">Neat, anything else I should know?</a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 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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.\"]}]]}]]}]}]]}]}]]}],null]}]]\n"])</script><script>self.__next_f.push([1,"12:I[1514,[\"326\",\"static/chunks/326-3a90a6443b9c824c.js\",\"413\",\"static/chunks/413-f9f40b83f7bb3f22.js\",\"980\",\"static/chunks/980-6e243f9cd384c7d2.js\",\"797\",\"static/chunks/app/blog/%5B...slug%5D/page-502e08b6677b55da.js\"],\"\"]\n13:I[2529,[\"326\",\"static/chunks/326-3a90a6443b9c824c.js\",\"413\",\"static/chunks/413-f9f40b83f7bb3f22.js\",\"980\",\"static/chunks/980-6e243f9cd384c7d2.js\",\"797\",\"static/chunks/app/blog/%5B...slug%5D/page-502e08b6677b55da.js\"],\"\"]\n14:I[5185,[\"326\",\"static/chunks/326-3a90a6443b9c824c.js\",\"413\",\""])</script><script>self.__next_f.push([1,"static/chunks/413-f9f40b83f7bb3f22.js\",\"980\",\"static/chunks/980-6e243f9cd384c7d2.js\",\"797\",\"static/chunks/app/blog/%5B...slug%5D/page-502e08b6677b55da.js\"],\"\"]\n"])</script><script>self.__next_f.push([1,"10:[[\"$\",\"script\",null,{\"type\":\"application/ld+json\",\"dangerouslySetInnerHTML\":{\"__html\":\"{\\\"@context\\\":\\\"https://schema.org\\\",\\\"@type\\\":\\\"BlogPosting\\\",\\\"headline\\\":\\\"Apache Pinot™ 0.12 - Configurable Time Boundary\\\",\\\"datePublished\\\":\\\"2023-02-21T00:00:00.000Z\\\",\\\"dateModified\\\":\\\"2023-02-21T00:00:00.000Z\\\",\\\"description\\\":\\\"This post will explore the ability to configure the time boundary when working with hybrid tables.\\\",\\\"image\\\":\\\"/static/images/twitter-card.png\\\",\\\"url\\\":\\\"https://pinot.apache.org/blog/2023-02-21-Apache-Pinot-0-12-Configurable-Time-Boundary\\\",\\\"author\\\":[{\\\"@type\\\":\\\"Person\\\",\\\"name\\\":\\\"Mark Needham\\\"}]}\"}}],[\"$\",\"section\",null,{\"className\":\" px-5 pt-10 md:px-[13.313rem] md:py-16\",\"children\":[[\"$\",\"$L12\",null,{}],[\"$\",\"article\",null,{\"className\":\"\",\"children\":[\"$\",\"div\",null,{\"className\":\"mx-auto lg:flex\",\"children\":[[\"$\",\"div\",null,{\"className\":\"lg:pr-12\",\"children\":[[\"$\",\"header\",null,{\"className\":\"pt-6 md:pr-10\",\"children\":[[\"$\",\"h1\",null,{\"className\":\"text-4xl font-semibold\",\"children\":\"Apache Pinot™ 0.12 - Configurable Time Boundary\"}],[\"$\",\"p\",null,{\"className\":\"pt-2 text-lg\",\"children\":[\"By: \",\"Mark Needham\"]}],[\"$\",\"p\",null,{\"className\":\"py-2 text-sm\",\"children\":[\"February 21st, 2023\",\" • \",\"4 min read\"]}]]}],[\"$\",\"div\",null,{\"className\":\"flex flex-col lg:flex-row\",\"children\":[\"$\",\"main\",null,{\"className\":\"\",\"children\":[\"$\",\"div\",null,{\"className\":\"prose max-w-[45rem] pb-8 pt-10 dark:prose-invert\",\"children\":[[\"$\",\"p\",null,{\"children\":[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://youtu.be/lB3RaKJ0Hbs\",\"children\":[\"$\",\"img\",null,{\"alt\":\"Watch the video\",\"src\":\"https://i3.ytimg.com/vi/lB3RaKJ0Hbs/maxresdefault.jpg\"}]}]}],[\"$\",\"p\",null,{\"children\":[\"The Apache Pinot community recently released version \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/basics/releases/0.12.0\",\"children\":\"0.12.0\"}],\", which has lots of goodies for you to play with. 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The offline table stores historical data, while the real-time data continuously ingests data from a streaming data platform.\"}],[\"$\",\"h2\",null,{\"id\":\"how-do-you-query-a-hybrid-table\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#how-do-you-query-a-hybrid-table\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"How do you query a hybrid table?\"]}],[\"$\",\"p\",null,{\"children\":\"When you write a query against a hybrid table, the Pinot query engine needs to work out which records to read from the offline table and which to read from the real-time table.\"}],[\"$\",\"p\",null,{\"children\":\"It does this by computing the time boundary, determined by looking at the maximum end time of segments in the offline table and the segment ingestion frequency specified for the offline table.\"}],[\"$\",\"$L13\",null,{\"className\":\"language-js\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-js\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"timeBoundary \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003c\"}],[\"$\",\"span\",null,{\"className\":\"token maybe-class-name\",\"children\":\"Maximum\"}],\" end time \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"of\"}],\" offline segments\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003c\"}],[\"$\",\"span\",null,{\"className\":\"token maybe-class-name\",\"children\":\"Ingestion\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token maybe-class-name\",\"children\":\"Frequency\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e\"}],\"\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"The ingestion frequency can either be 1 hour or 1 day, so one of these values will be used.\"}],[\"$\",\"p\",null,{\"children\":\"When a query for a hybrid table is received by a Pinot Broker, the broker sends a time boundary annotated version of the query to the offline and real-time tables. Any records from or before the time boundary are read from the offline table; anything greater than the boundary comes from the real-time table.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot computing the time boundary\",\"src\":\"https://www.datocms-assets.com/75153/1676991003-image2.png\",\"title\":\"Apache Pinot computing the time boundary\"}]}],[\"$\",\"p\",null,{\"children\":\"For example, if we executed the following query:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\" events\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The broker would send the following query to the offline table:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\" events_OFFLINE\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\" timeColumn \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003c=\"}],\" $timeBoundary\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"And the following query to the real-time table:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\" events_REALTIME\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\" timeColumn \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e\"}],\" $timeBoundary\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The results of the two queries are merged by the broker before being returned to the client.\"}],[\"$\",\"h2\",null,{\"id\":\"so-whats-the-problem\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#so-whats-the-problem\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"So, what’s the problem?\"]}],[\"$\",\"p\",null,{\"children\":\"If we have some overlap in the data in our offline and real-time tables, this approach works well, but if we have no overlap, we will end up with unexpected results.\"}],[\"$\",\"p\",null,{\"children\":\"For example, let’s say that the most recent timestamp in the events offline table is 2023-01-09T18:41:17, our ingestion frequency is 1 hour, and the real-time table has data starting from 2023-01-09T18:41:18.\"}],[\"$\",\"p\",null,{\"children\":\"This will result in a boundary time of 2023-01-09T17:41:17, which means that any records with timestamps between 17:41 and 18:41 will be excluded from query results.\"}],[\"$\",\"h2\",null,{\"id\":\"and-the-solution\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#and-the-solution\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"And the solution?\"]}],[\"$\",\"p\",null,{\"children\":[\"The 0.12 release sees the addition of the \",[\"$\",\"code\",null,{\"children\":\"tables/{tableName}/timeBoundary\"}],\" API, which lets us set the time boundary to the maximum end time of all offline segments.\"]}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"curl\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-X\"}],\" POST \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://localhost:9000/tables/{tableName}/timeBoundary\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-H\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"accept: application/json\\\"\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"In this case, that will result in a new boundary time of 2023-01-09T18:41:17, which is exactly what we need.\"}],[\"$\",\"p\",null,{\"children\":\"We’ll then be able to query the events table and have it read the offline table to get all records on or before 2023-01-09T18:41:17 and the real-time table for everything else.\"}],[\"$\",\"h2\",null,{\"id\":\"neat-anything-else-i-should-know\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#neat-anything-else-i-should-know\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Neat, anything else I should know?\"]}],[\"$\",\"p\",null,{\"children\":\"Something to keep in mind when updating the time boundary is that it’s a one-off operation. It won’t be automatically updated if you add a new, more recent segment to the offline table.\"}],[\"$\",\"p\",null,{\"children\":[\"In this scenario, you need to call the \",[\"$\",\"code\",null,{\"children\":\"tables/{tableName}/timeBoundary\"}],\" API again.\"]}],[\"$\",\"p\",null,{\"children\":\"And if you want to revert to the previous behavior where the time boundary is computed by subtracting the ingestion frequency from the latest end time, you can do that too:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"curl\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-X\"}],\" DELETE \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://localhost:9000/tables/{tableName}/timeBoundary\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-H\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"accept: application/json\\\"\"}],\"\\n\"]}]]}]}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":\"I love this feature, and it solves a problem I’ve struggled with when using my datasets. I hope you’ll find it just as useful.\"}],[\"$\",\"p\",null,{\"children\":[\"Give it a try, and let us know how you get on. If you have any questions about this feature, feel free to join us on \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://stree.ai/slack\",\"children\":\"Slack\"}],\", where we’ll be happy to help you out.\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"What is a hybrid table?\",\"url\":\"#what-is-a-hybrid-table\",\"depth\":2},{\"value\":\"How do you query a hybrid table?\",\"url\":\"#how-do-you-query-a-hybrid-table\",\"depth\":2},{\"value\":\"So, what’s the problem?\",\"url\":\"#so-whats-the-problem\",\"depth\":2},{\"value\":\"And the solution?\",\"url\":\"#and-the-solution\",\"depth\":2},{\"value\":\"Neat, anything else I should know?\",\"url\":\"#neat-anything-else-i-should-know\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Apache Pinot™ 0.12 - Configurable Time Boundary | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"This post will explore the ability to configure the time boundary when working with hybrid tables.\"}],[\"$\",\"meta\",\"4\",{\"name\":\"robots\",\"content\":\"index, follow\"}],[\"$\",\"meta\",\"5\",{\"name\":\"googlebot\",\"content\":\"index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1\"}],[\"$\",\"link\",\"6\",{\"rel\":\"canonical\",\"href\":\"https://pinot.apache.org/blog/2023/02/21/Apache-Pinot-0-12-Configurable-Time-Boundary\"}],[\"$\",\"link\",\"7\",{\"rel\":\"alternate\",\"type\":\"application/rss+xml\",\"href\":\"https://pinot.apache.org/feed.xml\"}],[\"$\",\"meta\",\"8\",{\"property\":\"og:title\",\"content\":\"Apache Pinot™ 0.12 - Configurable Time Boundary\"}],[\"$\",\"meta\",\"9\",{\"property\":\"og:description\",\"content\":\"This post will explore the ability to configure the time boundary when working with hybrid tables.\"}],[\"$\",\"meta\",\"10\",{\"property\":\"og:url\",\"content\":\"https://pinot.apache.org/blog/2023/02/21/Apache-Pinot-0-12-Configurable-Time-Boundary\"}],[\"$\",\"meta\",\"11\",{\"property\":\"og:site_name\",\"content\":\"Apache Pinot™\"}],[\"$\",\"meta\",\"12\",{\"property\":\"og:locale\",\"content\":\"en_US\"}],[\"$\",\"meta\",\"13\",{\"property\":\"og:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"14\",{\"property\":\"og:type\",\"content\":\"article\"}],[\"$\",\"meta\",\"15\",{\"property\":\"article:published_time\",\"content\":\"2023-02-21T00:00:00.000Z\"}],[\"$\",\"meta\",\"16\",{\"property\":\"article:modified_time\",\"content\":\"2023-02-21T00:00:00.000Z\"}],[\"$\",\"meta\",\"17\",{\"property\":\"article:author\",\"content\":\"Mark Needham\"}],[\"$\",\"meta\",\"18\",{\"name\":\"twitter:card\",\"content\":\"summary_large_image\"}],[\"$\",\"meta\",\"19\",{\"name\":\"twitter:title\",\"content\":\"Apache Pinot™ 0.12 - Configurable Time Boundary\"}],[\"$\",\"meta\",\"20\",{\"name\":\"twitter:description\",\"content\":\"This post will explore the ability to configure the time boundary when working with hybrid tables.\"}],[\"$\",\"meta\",\"21\",{\"name\":\"twitter:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"22\",{\"name\":\"next-size-adjust\"}]]\n"])</script><script>self.__next_f.push([1,"f:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>
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+</span></code></pre></div><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>I love this feature, and it solves a problem I’ve struggled with when using my datasets. I hope you’ll find it just as useful.</p><p>Give it a try, and let us know how you get on. If you have any questions about this feature, feel free to join us on <a target="_blank" rel="noopener noreferrer" href="https://stree.ai/slack">Slack</a>, where we’ll be happy to help you out.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#what-is-a-hybrid-table">What is a hybrid table?</a></li><li class="text-sm font-normal leading-tight"><a href="#how-do-you-query-a-hybrid-table">How do you query a hybrid table?</a></li><li class="text-sm font-normal leading-tight"><a href="#so-whats-the-problem">So, what’s the problem?</a></li><li class="text-sm font-normal leading-tight"><a href="#and-the-solution">And the solution?</a></li><li class="text-sm font-normal leading-tight"><a href="#neat-anything-else-i-should-know">Neat, anything else I should know?</a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 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This is the first in a series of blog posts showing off some of the new features in this release.\"]}],[\"$\",\"p\",null,{\"children\":\"This post will explore the ability to configure the time boundary when working with hybrid tables.\"}],[\"$\",\"h2\",null,{\"id\":\"what-is-a-hybrid-table\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#what-is-a-hybrid-table\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"What is a hybrid table?\"]}],[\"$\",\"p\",null,{\"children\":\"A hybrid table is the term used to describe a situation where we have an offline and real-time table with the same name. The offline table stores historical data, while the real-time data continuously ingests data from a streaming data platform.\"}],[\"$\",\"h2\",null,{\"id\":\"how-do-you-query-a-hybrid-table\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#how-do-you-query-a-hybrid-table\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"How do you query a hybrid table?\"]}],[\"$\",\"p\",null,{\"children\":\"When you write a query against a hybrid table, the Pinot query engine needs to work out which records to read from the offline table and which to read from the real-time table.\"}],[\"$\",\"p\",null,{\"children\":\"It does this by computing the time boundary, determined by looking at the maximum end time of segments in the offline table and the segment ingestion frequency specified for the offline table.\"}],[\"$\",\"$L13\",null,{\"className\":\"language-js\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-js\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"timeBoundary \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003c\"}],[\"$\",\"span\",null,{\"className\":\"token maybe-class-name\",\"children\":\"Maximum\"}],\" end time \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"of\"}],\" offline segments\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003c\"}],[\"$\",\"span\",null,{\"className\":\"token maybe-class-name\",\"children\":\"Ingestion\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token maybe-class-name\",\"children\":\"Frequency\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e\"}],\"\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"The ingestion frequency can either be 1 hour or 1 day, so one of these values will be used.\"}],[\"$\",\"p\",null,{\"children\":\"When a query for a hybrid table is received by a Pinot Broker, the broker sends a time boundary annotated version of the query to the offline and real-time tables. Any records from or before the time boundary are read from the offline table; anything greater than the boundary comes from the real-time table.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot computing the time boundary\",\"src\":\"https://www.datocms-assets.com/75153/1676991003-image2.png\",\"title\":\"Apache Pinot computing the time boundary\"}]}],[\"$\",\"p\",null,{\"children\":\"For example, if we executed the following query:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\" events\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The broker would send the following query to the offline table:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token 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icon-link\"}]}],\"So, what’s the problem?\"]}],[\"$\",\"p\",null,{\"children\":\"If we have some overlap in the data in our offline and real-time tables, this approach works well, but if we have no overlap, we will end up with unexpected results.\"}],[\"$\",\"p\",null,{\"children\":\"For example, let’s say that the most recent timestamp in the events offline table is 2023-01-09T18:41:17, our ingestion frequency is 1 hour, and the real-time table has data starting from 2023-01-09T18:41:18.\"}],[\"$\",\"p\",null,{\"children\":\"This will result in a boundary time of 2023-01-09T17:41:17, which means that any records with timestamps between 17:41 and 18:41 will be excluded from query results.\"}],[\"$\",\"h2\",null,{\"id\":\"and-the-solution\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#and-the-solution\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"And the solution?\"]}],[\"$\",\"p\",null,{\"children\":[\"The 0.12 release sees the addition of the \",[\"$\",\"code\",null,{\"children\":\"tables/{tableName}/timeBoundary\"}],\" API, which lets us set the time boundary to the maximum end time of all offline segments.\"]}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"curl\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-X\"}],\" POST \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://localhost:9000/tables/{tableName}/timeBoundary\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-H\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"accept: application/json\\\"\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"In this case, that will result in a new boundary time of 2023-01-09T18:41:17, which is exactly what we need.\"}],[\"$\",\"p\",null,{\"children\":\"We’ll then be able to query the events table and have it read the offline table to get all records on or before 2023-01-09T18:41:17 and the real-time table for everything else.\"}],[\"$\",\"h2\",null,{\"id\":\"neat-anything-else-i-should-know\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#neat-anything-else-i-should-know\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Neat, anything else I should know?\"]}],[\"$\",\"p\",null,{\"children\":\"Something to keep in mind when updating the time boundary is that it’s a one-off operation. It won’t be automatically updated if you add a new, more recent segment to the offline table.\"}],[\"$\",\"p\",null,{\"children\":[\"In this scenario, you need to call the \",[\"$\",\"code\",null,{\"children\":\"tables/{tableName}/timeBoundary\"}],\" API again.\"]}],[\"$\",\"p\",null,{\"children\":\"And if you want to revert to the previous behavior where the time boundary is computed by subtracting the ingestion frequency from the latest end time, you can do that too:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"curl\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-X\"}],\" DELETE \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://localhost:9000/tables/{tableName}/timeBoundary\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-H\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"accept: application/json\\\"\"}],\"\\n\"]}]]}]}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":\"I love this feature, and it solves a problem I’ve struggled with when using my datasets. I hope you’ll find it just as useful.\"}],[\"$\",\"p\",null,{\"children\":[\"Give it a try, and let us know how you get on. If you have any questions about this feature, feel free to join us on \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://stree.ai/slack\",\"children\":\"Slack\"}],\", where we’ll be happy to help you out.\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"What is a hybrid table?\",\"url\":\"#what-is-a-hybrid-table\",\"depth\":2},{\"value\":\"How do you query a hybrid table?\",\"url\":\"#how-do-you-query-a-hybrid-table\",\"depth\":2},{\"value\":\"So, what’s the problem?\",\"url\":\"#so-whats-the-problem\",\"depth\":2},{\"value\":\"And the solution?\",\"url\":\"#and-the-solution\",\"depth\":2},{\"value\":\"Neat, anything else I should know?\",\"url\":\"#neat-anything-else-i-should-know\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Apache Pinot™ 0.12 - Configurable Time Boundary | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"This post will explore the ability to configure the time boundary when working with hybrid tables.\"}],[\"$\",\"meta\",\"4\",{\"name\":\"robots\",\"content\":\"index, follow\"}],[\"$\",\"meta\",\"5\",{\"name\":\"googlebot\",\"content\":\"index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1\"}],[\"$\",\"link\",\"6\",{\"rel\":\"canonical\",\"href\":\"https://pinot.apache.org/blog/2023/02/21/Apache-Pinot-0-12-Configurable-Time-Boundary\"}],[\"$\",\"link\",\"7\",{\"rel\":\"alternate\",\"type\":\"application/rss+xml\",\"href\":\"https://pinot.apache.org/feed.xml\"}],[\"$\",\"meta\",\"8\",{\"property\":\"og:title\",\"content\":\"Apache Pinot™ 0.12 - Configurable Time Boundary\"}],[\"$\",\"meta\",\"9\",{\"property\":\"og:description\",\"content\":\"This post will explore the ability to configure the time boundary when working with hybrid tables.\"}],[\"$\",\"meta\",\"10\",{\"property\":\"og:url\",\"content\":\"https://pinot.apache.org/blog/2023/02/21/Apache-Pinot-0-12-Configurable-Time-Boundary\"}],[\"$\",\"meta\",\"11\",{\"property\":\"og:site_name\",\"content\":\"Apache Pinot™\"}],[\"$\",\"meta\",\"12\",{\"property\":\"og:locale\",\"content\":\"en_US\"}],[\"$\",\"meta\",\"13\",{\"property\":\"og:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"14\",{\"property\":\"og:type\",\"content\":\"article\"}],[\"$\",\"meta\",\"15\",{\"property\":\"article:published_time\",\"content\":\"2023-02-21T00:00:00.000Z\"}],[\"$\",\"meta\",\"16\",{\"property\":\"article:modified_time\",\"content\":\"2023-02-21T00:00:00.000Z\"}],[\"$\",\"meta\",\"17\",{\"property\":\"article:author\",\"content\":\"Mark Needham\"}],[\"$\",\"meta\",\"18\",{\"name\":\"twitter:card\",\"content\":\"summary_large_image\"}],[\"$\",\"meta\",\"19\",{\"name\":\"twitter:title\",\"content\":\"Apache Pinot™ 0.12 - Configurable Time Boundary\"}],[\"$\",\"meta\",\"20\",{\"name\":\"twitter:description\",\"content\":\"This post will explore the ability to configure the time boundary when working with hybrid tables.\"}],[\"$\",\"meta\",\"21\",{\"name\":\"twitter:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"22\",{\"name\":\"next-size-adjust\"}]]\n"])</script><script>self.__next_f.push([1,"f:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>
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diff --git a/blog/2023/02/21/Apache-Pinot-0-12-Configurable-Time-Boundary.txt b/blog/2023/02/21/Apache-Pinot-0-12-Configurable-Time-Boundary.txt
index 2f83d0b..b7d3f94 100644
--- a/blog/2023/02/21/Apache-Pinot-0-12-Configurable-Time-Boundary.txt
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diff --git a/blog/2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag.html b/blog/2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag.html
index a391e7f..843a5d1 100644
--- a/blog/2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag.html
+++ b/blog/2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag.html
@@ -138,4 +138,4 @@
</span></code></pre></div><p>Give permissions to run it as a script:</p><p><code>chmod u+x watch\_consuming\_info.sh</code></p><p>And finally, run it:</p><p><code>./watch\_consuming\_info.sh</code></p><p>This will print out a new table every two seconds. Let’s now make things more interesting by removing the sleep from our ingestion command:</p><div class="relative"><pre><code class="code-highlight language-bash"><span class="code-line">python datagen.py <span class="token operator"><span class="token file-descriptor important">2</span>></span>/dev/null <span class="token operator">|</span>
</span><span class="code-line">jq <span class="token parameter variable">-cr</span> <span class="token parameter variable">--arg</span> sep ø <span class="token string">'[.uuid, tostring] | join($sep)'</span> <span class="token operator">|</span>
</span><span class="code-line">kcat <span class="token parameter variable">-P</span> <span class="token parameter variable">-b</span> localhost:9092 <span class="token parameter variable">-t</span> events -Kø
-</span></code></pre></div><p>And now if we look at the watch output:</p><p><img alt="Apache Pinot Consumer Record Lag" src="https://www.datocms-assets.com/75153/1680190286-image1.png" title="Apache Pinot Consumer Record Lag"/></p><p>We get some transitory lag, but it generally goes away by the next time the command is run.</p><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>I love this feature, and it solves a problem I’ve struggled with when using my datasets. I hope you’ll find it just as useful.</p><p>Give it a try, and let us know how you get on. If you have any questions about this feature, feel free to join us on <a target="_blank" rel="noopener noreferrer" href="https://stree.ai/slack">Slack</a>, where we’ll be happy to help you out.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#why-do-we-need-this">Why do we need this?</a></li><li class="text-sm font-normal leading-tight"><a href="#worked-example">Worked Example</a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 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Apache Pinot, Pinot, Apache, the Apache feather logo, and the Apache Pinot project logo are registered trademarks of The Apache Software Foundation. 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.\"]}]]}]]}]}]]}]}]]}],null]}]]\n"])</script><script>self.__next_f.push([1,"12:I[1514,[\"326\",\"static/chunks/326-3a90a6443b9c824c.js\",\"413\",\"static/chunks/413-f9f40b83f7bb3f22.js\",\"980\",\"static/chunks/980-6e243f9cd384c7d2.js\",\"797\",\"static/chunks/app/blog/%5B...slug%5D/page-502e08b6677b55da.js\"],\"\"]\n13:I[2529,[\"326\",\"static/chunks/326-3a90a6443b9c824c.js\",\"413\",\"static/chunks/413-f9f40b83f7bb3f22.js\",\"980\",\"static/chunks/980-6e243f9cd384c7d2.js\",\"797\",\"static/chunks/app/blog/%5B...slug%5D/page-502e08b6677b55da.js\"],\"\"]\n14:I[5185,[\"326\",\"static/chunks/326-3a90a6443b9c824c.js\",\"413\",\""])</script><script>self.__next_f.push([1,"static/chunks/413-f9f40b83f7bb3f22.js\",\"980\",\"static/chunks/980-6e243f9cd384c7d2.js\",\"797\",\"static/chunks/app/blog/%5B...slug%5D/page-502e08b6677b55da.js\"],\"\"]\n"])</script><script>self.__next_f.push([1,"10:[[\"$\",\"script\",null,{\"type\":\"application/ld+json\",\"dangerouslySetInnerHTML\":{\"__html\":\"{\\\"@context\\\":\\\"https://schema.org\\\",\\\"@type\\\":\\\"BlogPosting\\\",\\\"headline\\\":\\\"Apache Pinot™ 0.12 - Consumer Record Lag\\\",\\\"datePublished\\\":\\\"2023-03-30T00:00:00.000Z\\\",\\\"dateModified\\\":\\\"2023-03-30T00:00:00.000Z\\\",\\\"description\\\":\\\"This post will explore a new API endpoint that lets you check how much Pinot is lagging when ingesting from Apache Kafka.\\\",\\\"image\\\":\\\"/static/images/twitter-card.png\\\",\\\"url\\\":\\\"https://pinot.apache.org/blog/2023-03-30-Apache-Pinot-0-12-Consumer-Record-Lag\\\",\\\"author\\\":[{\\\"@type\\\":\\\"Person\\\",\\\"name\\\":\\\"Mark Needham\\\"}]}\"}}],[\"$\",\"section\",null,{\"className\":\" px-5 pt-10 md:px-[13.313rem] md:py-16\",\"children\":[[\"$\",\"$L12\",null,{}],[\"$\",\"article\",null,{\"className\":\"\",\"children\":[\"$\",\"div\",null,{\"className\":\"mx-auto lg:flex\",\"children\":[[\"$\",\"div\",null,{\"className\":\"lg:pr-12\",\"children\":[[\"$\",\"header\",null,{\"className\":\"pt-6 md:pr-10\",\"children\":[[\"$\",\"h1\",null,{\"className\":\"text-4xl font-semibold\",\"children\":\"Apache Pinot™ 0.12 - Consumer Record Lag\"}],[\"$\",\"p\",null,{\"className\":\"pt-2 text-lg\",\"children\":[\"By: \",\"Mark Needham\"]}],[\"$\",\"p\",null,{\"className\":\"py-2 text-sm\",\"children\":[\"March 30th, 2023\",\" • \",\"5 min read\"]}]]}],[\"$\",\"div\",null,{\"className\":\"flex flex-col lg:flex-row\",\"children\":[\"$\",\"main\",null,{\"className\":\"\",\"children\":[\"$\",\"div\",null,{\"className\":\"prose max-w-[45rem] pb-8 pt-10 dark:prose-invert\",\"children\":[[\"$\",\"p\",null,{\"children\":[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://youtu.be/JJEh_kBfJts\",\"children\":[\"$\",\"img\",null,{\"alt\":\"Watch the video\",\"src\":\"https://i3.ytimg.com/vi/JJEh_kBfJts/maxresdefault.jpg\"}]}]}],[\"$\",\"p\",null,{\"children\":[\"The Apache Pinot community recently released version \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/basics/releases/0.12.0\",\"children\":\"0.12.0\"}],\", which has lots of goodies for you to play with. I’ve been exploring and writing about those features in a series of blog posts.\"]}],[\"$\",\"p\",null,{\"children\":\"This post will explore a new API endpoint that lets you check how much Pinot is lagging when ingesting from Apache Kafka.\"}],[\"$\",\"h2\",null,{\"id\":\"why-do-we-need-this\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#why-do-we-need-this\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Why do we need this?\"]}],[\"$\",\"p\",null,{\"children\":\"A common question in the Pinot community is how to work out the consumption status of real-time tables.\"}],[\"$\",\"p\",null,{\"children\":\"This was a tricky one to answer, but Pinot 0.12 sees the addition of a new API that lets us see exactly what’s going 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punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"print\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"json\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"dumps\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"tsString\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" ts\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" 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punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"if\"}],\" __name__ \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"==\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'__main__'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" generate\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"We can see an example of the messages generated by this script by running the 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\",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-c\"}],\"\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"You should see something like this:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tsString\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"2023-03-17T12:10:03.854680Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token 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number\",\"children\":\"154\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"So far, so good. 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operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"We can create both the table and schema using the \",[\"$\",\"em\",null,{\"children\":\"AddTable\"}],\" command:\"]}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" 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\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-tableConfigFile\"}],\" /config/table.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-controllerHost\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"pinot-controller-lag-blog\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-exec\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Now let’s call the /consumingSegmentsInfo endpoint to see what’s going on:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"curl \\\"http://localhost:9000/tables/events/consumingSegmentsInfo\\\" 2\u003e/dev/null | jq\"}]}],[\"$\",\"p\",null,{\"children\":\"The output of calling this end point is shown below:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"_segmentToConsumingInfoMap\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"events__0__0__20230317T1133Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"serverName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Server_172.29.0.4_8098\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"consumerState\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"CONSUMING\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"lastConsumedTimestamp\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1679052823350\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"partitionToOffsetMap\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"0\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"969\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"partitionOffsetInfo\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"currentOffsetsMap\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"0\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"969\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"latestUpstreamOffsetMap\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"0\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"969\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"recordsLagMap\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"0\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"0\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"availabilityLagMsMap\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"0\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"26\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"…\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"If we look under \",[\"$\",\"em\",null,{\"children\":\"partitionOffsetInfo\"}],\", we can see what’s going on:\"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"currentOffsetsMap is Pinot’s current offset\"}],[\"$\",\"li\",null,{\"children\":\"latestUpstreamOffsetMap is Kafka’s offset\"}],[\"$\",\"li\",null,{\"children\":\"recordsLagMap is the record lag\"}],[\"$\",\"li\",null,{\"children\":\"availabilityLagMsMap is the time lag\"}]]}],[\"$\",\"p\",null,{\"children\":\"This output is a bit unwieldy, so let’s create a bash function to tidy up the output into something that’s easier to consume:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-python\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-python\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"function consuming_info\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" curl \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://localhost:9000/tables/events/consumingSegmentsInfo\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"2\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"/\"}],\"dev\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"/\"}],\"null \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" jq \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"rc '\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"_segmentToConsumingInfoMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" segment\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" $k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" kafka\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"partitionOffsetInfo\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"currentOffsetsMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" pinot\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"partitionOffsetInfo\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"latestUpstreamOffsetMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" recordLag\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"partitionOffsetInfo\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"recordsLagMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" timeLagMs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"partitionOffsetInfo\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"availabilityLagMsMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"0\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"keys_unsorted \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" @tsv\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],[\"$\",\"span\",null,{\"className\":\"token builtin\",\"children\":\"map\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"@tsv\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"' \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" column \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"t\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" printf \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"\\\\n\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Let’s call the function:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"consuming\\\\_info\"}]}],[\"$\",\"p\",null,{\"children\":\"We’ll see the following output:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Consumer record lag output\",\"src\":\"https://www.datocms-assets.com/75153/1680190272-image2.png\",\"title\":\"Consumer record lag output\"}]}],[\"$\",\"p\",null,{\"children\":\"Now let’s put it in a script and call the watch command so that it will be refreshed every couple of seconds:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-python\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-python\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"!\",[\"$\",\"span\",null,{\"className\":\"token comment\",\"children\":\"#/bin/bash\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"function consuming_info\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" curl \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://localhost:9000/tables/events/consumingSegmentsInfo\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"2\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"/\"}],\"dev\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"/\"}],\"null \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" jq \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"rc '\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"_segmentToConsumingInfoMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" segment\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" $k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" kafka\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"partitionOffsetInfo\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"currentOffsetsMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" pinot\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"partitionOffsetInfo\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"latestUpstreamOffsetMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" recordLag\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"partitionOffsetInfo\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"recordsLagMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" timeLagMs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"partitionOffsetInfo\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"availabilityLagMsMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"0\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"keys_unsorted \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" @tsv\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],[\"$\",\"span\",null,{\"className\":\"token builtin\",\"children\":\"map\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"@tsv\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"' \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" column \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"t\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" printf \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"\\\\n\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"export \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"f consuming_info\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"watch bash \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"c consuming_info\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}]]}]}],[\"$\",\"p\",null,{\"children\":\"Give permissions to run it as a script:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"chmod u+x watch\\\\_consuming\\\\_info.sh\"}]}],[\"$\",\"p\",null,{\"children\":\"And finally, run it:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"./watch\\\\_consuming\\\\_info.sh\"}]}],[\"$\",\"p\",null,{\"children\":\"This will print out a new table every two seconds. Let’s now make things more interesting by removing the sleep from our ingestion command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"python datagen.py \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token file-descriptor important\",\"children\":\"2\"}],\"\u003e\"]}],\"/dev/null \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"jq \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-cr\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"--arg\"}],\" sep ø \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'[.uuid, tostring] | join($sep)'\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"kcat \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-P\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-b\"}],\" localhost:9092 \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-t\"}],\" events -Kø\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"And now if we look at the watch output:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot Consumer Record Lag\",\"src\":\"https://www.datocms-assets.com/75153/1680190286-image1.png\",\"title\":\"Apache Pinot Consumer Record Lag\"}]}],[\"$\",\"p\",null,{\"children\":\"We get some transitory lag, but it generally goes away by the next time the command is run.\"}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":\"I love this feature, and it solves a problem I’ve struggled with when using my datasets. I hope you’ll find it just as useful.\"}],[\"$\",\"p\",null,{\"children\":[\"Give it a try, and let us know how you get on. If you have any questions about this feature, feel free to join us on \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://stree.ai/slack\",\"children\":\"Slack\"}],\", where we’ll be happy to help you out.\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"Why do we need this?\",\"url\":\"#why-do-we-need-this\",\"depth\":2},{\"value\":\"Worked Example\",\"url\":\"#worked-example\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Apache Pinot™ 0.12 - Consumer Record Lag | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"This post will explore a new API endpoint that lets you check how much Pinot is lagging when ingesting from Apache Kafka.\"}],[\"$\",\"meta\",\"4\",{\"name\":\"robots\",\"content\":\"index, follow\"}],[\"$\",\"meta\",\"5\",{\"name\":\"googlebot\",\"content\":\"index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1\"}],[\"$\",\"link\",\"6\",{\"rel\":\"canonical\",\"href\":\"https://pinot.apache.org/blog/2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag\"}],[\"$\",\"link\",\"7\",{\"rel\":\"alternate\",\"type\":\"application/rss+xml\",\"href\":\"https://pinot.apache.org/feed.xml\"}],[\"$\",\"meta\",\"8\",{\"property\":\"og:title\",\"content\":\"Apache Pinot™ 0.12 - Consumer Record Lag\"}],[\"$\",\"meta\",\"9\",{\"property\":\"og:description\",\"content\":\"This post will explore a new API endpoint that lets you check how much Pinot is lagging when ingesting from Apache Kafka.\"}],[\"$\",\"meta\",\"10\",{\"property\":\"og:url\",\"content\":\"https://pinot.apache.org/blog/2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag\"}],[\"$\",\"meta\",\"11\",{\"property\":\"og:site_name\",\"content\":\"Apache Pinot™\"}],[\"$\",\"meta\",\"12\",{\"property\":\"og:locale\",\"content\":\"en_US\"}],[\"$\",\"meta\",\"13\",{\"property\":\"og:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"14\",{\"property\":\"og:type\",\"content\":\"article\"}],[\"$\",\"meta\",\"15\",{\"property\":\"article:published_time\",\"content\":\"2023-03-30T00:00:00.000Z\"}],[\"$\",\"meta\",\"16\",{\"property\":\"article:modified_time\",\"content\":\"2023-03-30T00:00:00.000Z\"}],[\"$\",\"meta\",\"17\",{\"property\":\"article:author\",\"content\":\"Mark Needham\"}],[\"$\",\"meta\",\"18\",{\"name\":\"twitter:card\",\"content\":\"summary_large_image\"}],[\"$\",\"meta\",\"19\",{\"name\":\"twitter:title\",\"content\":\"Apache Pinot™ 0.12 - Consumer Record Lag\"}],[\"$\",\"meta\",\"20\",{\"name\":\"twitter:description\",\"content\":\"This post will explore a new API endpoint that lets you check how much Pinot is lagging when ingesting from Apache Kafka.\"}],[\"$\",\"meta\",\"21\",{\"name\":\"twitter:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"22\",{\"name\":\"next-size-adjust\"}]]\n"])</script><script>self.__next_f.push([1,"f:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>
\ No newline at end of file
+</span></code></pre></div><p>And now if we look at the watch output:</p><p><img alt="Apache Pinot Consumer Record Lag" src="https://www.datocms-assets.com/75153/1680190286-image1.png" title="Apache Pinot Consumer Record Lag"/></p><p>We get some transitory lag, but it generally goes away by the next time the command is run.</p><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>I love this feature, and it solves a problem I’ve struggled with when using my datasets. I hope you’ll find it just as useful.</p><p>Give it a try, and let us know how you get on. If you have any questions about this feature, feel free to join us on <a target="_blank" rel="noopener noreferrer" href="https://stree.ai/slack">Slack</a>, where we’ll be happy to help you out.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#why-do-we-need-this">Why do we need this?</a></li><li class="text-sm font-normal leading-tight"><a href="#worked-example">Worked Example</a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 2.426-2.068l.002-.002m6.452 5.605c-.445-.793-1.032-1.395-1.76-1.808a4.72 4.72 0 0 0-2.362-.618c-.847 0-1.602.211-2.33.635-.728.423-1.315 1.038-1.76 1.841-.445.804-.668 1.749-.668 2.835 0 1.087.221 2.032.668 2.835.445.804 1.032 1.417 1.76 1.842a4.557 4.557 0 0 0 2.33.635 4.57 4.57 0 0 0 2.362-.652c.728-.435 1.313-1.053 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punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"if\"}],\" __name__ \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"==\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'__main__'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" generate\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"We can see an example of the messages generated by this script by running the 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string\",\"children\":\"\\\"2023-03-17T12:10:03.864815Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"eac57622-4b58-4456-bb38-96d1ef5a1ed5\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"522\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tsString\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"2023-03-17T12:10:03.875723Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"uuid\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"65926a80-208a-408b-90d0-36cf74c8923a\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"154\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"So far, so good. Let’s now ingest this data into Kafka:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"python datagen.py \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"--sleep\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"0.01\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token file-descriptor important\",\"children\":\"2\"}],\"\u003e\"]}],\"/dev/null \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"jq \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-cr\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"--arg\"}],\" sep ø \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'[.uuid, tostring] | join($sep)'\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"kcat \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-P\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-b\"}],\" localhost:9092 \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-t\"}],\" events \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-K\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Next we’re going to create a Pinot schema and table. 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string\",\"children\":\"\\\"smallest\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.factory.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"realtime.segment.flush.threshold.rows\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"10000000\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"ingestionConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"columnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformFunction\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"FromDateTime(tsString, 'YYYY-MM-dd''T''HH:mm:ss.SSSSSS''Z''')\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tenants\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metadata\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"We can create both the table and schema using the \",[\"$\",\"em\",null,{\"children\":\"AddTable\"}],\" command:\"]}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"--network\"}],\" lag_blog \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/config:/config \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.12.0-arm64 AddTable \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-schemaFile\"}],\" /config/schema.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-tableConfigFile\"}],\" /config/table.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-controllerHost\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"pinot-controller-lag-blog\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-exec\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Now let’s call the /consumingSegmentsInfo endpoint to see what’s going on:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"curl \\\"http://localhost:9000/tables/events/consumingSegmentsInfo\\\" 2\u003e/dev/null | jq\"}]}],[\"$\",\"p\",null,{\"children\":\"The output of calling this end point is shown below:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"_segmentToConsumingInfoMap\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"events__0__0__20230317T1133Z\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"serverName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Server_172.29.0.4_8098\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"consumerState\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"CONSUMING\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"lastConsumedTimestamp\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1679052823350\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"partitionToOffsetMap\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"0\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"969\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"partitionOffsetInfo\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"currentOffsetsMap\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"0\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"969\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"latestUpstreamOffsetMap\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"0\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"969\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"recordsLagMap\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"0\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"0\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"availabilityLagMsMap\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"0\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"26\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"…\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"If we look under \",[\"$\",\"em\",null,{\"children\":\"partitionOffsetInfo\"}],\", we can see what’s going on:\"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"currentOffsetsMap is Pinot’s current offset\"}],[\"$\",\"li\",null,{\"children\":\"latestUpstreamOffsetMap is Kafka’s offset\"}],[\"$\",\"li\",null,{\"children\":\"recordsLagMap is the record lag\"}],[\"$\",\"li\",null,{\"children\":\"availabilityLagMsMap is the time lag\"}]]}],[\"$\",\"p\",null,{\"children\":\"This output is a bit unwieldy, so let’s create a bash function to tidy up the output into something that’s easier to consume:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-python\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-python\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"function consuming_info\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" curl \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://localhost:9000/tables/events/consumingSegmentsInfo\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"2\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"/\"}],\"dev\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"/\"}],\"null \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" jq \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"rc '\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"_segmentToConsumingInfoMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" segment\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" $k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" kafka\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"partitionOffsetInfo\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"currentOffsetsMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" pinot\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"partitionOffsetInfo\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"latestUpstreamOffsetMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" recordLag\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"partitionOffsetInfo\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"recordsLagMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" timeLagMs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"partitionOffsetInfo\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"availabilityLagMsMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"0\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"keys_unsorted \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" @tsv\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],[\"$\",\"span\",null,{\"className\":\"token builtin\",\"children\":\"map\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"@tsv\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"' \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" column \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"t\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" printf \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"\\\\n\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Let’s call the function:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"consuming\\\\_info\"}]}],[\"$\",\"p\",null,{\"children\":\"We’ll see the following output:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Consumer record lag output\",\"src\":\"https://www.datocms-assets.com/75153/1680190272-image2.png\",\"title\":\"Consumer record lag output\"}]}],[\"$\",\"p\",null,{\"children\":\"Now let’s put it in a script and call the watch command so that it will be refreshed every couple of seconds:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-python\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-python\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"!\",[\"$\",\"span\",null,{\"className\":\"token comment\",\"children\":\"#/bin/bash\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"function consuming_info\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" curl \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://localhost:9000/tables/events/consumingSegmentsInfo\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"2\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"/\"}],\"dev\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"/\"}],\"null \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" jq \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"rc '\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"_segmentToConsumingInfoMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" segment\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" $k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" kafka\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"partitionOffsetInfo\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"currentOffsetsMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" pinot\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"partitionOffsetInfo\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"latestUpstreamOffsetMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" recordLag\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"partitionOffsetInfo\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"recordsLagMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" timeLagMs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"partitionOffsetInfo\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"availabilityLagMsMap \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" keys\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" $k \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"$$k\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"0\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"keys_unsorted \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" @tsv\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],[\"$\",\"span\",null,{\"className\":\"token builtin\",\"children\":\"map\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"@tsv\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"' \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" column \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"t\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" printf \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"\\\\n\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"export \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"f consuming_info\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"watch bash \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"-\"}],\"c consuming_info\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}]]}]}],[\"$\",\"p\",null,{\"children\":\"Give permissions to run it as a script:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"chmod u+x watch\\\\_consuming\\\\_info.sh\"}]}],[\"$\",\"p\",null,{\"children\":\"And finally, run it:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"./watch\\\\_consuming\\\\_info.sh\"}]}],[\"$\",\"p\",null,{\"children\":\"This will print out a new table every two seconds. Let’s now make things more interesting by removing the sleep from our ingestion command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"python datagen.py \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token file-descriptor important\",\"children\":\"2\"}],\"\u003e\"]}],\"/dev/null \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"jq \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-cr\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"--arg\"}],\" sep ø \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'[.uuid, tostring] | join($sep)'\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"kcat \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-P\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-b\"}],\" localhost:9092 \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-t\"}],\" events -Kø\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"And now if we look at the watch output:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot Consumer Record Lag\",\"src\":\"https://www.datocms-assets.com/75153/1680190286-image1.png\",\"title\":\"Apache Pinot Consumer Record Lag\"}]}],[\"$\",\"p\",null,{\"children\":\"We get some transitory lag, but it generally goes away by the next time the command is run.\"}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":\"I love this feature, and it solves a problem I’ve struggled with when using my datasets. I hope you’ll find it just as useful.\"}],[\"$\",\"p\",null,{\"children\":[\"Give it a try, and let us know how you get on. If you have any questions about this feature, feel free to join us on \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://stree.ai/slack\",\"children\":\"Slack\"}],\", where we’ll be happy to help you out.\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"Why do we need this?\",\"url\":\"#why-do-we-need-this\",\"depth\":2},{\"value\":\"Worked Example\",\"url\":\"#worked-example\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Apache Pinot™ 0.12 - Consumer Record Lag | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"This post will explore a new API endpoint that lets you check how much Pinot is lagging when ingesting from Apache Kafka.\"}],[\"$\",\"meta\",\"4\",{\"name\":\"robots\",\"content\":\"index, follow\"}],[\"$\",\"meta\",\"5\",{\"name\":\"googlebot\",\"content\":\"index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1\"}],[\"$\",\"link\",\"6\",{\"rel\":\"canonical\",\"href\":\"https://pinot.apache.org/blog/2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag\"}],[\"$\",\"link\",\"7\",{\"rel\":\"alternate\",\"type\":\"application/rss+xml\",\"href\":\"https://pinot.apache.org/feed.xml\"}],[\"$\",\"meta\",\"8\",{\"property\":\"og:title\",\"content\":\"Apache Pinot™ 0.12 - Consumer Record Lag\"}],[\"$\",\"meta\",\"9\",{\"property\":\"og:description\",\"content\":\"This post will explore a new API endpoint that lets you check how much Pinot is lagging when ingesting from Apache Kafka.\"}],[\"$\",\"meta\",\"10\",{\"property\":\"og:url\",\"content\":\"https://pinot.apache.org/blog/2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag\"}],[\"$\",\"meta\",\"11\",{\"property\":\"og:site_name\",\"content\":\"Apache Pinot™\"}],[\"$\",\"meta\",\"12\",{\"property\":\"og:locale\",\"content\":\"en_US\"}],[\"$\",\"meta\",\"13\",{\"property\":\"og:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"14\",{\"property\":\"og:type\",\"content\":\"article\"}],[\"$\",\"meta\",\"15\",{\"property\":\"article:published_time\",\"content\":\"2023-03-30T00:00:00.000Z\"}],[\"$\",\"meta\",\"16\",{\"property\":\"article:modified_time\",\"content\":\"2023-03-30T00:00:00.000Z\"}],[\"$\",\"meta\",\"17\",{\"property\":\"article:author\",\"content\":\"Mark Needham\"}],[\"$\",\"meta\",\"18\",{\"name\":\"twitter:card\",\"content\":\"summary_large_image\"}],[\"$\",\"meta\",\"19\",{\"name\":\"twitter:title\",\"content\":\"Apache Pinot™ 0.12 - Consumer Record Lag\"}],[\"$\",\"meta\",\"20\",{\"name\":\"twitter:description\",\"content\":\"This post will explore a new API endpoint that lets you check how much Pinot is lagging when ingesting from Apache Kafka.\"}],[\"$\",\"meta\",\"21\",{\"name\":\"twitter:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"22\",{\"name\":\"next-size-adjust\"}]]\n"])</script><script>self.__next_f.push([1,"f:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>
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diff --git a/blog/2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag.txt b/blog/2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag.txt
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</span><span class="code-line"><span class="token keyword">limit</span> <span class="token number">10</span>
</span></code></pre></div><p>The results from running the query are shown below:</p><p><img alt="Sample Geospatial Indexing In Apache Pinot Query Result" src="https://www.datocms-assets.com/75153/1683813749-image4.png" title="Sample Geospatial Indexing In Apache Pinot Query Result"/></p><p>If we change the query to show trains outside of a central London polygon, we’d see the following results:</p><p><img alt="Sample Geospatial Indexing In Apache Pinot Query Result" src="https://www.datocms-assets.com/75153/1683813705-image3.png" title="Sample Geospatial Indexing In Apache Pinot Query Result"/></p><p>So what’s actually happening when we run this query?</p><p>The polygon covers the area inside the white shape as shown below:</p><p><img alt="Geospatial Indexing In Apache Pinot Polygon" src="https://www.datocms-assets.com/75153/1683813802-image2.png" title="Geospatial Indexing In Apache Pinot Polygon"/></p><p>Pinot’s query planner will first find all the coordinates on the exterior of the polygon. It will then find the hexagons that fit within that geofence. Those hexagons get added to the potential cells list.</p><p>The query planner then takes each of those hexagons and checks whether they fit completely inside the original polygon. If they do, then they get added to the fully contained cells list. If we have any cells in both lists, we remove them from the potential cells list.</p><p>Next, we find the records for the fully contained cells list and those for the potential cells list.</p><p>If we are finding records that fit inside the polygon, we return those in the fully contained list and apply the STWithin/StContains predicate to work out which records to return from the potential list.</p><p>If we are finding records outside the polygon, we will create a new fully contained list, which will actually contain the records that are outside the polygon. This list contains all of the records in the database except the ones in the potential list and those in the initial fully contained list.</p><p>This one was a bit tricky for me to get my head around, so let’s just quickly go through an example. Imagine that we store 10 records in our database and our potential and fully contained lists hold the following values:</p><div class="relative"><pre><code class="code-highlight language-python"><span class="code-line">potential <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">,</span><span class="token number">1</span><span class="token punctuation">,</span><span class="token number">2</span><span class="token punctuation">,</span><span class="token number">3</span><span class="token punctuation">]</span>
</span><span class="code-line">fullyContained <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token number">4</span><span class="token punctuation">,</span><span class="token number">5</span><span class="token punctuation">,</span><span class="token number">6</span><span class="token punctuation">]</span>
-</span></code></pre></div><p>First, compute newFullyContained to find all the records not in potential:</p><p><code>newFullyContained = [4,5,6,7,8,9]</code></p><p>Then we can remove the values in fullyContained, which gives us:</p><p><code>newFullyContained = [7,8,9]</code></p><p>We will return all the records in <code>newFullyContained</code> and apply the <code>STWithin</code> or <code>StContains</code> predicate to work out which records to return from the potential list.</p><h2 id="how-do-you-know-the-index-usage"><a href="#how-do-you-know-the-index-usage" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>How do you know the index usage?</h2><p>We can write queries that use <code>STDistance</code>, <code>STWithin</code>, and <code>STContains</code> without using a geospatial index, but if we’ve got one defined, we’ll want to get the peace of mind of its actual use.</p><p>You can check by prefixing a query with <code>EXPLAIN PLAN FOR</code>, which will return a list of the operators in the query plan.</p><p>If our query uses <code>STDistance</code>, we should expect to see the ​<code>​FILTER\_H3\_INDEX</code> operator. If it uses STWithin or STContains, we should expect to see the INCLUSION_FILTER_H3_INDEX operator.</p><p>See this example query plan:</p><p><img alt="Apache Pinot Geospatial Indexing Query Plan" src="https://www.datocms-assets.com/75153/1683813851-image6.png" title="Apache Pinot Geospatial Indexing Query Plan"/></p><p>The <a target="_blank" rel="noopener noreferrer" href="https://dev.startree.ai/">StarTree Developer Hub</a> contains a <a target="_blank" rel="noopener noreferrer" href="https://dev.startree.ai/docs/pinot/recipes/geospatial-indexing#how-do-i-check-that-the-geospatial-index-is-being-used">geospatial indexing guide</a> that goes through this in more detail.</p><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>I hope you found this blog post useful and now understand how geospatial indexes work and when to use them in Apache Pinot.</p><p>Give them a try, and let us know how you get on! If you want to use, or are already using geospatial queries in Apache Pinot, we’d love to hear how — feel free to <a href="/contact-us">contact us</a> and tell us more! To help get you started, <a href="/saas-signup">sign up for a free trial of fully managed Apache Pinot</a>. And if you run into any technical questions, feel free to find me on the <a target="_blank" rel="noopener noreferrer" href="https://stree.ai/slack">StarTree Community Slack</a>.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#what-is-geospatial-indexing">What is geospatial indexing?</a></li><li class="text-sm font-normal leading-tight"><a href="#how-do-geospatial-indexes-work-in-apache-pinot">How do geospatial indexes work in Apache Pinot?</a></li><li class="text-sm font-normal leading-tight"><a href="#when-is-the-geospatial-index-used">When is the geospatial index used?</a></li><li class="text-sm font-normal leading-tight"><a href="#how-do-you-know-the-index-usage">How do you know the index usage?</a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 2.426-2.068l.002-.002m6.452 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We’ll see how that column gets populated from our table config, as shown below:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"trains\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"REALTIME\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"segmentsConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"timeColumnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schemaName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"trains\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"replication\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"replicasPerPartition\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"fieldConfigList\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"point\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"encodingType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"RAW\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"indexType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"H3\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"properties\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"resolutions\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"7\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableIndexConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" 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punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"streamConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"streamType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"kafka\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.topic.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"trains\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.broker.list\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"kafka-geospatial:9093\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.type\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"lowlevel\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.prop.auto.offset.reset\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"smallest\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.factory.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"ingestionConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"columnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"point\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformFunction\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"STPoint(lon, lat, 1)\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tenants\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metadata\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"The point column is populated by the following function under \",[\"$\",\"code\",null,{\"children\":\"transformConfigs\"}],\":\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"STPoint(lon, lat, 1)\"}]}],[\"$\",\"p\",null,{\"children\":\"In earlier versions of Pinot, you’d need to ensure that the schema included lat and lon columns, but that no longer applies.\"}],[\"$\",\"p\",null,{\"children\":[\"We define the geospatial index on the point column under \",[\"$\",\"code\",null,{\"children\":\"fieldConfigList\"}],\". We can configure what H3 calls \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://h3geo.org/docs/core-library/restable\",\"children\":\"resolutions\"}],\", which defines the size of a hexagon and their number. A resolution of 7 means that there will be 98,825,150 hexagons, each covering an area of 5.16 km². We also need to add the geospatial column to \",[\"$\",\"code\",null,{\"children\":\"tableIndexConfig.noDictionaryColumns\"}],\".\"]}],[\"$\",\"p\",null,{\"children\":[\"We can go ahead and create that table using the \",[\"$\",\"code\",null,{\"children\":\"AddTable\"}],\" command and Pinot will automatically start ingesting data from Kafka.\"]}],[\"$\",\"h2\",null,{\"id\":\"when-is-the-geospatial-index-used\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#when-is-the-geospatial-index-used\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"When is the geospatial index used?\"]}],[\"$\",\"p\",null,{\"children\":\"The geospatial index is used when a WHERE clause in a query calls the StDistance, StWithin, or StContains functions.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"ST\\\\_Distance\"}]}],[\"$\",\"p\",null,{\"children\":\"Let’s say we want to find all the trains within a 10 km radius of Westminster. We could write a query to answer this question using the STDistance function. The query might look like this:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" ts\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" trainId\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" atocCode\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" trainCompany\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" ST\\\\_AsText\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"point\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" STDistance\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"point\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" toSphericalGeography\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"ST_GeomFromText\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'POINT (-0.13624 51.499507)'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"AS\"}],\" distance\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" trains\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\" distance \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003c\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10000\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ORDER\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" distance\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" ts \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"DESC\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"These results from running the query would follow:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample Geospatial Indexing In Apache Pinot Query Result\",\"src\":\"https://www.datocms-assets.com/75153/1683813581-image1.png\",\"title\":\"Sample Geospatial Indexing In Apache Pinot Query Result\"}]}],[\"$\",\"p\",null,{\"children\":\"Let’s now go into a bit more detail about what happens when we run the query.\"}],[\"$\",\"p\",null,{\"children\":\"The 10 km radius covers the area inside the white circle on the diagram below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Geospatial Indexing In Apache Pinot Circle\",\"src\":\"https://www.datocms-assets.com/75153/1683813641-image7.png\",\"title\":\"Geospatial Indexing In Apache Pinot Circle\"}]}],[\"$\",\"p\",null,{\"children\":\"Pinot’s query planner will first translate the distance of 10 km into a number of rings, in this case, two. It will then find all the hexagons located two rings away from the white one. Some of these hexagons will fit completely inside the white circle, and some will overlap with the circle.\"}],[\"$\",\"p\",null,{\"children\":\"If a hexagon fully fits, then we can get all the records inside this hexagon and return them. For those that partially fit, we’ll need to apply the distance predicate before working out which records to return.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"ST\\\\_Within/ST\\\\_Contains\"}]}],[\"$\",\"p\",null,{\"children\":[\"Let’s say that rather than specifying a distance, we instead want to draw a polygon and find the trains that fit inside that polygon. We could use either the \",[\"$\",\"code\",null,{\"children\":\"ST\\\\_Within\"}],\" or \",[\"$\",\"code\",null,{\"children\":\"ST\\\\_Contains\"}],\" functions to answer this question.\"]}],[\"$\",\"p\",null,{\"children\":\"The query might look like this:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" ts\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" trainId\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" atocCode\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" trainCompany\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" ST\\\\_AsText\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"point\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" trains\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\" StWithin\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"point\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" toSphericalGeography\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"ST_GeomFromText\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'POLYGON((\\n\"}]]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\" -0.1296371966600418 51.508053828550544,\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\" -0.1538461446762085 51.497007194317064,\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\" -0.13032652437686923 51.488276935884414,\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\" -0.10458670556545259 51.497003019756846,\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\" -0.10864421725273131 51.50817152245844,\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\" -0.1296371966600418 51.508053828550544))'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ORDER\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" ts \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"DESC\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The results from running the query are shown below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample Geospatial Indexing In Apache Pinot Query Result\",\"src\":\"https://www.datocms-assets.com/75153/1683813749-image4.png\",\"title\":\"Sample Geospatial Indexing In Apache Pinot Query Result\"}]}],[\"$\",\"p\",null,{\"children\":\"If we change the query to show trains outside of a central London polygon, we’d see the following results:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample Geospatial Indexing In Apache Pinot Query Result\",\"src\":\"https://www.datocms-assets.com/75153/1683813705-image3.png\",\"title\":\"Sample Geospatial Indexing In Apache Pinot Query Result\"}]}],[\"$\",\"p\",null,{\"children\":\"So what’s actually happening when we run this query?\"}],[\"$\",\"p\",null,{\"children\":\"The polygon covers the area inside the white shape as shown below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Geospatial Indexing In Apache Pinot Polygon\",\"src\":\"https://www.datocms-assets.com/75153/1683813802-image2.png\",\"title\":\"Geospatial Indexing In Apache Pinot Polygon\"}]}],[\"$\",\"p\",null,{\"children\":\"Pinot’s query planner will first find all the coordinates on the exterior of the polygon. It will then find the hexagons that fit within that geofence. Those hexagons get added to the potential cells list.\"}],[\"$\",\"p\",null,{\"children\":\"The query planner then takes each of those hexagons and checks whether they fit completely inside the original polygon. If they do, then they get added to the fully contained cells list. If we have any cells in both lists, we remove them from the potential cells list.\"}],[\"$\",\"p\",null,{\"children\":\"Next, we find the records for the fully contained cells list and those for the potential cells list.\"}],[\"$\",\"p\",null,{\"children\":\"If we are finding records that fit inside the polygon, we return those in the fully contained list and apply the STWithin/StContains predicate to work out which records to return from the potential list.\"}],[\"$\",\"p\",null,{\"children\":\"If we are finding records outside the polygon, we will create a new fully contained list, which will actually contain the records that are outside the polygon. This list contains all of the records in the database except the ones in the potential list and those in the initial fully contained list.\"}],[\"$\",\"p\",null,{\"children\":\"This one was a bit tricky for me to get my head around, so let’s just quickly go through an example. Imagine that we store 10 records in our database and our potential and fully contained lists hold the following values:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-python\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-python\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"potential \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"0\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"2\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"3\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"fullyContained \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"4\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"5\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"6\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"First, compute newFullyContained to find all the records not in potential:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"newFullyContained = [4,5,6,7,8,9]\"}]}],[\"$\",\"p\",null,{\"children\":\"Then we can remove the values in fullyContained, which gives us:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"newFullyContained = [7,8,9]\"}]}],[\"$\",\"p\",null,{\"children\":[\"We will return all the records in \",[\"$\",\"code\",null,{\"children\":\"newFullyContained\"}],\" and apply the \",[\"$\",\"code\",null,{\"children\":\"STWithin\"}],\" or \",[\"$\",\"code\",null,{\"children\":\"StContains\"}],\" predicate to work out which records to return from the potential list.\"]}],[\"$\",\"h2\",null,{\"id\":\"how-do-you-know-the-index-usage\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#how-do-you-know-the-index-usage\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"How do you know the index usage?\"]}],[\"$\",\"p\",null,{\"children\":[\"We can write queries that use \",[\"$\",\"code\",null,{\"children\":\"STDistance\"}],\", \",[\"$\",\"code\",null,{\"children\":\"STWithin\"}],\", and \",[\"$\",\"code\",null,{\"children\":\"STContains\"}],\" without using a geospatial index, but if we’ve got one defined, we’ll want to get the peace of mind of its actual use.\"]}],[\"$\",\"p\",null,{\"children\":[\"You can check by prefixing a query with \",[\"$\",\"code\",null,{\"children\":\"EXPLAIN PLAN FOR\"}],\", which will return a list of the operators in the query plan.\"]}],[\"$\",\"p\",null,{\"children\":[\"If our query uses \",[\"$\",\"code\",null,{\"children\":\"STDistance\"}],\", we should expect to see the ​\",[\"$\",\"code\",null,{\"children\":\"​FILTER\\\\_H3\\\\_INDEX\"}],\" operator. If it uses STWithin or STContains, we should expect to see the INCLUSION_FILTER_H3_INDEX operator.\"]}],[\"$\",\"p\",null,{\"children\":\"See this example query plan:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot Geospatial Indexing Query Plan\",\"src\":\"https://www.datocms-assets.com/75153/1683813851-image6.png\",\"title\":\"Apache Pinot Geospatial Indexing Query Plan\"}]}],[\"$\",\"p\",null,{\"children\":[\"The \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://dev.startree.ai/\",\"children\":\"StarTree Developer Hub\"}],\" contains a \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://dev.startree.ai/docs/pinot/recipes/geospatial-indexing#how-do-i-check-that-the-geospatial-index-is-being-used\",\"children\":\"geospatial indexing guide\"}],\" that goes through this in more detail.\"]}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":\"I hope you found this blog post useful and now understand how geospatial indexes work and when to use them in Apache Pinot.\"}],[\"$\",\"p\",null,{\"children\":[\"Give them a try, and let us know how you get on! If you want to use, or are already using geospatial queries in Apache Pinot, we’d love to hear how — feel free to \",[\"$\",\"$Le\",null,{\"href\":\"/contact-us\",\"children\":\"contact us\"}],\" and tell us more! To help get you started, \",[\"$\",\"$Le\",null,{\"href\":\"/saas-signup\",\"children\":\"sign up for a free trial of fully managed Apache Pinot\"}],\". And if you run into any technical questions, feel free to find me on the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://stree.ai/slack\",\"children\":\"StarTree Community Slack\"}],\".\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"What is geospatial indexing?\",\"url\":\"#what-is-geospatial-indexing\",\"depth\":2},{\"value\":\"How do geospatial indexes work in Apache Pinot?\",\"url\":\"#how-do-geospatial-indexes-work-in-apache-pinot\",\"depth\":2},{\"value\":\"When is the geospatial index used?\",\"url\":\"#when-is-the-geospatial-index-used\",\"depth\":2},{\"value\":\"How do you know the index usage?\",\"url\":\"#how-do-you-know-the-index-usage\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Geospatial Indexing in Apache Pinot | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"This post will explore a new API endpoint that lets you check how much Pinot is lagging when ingesting from Apache Kafka.\"}],[\"$\",\"meta\",\"4\",{\"name\":\"robots\",\"content\":\"index, follow\"}],[\"$\",\"meta\",\"5\",{\"name\":\"googlebot\",\"content\":\"index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1\"}],[\"$\",\"link\",\"6\",{\"rel\":\"canonical\",\"href\":\"https://pinot.apache.org/blog/2023/05/11/Geospatial-Indexing-in-Apache-Pinot\"}],[\"$\",\"link\",\"7\",{\"rel\":\"alternate\",\"type\":\"application/rss+xml\",\"href\":\"https://pinot.apache.org/feed.xml\"}],[\"$\",\"meta\",\"8\",{\"property\":\"og:title\",\"content\":\"Geospatial Indexing in Apache Pinot\"}],[\"$\",\"meta\",\"9\",{\"property\":\"og:description\",\"content\":\"This post will explore a new API endpoint that lets you check how much Pinot is lagging when ingesting from Apache Kafka.\"}],[\"$\",\"meta\",\"10\",{\"property\":\"og:url\",\"content\":\"https://pinot.apache.org/blog/2023/05/11/Geospatial-Indexing-in-Apache-Pinot\"}],[\"$\",\"meta\",\"11\",{\"property\":\"og:site_name\",\"content\":\"Apache Pinot™\"}],[\"$\",\"meta\",\"12\",{\"property\":\"og:locale\",\"content\":\"en_US\"}],[\"$\",\"meta\",\"13\",{\"property\":\"og:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"14\",{\"property\":\"og:type\",\"content\":\"article\"}],[\"$\",\"meta\",\"15\",{\"property\":\"article:published_time\",\"content\":\"2023-05-11T00:00:00.000Z\"}],[\"$\",\"meta\",\"16\",{\"property\":\"article:modified_time\",\"content\":\"2023-05-11T00:00:00.000Z\"}],[\"$\",\"meta\",\"17\",{\"property\":\"article:author\",\"content\":\"Mark Needham\"}],[\"$\",\"meta\",\"18\",{\"name\":\"twitter:card\",\"content\":\"summary_large_image\"}],[\"$\",\"meta\",\"19\",{\"name\":\"twitter:title\",\"content\":\"Geospatial Indexing in Apache Pinot\"}],[\"$\",\"meta\",\"20\",{\"name\":\"twitter:description\",\"content\":\"This post will explore a new API endpoint that lets you check how much Pinot is lagging when ingesting from Apache Kafka.\"}],[\"$\",\"meta\",\"21\",{\"name\":\"twitter:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"22\",{\"name\":\"next-size-adjust\"}]]\n"])</script><script>self.__next_f.push([1,"f:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>
\ No newline at end of file
+</span></code></pre></div><p>First, compute newFullyContained to find all the records not in potential:</p><p><code>newFullyContained = [4,5,6,7,8,9]</code></p><p>Then we can remove the values in fullyContained, which gives us:</p><p><code>newFullyContained = [7,8,9]</code></p><p>We will return all the records in <code>newFullyContained</code> and apply the <code>STWithin</code> or <code>StContains</code> predicate to work out which records to return from the potential list.</p><h2 id="how-do-you-know-the-index-usage"><a href="#how-do-you-know-the-index-usage" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>How do you know the index usage?</h2><p>We can write queries that use <code>STDistance</code>, <code>STWithin</code>, and <code>STContains</code> without using a geospatial index, but if we’ve got one defined, we’ll want to get the peace of mind of its actual use.</p><p>You can check by prefixing a query with <code>EXPLAIN PLAN FOR</code>, which will return a list of the operators in the query plan.</p><p>If our query uses <code>STDistance</code>, we should expect to see the ​<code>​FILTER\_H3\_INDEX</code> operator. If it uses STWithin or STContains, we should expect to see the INCLUSION_FILTER_H3_INDEX operator.</p><p>See this example query plan:</p><p><img alt="Apache Pinot Geospatial Indexing Query Plan" src="https://www.datocms-assets.com/75153/1683813851-image6.png" title="Apache Pinot Geospatial Indexing Query Plan"/></p><p>The <a target="_blank" rel="noopener noreferrer" href="https://dev.startree.ai/">StarTree Developer Hub</a> contains a <a target="_blank" rel="noopener noreferrer" href="https://dev.startree.ai/docs/pinot/recipes/geospatial-indexing#how-do-i-check-that-the-geospatial-index-is-being-used">geospatial indexing guide</a> that goes through this in more detail.</p><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>I hope you found this blog post useful and now understand how geospatial indexes work and when to use them in Apache Pinot.</p><p>Give them a try, and let us know how you get on! If you want to use, or are already using geospatial queries in Apache Pinot, we’d love to hear how — feel free to <a href="/contact-us">contact us</a> and tell us more! To help get you started, <a href="/saas-signup">sign up for a free trial of fully managed Apache Pinot</a>. And if you run into any technical questions, feel free to find me on the <a target="_blank" rel="noopener noreferrer" href="https://stree.ai/slack">StarTree Community Slack</a>.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#what-is-geospatial-indexing">What is geospatial indexing?</a></li><li class="text-sm font-normal leading-tight"><a href="#how-do-geospatial-indexes-work-in-apache-pinot">How do geospatial indexes work in Apache Pinot?</a></li><li class="text-sm font-normal leading-tight"><a href="#when-is-the-geospatial-index-used">When is the geospatial index used?</a></li><li class="text-sm font-normal leading-tight"><a href="#how-do-you-know-the-index-usage">How do you know the index usage?</a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 2.426-2.068l.002-.002m6.452 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Using geospatial queries in combination with time series queries in Pinot, you can perform complex spatiotemporal analysis, such as analyzing changes in weather patterns over time or tracking the movement of objects, vehicles, or people. 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property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"STRING\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"atocCode\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"STRING\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"point\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"BYTES\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dateTimeFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"TIMESTAMP\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"format\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS:EPOCH\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"granularity\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The point column will store a point object that represents the current location of a train. We’ll see how that column gets populated from our table config, as shown below:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"trains\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"REALTIME\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"segmentsConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"timeColumnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token 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punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"replicasPerPartition\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"fieldConfigList\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token 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property\",\"children\":\"\\\"stream.kafka.topic.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"trains\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.broker.list\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"kafka-geospatial:9093\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.type\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"lowlevel\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.prop.auto.offset.reset\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"smallest\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.factory.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.stream.kafka.KafkaJSONMessageDecoder\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"ingestionConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"columnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"point\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"transformFunction\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"STPoint(lon, lat, 1)\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tenants\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metadata\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"The point column is populated by the following function under \",[\"$\",\"code\",null,{\"children\":\"transformConfigs\"}],\":\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"STPoint(lon, lat, 1)\"}]}],[\"$\",\"p\",null,{\"children\":\"In earlier versions of Pinot, you’d need to ensure that the schema included lat and lon columns, but that no longer applies.\"}],[\"$\",\"p\",null,{\"children\":[\"We define the geospatial index on the point column under \",[\"$\",\"code\",null,{\"children\":\"fieldConfigList\"}],\". We can configure what H3 calls \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://h3geo.org/docs/core-library/restable\",\"children\":\"resolutions\"}],\", which defines the size of a hexagon and their number. A resolution of 7 means that there will be 98,825,150 hexagons, each covering an area of 5.16 km². We also need to add the geospatial column to \",[\"$\",\"code\",null,{\"children\":\"tableIndexConfig.noDictionaryColumns\"}],\".\"]}],[\"$\",\"p\",null,{\"children\":[\"We can go ahead and create that table using the \",[\"$\",\"code\",null,{\"children\":\"AddTable\"}],\" command and Pinot will automatically start ingesting data from Kafka.\"]}],[\"$\",\"h2\",null,{\"id\":\"when-is-the-geospatial-index-used\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#when-is-the-geospatial-index-used\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"When is the geospatial index used?\"]}],[\"$\",\"p\",null,{\"children\":\"The geospatial index is used when a WHERE clause in a query calls the StDistance, StWithin, or StContains functions.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"ST\\\\_Distance\"}]}],[\"$\",\"p\",null,{\"children\":\"Let’s say we want to find all the trains within a 10 km radius of Westminster. We could write a query to answer this question using the STDistance function. The query might look like this:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" ts\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" trainId\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" atocCode\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" trainCompany\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" ST\\\\_AsText\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"point\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" STDistance\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"point\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" toSphericalGeography\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"ST_GeomFromText\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'POINT (-0.13624 51.499507)'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"AS\"}],\" distance\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" trains\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\" distance \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003c\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10000\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ORDER\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" distance\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" ts \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"DESC\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"These results from running the query would follow:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample Geospatial Indexing In Apache Pinot Query Result\",\"src\":\"https://www.datocms-assets.com/75153/1683813581-image1.png\",\"title\":\"Sample Geospatial Indexing In Apache Pinot Query Result\"}]}],[\"$\",\"p\",null,{\"children\":\"Let’s now go into a bit more detail about what happens when we run the query.\"}],[\"$\",\"p\",null,{\"children\":\"The 10 km radius covers the area inside the white circle on the diagram below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Geospatial Indexing In Apache Pinot Circle\",\"src\":\"https://www.datocms-assets.com/75153/1683813641-image7.png\",\"title\":\"Geospatial Indexing In Apache Pinot Circle\"}]}],[\"$\",\"p\",null,{\"children\":\"Pinot’s query planner will first translate the distance of 10 km into a number of rings, in this case, two. It will then find all the hexagons located two rings away from the white one. Some of these hexagons will fit completely inside the white circle, and some will overlap with the circle.\"}],[\"$\",\"p\",null,{\"children\":\"If a hexagon fully fits, then we can get all the records inside this hexagon and return them. For those that partially fit, we’ll need to apply the distance predicate before working out which records to return.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"ST\\\\_Within/ST\\\\_Contains\"}]}],[\"$\",\"p\",null,{\"children\":[\"Let’s say that rather than specifying a distance, we instead want to draw a polygon and find the trains that fit inside that polygon. We could use either the \",[\"$\",\"code\",null,{\"children\":\"ST\\\\_Within\"}],\" or \",[\"$\",\"code\",null,{\"children\":\"ST\\\\_Contains\"}],\" functions to answer this question.\"]}],[\"$\",\"p\",null,{\"children\":\"The query might look like this:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" ts\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" trainId\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" atocCode\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" trainCompany\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" ST\\\\_AsText\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"point\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" trains\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\" StWithin\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"point\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" toSphericalGeography\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"ST_GeomFromText\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'POLYGON((\\n\"}]]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\" -0.1296371966600418 51.508053828550544,\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\" -0.1538461446762085 51.497007194317064,\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\" -0.13032652437686923 51.488276935884414,\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\" -0.10458670556545259 51.497003019756846,\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\" -0.10864421725273131 51.50817152245844,\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\" -0.1296371966600418 51.508053828550544))'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ORDER\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\" ts \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"DESC\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The results from running the query are shown below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample Geospatial Indexing In Apache Pinot Query Result\",\"src\":\"https://www.datocms-assets.com/75153/1683813749-image4.png\",\"title\":\"Sample Geospatial Indexing In Apache Pinot Query Result\"}]}],[\"$\",\"p\",null,{\"children\":\"If we change the query to show trains outside of a central London polygon, we’d see the following results:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Sample Geospatial Indexing In Apache Pinot Query Result\",\"src\":\"https://www.datocms-assets.com/75153/1683813705-image3.png\",\"title\":\"Sample Geospatial Indexing In Apache Pinot Query Result\"}]}],[\"$\",\"p\",null,{\"children\":\"So what’s actually happening when we run this query?\"}],[\"$\",\"p\",null,{\"children\":\"The polygon covers the area inside the white shape as shown below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Geospatial Indexing In Apache Pinot Polygon\",\"src\":\"https://www.datocms-assets.com/75153/1683813802-image2.png\",\"title\":\"Geospatial Indexing In Apache Pinot Polygon\"}]}],[\"$\",\"p\",null,{\"children\":\"Pinot’s query planner will first find all the coordinates on the exterior of the polygon. It will then find the hexagons that fit within that geofence. Those hexagons get added to the potential cells list.\"}],[\"$\",\"p\",null,{\"children\":\"The query planner then takes each of those hexagons and checks whether they fit completely inside the original polygon. If they do, then they get added to the fully contained cells list. If we have any cells in both lists, we remove them from the potential cells list.\"}],[\"$\",\"p\",null,{\"children\":\"Next, we find the records for the fully contained cells list and those for the potential cells list.\"}],[\"$\",\"p\",null,{\"children\":\"If we are finding records that fit inside the polygon, we return those in the fully contained list and apply the STWithin/StContains predicate to work out which records to return from the potential list.\"}],[\"$\",\"p\",null,{\"children\":\"If we are finding records outside the polygon, we will create a new fully contained list, which will actually contain the records that are outside the polygon. This list contains all of the records in the database except the ones in the potential list and those in the initial fully contained list.\"}],[\"$\",\"p\",null,{\"children\":\"This one was a bit tricky for me to get my head around, so let’s just quickly go through an example. Imagine that we store 10 records in our database and our potential and fully contained lists hold the following values:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-python\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-python\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"potential \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"0\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"2\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"3\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"fullyContained \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"4\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"5\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"6\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"First, compute newFullyContained to find all the records not in potential:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"newFullyContained = [4,5,6,7,8,9]\"}]}],[\"$\",\"p\",null,{\"children\":\"Then we can remove the values in fullyContained, which gives us:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"newFullyContained = [7,8,9]\"}]}],[\"$\",\"p\",null,{\"children\":[\"We will return all the records in \",[\"$\",\"code\",null,{\"children\":\"newFullyContained\"}],\" and apply the \",[\"$\",\"code\",null,{\"children\":\"STWithin\"}],\" or \",[\"$\",\"code\",null,{\"children\":\"StContains\"}],\" predicate to work out which records to return from the potential list.\"]}],[\"$\",\"h2\",null,{\"id\":\"how-do-you-know-the-index-usage\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#how-do-you-know-the-index-usage\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"How do you know the index usage?\"]}],[\"$\",\"p\",null,{\"children\":[\"We can write queries that use \",[\"$\",\"code\",null,{\"children\":\"STDistance\"}],\", \",[\"$\",\"code\",null,{\"children\":\"STWithin\"}],\", and \",[\"$\",\"code\",null,{\"children\":\"STContains\"}],\" without using a geospatial index, but if we’ve got one defined, we’ll want to get the peace of mind of its actual use.\"]}],[\"$\",\"p\",null,{\"children\":[\"You can check by prefixing a query with \",[\"$\",\"code\",null,{\"children\":\"EXPLAIN PLAN FOR\"}],\", which will return a list of the operators in the query plan.\"]}],[\"$\",\"p\",null,{\"children\":[\"If our query uses \",[\"$\",\"code\",null,{\"children\":\"STDistance\"}],\", we should expect to see the ​\",[\"$\",\"code\",null,{\"children\":\"​FILTER\\\\_H3\\\\_INDEX\"}],\" operator. If it uses STWithin or STContains, we should expect to see the INCLUSION_FILTER_H3_INDEX operator.\"]}],[\"$\",\"p\",null,{\"children\":\"See this example query plan:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot Geospatial Indexing Query Plan\",\"src\":\"https://www.datocms-assets.com/75153/1683813851-image6.png\",\"title\":\"Apache Pinot Geospatial Indexing Query Plan\"}]}],[\"$\",\"p\",null,{\"children\":[\"The \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://dev.startree.ai/\",\"children\":\"StarTree Developer Hub\"}],\" contains a \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://dev.startree.ai/docs/pinot/recipes/geospatial-indexing#how-do-i-check-that-the-geospatial-index-is-being-used\",\"children\":\"geospatial indexing guide\"}],\" that goes through this in more detail.\"]}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":\"I hope you found this blog post useful and now understand how geospatial indexes work and when to use them in Apache Pinot.\"}],[\"$\",\"p\",null,{\"children\":[\"Give them a try, and let us know how you get on! If you want to use, or are already using geospatial queries in Apache Pinot, we’d love to hear how — feel free to \",[\"$\",\"$Le\",null,{\"href\":\"/contact-us\",\"children\":\"contact us\"}],\" and tell us more! To help get you started, \",[\"$\",\"$Le\",null,{\"href\":\"/saas-signup\",\"children\":\"sign up for a free trial of fully managed Apache Pinot\"}],\". And if you run into any technical questions, feel free to find me on the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://stree.ai/slack\",\"children\":\"StarTree Community Slack\"}],\".\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"What is geospatial indexing?\",\"url\":\"#what-is-geospatial-indexing\",\"depth\":2},{\"value\":\"How do geospatial indexes work in Apache Pinot?\",\"url\":\"#how-do-geospatial-indexes-work-in-apache-pinot\",\"depth\":2},{\"value\":\"When is the geospatial index used?\",\"url\":\"#when-is-the-geospatial-index-used\",\"depth\":2},{\"value\":\"How do you know the index usage?\",\"url\":\"#how-do-you-know-the-index-usage\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Geospatial Indexing in Apache Pinot | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"This post will explore a new API endpoint that lets you check how much Pinot is lagging when ingesting from Apache Kafka.\"}],[\"$\",\"meta\",\"4\",{\"name\":\"robots\",\"content\":\"index, follow\"}],[\"$\",\"meta\",\"5\",{\"name\":\"googlebot\",\"content\":\"index, follow, max-video-preview:-1, max-image-preview:large, max-snippet:-1\"}],[\"$\",\"link\",\"6\",{\"rel\":\"canonical\",\"href\":\"https://pinot.apache.org/blog/2023/05/11/Geospatial-Indexing-in-Apache-Pinot\"}],[\"$\",\"link\",\"7\",{\"rel\":\"alternate\",\"type\":\"application/rss+xml\",\"href\":\"https://pinot.apache.org/feed.xml\"}],[\"$\",\"meta\",\"8\",{\"property\":\"og:title\",\"content\":\"Geospatial Indexing in Apache Pinot\"}],[\"$\",\"meta\",\"9\",{\"property\":\"og:description\",\"content\":\"This post will explore a new API endpoint that lets you check how much Pinot is lagging when ingesting from Apache Kafka.\"}],[\"$\",\"meta\",\"10\",{\"property\":\"og:url\",\"content\":\"https://pinot.apache.org/blog/2023/05/11/Geospatial-Indexing-in-Apache-Pinot\"}],[\"$\",\"meta\",\"11\",{\"property\":\"og:site_name\",\"content\":\"Apache Pinot™\"}],[\"$\",\"meta\",\"12\",{\"property\":\"og:locale\",\"content\":\"en_US\"}],[\"$\",\"meta\",\"13\",{\"property\":\"og:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"14\",{\"property\":\"og:type\",\"content\":\"article\"}],[\"$\",\"meta\",\"15\",{\"property\":\"article:published_time\",\"content\":\"2023-05-11T00:00:00.000Z\"}],[\"$\",\"meta\",\"16\",{\"property\":\"article:modified_time\",\"content\":\"2023-05-11T00:00:00.000Z\"}],[\"$\",\"meta\",\"17\",{\"property\":\"article:author\",\"content\":\"Mark Needham\"}],[\"$\",\"meta\",\"18\",{\"name\":\"twitter:card\",\"content\":\"summary_large_image\"}],[\"$\",\"meta\",\"19\",{\"name\":\"twitter:title\",\"content\":\"Geospatial Indexing in Apache Pinot\"}],[\"$\",\"meta\",\"20\",{\"name\":\"twitter:description\",\"content\":\"This post will explore a new API endpoint that lets you check how much Pinot is lagging when ingesting from Apache Kafka.\"}],[\"$\",\"meta\",\"21\",{\"name\":\"twitter:image\",\"content\":\"https://pinot.apache.org/static/images/twitter-card.png\"}],[\"$\",\"meta\",\"22\",{\"name\":\"next-size-adjust\"}]]\n"])</script><script>self.__next_f.push([1,"f:null\n"])</script><script>self.__next_f.push([1,""])</script></body></html>
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diff --git a/blog/2023/05/16/star-tree-indexes-in-apache-pinot-part-1-understanding-the-impact-on-query-performance.html b/blog/2023/05/16/star-tree-indexes-in-apache-pinot-part-1-understanding-the-impact-on-query-performance.html
index 602b3d9..f96380b 100644
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@@ -75,4 +75,4 @@
</span><span class="code-line"> <span class="token string">"Date"</span> <span class="token keyword">ASC</span>
</span><span class="code-line"><span class="token keyword">limit</span> <span class="token number">1000</span>
</span><span class="code-line"><span class="token keyword">option</span><span class="token punctuation">(</span>useStarTree<span class="token operator">=</span><span class="token boolean">true</span><span class="token punctuation">)</span>
-</span></code></pre></div><p><img alt="" src="https://www.datocms-assets.com/75153/1684246852-image9.png"/></p><p>Result: The query completed in 4 milliseconds! Notice in particular that the numDocsScanned came down from ~146M to 409!</p><h3 id="comparison"><a href="#comparison" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Comparison:</h3><p>Let’s take a closer look at the <a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/users/api/querying-pinot-using-standard-sql/response-format">query response stats</a> across all three iterations to understand the “how” part of this magic of indexing in Apache Pinot.</p><p><img alt="" src="https://www.datocms-assets.com/75153/1684246748-image3.png"/></p><ol><li><p>The dataset has 633,694,594 records (documents) spread across 130 segments.</p></li><li><p>Query Stats:</p><ol><li>w/o any index optimizations (Iteration #1), the query scanned ALL 633,694,594 records (check numEntriesScannedInFilter) during processing. Also, numEntriesScannedPostFilter was 584,147,312 (numDocsScanned = ~146M). All 130 segments were processed which was very inefficient.</li><li>w/ Inverted Index (Iteration #2), numEntriesScannedInFilter was 0; numEntriesScannedPostFilter was 584,147,312 (numDocsScanned = ~146M) which meant that the query selectivity was low (the query had to scan a lot of records during post filter phase; about 92% of overall records). This is an indication of when a star-tree index could help.</li><li>w/ Star-tree Index (Iteration #3), numEntriesScannedInFilter was 0; numEntriesScannedPostFilter was only 2,045 (numDocsScanned = 409). The star-tree index helped improve query performance tremendously by providing high query selectivity.</li></ol></li></ol><h2 id="5-impact-summary"><a href="#5-impact-summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>5. Impact Summary:</h2><p><img alt="" src="https://www.datocms-assets.com/75153/1684246766-image4.png"/></p><ol><li>356,968x improvement (or 99.999% drop) in num docs scanned from ~146M to 409.</li><li>378.5x improvement (~99.736% drop) in query latency from 1,513 ms to 4 ms.</li></ol><h3 id="key-benefits-of-the-star-tree-index"><a href="#key-benefits-of-the-star-tree-index" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Key Benefits of the Star-Tree Index:</h3><ul><li><p>User controllable: Tune space vs. time overhead</p></li><li><p>Flexible: create any number of indexes. The right index is chosen based on the query structure.</p></li><li><p>Transparent: Unlike traditional MVs, users don’t need to know about the existence of a star-tree index. The same query will be accelerated with a star-tree index in place.</p></li><li><p>Dynamic: Very easy to generate a new index at any point of time.</p></li><li><p>Disk IO is the most expensive operation in query processing. Latency is linear to the number of disk reads a query has to perform. Star-Tree Index brings the number of disk reads down exponentially.</p><ul><li>In this example, star-tree Index reduced the disk reads by 99.999% from ~584 Million entries (~146 Million documents or records) in case of an inverted index to 2,045 entries (409 documents or records). Query latency came down from 1,513 ms to 4 ms!</li></ul></li></ul><p><a target="_blank" rel="noopener noreferrer" href="https://startree.ai/blog/star-tree-indexes-in-apache-pinot-part-2-understanding-the-impact-during-high-concurrency">In part 2 of this series,</a> we will perform throughput tests to measure the impact of star-tree index under high load.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#1-the-dataset">1. The Dataset:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#schema">Schema:</a></li><li class="text-sm font-normal leading-tight"><a href="#2-query-pattern">2. Query Pattern</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#star-tree-index-config">Star-Tree Index Config:</a></li><li class="text-sm font-normal leading-tight"><a href="#4-query-results-and-stats">4. Query Results and Stats</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#iteration-1-wo-any-apache-pinot-optimizations">Iteration #1: w/o any Apache Pinot optimizations:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#iteration-2-w-inverted-index">Iteration #2: w/ Inverted Index </a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#iteration-3-w-star-tree-index">Iteration #3: w/ Star-Tree Index: </a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#comparison">Comparison:</a></li><li class="text-sm font-normal leading-tight"><a href="#5-impact-summary">5. Impact Summary:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#key-benefits-of-the-star-tree-index">Key Benefits of the Star-Tree Index:</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 2.426-2.068l.002-.002m6.452 5.605c-.445-.793-1.032-1.395-1.76-1.808a4.72 4.72 0 0 0-2.362-.618c-.847 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This index dynamically builds a tree structure to maintain aggregates for a group of dimensions. With star-tree Index, the query latency becomes a function of just a tree traversal with computational complexity of log(\",[\"$\",\"em\",null,{\"children\":\"n\"}],\").\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/blog/a-tale-of-three-real-time-olap-databases#query\",\"children\":\"This comprehensive blog\"}],\" explains in depth how the star-tree Index differs from traditional materialized views (MVs). In particular, read the section Star-Tree Index: Pinot’s intelligent materialized view. 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On the other hand, the star-tree index allows us to partially materialize the computations and provide the ability to tune the space-time tradeoff by providing a configurable threshold between pre-aggregation and data scans.\"}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1684246698-image5.png\"}]}],[\"$\",\"p\",null,{\"children\":\"In this three-part blog series, we will compare and contrast query performance of a star-tree index with an inverted index, something that most of the OLAP databases end up using for such queries.\"}],[\"$\",\"p\",null,{\"children\":\"In this first part, we will showcase how a star-tree index brought down standalone query latency on a sizable dataset of ~633M records from 1,513ms to 4ms! — nearly 380x faster.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1684246806-image7.png\"}]}],[\"$\",\"h2\",null,{\"id\":\"1-the-dataset\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#1-the-dataset\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"1. The Dataset:\"]}],[\"$\",\"p\",null,{\"children\":[\"We used New York City Taxi Data for this comparison. Original source: \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://www.kaggle.com/c/nyc-taxi-trip-duration\",\"children\":\"here\"}],\". Below are the high level details about this dataset.\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1684246816-image6.png\"}]}],[\"$\",\"h3\",null,{\"id\":\"schema\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#schema\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Schema:\"]}],[\"$\",\"p\",null,{\"children\":\"The dataset has 8 dimension fields and 11 metric columns as listed below.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1684246732-image2.png\"}]}],[\"$\",\"h2\",null,{\"id\":\"2-query-pattern\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#2-query-pattern\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"2. Query Pattern\"]}],[\"$\",\"p\",null,{\"children\":\"The query pattern involved slicing and dicing the data (GROUPING) BY various dimensions (Date, Month and Year), aggregating different metrics (total trips, distance and passengers count) and FILTERING BY a time range that could go as wide as 1 year.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1684246872-image10.png\"}]}],[\"$\",\"p\",null,{\"children\":\"Note: A key thing to note is that a single star-tree index covers a wide range of OLAP queries that comprise the dimensions, metrics and aggregate functions specified in it.\"}],[\"$\",\"h3\",null,{\"id\":\"star-tree-index-config\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#star-tree-index-config\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Star-Tree Index Config:\"]}],[\"$\",\"p\",null,{\"children\":\"To support the various query patterns specified above, we defined the following star-tree index.\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"starTreeIndexConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dimensionsSplitOrder\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" 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punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"skipStarNodeCreationForDimensions\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"functionColumnPairs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"COUNT__*\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"SUM__passenger_count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"SUM__total_amount\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"SUM__trip_distance\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"AVG__passenger_count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"AVG__total_amount\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"AVG__trip_distance\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MIN__passenger_count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MIN__total_amount\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MIN__trip_distance\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MAX__passenger_count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MAX__total_amount\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MAX__trip_distance\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"maxLeafRecords\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10000\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"This one star-tree index can get us insights to questions such as:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"How many trips were completed in a given day, month or year?\"}],[\"$\",\"li\",null,{\"children\":\"How many passengers traveled in a given day, month or year?\"}],[\"$\",\"li\",null,{\"children\":\"What is the daily / monthly / annual average trip revenue?\"}],[\"$\",\"li\",null,{\"children\":\"What is the daily / monthly / annual average trip revenue, trip duration and distance traveled?\"}],[\"$\",\"li\",null,{\"children\":\"What is the daily / monthly / annual breakdown of total number of trips, total distance traveled and total revenue generated in 2015?\"}],[\"$\",\"li\",null,{\"children\":\"And many more…\"}]]}],[\"$\",\"p\",null,{\"children\":\"We will use one such variant query for this illustration:\"}],[\"$\",\"ul\",null,{\"children\":[\"$\",\"li\",null,{\"children\":\"What is the total number of trips, total distance traveled and total revenue generated by day in 2015?\"}]}],[\"$\",\"p\",null,{\"children\":\"We used a very small infrastructure footprint for this comparison test.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1687549350-screen-shot-2023-06-22-at-1-32-51-pm.png\"}]}],[\"$\",\"h2\",null,{\"id\":\"4-query-results-and-stats\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#4-query-results-and-stats\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"4. Query Results and Stats\"]}],[\"$\",\"h3\",null,{\"id\":\"iteration-1-wo-any-apache-pinot-optimizations\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#iteration-1-wo-any-apache-pinot-optimizations\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Iteration #1: w/o any Apache Pinot optimizations:\"]}],[\"$\",\"p\",null,{\"children\":\"First, we ran the query without any optimizations offered in Apache Pinot.\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token comment\",\"children\":\"-- Iteration #1: w/o optimizations \u003e 120s\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" toDateTime\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"tpep_dropoff_datetime\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"/\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1000\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'yyyy-MM-dd'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Total # of Trips\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"trip_distance\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Total distance traveled\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"passenger_count\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Total # of Passengers\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"total_amount\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Total Revenue\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" nyc_taxi_demo\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"BETWEEN\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2015-01-01'\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"AND\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2015-12-31'\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"GROUP\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ORDER\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ASC\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1000\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"This was a wide time range query (365 days). It required scanning across ~146M out of ~633M documents. In addition, it involved performing an expensive ToDateTime transformation on the tpep_dropoff_datetime entry in each of those ~146M documents during query time.\"}],[\"$\",\"p\",null,{\"children\":\"Result: The query took 131,425 milliseconds (~131.4s; ~2m 11s) to complete.\"}],[\"$\",\"h3\",null,{\"id\":\"iteration-2-w-inverted-index\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#iteration-2-w-inverted-index\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Iteration #2: w/ Inverted Index \"]}],[\"$\",\"p\",null,{\"children\":\"In this iteration, we used a derived column - dropoff_date_str - which performed the ToDateTime transformation for every record during ingestion time. 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(~1.5s); from ~131s to ~1.5s was a BIG improvement. However, results still took more than a second — which is a relatively long time for an OLAP database, especially if it is faced with multiple concurrent queries.\"}],[\"$\",\"h3\",null,{\"id\":\"iteration-3-w-star-tree-index\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#iteration-3-w-star-tree-index\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Iteration #3: w/ Star-Tree Index: \"]}],[\"$\",\"p\",null,{\"children\":\"In this iteration, we ran the same query with star-tree index enabled.\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token comment\",\"children\":\"-- Iteration #3: w/ Ingestion Time Transformation + StarTree Index\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" dropoff_date_str \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Total # 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of Passengers\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"total_amount\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Total Revenue\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" nyc_taxi_demo\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"BETWEEN\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2015-01-01'\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"AND\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2015-12-31'\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"GROUP\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ORDER\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ASC\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1000\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"option\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"useStarTree\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"true\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1684246852-image9.png\"}]}],[\"$\",\"p\",null,{\"children\":\"Result: The query completed in 4 milliseconds! Notice in particular that the numDocsScanned came down from ~146M to 409!\"}],[\"$\",\"h3\",null,{\"id\":\"comparison\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#comparison\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Comparison:\"]}],[\"$\",\"p\",null,{\"children\":[\"Let’s take a closer look at the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/users/api/querying-pinot-using-standard-sql/response-format\",\"children\":\"query response stats\"}],\" across all three iterations to understand the “how” part of this magic of indexing in Apache Pinot.\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1684246748-image3.png\"}]}],[\"$\",\"ol\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":[\"$\",\"p\",null,{\"children\":\"The dataset has 633,694,594 records (documents) spread across 130 segments.\"}]}],[\"$\",\"li\",null,{\"children\":[[\"$\",\"p\",null,{\"children\":\"Query Stats:\"}],[\"$\",\"ol\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"w/o any index optimizations (Iteration #1), the query scanned ALL 633,694,594 records (check numEntriesScannedInFilter) during processing. Also, numEntriesScannedPostFilter was 584,147,312 (numDocsScanned = ~146M). All 130 segments were processed which was very inefficient.\"}],[\"$\",\"li\",null,{\"children\":\"w/ Inverted Index (Iteration #2), numEntriesScannedInFilter was 0; numEntriesScannedPostFilter was 584,147,312 (numDocsScanned = ~146M) which meant that the query selectivity was low (the query had to scan a lot of records during post filter phase; about 92% of overall records). This is an indication of when a star-tree index could help.\"}],[\"$\",\"li\",null,{\"children\":\"w/ Star-tree Index (Iteration #3), numEntriesScannedInFilter was 0; numEntriesScannedPostFilter was only 2,045 (numDocsScanned = 409). The star-tree index helped improve query performance tremendously by providing high query selectivity.\"}]]}]]}]]}],[\"$\",\"h2\",null,{\"id\":\"5-impact-summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#5-impact-summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"5. Impact Summary:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1684246766-image4.png\"}]}],[\"$\",\"ol\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"356,968x improvement (or 99.999% drop) in num docs scanned from ~146M to 409.\"}],[\"$\",\"li\",null,{\"children\":\"378.5x improvement (~99.736% drop) in query latency from 1,513 ms to 4 ms.\"}]]}],[\"$\",\"h3\",null,{\"id\":\"key-benefits-of-the-star-tree-index\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#key-benefits-of-the-star-tree-index\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Key Benefits of the Star-Tree Index:\"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":[\"$\",\"p\",null,{\"children\":\"User controllable: Tune space vs. time overhead\"}]}],[\"$\",\"li\",null,{\"children\":[\"$\",\"p\",null,{\"children\":\"Flexible: create any number of indexes. 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+</span></code></pre></div><p><img alt="" src="https://www.datocms-assets.com/75153/1684246852-image9.png"/></p><p>Result: The query completed in 4 milliseconds! Notice in particular that the numDocsScanned came down from ~146M to 409!</p><h3 id="comparison"><a href="#comparison" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Comparison:</h3><p>Let’s take a closer look at the <a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/users/api/querying-pinot-using-standard-sql/response-format">query response stats</a> across all three iterations to understand the “how” part of this magic of indexing in Apache Pinot.</p><p><img alt="" src="https://www.datocms-assets.com/75153/1684246748-image3.png"/></p><ol><li><p>The dataset has 633,694,594 records (documents) spread across 130 segments.</p></li><li><p>Query Stats:</p><ol><li>w/o any index optimizations (Iteration #1), the query scanned ALL 633,694,594 records (check numEntriesScannedInFilter) during processing. Also, numEntriesScannedPostFilter was 584,147,312 (numDocsScanned = ~146M). All 130 segments were processed which was very inefficient.</li><li>w/ Inverted Index (Iteration #2), numEntriesScannedInFilter was 0; numEntriesScannedPostFilter was 584,147,312 (numDocsScanned = ~146M) which meant that the query selectivity was low (the query had to scan a lot of records during post filter phase; about 92% of overall records). This is an indication of when a star-tree index could help.</li><li>w/ Star-tree Index (Iteration #3), numEntriesScannedInFilter was 0; numEntriesScannedPostFilter was only 2,045 (numDocsScanned = 409). The star-tree index helped improve query performance tremendously by providing high query selectivity.</li></ol></li></ol><h2 id="5-impact-summary"><a href="#5-impact-summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>5. Impact Summary:</h2><p><img alt="" src="https://www.datocms-assets.com/75153/1684246766-image4.png"/></p><ol><li>356,968x improvement (or 99.999% drop) in num docs scanned from ~146M to 409.</li><li>378.5x improvement (~99.736% drop) in query latency from 1,513 ms to 4 ms.</li></ol><h3 id="key-benefits-of-the-star-tree-index"><a href="#key-benefits-of-the-star-tree-index" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Key Benefits of the Star-Tree Index:</h3><ul><li><p>User controllable: Tune space vs. time overhead</p></li><li><p>Flexible: create any number of indexes. The right index is chosen based on the query structure.</p></li><li><p>Transparent: Unlike traditional MVs, users don’t need to know about the existence of a star-tree index. The same query will be accelerated with a star-tree index in place.</p></li><li><p>Dynamic: Very easy to generate a new index at any point of time.</p></li><li><p>Disk IO is the most expensive operation in query processing. Latency is linear to the number of disk reads a query has to perform. Star-Tree Index brings the number of disk reads down exponentially.</p><ul><li>In this example, star-tree Index reduced the disk reads by 99.999% from ~584 Million entries (~146 Million documents or records) in case of an inverted index to 2,045 entries (409 documents or records). Query latency came down from 1,513 ms to 4 ms!</li></ul></li></ul><p><a target="_blank" rel="noopener noreferrer" href="https://startree.ai/blog/star-tree-indexes-in-apache-pinot-part-2-understanding-the-impact-during-high-concurrency">In part 2 of this series,</a> we will perform throughput tests to measure the impact of star-tree index under high load.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#1-the-dataset">1. The Dataset:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#schema">Schema:</a></li><li class="text-sm font-normal leading-tight"><a href="#2-query-pattern">2. Query Pattern</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#star-tree-index-config">Star-Tree Index Config:</a></li><li class="text-sm font-normal leading-tight"><a href="#4-query-results-and-stats">4. Query Results and Stats</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#iteration-1-wo-any-apache-pinot-optimizations">Iteration #1: w/o any Apache Pinot optimizations:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#iteration-2-w-inverted-index">Iteration #2: w/ Inverted Index </a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#iteration-3-w-star-tree-index">Iteration #3: w/ Star-Tree Index: </a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#comparison">Comparison:</a></li><li class="text-sm font-normal leading-tight"><a href="#5-impact-summary">5. Impact Summary:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#key-benefits-of-the-star-tree-index">Key Benefits of the Star-Tree Index:</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 2.426-2.068l.002-.002m6.452 5.605c-.445-.793-1.032-1.395-1.76-1.808a4.72 4.72 0 0 0-2.362-.618c-.847 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By implementing a star-tree index, query performance significantly improved, reducing query latency from 1,513 ms to just 4 ms and drastically reducing disk reads by 99.999%.\\\",\\\"image\\\":\\\"/static/images/twitter-card.png\\\",\\\"url\\\":\\\"https://pinot.apache.org/blog/2023-05-16-star-tree-indexes-in-apache-pinot-part-1-understanding-the-impact-on-query-performance\\\",\\\"author\\\":[{\\\"@type\\\":\\\"Person\\\",\\\"name\\\":\\\"Sandeep Dabade\\\"}]}\"}}],[\"$\",\"section\",null,{\"className\":\" px-5 pt-10 md:px-[13.313rem] md:py-16\",\"children\":[[\"$\",\"$L12\",null,{}],[\"$\",\"article\",null,{\"className\":\"\",\"children\":[\"$\",\"div\",null,{\"className\":\"mx-auto lg:flex\",\"children\":[[\"$\",\"div\",null,{\"className\":\"lg:pr-12\",\"children\":[[\"$\",\"header\",null,{\"className\":\"pt-6 md:pr-10\",\"children\":[[\"$\",\"h1\",null,{\"className\":\"text-4xl font-semibold\",\"children\":\"StarTree Indexes in Apache Pinot Part-1 - Understanding the Impact on Query Performance\"}],[\"$\",\"p\",null,{\"className\":\"pt-2 text-lg\",\"children\":[\"By: \",\"Sandeep Dabade\"]}],[\"$\",\"p\",null,{\"className\":\"py-2 text-sm\",\"children\":[\"May 16th, 2023\",\" • \",\"7 min read\"]}]]}],[\"$\",\"div\",null,{\"className\":\"flex flex-col lg:flex-row\",\"children\":[\"$\",\"main\",null,{\"className\":\"\",\"children\":[\"$\",\"div\",null,{\"className\":\"prose max-w-[45rem] pb-8 pt-10 dark:prose-invert\",\"children\":[[\"$\",\"p\",null,{\"children\":[\"Star-tree is a \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/basics/indexing/star-tree-index\",\"children\":\"specialized index\"}],\" in \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/resources/what-is-apache-pinot\",\"children\":\"Apache Pinot™\"}],\". This index dynamically builds a tree structure to maintain aggregates for a group of dimensions. With star-tree Index, the query latency becomes a function of just a tree traversal with computational complexity of log(\",[\"$\",\"em\",null,{\"children\":\"n\"}],\").\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/blog/a-tale-of-three-real-time-olap-databases#query\",\"children\":\"This comprehensive blog\"}],\" explains in depth how the star-tree Index differs from traditional materialized views (MVs). In particular, read the section Star-Tree Index: Pinot’s intelligent materialized view. Particularly this one key passage:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"em\",null,{\"children\":\"Star-Tree Index: Pinot’s Intelligent Materialized View:\"}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"em\",null,{\"children\":\"The star-tree index provides an intelligent way to build materialized views within Pinot. Traditional MVs work by fully materializing the computation for each source record that matches the specified predicates. Although useful, this can result in non-trivial storage overhead. On the other hand, the star-tree index allows us to partially materialize the computations and provide the ability to tune the space-time tradeoff by providing a configurable threshold between pre-aggregation and data scans.\"}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1684246698-image5.png\"}]}],[\"$\",\"p\",null,{\"children\":\"In this three-part blog series, we will compare and contrast query performance of a star-tree index with an inverted index, something that most of the OLAP databases end up using for such queries.\"}],[\"$\",\"p\",null,{\"children\":\"In this first part, we will showcase how a star-tree index brought down standalone query latency on a sizable dataset of ~633M records from 1,513ms to 4ms! — nearly 380x faster.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1684246806-image7.png\"}]}],[\"$\",\"h2\",null,{\"id\":\"1-the-dataset\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#1-the-dataset\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"1. The Dataset:\"]}],[\"$\",\"p\",null,{\"children\":[\"We used New York City Taxi Data for this comparison. Original source: \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://www.kaggle.com/c/nyc-taxi-trip-duration\",\"children\":\"here\"}],\". Below are the high level details about this dataset.\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1684246816-image6.png\"}]}],[\"$\",\"h3\",null,{\"id\":\"schema\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#schema\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Schema:\"]}],[\"$\",\"p\",null,{\"children\":\"The dataset has 8 dimension fields and 11 metric columns as listed below.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1684246732-image2.png\"}]}],[\"$\",\"h2\",null,{\"id\":\"2-query-pattern\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#2-query-pattern\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"2. Query Pattern\"]}],[\"$\",\"p\",null,{\"children\":\"The query pattern involved slicing and dicing the data (GROUPING) BY various dimensions (Date, Month and Year), aggregating different metrics (total trips, distance and passengers count) and FILTERING BY a time range that could go as wide as 1 year.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1684246872-image10.png\"}]}],[\"$\",\"p\",null,{\"children\":\"Note: A key thing to note is that a single star-tree index covers a wide range of OLAP queries that comprise the dimensions, metrics and aggregate functions specified in it.\"}],[\"$\",\"h3\",null,{\"id\":\"star-tree-index-config\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#star-tree-index-config\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Star-Tree Index Config:\"]}],[\"$\",\"p\",null,{\"children\":\"To support the various query patterns specified above, we defined the following star-tree index.\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"starTreeIndexConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dimensionsSplitOrder\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"dropoff_date_str\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"dropoff_month\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"dropoff_year\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"skipStarNodeCreationForDimensions\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"functionColumnPairs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"COUNT__*\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"SUM__passenger_count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"SUM__total_amount\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"SUM__trip_distance\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"AVG__passenger_count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"AVG__total_amount\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"AVG__trip_distance\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MIN__passenger_count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MIN__total_amount\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MIN__trip_distance\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MAX__passenger_count\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MAX__total_amount\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MAX__trip_distance\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"maxLeafRecords\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10000\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"This one star-tree index can get us insights to questions such as:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"How many trips were completed in a given day, month or year?\"}],[\"$\",\"li\",null,{\"children\":\"How many passengers traveled in a given day, month or year?\"}],[\"$\",\"li\",null,{\"children\":\"What is the daily / monthly / annual average trip revenue?\"}],[\"$\",\"li\",null,{\"children\":\"What is the daily / monthly / annual average trip revenue, trip duration and distance traveled?\"}],[\"$\",\"li\",null,{\"children\":\"What is the daily / monthly / annual breakdown of total number of trips, total distance traveled and total revenue generated in 2015?\"}],[\"$\",\"li\",null,{\"children\":\"And many more…\"}]]}],[\"$\",\"p\",null,{\"children\":\"We will use one such variant query for this illustration:\"}],[\"$\",\"ul\",null,{\"children\":[\"$\",\"li\",null,{\"children\":\"What is the total number of trips, total distance traveled and total revenue generated by day in 2015?\"}]}],[\"$\",\"p\",null,{\"children\":\"We used a very small infrastructure footprint for this comparison test.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1687549350-screen-shot-2023-06-22-at-1-32-51-pm.png\"}]}],[\"$\",\"h2\",null,{\"id\":\"4-query-results-and-stats\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#4-query-results-and-stats\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"4. Query Results and Stats\"]}],[\"$\",\"h3\",null,{\"id\":\"iteration-1-wo-any-apache-pinot-optimizations\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#iteration-1-wo-any-apache-pinot-optimizations\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Iteration #1: w/o any Apache Pinot optimizations:\"]}],[\"$\",\"p\",null,{\"children\":\"First, we ran the query without any optimizations offered in Apache Pinot.\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token comment\",\"children\":\"-- Iteration #1: w/o optimizations \u003e 120s\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" toDateTime\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"tpep_dropoff_datetime\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"/\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1000\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'yyyy-MM-dd'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Total # of Trips\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"trip_distance\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Total distance traveled\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"passenger_count\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Total # of Passengers\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"total_amount\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Total Revenue\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" nyc_taxi_demo\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"BETWEEN\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2015-01-01'\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"AND\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2015-12-31'\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"GROUP\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ORDER\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ASC\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1000\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"This was a wide time range query (365 days). It required scanning across ~146M out of ~633M documents. In addition, it involved performing an expensive ToDateTime transformation on the tpep_dropoff_datetime entry in each of those ~146M documents during query time.\"}],[\"$\",\"p\",null,{\"children\":\"Result: The query took 131,425 milliseconds (~131.4s; ~2m 11s) to complete.\"}],[\"$\",\"h3\",null,{\"id\":\"iteration-2-w-inverted-index\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#iteration-2-w-inverted-index\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Iteration #2: w/ Inverted Index \"]}],[\"$\",\"p\",null,{\"children\":\"In this iteration, we used a derived column - dropoff_date_str - which performed the ToDateTime transformation for every record during ingestion time. Since the cardinality of this derived column was much lower (granularity was at Day level instead of milliseconds), this enabled us to use an inverted index on this column.\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token comment\",\"children\":\"-- Iteration #2: w/ Ingestion Time Transformation\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" dropoff_date_str \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Total # of Trips\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"trip_distance\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Total distance traveled\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"passenger_count\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Total # of Passengers\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"total_amount\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Total Revenue\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" nyc_taxi_demo\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"BETWEEN\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2015-01-01'\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"AND\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2015-12-31'\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"GROUP\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ORDER\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ASC\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1000\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"option\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"useStarTree\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" timeoutMs\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"20000\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1684246716-image1.png\"}]}],[\"$\",\"p\",null,{\"children\":\"Result: The query completed in 1,513 milliseconds. (~1.5s); from ~131s to ~1.5s was a BIG improvement. However, results still took more than a second — which is a relatively long time for an OLAP database, especially if it is faced with multiple concurrent queries.\"}],[\"$\",\"h3\",null,{\"id\":\"iteration-3-w-star-tree-index\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#iteration-3-w-star-tree-index\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Iteration #3: w/ Star-Tree Index: \"]}],[\"$\",\"p\",null,{\"children\":\"In this iteration, we ran the same query with star-tree index enabled.\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token comment\",\"children\":\"-- Iteration #3: w/ Ingestion Time Transformation + StarTree Index\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"SELECT\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" dropoff_date_str \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Total # 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of Passengers\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"total_amount\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Total Revenue\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FROM\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" nyc_taxi_demo\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"WHERE\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"BETWEEN\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2015-01-01'\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"AND\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'2015-12-31'\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"GROUP\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ORDER\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"BY\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Date\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ASC\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1000\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"option\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"useStarTree\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"true\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1684246852-image9.png\"}]}],[\"$\",\"p\",null,{\"children\":\"Result: The query completed in 4 milliseconds! Notice in particular that the numDocsScanned came down from ~146M to 409!\"}],[\"$\",\"h3\",null,{\"id\":\"comparison\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#comparison\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Comparison:\"]}],[\"$\",\"p\",null,{\"children\":[\"Let’s take a closer look at the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/users/api/querying-pinot-using-standard-sql/response-format\",\"children\":\"query response stats\"}],\" across all three iterations to understand the “how” part of this magic of indexing in Apache Pinot.\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1684246748-image3.png\"}]}],[\"$\",\"ol\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":[\"$\",\"p\",null,{\"children\":\"The dataset has 633,694,594 records (documents) spread across 130 segments.\"}]}],[\"$\",\"li\",null,{\"children\":[[\"$\",\"p\",null,{\"children\":\"Query Stats:\"}],[\"$\",\"ol\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"w/o any index optimizations (Iteration #1), the query scanned ALL 633,694,594 records (check numEntriesScannedInFilter) during processing. Also, numEntriesScannedPostFilter was 584,147,312 (numDocsScanned = ~146M). All 130 segments were processed which was very inefficient.\"}],[\"$\",\"li\",null,{\"children\":\"w/ Inverted Index (Iteration #2), numEntriesScannedInFilter was 0; numEntriesScannedPostFilter was 584,147,312 (numDocsScanned = ~146M) which meant that the query selectivity was low (the query had to scan a lot of records during post filter phase; about 92% of overall records). This is an indication of when a star-tree index could help.\"}],[\"$\",\"li\",null,{\"children\":\"w/ Star-tree Index (Iteration #3), numEntriesScannedInFilter was 0; numEntriesScannedPostFilter was only 2,045 (numDocsScanned = 409). The star-tree index helped improve query performance tremendously by providing high query selectivity.\"}]]}]]}]]}],[\"$\",\"h2\",null,{\"id\":\"5-impact-summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#5-impact-summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"5. Impact Summary:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"\",\"src\":\"https://www.datocms-assets.com/75153/1684246766-image4.png\"}]}],[\"$\",\"ol\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"356,968x improvement (or 99.999% drop) in num docs scanned from ~146M to 409.\"}],[\"$\",\"li\",null,{\"children\":\"378.5x improvement (~99.736% drop) in query latency from 1,513 ms to 4 ms.\"}]]}],[\"$\",\"h3\",null,{\"id\":\"key-benefits-of-the-star-tree-index\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#key-benefits-of-the-star-tree-index\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Key Benefits of the Star-Tree Index:\"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":[\"$\",\"p\",null,{\"children\":\"User controllable: Tune space vs. time overhead\"}]}],[\"$\",\"li\",null,{\"children\":[\"$\",\"p\",null,{\"children\":\"Flexible: create any number of indexes. The right index is chosen based on the query structure.\"}]}],[\"$\",\"li\",null,{\"children\":[\"$\",\"p\",null,{\"children\":\"Transparent: Unlike traditional MVs, users don’t need to know about the existence of a star-tree index. The same query will be accelerated with a star-tree index in place.\"}]}],[\"$\",\"li\",null,{\"children\":[\"$\",\"p\",null,{\"children\":\"Dynamic: Very easy to generate a new index at any point of time.\"}]}],[\"$\",\"li\",null,{\"children\":[[\"$\",\"p\",null,{\"children\":\"Disk IO is the most expensive operation in query processing. Latency is linear to the number of disk reads a query has to perform. Star-Tree Index brings the number of disk reads down exponentially.\"}],[\"$\",\"ul\",null,{\"children\":[\"$\",\"li\",null,{\"children\":\"In this example, star-tree Index reduced the disk reads by 99.999% from ~584 Million entries (~146 Million documents or records) in case of an inverted index to 2,045 entries (409 documents or records). Query latency came down from 1,513 ms to 4 ms!\"}]}]]}]]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/blog/star-tree-indexes-in-apache-pinot-part-2-understanding-the-impact-during-high-concurrency\",\"children\":\"In part 2 of this series,\"}],\" we will perform throughput tests to measure the impact of star-tree index under high load.\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"1. The Dataset:\",\"url\":\"#1-the-dataset\",\"depth\":2},{\"value\":\"Schema:\",\"url\":\"#schema\",\"depth\":3},{\"value\":\"2. Query Pattern\",\"url\":\"#2-query-pattern\",\"depth\":2},{\"value\":\"Star-Tree Index Config:\",\"url\":\"#star-tree-index-config\",\"depth\":3},{\"value\":\"4. Query Results and Stats\",\"url\":\"#4-query-results-and-stats\",\"depth\":2},{\"value\":\"Iteration #1: w/o any Apache Pinot optimizations:\",\"url\":\"#iteration-1-wo-any-apache-pinot-optimizations\",\"depth\":3},{\"value\":\"Iteration #2: w/ Inverted Index \",\"url\":\"#iteration-2-w-inverted-index\",\"depth\":3},{\"value\":\"Iteration #3: w/ Star-Tree Index: \",\"url\":\"#iteration-3-w-star-tree-index\",\"depth\":3},{\"value\":\"Comparison:\",\"url\":\"#comparison\",\"depth\":3},{\"value\":\"5. 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diff --git a/blog/2023/05/16/star-tree-indexes-in-apache-pinot-part-1-understanding-the-impact-on-query-performance.txt b/blog/2023/05/16/star-tree-indexes-in-apache-pinot-part-1-understanding-the-impact-on-query-performance.txt
index 86aa0bc..45ea60c 100644
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+++ b/blog/2023/05/16/star-tree-indexes-in-apache-pinot-part-1-understanding-the-impact-on-query-performance.txt
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diff --git a/blog/2023/05/18/apache-pinot-tutorial-for-getting-started-a-step-by-step-guide.html b/blog/2023/05/18/apache-pinot-tutorial-for-getting-started-a-step-by-step-guide.html
index 7c17e98..7900d30 100644
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+++ b/blog/2023/05/18/apache-pinot-tutorial-for-getting-started-a-step-by-step-guide.html
@@ -42,4 +42,4 @@
</span><span class="code-line">
</span></code></pre></div><p>If you run into trouble on this step like I did, edit the ingestJobSpec.yaml file using Docker Desktop to change the inputDirURI from relative to absolute path. Then rerun the above command.</p><p><img alt="Editing the .yaml file with Docker Desktop" src="https://www.datocms-assets.com/75153/1684419802-image1.png" title="Editing the .yaml file with Docker Desktop"/></p><p>You should now be able to see the table has been populated like so:</p><p><img alt="Apache Pinot table populated" src="https://www.datocms-assets.com/75153/1684421215-image8.png" title="Apache Pinot table populated"/></p><p>Now, let’s run some queries. From localhost:9000, select the Query Console in the left-hand menu. Then type in some of these queries:</p><div class="relative"><pre><code class="code-highlight language-sql"><span class="code-line"><span class="token keyword">select</span> <span class="token operator">*</span> <span class="token keyword">from</span> baseballStats <span class="token keyword">limit</span> <span class="token number">10</span>
</span><span class="code-line"><span class="token keyword">select</span> <span class="token function">sum</span><span class="token punctuation">(</span>runs<span class="token punctuation">)</span><span class="token punctuation">,</span> playerName <span class="token keyword">from</span> baseballStats <span class="token keyword">group</span> <span class="token keyword">by</span> playerName <span class="token keyword">order</span> <span class="token keyword">by</span> <span class="token function">sum</span><span class="token punctuation">(</span>runs<span class="token punctuation">)</span> <span class="token keyword">desc</span>
-</span></code></pre></div><p>You should see results like so:</p><p><img alt="Apache Pinot query console" src="https://www.datocms-assets.com/75153/1684421163-image6.png" title="Apache Pinot query console"/></p><p>And there you have it!</p><h2 id="whats-under-the-hood"><a href="#whats-under-the-hood" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>What’s under the hood?</h2><p>If you’re curious to go a step further and see what the segments look like and what the actual data on disk looks like, keep reading! In the Tables section of localhost:9000, you can scroll down to find a segment:</p><p><img alt="Apache Pinot data on disk segment" src="https://www.datocms-assets.com/75153/1684421358-image9.png" title="Apache Pinot data on disk segment"/></p><p>Clicking on this gives the specifics of the segment:</p><p><img alt="Segment specifics in Pinot UI" src="https://www.datocms-assets.com/75153/1684420155-image5.png" title="Segment specifics in Pinot UI"/></p><p>Pinot allows you to easily inspect your segments and tables in one easy-to-use UI. You can find what’s where and keep an eye on size, location, number of documents, etc.</p><h2 id="conclusion"><a href="#conclusion" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Conclusion</h2><p>Congratulations!</p><p>Together, we’ve:</p><ul><li>Installed and ran Apache Pinot components</li><li>Created a table schema and a table</li><li>Loaded data in a table</li><li>Ran a few queries</li><li>Explored the Pinot UI</li></ul><p>In my next article, we’ll consume event streaming data using Apache Pinot and Apache Kafka®.</p><p>In the meantime, run more queries, load more data, and don’t forget to <a target="_blank" rel="noopener noreferrer" href="https://communityinviter.com/apps/startreedata/startree-community">join the Community Slack</a> for support if you get stuck!</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#the-obligatory-what-is-apache-pinot-and-startree-section">The Obligatory What is Apache Pinot and StarTree Section</a></li><li class="text-sm font-normal leading-tight"><a href="#what-do-you-need-to-run-apache-pinot">What do you need to run Apache Pinot?</a></li><li class="text-sm font-normal leading-tight"><a href="#step-by-step-installation-of-apache-pinot">Step-by-step installation of Apache Pinot</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#step-1">Step 1: </a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#step-2">Step 2:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#step-3">Step 3:</a></li><li class="text-sm leading-tight ml-6 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To accommodate this, we recommend at a minimum the following resources in order to run the sample:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"CPUs: four or more\"}],[\"$\",\"li\",null,{\"children\":\"Memory: 8 GB or more\"}],[\"$\",\"li\",null,{\"children\":\"Swap: 2 GB or more\"}],[\"$\",\"li\",null,{\"children\":\"Disk space: 10 GB or more\"}]]}],[\"$\",\"p\",null,{\"children\":\"Note: When importing custom data or event streaming, you may need more resources. Additionally, note that if not set, Docker will use resources from the host environment as needed and available.\"}],[\"$\",\"h2\",null,{\"id\":\"step-by-step-installation-of-apache-pinot\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#step-by-step-installation-of-apache-pinot\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Step-by-step installation of Apache Pinot\"]}],[\"$\",\"p\",null,{\"children\":\"For this intro tutorial, we will use Docker. Alternatively, you can run Pinot locally if you wish.\"}],[\"$\",\"p\",null,{\"children\":\"The instructions use a Windows 11 computer, but they will work on Macs as well. Also note that I am using VS Code with the Docker extension installed.\"}],[\"$\",\"h3\",null,{\"id\":\"step-1\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#step-1\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Step 1: \"]}],[\"$\",\"p\",null,{\"children\":[\"Make sure you have \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.docker.com/get-docker/\",\"children\":\"Docker installed\"}],\" on your machine.\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"em\",null,{\"children\":\"Docker is a set of platform as a service (PaaS) products that use OS-level virtualization to deliver software in packages called containers.\"}]}],[\"$\",\"h3\",null,{\"id\":\"step-2\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#step-2\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Step 2:\"]}],[\"$\",\"p\",null,{\"children\":\"Now, let’s download the Docker image. On a Windows machine, start a new PowerShell command window. Note that this is not the same as a Windows PowerShell command window, as shown below.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Download Docker image on Windows with PowerShell command window\",\"src\":\"https://www.datocms-assets.com/75153/1684419409-image7.png\",\"title\":\"Download Docker image on Windows with PowerShell command window\"}]}],[\"$\",\"p\",null,{\"children\":\"Use the following command to get (pull) the image we are looking for:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" pull apachepinot/pinot:0.12.0\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"You can also download the latest version like so:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" pull apachepinot/pinot:latest\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"Here, apachepinot is the name of the repository in Docker Hub, pinot is the name of the image, and :latest or :0.12.0 is the version for the image. Note that we will be using the 0.12.0 version for this blog post.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"em\",null,{\"children\":\"Docker Hub is the world’s largest repository of container images in the world.\"}]}],[\"$\",\"p\",null,{\"children\":\"You can verify the image was downloaded or pulled by running the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" images\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"It should show you the image like so:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Docker images command\",\"src\":\"https://www.datocms-assets.com/75153/1684420004-image3.png\",\"title\":\"Docker images command\"}]}],[\"$\",\"h3\",null,{\"id\":\"step-3\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#step-3\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Step 3:\"]}],[\"$\",\"p\",null,{\"children\":\"Let’s run a container using the Docker image that we downloaded:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-it\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"--entrypoint\"}],\" /bin/bash \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-p\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"9000\"}],\":9000 apachepinot/pinot:0.12.0\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Running a container with downloaded Docker image\",\"src\":\"https://www.datocms-assets.com/75153/1684420103-image4.png\",\"title\":\"Running a container with downloaded Docker image\"}]}],[\"$\",\"p\",null,{\"children\":\"The docker run command runs the image. The -p 9000:00 option maps the docker container port 9000 to the local machine port 9000. This allows us to access the Pinot UI, which defaults to port 9000 to be accessible from the localhost. We are using the –entrypoint to override the default entrypoint and replace it with Bash. We want to override the default behavior so that we can start each component one at a time. The next parameter apachepinot/pinot:0.12.0 is the Docker image we pulled above.\"}],[\"$\",\"p\",null,{\"children\":\"After running the command, we’ll find ourselves in the Docker container instance running Bash shell. We can use ls to list the contents of the Docker container as shown above.\"}],[\"$\",\"p\",null,{\"children\":\"If you’re using VS Code, with the Docker extension installed, you can click on the Docker extension and see our container and its content:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"VS Code Docker extension open to see container and content\",\"src\":\"https://www.datocms-assets.com/75153/1684421493-image11.png\",\"title\":\"VS Code Docker extension open to see container and content\"}]}],[\"$\",\"p\",null,{\"children\":\"Click on the Docker icon in the left menu, and apachepinot/pinot:0.12.0. This should take a few seconds to connect to the running container. Now, you can navigate to the files and see what we have under the opt folder.\"}],[\"$\",\"h3\",null,{\"id\":\"step-4\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#step-4\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Step 4:\"]}],[\"$\",\"p\",null,{\"children\":\"Let’s run the components that are essential to running a Pinot cluster. Change directory to the bin folder and list the contents like so:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Running components, directory changed to bin folder and contents listed\",\"src\":\"https://www.datocms-assets.com/75153/1684421611-image10.png\",\"title\":\"Running components, directory changed to bin folder and contents listed\"}]}],[\"$\",\"p\",null,{\"children\":\"In order to start the Pinot cluster, we will need to run the following essential components:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Apache ZooKeeper™\"}],[\"$\",\"li\",null,{\"children\":\"Controller\"}],[\"$\",\"li\",null,{\"children\":\"Broker\"}],[\"$\",\"li\",null,{\"children\":\"Server\"}]]}],[\"$\",\"p\",null,{\"children\":\"Start ZooKeeper using the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"./pinot-admin.sh StartZookeeper \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u0026\"}],\"\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"pinot-admin.sh is a shell script for starting the various components. The \u0026 allows us to continue using the Bash shell. ZooKeeper is responsible for the configuration for the Pinot cluster and needs to be started first.\"}],[\"$\",\"p\",null,{\"children\":\"We can start the remaining components using these commands one at a time:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"./pinot-admin.sh StartController \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u0026\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"./pinot-admin.sh StartBroker \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u0026\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"./pinot-admin.sh StartServer \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u0026\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"The controller controls the cluster health and coordinates with ZooKeeper for configuration and status changes. The broker is responsible for query distribution and result collation, sometimes called Scatter-Gather. Servers manage individual table segments and perform the actual read/writes. To get a better understanding of each component, read this \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/blog/introduction-to-apache-pinot-a-beginners-guide\",\"children\":\"intro to Apache Pinot\"}],\".\"]}],[\"$\",\"p\",null,{\"children\":\"At this time, we should have a running Pinot cluster. We can verify via the Pinot Data Explorer by browsing to localhost:9000. You should see something like this:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Pinot data explorer\",\"src\":\"https://www.datocms-assets.com/75153/1684419932-image2.png\",\"title\":\"Pinot data explorer\"}]}],[\"$\",\"p\",null,{\"children\":\"What just happened?\"}],[\"$\",\"p\",null,{\"children\":\"Let’s dive in.\"}],[\"$\",\"p\",null,{\"children\":\"We have started the four essential components of Pinot, however, you will note that there is not yet any data in our fresh new instance.\"}],[\"$\",\"p\",null,{\"children\":\"Before we create a table and load data, notice the four navigation menus on the left-hand side. You can look at the cluster status, run queries, inspect ZooKeeper, or launch the Swagger endpoints for the REST API that Pinot supports.\"}],[\"$\",\"p\",null,{\"children\":\"On the cluster, we notice that we have the essentials deployed: controller, broker, and server. Currently, there are no tables and no minions—dispatchable components used for task management—exist, though Notice also that multi-tenancy support is available in the cluster manager.\"}],[\"$\",\"h3\",null,{\"id\":\"step-5\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#step-5\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Step 5:\"]}],[\"$\",\"p\",null,{\"children\":\"Now that we have our Apache Pinot cluster ready, let’s load some data. Of course, before we do that, we have to create a schema.\"}],[\"$\",\"p\",null,{\"children\":\"Let’s navigate to the folder:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token builtin class-name\",\"children\":\"cd\"}],\" /opt/pinot/examples/batch/baseballStats\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"You will notice that there are the following files listed here:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-js\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-js\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"baseballStats_offline_table_config\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token property-access\",\"children\":\"json\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"baseballStats_schema\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token property-access\",\"children\":\"json\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"ingestionJobSpec\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token property-access\",\"children\":\"yaml\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"sparkIngestionJobSpec\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token property-access\",\"children\":\"yaml\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"rawdata\\n\"}]]}]}],[\"$\",\"p\",null,{\"children\":\"From the names, we can see that there is a schema file, a table config file, an ingestion job, and Apache Spark™ ingestion job files as well as a raw data folder.\"}],[\"$\",\"p\",null,{\"children\":\"The content of the schema file contains both metric and dimension like so (abbreviated):\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metricFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"INT\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"playerStint\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" …\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"INT\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"baseOnBalls\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dimensionFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"STRING\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"playerID\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" ….\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"STRING\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"playerName\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schemaName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"baseballStats\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"To create a schema and table for the baseball stats file, run the following command from the /app/pinot/bin folder:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"./pinot-admin.sh AddTable \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-schemaFile\"}],\" /opt/pinot/examples/batch/baseballStats/baseballStats_schema.json \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-tableConfigFile\"}],\" /opt/pinot/examples/batch/baseballStats/baseballStats_offline_table_config.json \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-exec\"}],\"\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"You should now see the schema and table created:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot tables created\",\"src\":\"https://www.datocms-assets.com/75153/1684421406-image12.png\",\"title\":\"Apache Pinot tables created\"}]}],[\"$\",\"p\",null,{\"children\":\"Next, we’ll want to load some data into the table that we created. We have some sample data in the folder rawdata that we can use to load. We will need a YAML file to perform the actual ingestion job and can use the following command to import data:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"./pinot-admin.sh LaunchDataIngestionJob \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-jobSpecFile\"}],\" /opt/pinot/examples/batch/baseballStats/ingestionJobSpec.yaml\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}]]}]}],[\"$\",\"p\",null,{\"children\":\"If you run into trouble on this step like I did, edit the ingestJobSpec.yaml file using Docker Desktop to change the inputDirURI from relative to absolute path. Then rerun the above command.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Editing the .yaml file with Docker Desktop\",\"src\":\"https://www.datocms-assets.com/75153/1684419802-image1.png\",\"title\":\"Editing the .yaml file with Docker Desktop\"}]}],[\"$\",\"p\",null,{\"children\":\"You should now be able to see the table has been populated like so:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot table populated\",\"src\":\"https://www.datocms-assets.com/75153/1684421215-image8.png\",\"title\":\"Apache Pinot table populated\"}]}],[\"$\",\"p\",null,{\"children\":\"Now, let’s run some queries. From localhost:9000, select the Query Console in the left-hand menu. Then type in some of these queries:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" baseballStats \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"runs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" playerName \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" baseballStats \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"group\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" playerName \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"order\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"runs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"desc\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"You should see results like so:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot query console\",\"src\":\"https://www.datocms-assets.com/75153/1684421163-image6.png\",\"title\":\"Apache Pinot query console\"}]}],[\"$\",\"p\",null,{\"children\":\"And there you have it!\"}],[\"$\",\"h2\",null,{\"id\":\"whats-under-the-hood\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#whats-under-the-hood\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"What’s under the hood?\"]}],[\"$\",\"p\",null,{\"children\":\"If you’re curious to go a step further and see what the segments look like and what the actual data on disk looks like, keep reading! In the Tables section of localhost:9000, you can scroll down to find a segment:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot data on disk segment\",\"src\":\"https://www.datocms-assets.com/75153/1684421358-image9.png\",\"title\":\"Apache Pinot data on disk segment\"}]}],[\"$\",\"p\",null,{\"children\":\"Clicking on this gives the specifics of the segment:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Segment specifics in Pinot UI\",\"src\":\"https://www.datocms-assets.com/75153/1684420155-image5.png\",\"title\":\"Segment specifics in Pinot UI\"}]}],[\"$\",\"p\",null,{\"children\":\"Pinot allows you to easily inspect your segments and tables in one easy-to-use UI. You can find what’s where and keep an eye on size, location, number of documents, etc.\"}],[\"$\",\"h2\",null,{\"id\":\"conclusion\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#conclusion\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Conclusion\"]}],[\"$\",\"p\",null,{\"children\":\"Congratulations!\"}],[\"$\",\"p\",null,{\"children\":\"Together, we’ve:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Installed and ran Apache Pinot components\"}],[\"$\",\"li\",null,{\"children\":\"Created a table schema and a table\"}],[\"$\",\"li\",null,{\"children\":\"Loaded data in a table\"}],[\"$\",\"li\",null,{\"children\":\"Ran a few queries\"}],[\"$\",\"li\",null,{\"children\":\"Explored the Pinot UI\"}]]}],[\"$\",\"p\",null,{\"children\":\"In my next article, we’ll consume event streaming data using Apache Pinot and Apache Kafka®.\"}],[\"$\",\"p\",null,{\"children\":[\"In the meantime, run more queries, load more data, and don’t forget to \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://communityinviter.com/apps/startreedata/startree-community\",\"children\":\"join the Community Slack\"}],\" for support if you get stuck!\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"The Obligatory What is Apache Pinot and StarTree Section\",\"url\":\"#the-obligatory-what-is-apache-pinot-and-startree-section\",\"depth\":2},{\"value\":\"What do you need to run Apache Pinot?\",\"url\":\"#what-do-you-need-to-run-apache-pinot\",\"depth\":2},{\"value\":\"Step-by-step installation of Apache Pinot\",\"url\":\"#step-by-step-installation-of-apache-pinot\",\"depth\":2},{\"value\":\"Step 1: \",\"url\":\"#step-1\",\"depth\":3},{\"value\":\"Step 2:\",\"url\":\"#step-2\",\"depth\":3},{\"value\":\"Step 3:\",\"url\":\"#step-3\",\"depth\":3},{\"value\":\"Step 4:\",\"url\":\"#step-4\",\"depth\":3},{\"value\":\"Step 5:\",\"url\":\"#step-5\",\"depth\":3},{\"value\":\"What’s under the hood?\",\"url\":\"#whats-under-the-hood\",\"depth\":2},{\"value\":\"Conclusion\",\"url\":\"#conclusion\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Apache Pinot Tutorial for Getting Started - A Step-by-Step Guide | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"This blog post is a guide to getting started with Apache Pinot, including installing and running the necessary components of a Pinot cluster. 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+</span></code></pre></div><p>You should see results like so:</p><p><img alt="Apache Pinot query console" src="https://www.datocms-assets.com/75153/1684421163-image6.png" title="Apache Pinot query console"/></p><p>And there you have it!</p><h2 id="whats-under-the-hood"><a href="#whats-under-the-hood" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>What’s under the hood?</h2><p>If you’re curious to go a step further and see what the segments look like and what the actual data on disk looks like, keep reading! In the Tables section of localhost:9000, you can scroll down to find a segment:</p><p><img alt="Apache Pinot data on disk segment" src="https://www.datocms-assets.com/75153/1684421358-image9.png" title="Apache Pinot data on disk segment"/></p><p>Clicking on this gives the specifics of the segment:</p><p><img alt="Segment specifics in Pinot UI" src="https://www.datocms-assets.com/75153/1684420155-image5.png" title="Segment specifics in Pinot UI"/></p><p>Pinot allows you to easily inspect your segments and tables in one easy-to-use UI. You can find what’s where and keep an eye on size, location, number of documents, etc.</p><h2 id="conclusion"><a href="#conclusion" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Conclusion</h2><p>Congratulations!</p><p>Together, we’ve:</p><ul><li>Installed and ran Apache Pinot components</li><li>Created a table schema and a table</li><li>Loaded data in a table</li><li>Ran a few queries</li><li>Explored the Pinot UI</li></ul><p>In my next article, we’ll consume event streaming data using Apache Pinot and Apache Kafka®.</p><p>In the meantime, run more queries, load more data, and don’t forget to <a target="_blank" rel="noopener noreferrer" href="https://communityinviter.com/apps/startreedata/startree-community">join the Community Slack</a> for support if you get stuck!</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#the-obligatory-what-is-apache-pinot-and-startree-section">The Obligatory What is Apache Pinot and StarTree Section</a></li><li class="text-sm font-normal leading-tight"><a href="#what-do-you-need-to-run-apache-pinot">What do you need to run Apache Pinot?</a></li><li class="text-sm font-normal leading-tight"><a href="#step-by-step-installation-of-apache-pinot">Step-by-step installation of Apache Pinot</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#step-1">Step 1: </a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#step-2">Step 2:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#step-3">Step 3:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#step-4">Step 4:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#step-5">Step 5:</a></li><li class="text-sm font-normal leading-tight"><a href="#whats-under-the-hood">What’s under the hood?</a></li><li class="text-sm font-normal leading-tight"><a href="#conclusion">Conclusion</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 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text-gray-600\",\"children\":[\"Copyright © \",2024,\" The Apache Software Foundation. Apache Pinot, Pinot, Apache, the Apache feather logo, and the Apache Pinot project logo are registered trademarks of The Apache Software Foundation. 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.\"]}]]}]]}]}]]}]}]]}],null]}]]\n"])</script><script>self.__next_f.push([1,"12:I[1514,[\"326\",\"static/chunks/326-3a90a6443b9c824c.js\",\"413\",\"static/chunks/413-f9f40b83f7bb3f22.js\",\"980\",\"static/chunks/980-6e243f9cd384c7d2.js\",\"797\",\"static/chunks/app/blog/%5B...slug%5D/page-502e08b6677b55da.js\"],\"\"]\n13:I[2529,[\"326\",\"static/chunks/326-3a90a6443b9c824c.js\",\"413\",\"static/chunks/413-f9f40b83f7bb3f22.js\",\"980\",\"static/chunks/980-6e243f9cd384c7d2.js\",\"797\",\"static/chunks/app/blog/%5B...slug%5D/page-502e08b6677b55da.js\"],\"\"]\n14:I[5185,[\"326\",\"static/chunks/326-3a90a6443b9c824c.js\",\"413\",\""])</script><script>self.__next_f.push([1,"static/chunks/413-f9f40b83f7bb3f22.js\",\"980\",\"static/chunks/980-6e243f9cd384c7d2.js\",\"797\",\"static/chunks/app/blog/%5B...slug%5D/page-502e08b6677b55da.js\"],\"\"]\n"])</script><script>self.__next_f.push([1,"10:[[\"$\",\"script\",null,{\"type\":\"application/ld+json\",\"dangerouslySetInnerHTML\":{\"__html\":\"{\\\"@context\\\":\\\"https://schema.org\\\",\\\"@type\\\":\\\"BlogPosting\\\",\\\"headline\\\":\\\"Apache Pinot Tutorial for Getting Started - A Step-by-Step Guide\\\",\\\"datePublished\\\":\\\"2023-05-18T00:00:00.000Z\\\",\\\"dateModified\\\":\\\"2023-05-18T00:00:00.000Z\\\",\\\"description\\\":\\\"This blog post is a guide to getting started with Apache Pinot, including installing and running the necessary components of a Pinot cluster. It also explains how to set up schemas, tables, and load data into Pinot, as well as how to run queries using the Pinot Data Explorer. The next article in the series will cover consuming event streaming data with Apache Pinot and Apache Kafka.\\\",\\\"image\\\":\\\"/static/images/twitter-card.png\\\",\\\"url\\\":\\\"https://pinot.apache.org/blog/2023-05-18-apache-pinot-tutorial-for-getting-started-a-step-by-step-guide\\\",\\\"author\\\":[{\\\"@type\\\":\\\"Person\\\",\\\"name\\\":\\\"Barkha Herman\\\"}]}\"}}],[\"$\",\"section\",null,{\"className\":\" px-5 pt-10 md:px-[13.313rem] md:py-16\",\"children\":[[\"$\",\"$L12\",null,{}],[\"$\",\"article\",null,{\"className\":\"\",\"children\":[\"$\",\"div\",null,{\"className\":\"mx-auto lg:flex\",\"children\":[[\"$\",\"div\",null,{\"className\":\"lg:pr-12\",\"children\":[[\"$\",\"header\",null,{\"className\":\"pt-6 md:pr-10\",\"children\":[[\"$\",\"h1\",null,{\"className\":\"text-4xl font-semibold\",\"children\":\"Apache Pinot Tutorial for Getting Started - A Step-by-Step Guide\"}],[\"$\",\"p\",null,{\"className\":\"pt-2 text-lg\",\"children\":[\"By: \",\"Barkha Herman\"]}],[\"$\",\"p\",null,{\"className\":\"py-2 text-sm\",\"children\":[\"May 18th, 2023\",\" • \",\"8 min read\"]}]]}],[\"$\",\"div\",null,{\"className\":\"flex flex-col lg:flex-row\",\"children\":[\"$\",\"main\",null,{\"className\":\"\",\"children\":[\"$\",\"div\",null,{\"className\":\"prose max-w-[45rem] pb-8 pt-10 dark:prose-invert\",\"children\":[[\"$\",\"p\",null,{\"children\":[\"How do you get started with \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/resources/what-is-apache-pinot\",\"children\":\"Apache Pinot™\"}],\"? Good question! To save you the hassle of trying to tackle this on your own, here’s a handy guide that overviews all of the components that make up Pinot and how to set Pinot up.\"]}],[\"$\",\"h2\",null,{\"id\":\"the-obligatory-what-is-apache-pinot-and-startree-section\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#the-obligatory-what-is-apache-pinot-and-startree-section\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"The Obligatory What is Apache Pinot and StarTree Section\"]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/what-is-apache-pinot\",\"children\":\"Pinot\"}],\" is an open source, free-to-use, real-time, and distributed OLAP datastore, purpose built to provide ultra low-latency analytics at extremely high throughput.\"]}],[\"$\",\"p\",null,{\"children\":[\"StarTree offers a fully managed version of the Apache Pinot \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/resources/what-is-real-time-analytics\",\"children\":\"real-time analytics\"}],\" system and other tools around it, such as a real-time anomaly detection and root cause analysis tool, which you can \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/saas-signup\",\"children\":\"try for free\"}],\".\"]}],[\"$\",\"h2\",null,{\"id\":\"what-do-you-need-to-run-apache-pinot\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#what-do-you-need-to-run-apache-pinot\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"What do you need to run Apache Pinot?\"]}],[\"$\",\"p\",null,{\"children\":\"The Docker image that we will use runs multiple services. To accommodate this, we recommend at a minimum the following resources in order to run the sample:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"CPUs: four or more\"}],[\"$\",\"li\",null,{\"children\":\"Memory: 8 GB or more\"}],[\"$\",\"li\",null,{\"children\":\"Swap: 2 GB or more\"}],[\"$\",\"li\",null,{\"children\":\"Disk space: 10 GB or more\"}]]}],[\"$\",\"p\",null,{\"children\":\"Note: When importing custom data or event streaming, you may need more resources. Additionally, note that if not set, Docker will use resources from the host environment as needed and available.\"}],[\"$\",\"h2\",null,{\"id\":\"step-by-step-installation-of-apache-pinot\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#step-by-step-installation-of-apache-pinot\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Step-by-step installation of Apache Pinot\"]}],[\"$\",\"p\",null,{\"children\":\"For this intro tutorial, we will use Docker. Alternatively, you can run Pinot locally if you wish.\"}],[\"$\",\"p\",null,{\"children\":\"The instructions use a Windows 11 computer, but they will work on Macs as well. Also note that I am using VS Code with the Docker extension installed.\"}],[\"$\",\"h3\",null,{\"id\":\"step-1\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#step-1\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Step 1: \"]}],[\"$\",\"p\",null,{\"children\":[\"Make sure you have \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.docker.com/get-docker/\",\"children\":\"Docker installed\"}],\" on your machine.\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"em\",null,{\"children\":\"Docker is a set of platform as a service (PaaS) products that use OS-level virtualization to deliver software in packages called containers.\"}]}],[\"$\",\"h3\",null,{\"id\":\"step-2\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#step-2\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Step 2:\"]}],[\"$\",\"p\",null,{\"children\":\"Now, let’s download the Docker image. On a Windows machine, start a new PowerShell command window. Note that this is not the same as a Windows PowerShell command window, as shown below.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Download Docker image on Windows with PowerShell command window\",\"src\":\"https://www.datocms-assets.com/75153/1684419409-image7.png\",\"title\":\"Download Docker image on Windows with PowerShell command window\"}]}],[\"$\",\"p\",null,{\"children\":\"Use the following command to get (pull) the image we are looking for:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" pull apachepinot/pinot:0.12.0\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"You can also download the latest version like so:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" pull apachepinot/pinot:latest\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"Here, apachepinot is the name of the repository in Docker Hub, pinot is the name of the image, and :latest or :0.12.0 is the version for the image. Note that we will be using the 0.12.0 version for this blog post.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"em\",null,{\"children\":\"Docker Hub is the world’s largest repository of container images in the world.\"}]}],[\"$\",\"p\",null,{\"children\":\"You can verify the image was downloaded or pulled by running the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" images\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"It should show you the image like so:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Docker images command\",\"src\":\"https://www.datocms-assets.com/75153/1684420004-image3.png\",\"title\":\"Docker images command\"}]}],[\"$\",\"h3\",null,{\"id\":\"step-3\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#step-3\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Step 3:\"]}],[\"$\",\"p\",null,{\"children\":\"Let’s run a container using the Docker image that we downloaded:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-it\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"--entrypoint\"}],\" /bin/bash \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-p\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"9000\"}],\":9000 apachepinot/pinot:0.12.0\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Running a container with downloaded Docker image\",\"src\":\"https://www.datocms-assets.com/75153/1684420103-image4.png\",\"title\":\"Running a container with downloaded Docker image\"}]}],[\"$\",\"p\",null,{\"children\":\"The docker run command runs the image. The -p 9000:00 option maps the docker container port 9000 to the local machine port 9000. This allows us to access the Pinot UI, which defaults to port 9000 to be accessible from the localhost. We are using the –entrypoint to override the default entrypoint and replace it with Bash. We want to override the default behavior so that we can start each component one at a time. The next parameter apachepinot/pinot:0.12.0 is the Docker image we pulled above.\"}],[\"$\",\"p\",null,{\"children\":\"After running the command, we’ll find ourselves in the Docker container instance running Bash shell. We can use ls to list the contents of the Docker container as shown above.\"}],[\"$\",\"p\",null,{\"children\":\"If you’re using VS Code, with the Docker extension installed, you can click on the Docker extension and see our container and its content:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"VS Code Docker extension open to see container and content\",\"src\":\"https://www.datocms-assets.com/75153/1684421493-image11.png\",\"title\":\"VS Code Docker extension open to see container and content\"}]}],[\"$\",\"p\",null,{\"children\":\"Click on the Docker icon in the left menu, and apachepinot/pinot:0.12.0. This should take a few seconds to connect to the running container. Now, you can navigate to the files and see what we have under the opt folder.\"}],[\"$\",\"h3\",null,{\"id\":\"step-4\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#step-4\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Step 4:\"]}],[\"$\",\"p\",null,{\"children\":\"Let’s run the components that are essential to running a Pinot cluster. Change directory to the bin folder and list the contents like so:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Running components, directory changed to bin folder and contents listed\",\"src\":\"https://www.datocms-assets.com/75153/1684421611-image10.png\",\"title\":\"Running components, directory changed to bin folder and contents listed\"}]}],[\"$\",\"p\",null,{\"children\":\"In order to start the Pinot cluster, we will need to run the following essential components:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Apache ZooKeeper™\"}],[\"$\",\"li\",null,{\"children\":\"Controller\"}],[\"$\",\"li\",null,{\"children\":\"Broker\"}],[\"$\",\"li\",null,{\"children\":\"Server\"}]]}],[\"$\",\"p\",null,{\"children\":\"Start ZooKeeper using the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"./pinot-admin.sh StartZookeeper \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u0026\"}],\"\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"pinot-admin.sh is a shell script for starting the various components. The \u0026 allows us to continue using the Bash shell. ZooKeeper is responsible for the configuration for the Pinot cluster and needs to be started first.\"}],[\"$\",\"p\",null,{\"children\":\"We can start the remaining components using these commands one at a time:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"./pinot-admin.sh StartController \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u0026\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"./pinot-admin.sh StartBroker \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u0026\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"./pinot-admin.sh StartServer \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u0026\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"The controller controls the cluster health and coordinates with ZooKeeper for configuration and status changes. The broker is responsible for query distribution and result collation, sometimes called Scatter-Gather. Servers manage individual table segments and perform the actual read/writes. To get a better understanding of each component, read this \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/blog/introduction-to-apache-pinot-a-beginners-guide\",\"children\":\"intro to Apache Pinot\"}],\".\"]}],[\"$\",\"p\",null,{\"children\":\"At this time, we should have a running Pinot cluster. We can verify via the Pinot Data Explorer by browsing to localhost:9000. You should see something like this:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Pinot data explorer\",\"src\":\"https://www.datocms-assets.com/75153/1684419932-image2.png\",\"title\":\"Pinot data explorer\"}]}],[\"$\",\"p\",null,{\"children\":\"What just happened?\"}],[\"$\",\"p\",null,{\"children\":\"Let’s dive in.\"}],[\"$\",\"p\",null,{\"children\":\"We have started the four essential components of Pinot, however, you will note that there is not yet any data in our fresh new instance.\"}],[\"$\",\"p\",null,{\"children\":\"Before we create a table and load data, notice the four navigation menus on the left-hand side. You can look at the cluster status, run queries, inspect ZooKeeper, or launch the Swagger endpoints for the REST API that Pinot supports.\"}],[\"$\",\"p\",null,{\"children\":\"On the cluster, we notice that we have the essentials deployed: controller, broker, and server. Currently, there are no tables and no minions—dispatchable components used for task management—exist, though Notice also that multi-tenancy support is available in the cluster manager.\"}],[\"$\",\"h3\",null,{\"id\":\"step-5\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#step-5\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Step 5:\"]}],[\"$\",\"p\",null,{\"children\":\"Now that we have our Apache Pinot cluster ready, let’s load some data. Of course, before we do that, we have to create a schema.\"}],[\"$\",\"p\",null,{\"children\":\"Let’s navigate to the folder:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token builtin class-name\",\"children\":\"cd\"}],\" /opt/pinot/examples/batch/baseballStats\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"You will notice that there are the following files listed here:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-js\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-js\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"baseballStats_offline_table_config\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token property-access\",\"children\":\"json\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"baseballStats_schema\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token property-access\",\"children\":\"json\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"ingestionJobSpec\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token property-access\",\"children\":\"yaml\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"sparkIngestionJobSpec\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token property-access\",\"children\":\"yaml\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"rawdata\\n\"}]]}]}],[\"$\",\"p\",null,{\"children\":\"From the names, we can see that there is a schema file, a table config file, an ingestion job, and Apache Spark™ ingestion job files as well as a raw data folder.\"}],[\"$\",\"p\",null,{\"children\":\"The content of the schema file contains both metric and dimension like so (abbreviated):\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metricFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"INT\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"playerStint\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" …\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"INT\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"baseOnBalls\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dimensionFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"STRING\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"playerID\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" ….\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"STRING\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"playerName\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schemaName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"baseballStats\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"To create a schema and table for the baseball stats file, run the following command from the /app/pinot/bin folder:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"./pinot-admin.sh AddTable \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-schemaFile\"}],\" /opt/pinot/examples/batch/baseballStats/baseballStats_schema.json \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-tableConfigFile\"}],\" /opt/pinot/examples/batch/baseballStats/baseballStats_offline_table_config.json \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-exec\"}],\"\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"You should now see the schema and table created:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot tables created\",\"src\":\"https://www.datocms-assets.com/75153/1684421406-image12.png\",\"title\":\"Apache Pinot tables created\"}]}],[\"$\",\"p\",null,{\"children\":\"Next, we’ll want to load some data into the table that we created. We have some sample data in the folder rawdata that we can use to load. We will need a YAML file to perform the actual ingestion job and can use the following command to import data:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"./pinot-admin.sh LaunchDataIngestionJob \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-jobSpecFile\"}],\" /opt/pinot/examples/batch/baseballStats/ingestionJobSpec.yaml\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}]]}]}],[\"$\",\"p\",null,{\"children\":\"If you run into trouble on this step like I did, edit the ingestJobSpec.yaml file using Docker Desktop to change the inputDirURI from relative to absolute path. Then rerun the above command.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Editing the .yaml file with Docker Desktop\",\"src\":\"https://www.datocms-assets.com/75153/1684419802-image1.png\",\"title\":\"Editing the .yaml file with Docker Desktop\"}]}],[\"$\",\"p\",null,{\"children\":\"You should now be able to see the table has been populated like so:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot table populated\",\"src\":\"https://www.datocms-assets.com/75153/1684421215-image8.png\",\"title\":\"Apache Pinot table populated\"}]}],[\"$\",\"p\",null,{\"children\":\"Now, let’s run some queries. From localhost:9000, select the Query Console in the left-hand menu. Then type in some of these queries:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" baseballStats \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"limit\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"10\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"runs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" playerName \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" baseballStats \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"group\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" playerName \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"order\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"runs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"desc\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"You should see results like so:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot query console\",\"src\":\"https://www.datocms-assets.com/75153/1684421163-image6.png\",\"title\":\"Apache Pinot query console\"}]}],[\"$\",\"p\",null,{\"children\":\"And there you have it!\"}],[\"$\",\"h2\",null,{\"id\":\"whats-under-the-hood\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#whats-under-the-hood\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"What’s under the hood?\"]}],[\"$\",\"p\",null,{\"children\":\"If you’re curious to go a step further and see what the segments look like and what the actual data on disk looks like, keep reading! In the Tables section of localhost:9000, you can scroll down to find a segment:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot data on disk segment\",\"src\":\"https://www.datocms-assets.com/75153/1684421358-image9.png\",\"title\":\"Apache Pinot data on disk segment\"}]}],[\"$\",\"p\",null,{\"children\":\"Clicking on this gives the specifics of the segment:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Segment specifics in Pinot UI\",\"src\":\"https://www.datocms-assets.com/75153/1684420155-image5.png\",\"title\":\"Segment specifics in Pinot UI\"}]}],[\"$\",\"p\",null,{\"children\":\"Pinot allows you to easily inspect your segments and tables in one easy-to-use UI. You can find what’s where and keep an eye on size, location, number of documents, etc.\"}],[\"$\",\"h2\",null,{\"id\":\"conclusion\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#conclusion\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Conclusion\"]}],[\"$\",\"p\",null,{\"children\":\"Congratulations!\"}],[\"$\",\"p\",null,{\"children\":\"Together, we’ve:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Installed and ran Apache Pinot components\"}],[\"$\",\"li\",null,{\"children\":\"Created a table schema and a table\"}],[\"$\",\"li\",null,{\"children\":\"Loaded data in a table\"}],[\"$\",\"li\",null,{\"children\":\"Ran a few queries\"}],[\"$\",\"li\",null,{\"children\":\"Explored the Pinot UI\"}]]}],[\"$\",\"p\",null,{\"children\":\"In my next article, we’ll consume event streaming data using Apache Pinot and Apache Kafka®.\"}],[\"$\",\"p\",null,{\"children\":[\"In the meantime, run more queries, load more data, and don’t forget to \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://communityinviter.com/apps/startreedata/startree-community\",\"children\":\"join the Community Slack\"}],\" for support if you get stuck!\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"The Obligatory What is Apache Pinot and StarTree Section\",\"url\":\"#the-obligatory-what-is-apache-pinot-and-startree-section\",\"depth\":2},{\"value\":\"What do you need to run Apache Pinot?\",\"url\":\"#what-do-you-need-to-run-apache-pinot\",\"depth\":2},{\"value\":\"Step-by-step installation of Apache Pinot\",\"url\":\"#step-by-step-installation-of-apache-pinot\",\"depth\":2},{\"value\":\"Step 1: \",\"url\":\"#step-1\",\"depth\":3},{\"value\":\"Step 2:\",\"url\":\"#step-2\",\"depth\":3},{\"value\":\"Step 3:\",\"url\":\"#step-3\",\"depth\":3},{\"value\":\"Step 4:\",\"url\":\"#step-4\",\"depth\":3},{\"value\":\"Step 5:\",\"url\":\"#step-5\",\"depth\":3},{\"value\":\"What’s under the hood?\",\"url\":\"#whats-under-the-hood\",\"depth\":2},{\"value\":\"Conclusion\",\"url\":\"#conclusion\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Apache Pinot Tutorial for Getting Started - A Step-by-Step Guide | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"This blog post is a guide to getting started with Apache Pinot, including installing and running the necessary components of a Pinot cluster. 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+++ b/blog/2023/05/23/change-data-capture-with-apache-pinot-how-does-it-work.html
@@ -82,4 +82,4 @@
</span><span class="code-line"> <span class="token property">"primaryKeyColumns"</span><span class="token operator">:</span> <span class="token punctuation">[</span><span class="token string">"user_id"</span><span class="token punctuation">]</span>
</span><span class="code-line"><span class="token punctuation">}</span>
</span></code></pre></div><p>You can further configure the behavior of the upsert to allow for different behaviors: FULL or PARTIAL.</p><p>A FULL upsert means that a new record will replace the older record completely if they share the same primary key.</p><p>PARTIAL only allows updates to specific columns and employs additional strategies.</p><p><img alt="Table describing the strategy and descriptions of stream ingestion with upsert" src="https://www.datocms-assets.com/75153/1684857317-image6.png" title="Table describing the strategy and descriptions of stream ingestion with upsert"/></p><p>Source: <a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/basics/data-import/upsert">Stream Ingestion with Upsert</a></p><p>Here is a sample snippet of a table configuration containing the property that configures the upsert strategy:</p><div class="relative"><pre><code class="language-json code-highlight"><span class="code-line"><span class="token property">"upsertConfig"</span><span class="token operator">:</span> <span class="token punctuation">{</span> <span class="token property">"mode"</span><span class="token operator">:</span> <span class="token string">"FULL"</span> <span class="token punctuation">}</span><span class="token punctuation">,</span>
-</span></code></pre></div><p>Upsert simplifies client queries in an extremely powerful way. More importantly, upsert assures the accuracy of any aggregations applied to updated columns, which proves especially important when the analytics lead to critical decisions.</p><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>Change data capture is the best way to capture changes in a database. Other options require comparing snapshots or applying complex modified timestamp logic. Other solutions only emulate real-time, but change data capture embodies the only genuine real-time event streaming solution.</p><p><a target="_blank" rel="noopener noreferrer" href="https://debezium.io/documentation/reference/stable/index.html">Debezium provides many other CDC connectors</a> that you can find in their documentation. If you do not have a Kafka Connect cluster or do not use Kafka at all, you can use the Debezium server to run the CDC connectors and write to an alternative streaming system, such as Amazon Kinesis, Pub/Sub from Google Cloud, Apache® Pulsar™, Azure Event Hubs, and RabbitMQ.</p><p>Lastly, Apache Pinot enables upsert for any client sinking into it, which means the client does not need to implement upsert logic. Any client can generate a materialized view in Pinot. This makes the resulting table faster to query and provides more accurate analytics.</p><p>To try Pinot in the cloud, <a target="_blank" rel="noopener noreferrer" href="https://startree.ai/saas-signup">visit startree.ai for a free trial</a>.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#pre-image-post-image-or-diffs">Pre-Image, Post-Image, or Diffs?</a></li><li class="text-sm font-normal leading-tight"><a href="#how-to-capture-change-data-with-debezium">How to Capture Change Data with Debezium</a></li><li class="text-sm font-normal leading-tight"><a href="#debezium-data-format">Debezium Data Format</a></li><li class="text-sm font-normal leading-tight"><a href="#materialized-views">Materialized Views</a></li><li class="text-sm font-normal leading-tight"><a href="#upsert-in-apache-pinot">Upsert in Apache Pinot</a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 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It explains the elements of the format and its usage in indexing JSON fields. 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However, the means to ingest and analyze that data in \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/resources/what-is-apache-pinot\",\"children\":\"Apache Pinot™\"}],\" will generally remain the same.\"]}],[\"$\",\"p\",null,{\"children\":\"As your applications interact with their data stores, they automatically log the transaction in a construct called a write-ahead log (WAL) in real time. In fact, each transaction reflects an event that has been recorded, naturally giving the WAL event streaming properties. This approach is typically used by relational OLTP databases like PostgreSQL.\"}],[\"$\",\"p\",null,{\"children\":\"NOTE: NoSQL databases also have the ability to perform CDC but may use other mechanisms than a WAL. CDC for NoSQL databases is outside the scope of this post.\"}],[\"$\",\"p\",null,{\"children\":\"The WAL is an append-only, immutable stream of events designed to replicate its data to another instance of the data store for high availability in disaster recovery scenarios (see diagram below). The transactions occurring on the left data store (primary) get replicated to the data store to the right (secondary). The applications connect to the primary data store and replicate its data to the secondary data store. If the primary data store goes down, the application switches to the secondary data store.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Primary data store transactions being replicated to a secondary data store\",\"src\":\"https://www.datocms-assets.com/75153/1684857872-image3.png\",\"title\":\"Primary data store transactions being replicated to a secondary data store\"}]}],[\"$\",\"p\",null,{\"children\":\"The following diagram shows an example of a WAL in a data store. New transactions get appended to the end of the WAL. The old transactions are on the left, and the newer transactions are on the right.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"WAL in a data store with new transactions appended to the end of the WAL\",\"src\":\"https://www.datocms-assets.com/75153/1684857250-image5.png\",\"title\":\"WAL in a data store with new transactions appended to the end of the WAL\"}]}],[\"$\",\"p\",null,{\"children\":\"Change data capture enables you to listen to this WAL by capturing these transactions and sending them downstream for processing. The data processing occurs in a different system where we can view the latest version of each record in other applications. Because of the real-time nature of the data, the subscribing applications to the stream of transactions receive real-time transaction events.\"}],[\"$\",\"h2\",null,{\"id\":\"pre-image-post-image-or-diffs\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#pre-image-post-image-or-diffs\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Pre-Image, Post-Image, or Diffs?\"]}],[\"$\",\"p\",null,{\"children\":[\"An important consideration for CDC is what specific elements of change it captures. Not all CDC implementations are the same. Some provide only the \",[\"$\",\"em\",null,{\"children\":\"post-image\"}],\" — the complete state to which the record changes after an update. Some only provide the \",[\"$\",\"em\",null,{\"children\":\"diffs\"}],\" (or \",[\"$\",\"em\",null,{\"children\":\"deltas\"}],\") — the specific changes made to the record at the time of the update, not the complete current state of the record. And others can provide the pre-image as well — what the state of the record was before the changes were applied.\"]}],[\"$\",\"p\",null,{\"children\":[\"Different transactional databases may only provide one or two of these elements. Usually, it will provide the complete post-image or the diffs (or deltas) to the record. In other cases, a CDC implementation might provide all three data elements — pre-, post-, \",[\"$\",\"em\",null,{\"children\":\"and\"}],\" diffs. It is very important for you to understand what specific CDC data elements your transactional database provides because of how it limits the kind of analytics you can perform.\"]}],[\"$\",\"h2\",null,{\"id\":\"how-to-capture-change-data-with-debezium\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#how-to-capture-change-data-with-debezium\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"How to Capture Change Data with Debezium\"]}],[\"$\",\"p\",null,{\"children\":\"Capturing change events requires specific knowledge of the database from which the changes are occurring; and there are many transactional databases. Debezium, an open source project, provides a set of connectors that can subscribe to WALs in many different data stores, such as PostgreSQL, SQL Server, and MongoDB. Their implementation involves the Kafka Connect framework, an open source framework that enables integrations to Apache Kafka®. Two types of connectors exist: source and sink. Debezium connectors are source-only connectors.\"}],[\"$\",\"p\",null,{\"children\":\"Kafka connectors must run in a Kafka Connect cluster, a highly available and distributed system for running connectors. Kafka connectors cannot run on their own and require a server. The Debezium project provides a Debezium server that can also run Debezium connectors capable of writing to other event streaming platforms besides Kafka, for instance, Amazon Kinesis. The diagram below shows a Debezium connector reading the WAL and writing to a Debezium server. The Debezium server can then write to either Kafka or Kinesis.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Diagram showing a Debezium connector reading the WAL and writing to a Debezium server\",\"src\":\"https://www.datocms-assets.com/75153/1684857201-image4.png\",\"title\":\"Diagram showing a Debezium connector reading the WAL and writing to a Debezium server\"}]}],[\"$\",\"h2\",null,{\"id\":\"debezium-data-format\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#debezium-data-format\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Debezium Data Format\"]}],[\"$\",\"p\",null,{\"children\":[\"For details on the Debezium format, \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://debezium.io/documentation/reference/stable/tutorial.html\",\"children\":\"check out the tutorial\"}],\". 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property\",\"children\":\"\\\"ts_ms\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1486501486308\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"A few elements to note:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":[\"$\",\"p\",null,{\"children\":\"The schema element never changes and defines the schema of the payload\"}]}],[\"$\",\"li\",null,{\"children\":[[\"$\",\"p\",null,{\"children\":\"The payload element holds three different elements:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"before: shows the state 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\",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"format\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS:SIMPLE_DATE_FORMAT:yyyy-MM-dd'T'HH:mm:ss.SSS'Z'\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"granularity\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"primaryKeyColumns\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"user_id\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"You may have an alternative schema depending on your use case. You don’t need any of the fields I preserved. If at the end you only want the latest version, you can do that easily by only preserving the columns that matter to you.\"}],[\"$\",\"h2\",null,{\"id\":\"materialized-views\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#materialized-views\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Materialized Views\"]}],[\"$\",\"p\",null,{\"children\":\"When looking up your record in Pinot, you only need to provide a WHERE clause with the primary key. Pinot will only return one record—the latest version of the record, not the history of the record—as a true materialized view should. Otherwise, you would have to provide more logic in the SQL statement that selects for the latest record. This adds latency to the query and may make downstream aggregations less accurate. Pinot provides a materialized view by implementing upsert for real-time tables with a primary key.\"}],[\"$\",\"h2\",null,{\"id\":\"upsert-in-apache-pinot\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#upsert-in-apache-pinot\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Upsert in Apache Pinot\"]}],[\"$\",\"p\",null,{\"children\":[\"Unlike any other real-time OLAP, \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/basics/data-import/upsert\",\"children\":\"Pinot offers native support for upsert\"}],\" for real-time ingestion. Upsert logic says, “If the record exists, update it or otherwise insert it.”\"]}],[\"$\",\"p\",null,{\"children\":\"You need upsert capabilities for dimensional data to simply SELECT for the record’s primary key when retrieving it. Without upsert, you will need to find the latest version of a record by comparing the latest timestamps, which leaves room for error.\"}],[\"$\",\"p\",null,{\"children\":\"This JSON document shows a schema snippet in Pinot that contains a primaryKeyColumns property. By applying this property, Pinot automatically enables the upsert feature. Upsert is completely transparent to the sender and therefore no specific programming is required.\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-json code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"primaryKeyColumns\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"user_id\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"You can further configure the behavior of the upsert to allow for different behaviors: FULL or PARTIAL.\"}],[\"$\",\"p\",null,{\"children\":\"A FULL upsert means that a new record will replace the older record completely if they share the same primary key.\"}],[\"$\",\"p\",null,{\"children\":\"PARTIAL only allows updates to specific columns and employs additional strategies.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Table describing the strategy and descriptions of stream ingestion with upsert\",\"src\":\"https://www.datocms-assets.com/75153/1684857317-image6.png\",\"title\":\"Table describing the strategy and descriptions of stream ingestion with upsert\"}]}],[\"$\",\"p\",null,{\"children\":[\"Source: \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/basics/data-import/upsert\",\"children\":\"Stream Ingestion with Upsert\"}]]}],[\"$\",\"p\",null,{\"children\":\"Here is a sample snippet of a table configuration containing the property that configures the upsert strategy:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-json code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"upsertConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"mode\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"FULL\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"Upsert simplifies client queries in an extremely powerful way. More importantly, upsert assures the accuracy of any aggregations applied to updated columns, which proves especially important when the analytics lead to critical decisions.\"}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":\"Change data capture is the best way to capture changes in a database. Other options require comparing snapshots or applying complex modified timestamp logic. Other solutions only emulate real-time, but change data capture embodies the only genuine real-time event streaming solution.\"}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://debezium.io/documentation/reference/stable/index.html\",\"children\":\"Debezium provides many other CDC connectors\"}],\" that you can find in their documentation. If you do not have a Kafka Connect cluster or do not use Kafka at all, you can use the Debezium server to run the CDC connectors and write to an alternative streaming system, such as Amazon Kinesis, Pub/Sub from Google Cloud, Apache® Pulsar™, Azure Event Hubs, and RabbitMQ.\"]}],[\"$\",\"p\",null,{\"children\":\"Lastly, Apache Pinot enables upsert for any client sinking into it, which means the client does not need to implement upsert logic. Any client can generate a materialized view in Pinot. This makes the resulting table faster to query and provides more accurate analytics.\"}],[\"$\",\"p\",null,{\"children\":[\"To try Pinot in the cloud, \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/saas-signup\",\"children\":\"visit startree.ai for a free trial\"}],\".\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"Pre-Image, Post-Image, or Diffs?\",\"url\":\"#pre-image-post-image-or-diffs\",\"depth\":2},{\"value\":\"How to Capture Change Data with Debezium\",\"url\":\"#how-to-capture-change-data-with-debezium\",\"depth\":2},{\"value\":\"Debezium Data Format\",\"url\":\"#debezium-data-format\",\"depth\":2},{\"value\":\"Materialized Views\",\"url\":\"#materialized-views\",\"depth\":2},{\"value\":\"Upsert in Apache Pinot\",\"url\":\"#upsert-in-apache-pinot\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Change Data Capture with Apache Pinot - How Does It Work? | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"This blog post discusses the use of Change Data Capture (CDC) in Apache Pinot and the data format used in Debezium for efficient querying and analytics. 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\ No newline at end of file
+</span></code></pre></div><p>Upsert simplifies client queries in an extremely powerful way. More importantly, upsert assures the accuracy of any aggregations applied to updated columns, which proves especially important when the analytics lead to critical decisions.</p><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>Change data capture is the best way to capture changes in a database. Other options require comparing snapshots or applying complex modified timestamp logic. Other solutions only emulate real-time, but change data capture embodies the only genuine real-time event streaming solution.</p><p><a target="_blank" rel="noopener noreferrer" href="https://debezium.io/documentation/reference/stable/index.html">Debezium provides many other CDC connectors</a> that you can find in their documentation. If you do not have a Kafka Connect cluster or do not use Kafka at all, you can use the Debezium server to run the CDC connectors and write to an alternative streaming system, such as Amazon Kinesis, Pub/Sub from Google Cloud, Apache® Pulsar™, Azure Event Hubs, and RabbitMQ.</p><p>Lastly, Apache Pinot enables upsert for any client sinking into it, which means the client does not need to implement upsert logic. Any client can generate a materialized view in Pinot. This makes the resulting table faster to query and provides more accurate analytics.</p><p>To try Pinot in the cloud, <a target="_blank" rel="noopener noreferrer" href="https://startree.ai/saas-signup">visit startree.ai for a free trial</a>.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#pre-image-post-image-or-diffs">Pre-Image, Post-Image, or Diffs?</a></li><li class="text-sm font-normal leading-tight"><a href="#how-to-capture-change-data-with-debezium">How to Capture Change Data with Debezium</a></li><li class="text-sm font-normal leading-tight"><a href="#debezium-data-format">Debezium Data Format</a></li><li class="text-sm font-normal leading-tight"><a href="#materialized-views">Materialized Views</a></li><li class="text-sm font-normal leading-tight"><a href="#upsert-in-apache-pinot">Upsert in Apache Pinot</a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 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It explains the elements of the format and its usage in indexing JSON fields. 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However, the means to ingest and analyze that data in \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/resources/what-is-apache-pinot\",\"children\":\"Apache Pinot™\"}],\" will generally remain the same.\"]}],[\"$\",\"p\",null,{\"children\":\"As your applications interact with their data stores, they automatically log the transaction in a construct called a write-ahead log (WAL) in real time. In fact, each transaction reflects an event that has been recorded, naturally giving the WAL event streaming properties. This approach is typically used by relational OLTP databases like PostgreSQL.\"}],[\"$\",\"p\",null,{\"children\":\"NOTE: NoSQL databases also have the ability to perform CDC but may use other mechanisms than a WAL. CDC for NoSQL databases is outside the scope of this post.\"}],[\"$\",\"p\",null,{\"children\":\"The WAL is an append-only, immutable stream of events designed to replicate its data to another instance of the data store for high availability in disaster recovery scenarios (see diagram below). The transactions occurring on the left data store (primary) get replicated to the data store to the right (secondary). The applications connect to the primary data store and replicate its data to the secondary data store. If the primary data store goes down, the application switches to the secondary data store.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Primary data store transactions being replicated to a secondary data store\",\"src\":\"https://www.datocms-assets.com/75153/1684857872-image3.png\",\"title\":\"Primary data store transactions being replicated to a secondary data store\"}]}],[\"$\",\"p\",null,{\"children\":\"The following diagram shows an example of a WAL in a data store. New transactions get appended to the end of the WAL. The old transactions are on the left, and the newer transactions are on the right.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"WAL in a data store with new transactions appended to the end of the WAL\",\"src\":\"https://www.datocms-assets.com/75153/1684857250-image5.png\",\"title\":\"WAL in a data store with new transactions appended to the end of the WAL\"}]}],[\"$\",\"p\",null,{\"children\":\"Change data capture enables you to listen to this WAL by capturing these transactions and sending them downstream for processing. The data processing occurs in a different system where we can view the latest version of each record in other applications. Because of the real-time nature of the data, the subscribing applications to the stream of transactions receive real-time transaction events.\"}],[\"$\",\"h2\",null,{\"id\":\"pre-image-post-image-or-diffs\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#pre-image-post-image-or-diffs\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Pre-Image, Post-Image, or Diffs?\"]}],[\"$\",\"p\",null,{\"children\":[\"An important consideration for CDC is what specific elements of change it captures. Not all CDC implementations are the same. Some provide only the \",[\"$\",\"em\",null,{\"children\":\"post-image\"}],\" — the complete state to which the record changes after an update. Some only provide the \",[\"$\",\"em\",null,{\"children\":\"diffs\"}],\" (or \",[\"$\",\"em\",null,{\"children\":\"deltas\"}],\") — the specific changes made to the record at the time of the update, not the complete current state of the record. And others can provide the pre-image as well — what the state of the record was before the changes were applied.\"]}],[\"$\",\"p\",null,{\"children\":[\"Different transactional databases may only provide one or two of these elements. Usually, it will provide the complete post-image or the diffs (or deltas) to the record. In other cases, a CDC implementation might provide all three data elements — pre-, post-, \",[\"$\",\"em\",null,{\"children\":\"and\"}],\" diffs. It is very important for you to understand what specific CDC data elements your transactional database provides because of how it limits the kind of analytics you can perform.\"]}],[\"$\",\"h2\",null,{\"id\":\"how-to-capture-change-data-with-debezium\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#how-to-capture-change-data-with-debezium\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"How to Capture Change Data with Debezium\"]}],[\"$\",\"p\",null,{\"children\":\"Capturing change events requires specific knowledge of the database from which the changes are occurring; and there are many transactional databases. Debezium, an open source project, provides a set of connectors that can subscribe to WALs in many different data stores, such as PostgreSQL, SQL Server, and MongoDB. Their implementation involves the Kafka Connect framework, an open source framework that enables integrations to Apache Kafka®. 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\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"ts_ms\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"dataType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"LONG\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"format\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS:SIMPLE_DATE_FORMAT:yyyy-MM-dd'T'HH:mm:ss.SSS'Z'\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"granularity\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"primaryKeyColumns\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"user_id\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"You may have an alternative schema depending on your use case. You don’t need any of the fields I preserved. If at the end you only want the latest version, you can do that easily by only preserving the columns that matter to you.\"}],[\"$\",\"h2\",null,{\"id\":\"materialized-views\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#materialized-views\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Materialized Views\"]}],[\"$\",\"p\",null,{\"children\":\"When looking up your record in Pinot, you only need to provide a WHERE clause with the primary key. Pinot will only return one record—the latest version of the record, not the history of the record—as a true materialized view should. Otherwise, you would have to provide more logic in the SQL statement that selects for the latest record. This adds latency to the query and may make downstream aggregations less accurate. Pinot provides a materialized view by implementing upsert for real-time tables with a primary key.\"}],[\"$\",\"h2\",null,{\"id\":\"upsert-in-apache-pinot\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#upsert-in-apache-pinot\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Upsert in Apache Pinot\"]}],[\"$\",\"p\",null,{\"children\":[\"Unlike any other real-time OLAP, \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/basics/data-import/upsert\",\"children\":\"Pinot offers native support for upsert\"}],\" for real-time ingestion. Upsert logic says, “If the record exists, update it or otherwise insert it.”\"]}],[\"$\",\"p\",null,{\"children\":\"You need upsert capabilities for dimensional data to simply SELECT for the record’s primary key when retrieving it. Without upsert, you will need to find the latest version of a record by comparing the latest timestamps, which leaves room for error.\"}],[\"$\",\"p\",null,{\"children\":\"This JSON document shows a schema snippet in Pinot that contains a primaryKeyColumns property. By applying this property, Pinot automatically enables the upsert feature. Upsert is completely transparent to the sender and therefore no specific programming is required.\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-json code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"primaryKeyColumns\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"user_id\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"You can further configure the behavior of the upsert to allow for different behaviors: FULL or PARTIAL.\"}],[\"$\",\"p\",null,{\"children\":\"A FULL upsert means that a new record will replace the older record completely if they share the same primary key.\"}],[\"$\",\"p\",null,{\"children\":\"PARTIAL only allows updates to specific columns and employs additional strategies.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Table describing the strategy and descriptions of stream ingestion with upsert\",\"src\":\"https://www.datocms-assets.com/75153/1684857317-image6.png\",\"title\":\"Table describing the strategy and descriptions of stream ingestion with upsert\"}]}],[\"$\",\"p\",null,{\"children\":[\"Source: \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/basics/data-import/upsert\",\"children\":\"Stream Ingestion with Upsert\"}]]}],[\"$\",\"p\",null,{\"children\":\"Here is a sample snippet of a table configuration containing the property that configures the upsert strategy:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-json code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"upsertConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"mode\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"FULL\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"Upsert simplifies client queries in an extremely powerful way. More importantly, upsert assures the accuracy of any aggregations applied to updated columns, which proves especially important when the analytics lead to critical decisions.\"}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":\"Change data capture is the best way to capture changes in a database. Other options require comparing snapshots or applying complex modified timestamp logic. Other solutions only emulate real-time, but change data capture embodies the only genuine real-time event streaming solution.\"}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://debezium.io/documentation/reference/stable/index.html\",\"children\":\"Debezium provides many other CDC connectors\"}],\" that you can find in their documentation. If you do not have a Kafka Connect cluster or do not use Kafka at all, you can use the Debezium server to run the CDC connectors and write to an alternative streaming system, such as Amazon Kinesis, Pub/Sub from Google Cloud, Apache® Pulsar™, Azure Event Hubs, and RabbitMQ.\"]}],[\"$\",\"p\",null,{\"children\":\"Lastly, Apache Pinot enables upsert for any client sinking into it, which means the client does not need to implement upsert logic. Any client can generate a materialized view in Pinot. This makes the resulting table faster to query and provides more accurate analytics.\"}],[\"$\",\"p\",null,{\"children\":[\"To try Pinot in the cloud, \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/saas-signup\",\"children\":\"visit startree.ai for a free trial\"}],\".\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"Pre-Image, Post-Image, or Diffs?\",\"url\":\"#pre-image-post-image-or-diffs\",\"depth\":2},{\"value\":\"How to Capture Change Data with Debezium\",\"url\":\"#how-to-capture-change-data-with-debezium\",\"depth\":2},{\"value\":\"Debezium Data Format\",\"url\":\"#debezium-data-format\",\"depth\":2},{\"value\":\"Materialized Views\",\"url\":\"#materialized-views\",\"depth\":2},{\"value\":\"Upsert in Apache Pinot\",\"url\":\"#upsert-in-apache-pinot\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Change Data Capture with Apache Pinot - How Does It Work? | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"This blog post discusses the use of Change Data Capture (CDC) in Apache Pinot and the data format used in Debezium for efficient querying and analytics. 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</span><span class="code-line"> <span class="token property">"isDimTable"</span><span class="token operator">:</span> <span class="token boolean">false</span>
</span><span class="code-line"><span class="token punctuation">}</span>
</span></code></pre></div><p>Notice the section called streamConfigs, where we define the source as a Kafka stream, located at localhost:9092, and consume the topic wikipedia-events. That’s all it takes to consume a Kafka Topic into Pinot.</p><p>Don’t believe me? Give it a try!</p><p>Create the table by running the following command:</p><div class="relative"><pre><code class="language-bash code-highlight"><span class="code-line">/opt/pinot/bin/pinot-admin.sh AddTable <span class="token parameter variable">-schemaFile</span> /opt/realtime/realtime.schema.json <span class="token parameter variable">-tableConfigFile</span> /opt/realtime/realtime.tableconfig.json <span class="token parameter variable">-exec</span>
-</span></code></pre></div><p>Now, browse to the following location <a target="_blank" rel="noopener noreferrer" href="http://localhost:9000/#/tables">http://localhost:9000/#/tables,</a> and you should see the newly created table. However, where’s the real-time data, you say?</p><p>Run the node wikievents.js command, then query the newly created wikievents table to see the totalDocs increase in real time:</p><p><img alt="Apache Pinot query console" src="https://www.datocms-assets.com/75153/1685462248-image6.png" title="Apache Pinot query console"/></p><p>To avoid running out of space on your computer, make sure to stop the wikievents.js script when you’re done :-D</p><h2 id="conclusion"><a href="#conclusion" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Conclusion</h2><p>Congratulations! Using only the table config, we simultaneously consumed Kafka topics directly into Pinot tables and queried events. We also transformed JSON to map to the Pinot table. In the transformConfigs portion of the Pinot table config file, we consumed the nested block meta into a field called metaJson. In the subsequent steps, we referenced the metaJson field with jsonPath to extract fields such as id, stream, domain, and topic.</p><p>Not only does Pinot support easy ingestion from Kafka topics, but it also provides a robust way to transform JSON to OLAP tables.</p><p>In summary, we have:</p><ul><li>Installed and run Kafka</li><li>Consumed events from Wikipedia into Kafka</li><li>Created a real-time table schema and a table in Pinot</li><li>Streamed events from Wikipedia into Pinot tables via Kafka topics</li><li>Run multiple queries</li><li>Performed JSON transformations</li></ul><p>In some upcoming blog posts, we will explore more advanced topics, such as indexes and transformations, not to mention real-time anomaly detection with <a target="_blank" rel="noopener noreferrer" href="https://dev.startree.ai/docs/procedures/get-started-with-thirdeye/">ThirdEye</a>.</p><p>In the meantime, run more queries, load more data, and don’t forget to <a target="_blank" rel="noopener noreferrer" href="https://dev.startree.ai/slack-invite">join the community Slack for support</a> if you get stuck or would like to request a topic for me to write about—you know where to find us!</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight ml-6 font-bold"><a href="#the-obligatory-what-is-apache-pinot-and-startree-section">The obligatory “What is Apache Pinot and StarTree?” section</a></li><li class="text-sm font-normal leading-tight"><a href="#how-to-install-kafka-alongside-pinot">How to install Kafka alongside Pinot </a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#prerequisite">Prerequisite</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#step-1-install-kafka-on-your-pinot-docker-image">Step 1: Install Kafka on your Pinot Docker image</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#step-2-install-kafka-on-the-docker-container">Step 2: Install Kafka on the Docker container</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#step-3-ingest-data-into-kafka">Step 3: Ingest data into Kafka</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#step-4-connect-kafka-to-pinot">Step 4: Connect Kafka to Pinot</a></li><li class="text-sm font-normal leading-tight"><a href="#conclusion">Conclusion</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" 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StarTree offers a \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/saas-signup\",\"children\":\"fully managed version of the Apache Pinot real-time analytics system\"}],\" , plus other tools around it that you can try for free. 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To start the Docker image, run the following command:\"}],[\"$\",\"p\",null,{\"children\":\"docker run -it --entrypoint /bin/bash -p 9000:9000 apachepinot/pinot:0.12.0\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"PowerShell 7.3.4 docker run Apache Pinot\",\"src\":\"https://www.datocms-assets.com/75153/1685462020-image1.png\",\"title\":\"PowerShell 7.3.4 docker run Apache Pinot\"}]}],[\"$\",\"p\",null,{\"children\":\"We want to override the ENTRYPOINT and run Bash script within the Docker image. If you already have a container running, you can skip this step. I tend to tear down containers after use, so in my case, I created a brand new container.\"}],[\"$\",\"p\",null,{\"children\":\"Now, start each of the components one at a time like we did in the previous session:\"}],[\"$\",\"p\",null,{\"children\":\"bin/pinot-admin.sh StartZookeeper \u0026\"}],[\"$\",\"p\",null,{\"children\":\"bin/pinot-admin.sh StartController \u0026\"}],[\"$\",\"p\",null,{\"children\":\"bin/pinot-admin.sh StartBroker \u0026\"}],[\"$\",\"p\",null,{\"children\":\"bin/pinot-admin.sh StartServer \u0026\"}],[\"$\",\"p\",null,{\"children\":\"Run each of the commands one at a time. The \u0026 allows you to continue using the same Bash shell session. If you like, you can create different shells for each service:\"}],[\"$\",\"ol\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Get the container ID by running docker ps\"}],[\"$\",\"li\",null,{\"children\":[\"Run \",[\"$\",\"code\",null,{\"children\":\"docker exec -it DOCKER_CONTAINER_ID bash\"}],\" where DOCKER_CONTAINER_ID is the ID received from step 1.\"]}],[\"$\",\"li\",null,{\"children\":\"Run the pinot-admin.sh command to start the desired service\"}]]}],[\"$\",\"p\",null,{\"children\":\"It should look like this:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Docker with container ID, Image, Command, and Created\",\"src\":\"https://www.datocms-assets.com/75153/1685462274-image7.png\",\"title\":\"Docker with container ID, Image, Command, and Created\"}]}],[\"$\",\"p\",null,{\"children\":[\"You can now browse to \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/#/zookeeper\",\"children\":\"http://localhost:9000/#/zookeeper\"}],\" to see the running cluster:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Empty Zookeeper Browser\",\"src\":\"https://www.datocms-assets.com/75153/1685462203-image5.png\",\"title\":\"Empty Zookeeper Browser\"}]}],[\"$\",\"h3\",null,{\"id\":\"step-2-install-kafka-on-the-docker-container\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#step-2-install-kafka-on-the-docker-container\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Step 2: Install Kafka on the Docker container\"]}],[\"$\",\"p\",null,{\"children\":\"Next, let's install Kafka. We will be installing Kafka on the existing docker container. For this step, download the TAR file, extract the contents, and start Kafka.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"em\",null,{\"children\":\"Apache Kafka is an open source software platform that provides a unified, high-throughput, low-latency platform for handling real-time data feeds.\"}]}],[\"$\",\"p\",null,{\"children\":\"Use the following command to download the Kafka image:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token class-name builtin\",\"children\":\"cd\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"..\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"curl\"}],\" https://downloads.apache.org/kafka/3.4.0/kafka_2.12-3.4.0.tgz \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"--output\"}],\" kafka.tgz \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"--output\"}],\" kafka.tgz\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"It should look this:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Code with Apache Pinot speed results\",\"src\":\"https://www.datocms-assets.com/75153/1685462322-image8.png\",\"title\":\"Code with Apache Pinot speed results\"}]}],[\"$\",\"p\",null,{\"children\":\"Note that we’ve changed the directory to keep the Kafka folder separate from the Pinot folder.\"}],[\"$\",\"p\",null,{\"children\":\"Now, let’s expand the downloaded TAR file, rename the folder for convenience, and delete the downloaded file:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"tar\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-xvf\"}],\" kafka.tgz\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"mv\"}],\" kafka_2.12-3.4.0 kafka\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"rm\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-rf\"}],\" kafka.tgz\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"It should look like this:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Code with Apache Kafka\",\"src\":\"https://www.datocms-assets.com/75153/1685462061-image2.png\",\"title\":\"Code with Apache Kafka\"}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Code with kafka version\",\"src\":\"https://www.datocms-assets.com/75153/1685462480-image12.png\",\"title\":\"Code with kafka version\"}]}],[\"$\",\"p\",null,{\"children\":\"Now, Kafka and Pinot reside locally on our Docker container with Pinot up and running. Let’s run the Kafka service. Kafka will use the existing ZooKeeper for configuration management.\"}],[\"$\",\"p\",null,{\"children\":\"Use the following command to run Kafka:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token class-name builtin\",\"children\":\"cd\"}],\" kafka\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"./bin/kafka-server-start.sh config/server.properties\\n\"}]]}]}],[\"$\",\"p\",null,{\"children\":\"It should look like this:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Code with cd kafka\",\"src\":\"https://www.datocms-assets.com/75153/1685462400-image10.png\",\"title\":\"Code with cd kafka\"}]}],[\"$\",\"p\",null,{\"children\":[\"To verify that Kafka is running, let’s look at our ZooKeeper configs by browsing to \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/#/zookeeper\",\"children\":\"http://localhost:9000/#/zookeeper:\"}]]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Zookeeper Browser\",\"src\":\"https://www.datocms-assets.com/75153/1685462099-image3.png\",\"title\":\"Zookeeper Browser\"}]}],[\"$\",\"p\",null,{\"children\":\"You may have to refresh the page and find many more configuration items appear thanexpectedt. These are Kafka configurations.\"}],[\"$\",\"h3\",null,{\"id\":\"step-3-ingest-data-into-kafka\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#step-3-ingest-data-into-kafka\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Step 3: Ingest data into Kafka\"]}],[\"$\",\"p\",null,{\"children\":\"In this step, we will ingest data into Kafka. We will be using Wikipedia events since they are easily accessible. We will use a node script to ingest the Wikipedia events, then add them to a Kafka Topic.\"}],[\"$\",\"p\",null,{\"children\":\"Let’s first create some folders like this:\"}],[\"$\",\"p\",null,{\"children\":\"cd /opt\"}],[\"$\",\"p\",null,{\"children\":\"mkdir realtime\"}],[\"$\",\"p\",null,{\"children\":\"cd realtime\"}],[\"$\",\"p\",null,{\"children\":\"mkdir events\"}],[\"$\",\"p\",null,{\"children\":\"It should look like this:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Code with realtime\",\"src\":\"https://www.datocms-assets.com/75153/1685462150-image4.png\",\"title\":\"Code with realtime\"}]}],[\"$\",\"p\",null,{\"children\":\"You may have to start a new PowerShell window and connect to Docker for this. Now, let’s install Node.js and any dependencies we might need for the event consumption script:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"curl\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-fsSL\"}],\" https://deb.nodesource.com/setup_14.x \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"bash\"}],\" -\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"apt\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"install\"}],\" nodejs\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Node.js takes a few minutes to install. Next, we will create a script to consume the events called wikievents.js. Cut and paste the following code to this file:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-javascript\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-javascript\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"var\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token maybe-class-name\",\"children\":\"EventSource\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"require\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'eventsource'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"var\"}],\" fs \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"require\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'fs'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"var\"}],\" path \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"require\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'path'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"const\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token maybe-class-name\",\"children\":\"Kafka\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"require\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'kafkajs'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"var\"}],\" url \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'https://stream.wikimedia.org/v2/stream/recentchange'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"const\"}],\" kafka \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"new\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token class-name\",\"children\":\"Kafka\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property literal-property\",\"children\":\"clientId\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'wikievents'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" 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punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword control-flow\",\"children\":\"await\"}],\" producer\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token function property-access method\",\"children\":\"connect\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"startEvents\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"function\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"startEvents\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token class-name console\",\"children\":\"console\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token function property-access method\",\"children\":\"log\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token template-string\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token string template-punctuation\",\"children\":\"`\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"Connecting to EventStreams at \"}],[\"$\",\"span\",null,{\"className\":\"token interpolation\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation interpolation-punctuation\",\"children\":\"$${\"}],\"url\",[\"$\",\"span\",null,{\"className\":\"token punctuation 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console\",\"children\":\"console\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token function property-access method\",\"children\":\"log\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'--- Opened connection.'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" eventSource\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token function property-access method function-variable method-variable\",\"children\":\"onerror\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"function\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token parameter\",\"children\":\"event\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token class-name console\",\"children\":\"console\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token function property-access method\",\"children\":\"error\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'--- Encountered error'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" event\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" eventSource\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token function 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punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"start\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"You can use vi to create the file and save it. You can also use Docker Desktop to edit the file.\"}],[\"$\",\"p\",null,{\"children\":\"To install the two modules referenced in the file above, kafkajs and eventsource, run the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"npm\"}],\" i eventsource kafkajs\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"Let’s run the program. This will result in the download of many files, so I recommend running the program for just a few minutes. You can stop the run by using Ctrl-C.\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"node\"}],\" wikievents.js\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"Use Ctrl-C to stop the program. Navigate to the events folder to see some new folders created with the various language events downloaded from Wikipedia.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Wikievents node in code\",\"src\":\"https://www.datocms-assets.com/75153/1685462366-image9.png\",\"title\":\"Wikievents node in code\"}]}],[\"$\",\"p\",null,{\"children\":\"Navigate to the enwiki folder and review some of the downloaded JSON files.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Code with realtime wikievents\",\"src\":\"https://www.datocms-assets.com/75153/1685462441-image11.png\",\"title\":\"Code with realtime wikievents\"}]}],[\"$\",\"p\",null,{\"children\":[\"At \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/#/zookeeper\",\"children\":\"http://localhost:9000/#/zookeeper\"}],\", you can find the Kafka topic by locating the ZooKeeper config and expanding config \u003e topics. You may have to refresh your browser.\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Zookeeper browser in Apache Pinot topics\",\"src\":\"https://www.datocms-assets.com/75153/1685462510-image13.png\",\"title\":\"Zookeeper browser in Apache Pinot topics\"}]}],[\"$\",\"p\",null,{\"children\":\"Here, you should see the wikipedia-events topic that we created using the Node.js script. So far, so good.\"}],[\"$\",\"h3\",null,{\"id\":\"step-4-connect-kafka-to-pinot\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#step-4-connect-kafka-to-pinot\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Step 4: Connect Kafka to Pinot\"]}],[\"$\",\"p\",null,{\"children\":\"With Kafka installed and configured to receive events, we can connect it to Pinot.\"}],[\"$\",\"p\",null,{\"children\":\"To create a real-time table in Pinot that can consume the Kafka topic, create a schema and a configuration table. The schema configuration is very much like the schema that we created for our batch example. You can use vi to create a file named realtime.schema.json and cut and paste the content below.\"}],[\"$\",\"p\",null,{\"children\":\"Here’s the JSON for the wikievents schema:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schemaName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"wikievents\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" 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'$.topic')\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"isDimTable\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Notice the section called streamConfigs, where we define the source as a Kafka stream, located at localhost:9092, and consume the topic wikipedia-events. That’s all it takes to consume a Kafka Topic into Pinot.\"}],[\"$\",\"p\",null,{\"children\":\"Don’t believe me? Give it a try!\"}],[\"$\",\"p\",null,{\"children\":\"Create the table by running the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"/opt/pinot/bin/pinot-admin.sh AddTable \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-schemaFile\"}],\" /opt/realtime/realtime.schema.json \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-tableConfigFile\"}],\" /opt/realtime/realtime.tableconfig.json \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-exec\"}],\"\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":[\"Now, browse to the following location \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/#/tables\",\"children\":\"http://localhost:9000/#/tables,\"}],\" and you should see the newly created table. However, where’s the real-time data, you say?\"]}],[\"$\",\"p\",null,{\"children\":\"Run the node wikievents.js command, then query the newly created wikievents table to see the totalDocs increase in real time:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot query console\",\"src\":\"https://www.datocms-assets.com/75153/1685462248-image6.png\",\"title\":\"Apache Pinot query console\"}]}],[\"$\",\"p\",null,{\"children\":\"To avoid running out of space on your computer, make sure to stop the wikievents.js script when you’re done :-D\"}],[\"$\",\"h2\",null,{\"id\":\"conclusion\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#conclusion\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Conclusion\"]}],[\"$\",\"p\",null,{\"children\":\"Congratulations! Using only the table config, we simultaneously consumed Kafka topics directly into Pinot tables and queried events. We also transformed JSON to map to the Pinot table. In the transformConfigs portion of the Pinot table config file, we consumed the nested block meta into a field called metaJson. 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+</span></code></pre></div><p>Now, browse to the following location <a target="_blank" rel="noopener noreferrer" href="http://localhost:9000/#/tables">http://localhost:9000/#/tables,</a> and you should see the newly created table. However, where’s the real-time data, you say?</p><p>Run the node wikievents.js command, then query the newly created wikievents table to see the totalDocs increase in real time:</p><p><img alt="Apache Pinot query console" src="https://www.datocms-assets.com/75153/1685462248-image6.png" title="Apache Pinot query console"/></p><p>To avoid running out of space on your computer, make sure to stop the wikievents.js script when you’re done :-D</p><h2 id="conclusion"><a href="#conclusion" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Conclusion</h2><p>Congratulations! Using only the table config, we simultaneously consumed Kafka topics directly into Pinot tables and queried events. We also transformed JSON to map to the Pinot table. In the transformConfigs portion of the Pinot table config file, we consumed the nested block meta into a field called metaJson. In the subsequent steps, we referenced the metaJson field with jsonPath to extract fields such as id, stream, domain, and topic.</p><p>Not only does Pinot support easy ingestion from Kafka topics, but it also provides a robust way to transform JSON to OLAP tables.</p><p>In summary, we have:</p><ul><li>Installed and run Kafka</li><li>Consumed events from Wikipedia into Kafka</li><li>Created a real-time table schema and a table in Pinot</li><li>Streamed events from Wikipedia into Pinot tables via Kafka topics</li><li>Run multiple queries</li><li>Performed JSON transformations</li></ul><p>In some upcoming blog posts, we will explore more advanced topics, such as indexes and transformations, not to mention real-time anomaly detection with <a target="_blank" rel="noopener noreferrer" href="https://dev.startree.ai/docs/procedures/get-started-with-thirdeye/">ThirdEye</a>.</p><p>In the meantime, run more queries, load more data, and don’t forget to <a target="_blank" rel="noopener noreferrer" href="https://dev.startree.ai/slack-invite">join the community Slack for support</a> if you get stuck or would like to request a topic for me to write about—you know where to find us!</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight ml-6 font-bold"><a href="#the-obligatory-what-is-apache-pinot-and-startree-section">The obligatory “What is Apache Pinot and StarTree?” section</a></li><li class="text-sm font-normal leading-tight"><a href="#how-to-install-kafka-alongside-pinot">How to install Kafka alongside Pinot </a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#prerequisite">Prerequisite</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#step-1-install-kafka-on-your-pinot-docker-image">Step 1: Install Kafka on your Pinot Docker image</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#step-2-install-kafka-on-the-docker-container">Step 2: Install Kafka on the Docker container</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#step-3-ingest-data-into-kafka">Step 3: Ingest data into Kafka</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#step-4-connect-kafka-to-pinot">Step 4: Connect Kafka to Pinot</a></li><li class="text-sm font-normal leading-tight"><a href="#conclusion">Conclusion</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" 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StarTree offers a \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/saas-signup\",\"children\":\"fully managed version of the Apache Pinot real-time analytics system\"}],\" , plus other tools around it that you can try for free. 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To start the Docker image, run the following command:\"}],[\"$\",\"p\",null,{\"children\":\"docker run -it --entrypoint /bin/bash -p 9000:9000 apachepinot/pinot:0.12.0\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"PowerShell 7.3.4 docker run Apache Pinot\",\"src\":\"https://www.datocms-assets.com/75153/1685462020-image1.png\",\"title\":\"PowerShell 7.3.4 docker run Apache Pinot\"}]}],[\"$\",\"p\",null,{\"children\":\"We want to override the ENTRYPOINT and run Bash script within the Docker image. If you already have a container running, you can skip this step. I tend to tear down containers after use, so in my case, I created a brand new container.\"}],[\"$\",\"p\",null,{\"children\":\"Now, start each of the components one at a time like we did in the previous session:\"}],[\"$\",\"p\",null,{\"children\":\"bin/pinot-admin.sh StartZookeeper \u0026\"}],[\"$\",\"p\",null,{\"children\":\"bin/pinot-admin.sh StartController \u0026\"}],[\"$\",\"p\",null,{\"children\":\"bin/pinot-admin.sh StartBroker \u0026\"}],[\"$\",\"p\",null,{\"children\":\"bin/pinot-admin.sh StartServer \u0026\"}],[\"$\",\"p\",null,{\"children\":\"Run each of the commands one at a time. The \u0026 allows you to continue using the same Bash shell session. If you like, you can create different shells for each service:\"}],[\"$\",\"ol\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Get the container ID by running docker ps\"}],[\"$\",\"li\",null,{\"children\":[\"Run \",[\"$\",\"code\",null,{\"children\":\"docker exec -it DOCKER_CONTAINER_ID bash\"}],\" where DOCKER_CONTAINER_ID is the ID received from step 1.\"]}],[\"$\",\"li\",null,{\"children\":\"Run the pinot-admin.sh command to start the desired service\"}]]}],[\"$\",\"p\",null,{\"children\":\"It should look like this:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Docker with container ID, Image, Command, and Created\",\"src\":\"https://www.datocms-assets.com/75153/1685462274-image7.png\",\"title\":\"Docker with container ID, Image, Command, and Created\"}]}],[\"$\",\"p\",null,{\"children\":[\"You can now browse to \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/#/zookeeper\",\"children\":\"http://localhost:9000/#/zookeeper\"}],\" to see the running cluster:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Empty Zookeeper Browser\",\"src\":\"https://www.datocms-assets.com/75153/1685462203-image5.png\",\"title\":\"Empty Zookeeper Browser\"}]}],[\"$\",\"h3\",null,{\"id\":\"step-2-install-kafka-on-the-docker-container\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#step-2-install-kafka-on-the-docker-container\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Step 2: Install Kafka on the Docker container\"]}],[\"$\",\"p\",null,{\"children\":\"Next, let's install Kafka. We will be installing Kafka on the existing docker container. For this step, download the TAR file, extract the contents, and start Kafka.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"em\",null,{\"children\":\"Apache Kafka is an open source software platform that provides a unified, high-throughput, low-latency platform for handling real-time data feeds.\"}]}],[\"$\",\"p\",null,{\"children\":\"Use the following command to download the Kafka image:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token class-name builtin\",\"children\":\"cd\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"..\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"curl\"}],\" https://downloads.apache.org/kafka/3.4.0/kafka_2.12-3.4.0.tgz \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"--output\"}],\" kafka.tgz \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"--output\"}],\" kafka.tgz\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"It should look this:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Code with Apache Pinot speed results\",\"src\":\"https://www.datocms-assets.com/75153/1685462322-image8.png\",\"title\":\"Code with Apache Pinot speed results\"}]}],[\"$\",\"p\",null,{\"children\":\"Note that we’ve changed the directory to keep the Kafka folder separate from the Pinot folder.\"}],[\"$\",\"p\",null,{\"children\":\"Now, let’s expand the downloaded TAR file, rename the folder for convenience, and delete the downloaded file:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"tar\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-xvf\"}],\" kafka.tgz\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"mv\"}],\" kafka_2.12-3.4.0 kafka\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"rm\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-rf\"}],\" kafka.tgz\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"It should look like this:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Code with Apache Kafka\",\"src\":\"https://www.datocms-assets.com/75153/1685462061-image2.png\",\"title\":\"Code with Apache Kafka\"}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Code with kafka version\",\"src\":\"https://www.datocms-assets.com/75153/1685462480-image12.png\",\"title\":\"Code with kafka version\"}]}],[\"$\",\"p\",null,{\"children\":\"Now, Kafka and Pinot reside locally on our Docker container with Pinot up and running. Let’s run the Kafka service. Kafka will use the existing ZooKeeper for configuration management.\"}],[\"$\",\"p\",null,{\"children\":\"Use the following command to run Kafka:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token class-name builtin\",\"children\":\"cd\"}],\" kafka\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"./bin/kafka-server-start.sh config/server.properties\\n\"}]]}]}],[\"$\",\"p\",null,{\"children\":\"It should look like this:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Code with cd kafka\",\"src\":\"https://www.datocms-assets.com/75153/1685462400-image10.png\",\"title\":\"Code with cd kafka\"}]}],[\"$\",\"p\",null,{\"children\":[\"To verify that Kafka is running, let’s look at our ZooKeeper configs by browsing to \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/#/zookeeper\",\"children\":\"http://localhost:9000/#/zookeeper:\"}]]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Zookeeper Browser\",\"src\":\"https://www.datocms-assets.com/75153/1685462099-image3.png\",\"title\":\"Zookeeper Browser\"}]}],[\"$\",\"p\",null,{\"children\":\"You may have to refresh the page and find many more configuration items appear thanexpectedt. These are Kafka configurations.\"}],[\"$\",\"h3\",null,{\"id\":\"step-3-ingest-data-into-kafka\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#step-3-ingest-data-into-kafka\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Step 3: Ingest data into Kafka\"]}],[\"$\",\"p\",null,{\"children\":\"In this step, we will ingest data into Kafka. We will be using Wikipedia events since they are easily accessible. We will use a node script to ingest the Wikipedia events, then add them to a Kafka Topic.\"}],[\"$\",\"p\",null,{\"children\":\"Let’s first create some folders like this:\"}],[\"$\",\"p\",null,{\"children\":\"cd /opt\"}],[\"$\",\"p\",null,{\"children\":\"mkdir realtime\"}],[\"$\",\"p\",null,{\"children\":\"cd realtime\"}],[\"$\",\"p\",null,{\"children\":\"mkdir events\"}],[\"$\",\"p\",null,{\"children\":\"It should look like this:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Code with realtime\",\"src\":\"https://www.datocms-assets.com/75153/1685462150-image4.png\",\"title\":\"Code with realtime\"}]}],[\"$\",\"p\",null,{\"children\":\"You may have to start a new PowerShell window and connect to Docker for this. Now, let’s install Node.js and any dependencies we might need for the event consumption script:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"curl\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-fsSL\"}],\" https://deb.nodesource.com/setup_14.x \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"|\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"bash\"}],\" -\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"apt\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"install\"}],\" nodejs\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Node.js takes a few minutes to install. Next, we will create a script to consume the events called wikievents.js. Cut and paste the following code to this file:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-javascript\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-javascript\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"var\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token maybe-class-name\",\"children\":\"EventSource\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"require\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'eventsource'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"var\"}],\" fs \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"require\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'fs'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"var\"}],\" path \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"require\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'path'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"const\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token maybe-class-name\",\"children\":\"Kafka\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"require\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'kafkajs'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"var\"}],\" url \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'https://stream.wikimedia.org/v2/stream/recentchange'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"const\"}],\" kafka \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"new\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token class-name\",\"children\":\"Kafka\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property literal-property\",\"children\":\"clientId\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'wikievents'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" 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punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token keyword control-flow\",\"children\":\"await\"}],\" producer\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token function property-access method\",\"children\":\"connect\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"startEvents\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"function\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"startEvents\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token class-name console\",\"children\":\"console\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token function property-access method\",\"children\":\"log\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token template-string\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token string template-punctuation\",\"children\":\"`\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"Connecting to EventStreams at \"}],[\"$\",\"span\",null,{\"className\":\"token interpolation\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation interpolation-punctuation\",\"children\":\"$${\"}],\"url\",[\"$\",\"span\",null,{\"className\":\"token punctuation 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console\",\"children\":\"console\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token function property-access method\",\"children\":\"log\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'--- Opened connection.'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" eventSource\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token function property-access method function-variable method-variable\",\"children\":\"onerror\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"function\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token parameter\",\"children\":\"event\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token class-name console\",\"children\":\"console\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token function property-access method\",\"children\":\"error\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'--- Encountered error'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" event\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" eventSource\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],[\"$\",\"span\",null,{\"className\":\"token function 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punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"start\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"You can use vi to create the file and save it. You can also use Docker Desktop to edit the file.\"}],[\"$\",\"p\",null,{\"children\":\"To install the two modules referenced in the file above, kafkajs and eventsource, run the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"npm\"}],\" i eventsource kafkajs\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"Let’s run the program. This will result in the download of many files, so I recommend running the program for just a few minutes. You can stop the run by using Ctrl-C.\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"node\"}],\" wikievents.js\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"Use Ctrl-C to stop the program. Navigate to the events folder to see some new folders created with the various language events downloaded from Wikipedia.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Wikievents node in code\",\"src\":\"https://www.datocms-assets.com/75153/1685462366-image9.png\",\"title\":\"Wikievents node in code\"}]}],[\"$\",\"p\",null,{\"children\":\"Navigate to the enwiki folder and review some of the downloaded JSON files.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Code with realtime wikievents\",\"src\":\"https://www.datocms-assets.com/75153/1685462441-image11.png\",\"title\":\"Code with realtime wikievents\"}]}],[\"$\",\"p\",null,{\"children\":[\"At \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/#/zookeeper\",\"children\":\"http://localhost:9000/#/zookeeper\"}],\", you can find the Kafka topic by locating the ZooKeeper config and expanding config \u003e topics. You may have to refresh your browser.\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Zookeeper browser in Apache Pinot topics\",\"src\":\"https://www.datocms-assets.com/75153/1685462510-image13.png\",\"title\":\"Zookeeper browser in Apache Pinot topics\"}]}],[\"$\",\"p\",null,{\"children\":\"Here, you should see the wikipedia-events topic that we created using the Node.js script. So far, so good.\"}],[\"$\",\"h3\",null,{\"id\":\"step-4-connect-kafka-to-pinot\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#step-4-connect-kafka-to-pinot\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Step 4: Connect Kafka to Pinot\"]}],[\"$\",\"p\",null,{\"children\":\"With Kafka installed and configured to receive events, we can connect it to Pinot.\"}],[\"$\",\"p\",null,{\"children\":\"To create a real-time table in Pinot that can consume the Kafka topic, create a schema and a configuration table. The schema configuration is very much like the schema that we created for our batch example. You can use vi to create a file named realtime.schema.json and cut and paste the content below.\"}],[\"$\",\"p\",null,{\"children\":\"Here’s the JSON for the wikievents schema:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schemaName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"wikievents\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" 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'$.topic')\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"isDimTable\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token boolean\",\"children\":\"false\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Notice the section called streamConfigs, where we define the source as a Kafka stream, located at localhost:9092, and consume the topic wikipedia-events. That’s all it takes to consume a Kafka Topic into Pinot.\"}],[\"$\",\"p\",null,{\"children\":\"Don’t believe me? Give it a try!\"}],[\"$\",\"p\",null,{\"children\":\"Create the table by running the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-bash code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"/opt/pinot/bin/pinot-admin.sh AddTable \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-schemaFile\"}],\" /opt/realtime/realtime.schema.json \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-tableConfigFile\"}],\" /opt/realtime/realtime.tableconfig.json \",[\"$\",\"span\",null,{\"className\":\"token parameter variable\",\"children\":\"-exec\"}],\"\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":[\"Now, browse to the following location \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/#/tables\",\"children\":\"http://localhost:9000/#/tables,\"}],\" and you should see the newly created table. However, where’s the real-time data, you say?\"]}],[\"$\",\"p\",null,{\"children\":\"Run the node wikievents.js command, then query the newly created wikievents table to see the totalDocs increase in real time:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot query console\",\"src\":\"https://www.datocms-assets.com/75153/1685462248-image6.png\",\"title\":\"Apache Pinot query console\"}]}],[\"$\",\"p\",null,{\"children\":\"To avoid running out of space on your computer, make sure to stop the wikievents.js script when you’re done :-D\"}],[\"$\",\"h2\",null,{\"id\":\"conclusion\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#conclusion\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Conclusion\"]}],[\"$\",\"p\",null,{\"children\":\"Congratulations! Using only the table config, we simultaneously consumed Kafka topics directly into Pinot tables and queried events. We also transformed JSON to map to the Pinot table. In the transformConfigs portion of the Pinot table config file, we consumed the nested block meta into a field called metaJson. In the subsequent steps, we referenced the metaJson field with jsonPath to extract fields such as id, stream, domain, and topic.\"}],[\"$\",\"p\",null,{\"children\":\"Not only does Pinot support easy ingestion from Kafka topics, but it also provides a robust way to transform JSON to OLAP tables.\"}],[\"$\",\"p\",null,{\"children\":\"In summary, we have:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Installed and run Kafka\"}],[\"$\",\"li\",null,{\"children\":\"Consumed events from Wikipedia into Kafka\"}],[\"$\",\"li\",null,{\"children\":\"Created a real-time table schema and a table in Pinot\"}],[\"$\",\"li\",null,{\"children\":\"Streamed events from Wikipedia into Pinot tables via Kafka topics\"}],[\"$\",\"li\",null,{\"children\":\"Run multiple queries\"}],[\"$\",\"li\",null,{\"children\":\"Performed JSON transformations\"}]]}],[\"$\",\"p\",null,{\"children\":[\"In some upcoming blog posts, we will explore more advanced topics, such as indexes and transformations, not to mention real-time anomaly detection with \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://dev.startree.ai/docs/procedures/get-started-with-thirdeye/\",\"children\":\"ThirdEye\"}],\".\"]}],[\"$\",\"p\",null,{\"children\":[\"In the meantime, run more queries, load more data, and don’t forget to \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://dev.startree.ai/slack-invite\",\"children\":\"join the community Slack for support\"}],\" if you get stuck or would like to request a topic for me to write about—you know where to find us!\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"The obligatory “What is Apache Pinot and StarTree?” section\",\"url\":\"#the-obligatory-what-is-apache-pinot-and-startree-section\",\"depth\":3},{\"value\":\"How to install Kafka alongside Pinot \",\"url\":\"#how-to-install-kafka-alongside-pinot\",\"depth\":2},{\"value\":\"Prerequisite\",\"url\":\"#prerequisite\",\"depth\":3},{\"value\":\"Step 1: Install Kafka on your Pinot Docker image\",\"url\":\"#step-1-install-kafka-on-your-pinot-docker-image\",\"depth\":3},{\"value\":\"Step 2: Install Kafka on the Docker container\",\"url\":\"#step-2-install-kafka-on-the-docker-container\",\"depth\":3},{\"value\":\"Step 3: Ingest data into Kafka\",\"url\":\"#step-3-ingest-data-into-kafka\",\"depth\":3},{\"value\":\"Step 4: Connect Kafka to Pinot\",\"url\":\"#step-4-connect-kafka-to-pinot\",\"depth\":3},{\"value\":\"Conclusion\",\"url\":\"#conclusion\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"How to Ingest Streaming Data from Kafka to Apache Pinot™ | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"The blog post explains how to use Apache Kafka topics in Apache Pinot to ingest streaming data, with step-by-step instructions provided for installation and setup. 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diff --git a/blog/2023/05/30/how-to-ingest-streaming-data-from-kafka-to-apache-pinot.txt b/blog/2023/05/30/how-to-ingest-streaming-data-from-kafka-to-apache-pinot.txt
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diff --git a/blog/2023/06/01/real-time-mastodon-usage-with-apache-kafka-apache-pinot-and-streamlit.html b/blog/2023/06/01/real-time-mastodon-usage-with-apache-kafka-apache-pinot-and-streamlit.html
index 3029ebc..face619 100644
--- a/blog/2023/06/01/real-time-mastodon-usage-with-apache-kafka-apache-pinot-and-streamlit.html
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@@ -125,4 +125,4 @@
</span><span class="code-line">df <span class="token operator">=</span> pd<span class="token punctuation">.</span>DataFrame<span class="token punctuation">(</span>curs<span class="token punctuation">,</span> columns<span class="token operator">=</span><span class="token punctuation">[</span>item<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span> <span class="token keyword">for</span> item <span class="token keyword">in</span> curs<span class="token punctuation">.</span>description<span class="token punctuation">]</span><span class="token punctuation">)</span>
</span></code></pre></div><p>This produces the resulting DataFrame:</p><div class="relative"><pre><code class="code-highlight language-js"><span class="code-line"> <span class="token maybe-class-name">Num</span> toots <span class="token maybe-class-name">Num</span> users <span class="token maybe-class-name">Num</span> urls
</span><span class="code-line"><span class="token number">0</span> <span class="token number">552</span> <span class="token number">173</span> <span class="token number">192</span>
-</span></code></pre></div><h2 id="streamlit"><a href="#streamlit" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Streamlit</h2><p>Next, we’ll create a Streamlit dashboard to package up these queries. We’ll visualize the results using Plotly, which you can install using:</p><p>pip install streamlit plotly</p><p>I’ve created a Streamlit app in the file <a target="_blank" rel="noopener noreferrer" href="https://github.com/mneedham/mastodon-stream/blob/main/app.py">app.py</a>, which you can find in the GitHub repository. Let’s have a look at the kinds of visualizations that we can generate.</p><p>First, we’ll create metrics to show the number of toots, users, and URLs in the last <em>n</em> minutes. <em>n</em> will be configurable from the app as shown in the screenshot below:</p><p><img alt="Chart of real-time Mastodon usage" src="https://www.datocms-assets.com/75153/1685637876-image7.png" title="Chart of real-time Mastodon usage"/></p><p>From the screenshot, we can identify mastodon.cloud as the most active server, though it produces only 1,800 messages in 10 minutes or three messages per second. The values in green indicate the change in values compared to the previous 10 minutes.</p><p>We can also create a chart showing the number of messages per minute for the last 10 minutes:</p><p><img alt="Time of day Mastodon usage" src="https://www.datocms-assets.com/75153/1685637945-image9.png" title="Time of day Mastodon usage"/></p><p>Based on this chart, we can see that we’re creating anywhere from 200–900 messages per second. Part of the reason lies in the fact that the Mastodon servers sometimes disconnect our listener, and at the moment, I have to manually reconnect.</p><p>Finally, we can look at the toot length by language:</p><p><img alt="Chart of toot length by language usage" src="https://www.datocms-assets.com/75153/1685637644-image2.png" title="Chart of toot length by language usage"/></p><p>We see much bigger ranges here than Simon saw in his analysis. He saw a maximum length of 200 characters, whereas we see some messages of up to 4,200 characters.</p><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>We hope you enjoyed following along as we explored this fun use case for <a target="_blank" rel="noopener noreferrer" href="https://startree.ai/resources/what-is-real-time-analytics">real-time analytics</a>. As you can see, even though we’re pulling the data from many of the popular Mastodon servers, it’s still not all that much data!</p><p>Give the code a try and let us know how it goes. If you have any questions, feel free to <a target="_blank" rel="noopener noreferrer" href="https://stree.ai/slack">join us on Slack</a>, where we’ll gladly do our best to help you out.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#the-existing-solution-kafka-connect-parquet-seaborn-and-duckdb">The Existing Solution: Kafka Connect, Parquet, Seaborn and DuckDB </a></li><li class="text-sm font-normal leading-tight"><a href="#going-real-time-with-apache-pinot">Going Real-Time with Apache Pinot™</a></li><li class="text-sm font-normal leading-tight"><a href="#setup">Setup</a></li><li class="text-sm font-normal leading-tight"><a href="#pinot-schema-and-table">Pinot Schema and Table</a></li><li class="text-sm font-normal leading-tight"><a href="#ingest-data-into-kafka">Ingest Data into Kafka</a></li><li class="text-sm font-normal leading-tight"><a href="#query-pinot">Query Pinot</a></li><li class="text-sm font-normal leading-tight"><a href="#streamlit">Streamlit</a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 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He then wrote a Kafka Connect configuration that consumes messages from Kafka and flushes them after every 1,000 messages into Apache Parquet files stored in an Amazon S3 bucket.\"}],[\"$\",\"p\",null,{\"children\":\"Finally, he queried those Parquet files using DuckDB and created some charts using the Seaborn library, as reflected in the architecture diagram below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Flowchart of data collection to data processing\",\"src\":\"https://www.datocms-assets.com/75153/1685637607-image1.png\",\"title\":\"Flowchart of data collection to data processing\"}]}],[\"$\",\"p\",null,{\"children\":[\"Fig: \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://simonaubury.com/posts/202302_mastodon_duckdb/\",\"children\":\"Data Collection Architecture\"}]]}],[\"$\",\"p\",null,{\"children\":\"The awesome visualizations that Simon created make me wonder whether we can change what happens downstream of Kafka to make our queries even more real-time. Let’s find out!\"}],[\"$\",\"h2\",null,{\"id\":\"going-real-time-with-apache-pinot\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#going-real-time-with-apache-pinot\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Going Real-Time with Apache Pinot™\"]}],[\"$\",\"p\",null,{\"children\":[\"Now \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/resources/what-is-apache-pinot\",\"children\":\"Apache Pinot\"}],\" comes into the picture. Instead of using Kafka Connect to batch Mastodon toots into groups of 1,000 messages to generate Parquet files, we can stream the data immediately and directly, toot-by-toot into Pinot and then build a real-time dashboard using Streamlit:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Data collection in Mastodon, followed by processing in Apache Kafka, Apache Pinot, and Streamlit\",\"src\":\"https://www.datocms-assets.com/75153/1685637507-image4.png\",\"title\":\"Data collection in Mastodon, followed by processing in Apache Kafka, Apache Pinot, and Streamlit\"}]}],[\"$\",\"h2\",null,{\"id\":\"setup\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#setup\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Setup\"]}],[\"$\",\"p\",null,{\"children\":\"To follow along, first clone my fork of Simon’s GitHub repository:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"git\"}],\" clone git@github.com:mneedham/mastodon-stream.git\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token builtin class-name\",\"children\":\"cd\"}],\" mastodon-stream\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Then launch all of the components using Docker Compose:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker-compose\"}],\" up\\n\"]}]}]}],[\"$\",\"h2\",null,{\"id\":\"pinot-schema-and-table\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#pinot-schema-and-table\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Pinot Schema and Table\"]}],[\"$\",\"p\",null,{\"children\":\"Similar to what Simon did with DuckDB, we’ll ingest the Mastodon events into a table. Pinot tables have a schema that’s defined in a schema file.\"}],[\"$\",\"p\",null,{\"children\":\"To come up with a schema file, we need to know the structure of the ingested events. 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property\",\"children\":\"\\\"dateTimeFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"created_at\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token 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\",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Next up: our table config, shown below:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"mastodon\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"REALTIME\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"segmentsConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"timeColumnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"created_at\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"timeType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MILLISECONDS\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schemaName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"mastodon\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"replicasPerPartition\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tenants\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableIndexConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"loadMode\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MMAP\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"streamConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"streamType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"kafka\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.type\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"lowLevel\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.topic.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"mastodon-topic\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.inputformat.avro.confluent.KafkaConfluentSchemaRegistryAvroMessageDecoder\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.factory.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.prop.format\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"AVRO\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.prop.schema.registry.rest.url\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://schema-registry:8081\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.prop.schema.registry.schema.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"mastodon-topic-value\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.broker.list\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"broker:9093\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.prop.auto.offset.reset\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"smallest\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metadata\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"customConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"routing\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"instanceSelectorType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"strictReplicaGroup\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The following configs represent the most important ones for ingesting Apache Avro™ messages into Pinot:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.inputformat.avro.confluent.KafkaConfluentSchemaRegistryAvroMessageDecoder\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.prop.format\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"AVRO\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.prop.schema.registry.rest.url\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://schema-registry:8081\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.prop.schema.registry.schema.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"mastodon-topic-value\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The KafkaConfluentSchemaRegistryAvroMessageDecoder decoder calls the Schema Registry with the schema name to get back the schema that it will use to decode messages.\"}],[\"$\",\"p\",null,{\"children\":\"We can create the Pinot table by running the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--network\"}],\" mastodon \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/pinot:/config \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.12.0-arm64 AddTable \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-schemaFile\"}],\" /config/schema.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-tableConfigFile\"}],\" /config/table.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-controllerHost\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"pinot-controller\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-exec\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"We can then navigate to the table page of the Pinot UI:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/#/tenants/table/mastodon_REALTIME\",\"children\":\"http://localhost:9000/#/tenants/table/mastodon_REALTIME\"}]}],[\"$\",\"p\",null,{\"children\":\"Here, we’ll see the following:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot table config and schema\",\"src\":\"https://www.datocms-assets.com/75153/1685637837-image6.png\",\"title\":\"Apache Pinot table config and schema\"}]}],[\"$\",\"h2\",null,{\"id\":\"ingest-data-into-kafka\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#ingest-data-into-kafka\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Ingest Data into Kafka\"]}],[\"$\",\"p\",null,{\"children\":\"Now, we need to start ingesting data into Kafka. Simon created a script that accomplishes this for us, so we just need to indicate which Mastodon servers to query.\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"python mastodonlisten.py \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--baseURL\"}],\" https://data-folks.masto.host \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--public\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--enableKafka\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--quiet\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"python mastodonlisten.py \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--baseURL\"}],\" https://fosstodon.org/ \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--public\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--enableKafka\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--quiet\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"python mastodonlisten.py \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--baseURL\"}],\" https://mstdn.social/ \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--public\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--enableKafka\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--quiet\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"We can then check the ingestion of messages with the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.confluent.io/platform/current/clients/kafkacat-usage.html\",\"children\":\"kcat\"}],\" command line tool:\"]}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"kcat \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-C\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-b\"}],\" localhost:9092 \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-t\"}],\" mastodon-topic \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-s\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable assign-left\",\"children\":\"value\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"avro \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-r\"}],\" http://localhost:8081 \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-e\"}],\"\\n\"]}]]}]}],[\"$\",\"h2\",null,{\"id\":\"query-pinot\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#query-pinot\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Query Pinot\"]}],[\"$\",\"p\",null,{\"children\":\"Now, let’s go to the Pinot UI to see what data we’ve got to play with:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/\",\"children\":\"http://localhost:9000\"}]}],[\"$\",\"p\",null,{\"children\":\"We’ll see the following preview of the data in the mastodon table:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"SQL Editor, query response stats, and query result in Apache Pinot\",\"src\":\"https://www.datocms-assets.com/75153/1685637772-image5.png\",\"title\":\"SQL Editor, query response stats, and query result in Apache Pinot\"}]}],[\"$\",\"p\",null,{\"children\":\"We can then write a query to find the number of messages posted in the last five minutes:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Num toots\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"distinct\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"username\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Num users\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"distinct\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"url\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Num urls\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" mastodon\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"where\"}],\" created_at\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1000\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e\"}],\" ago\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'PT1M'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"order\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"DESC\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Query results for toots, users, and urls\",\"src\":\"https://www.datocms-assets.com/75153/1685637909-image8.png\",\"title\":\"Query results for toots, users, and urls\"}]}],[\"$\",\"p\",null,{\"children\":\"We can also query Pinot via the Python client, which we can install by running the following:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"pip \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"install\"}],\" pinotdb\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"Once we’ve done that, let’s open the Python REPL and run the following code:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-python\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-python\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" pinotdb \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"import\"}],\" connect\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"import\"}],\" pandas \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" pd\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"conn \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" connect\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"host\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'localhost'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" port\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"8099\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" path\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'/query/sql'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" scheme\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'http'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"curs \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" conn\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"cursor\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"st\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"header\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Daily Mastodon Usage\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"query \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string triple-quoted-string\",\"children\":\"\\\"\\\"\\\"\\n\"}]]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string triple-quoted-string\",\"children\":\"select count(*) as \\\"Num toots\\\"\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string triple-quoted-string\",\"children\":\", count(distinct(username)) as \\\"Num users\\\"\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string triple-quoted-string\",\"children\":\", count(distinct(url)) as \\\"Num urls\\\"\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string triple-quoted-string\",\"children\":\"from mastodon\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string triple-quoted-string\",\"children\":\"where created_at*1000 \u003e ago('PT1M')\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string triple-quoted-string\",\"children\":\"order by 1 DESC;\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token string triple-quoted-string\",\"children\":\"\\\"\\\"\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"curs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"execute\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"query\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"df \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" pd\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"DataFrame\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"curs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" columns\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"item\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"0\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"for\"}],\" item \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"in\"}],\" curs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"description\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"This produces the resulting DataFrame:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-js\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-js\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token maybe-class-name\",\"children\":\"Num\"}],\" toots \",[\"$\",\"span\",null,{\"className\":\"token maybe-class-name\",\"children\":\"Num\"}],\" users \",[\"$\",\"span\",null,{\"className\":\"token maybe-class-name\",\"children\":\"Num\"}],\" urls\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"0\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"552\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"173\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"192\"}],\"\\n\"]}]]}]}],[\"$\",\"h2\",null,{\"id\":\"streamlit\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#streamlit\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Streamlit\"]}],[\"$\",\"p\",null,{\"children\":\"Next, we’ll create a Streamlit dashboard to package up these queries. We’ll visualize the results using Plotly, which you can install using:\"}],[\"$\",\"p\",null,{\"children\":\"pip install streamlit plotly\"}],[\"$\",\"p\",null,{\"children\":[\"I’ve created a Streamlit app in the file \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/mneedham/mastodon-stream/blob/main/app.py\",\"children\":\"app.py\"}],\", which you can find in the GitHub repository. Let’s have a look at the kinds of visualizations that we can generate.\"]}],[\"$\",\"p\",null,{\"children\":[\"First, we’ll create metrics to show the number of toots, users, and URLs in the last \",[\"$\",\"em\",null,{\"children\":\"n\"}],\" minutes. \",[\"$\",\"em\",null,{\"children\":\"n\"}],\" will be configurable from the app as shown in the screenshot below:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Chart of real-time Mastodon usage\",\"src\":\"https://www.datocms-assets.com/75153/1685637876-image7.png\",\"title\":\"Chart of real-time Mastodon usage\"}]}],[\"$\",\"p\",null,{\"children\":\"From the screenshot, we can identify mastodon.cloud as the most active server, though it produces only 1,800 messages in 10 minutes or three messages per second. The values in green indicate the change in values compared to the previous 10 minutes.\"}],[\"$\",\"p\",null,{\"children\":\"We can also create a chart showing the number of messages per minute for the last 10 minutes:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Time of day Mastodon usage\",\"src\":\"https://www.datocms-assets.com/75153/1685637945-image9.png\",\"title\":\"Time of day Mastodon usage\"}]}],[\"$\",\"p\",null,{\"children\":\"Based on this chart, we can see that we’re creating anywhere from 200–900 messages per second. Part of the reason lies in the fact that the Mastodon servers sometimes disconnect our listener, and at the moment, I have to manually reconnect.\"}],[\"$\",\"p\",null,{\"children\":\"Finally, we can look at the toot length by language:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Chart of toot length by language usage\",\"src\":\"https://www.datocms-assets.com/75153/1685637644-image2.png\",\"title\":\"Chart of toot length by language usage\"}]}],[\"$\",\"p\",null,{\"children\":\"We see much bigger ranges here than Simon saw in his analysis. He saw a maximum length of 200 characters, whereas we see some messages of up to 4,200 characters.\"}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":[\"We hope you enjoyed following along as we explored this fun use case for \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/resources/what-is-real-time-analytics\",\"children\":\"real-time analytics\"}],\". As you can see, even though we’re pulling the data from many of the popular Mastodon servers, it’s still not all that much data!\"]}],[\"$\",\"p\",null,{\"children\":[\"Give the code a try and let us know how it goes. If you have any questions, feel free to \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://stree.ai/slack\",\"children\":\"join us on Slack\"}],\", where we’ll gladly do our best to help you out.\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"The Existing Solution: Kafka Connect, Parquet, Seaborn and DuckDB \",\"url\":\"#the-existing-solution-kafka-connect-parquet-seaborn-and-duckdb\",\"depth\":2},{\"value\":\"Going Real-Time with Apache Pinot™\",\"url\":\"#going-real-time-with-apache-pinot\",\"depth\":2},{\"value\":\"Setup\",\"url\":\"#setup\",\"depth\":2},{\"value\":\"Pinot Schema and Table\",\"url\":\"#pinot-schema-and-table\",\"depth\":2},{\"value\":\"Ingest Data into Kafka\",\"url\":\"#ingest-data-into-kafka\",\"depth\":2},{\"value\":\"Query Pinot\",\"url\":\"#query-pinot\",\"depth\":2},{\"value\":\"Streamlit\",\"url\":\"#streamlit\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Real-Time Mastodon Usage with Apache Kafka, Apache Pinot, and Streamlit | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"The blog post discusses analyzing user activity and server popularity on Mastodon using Kafka Connect, Parquet, Seaborn, and DuckDB. 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\ No newline at end of file
+</span></code></pre></div><h2 id="streamlit"><a href="#streamlit" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Streamlit</h2><p>Next, we’ll create a Streamlit dashboard to package up these queries. We’ll visualize the results using Plotly, which you can install using:</p><p>pip install streamlit plotly</p><p>I’ve created a Streamlit app in the file <a target="_blank" rel="noopener noreferrer" href="https://github.com/mneedham/mastodon-stream/blob/main/app.py">app.py</a>, which you can find in the GitHub repository. Let’s have a look at the kinds of visualizations that we can generate.</p><p>First, we’ll create metrics to show the number of toots, users, and URLs in the last <em>n</em> minutes. <em>n</em> will be configurable from the app as shown in the screenshot below:</p><p><img alt="Chart of real-time Mastodon usage" src="https://www.datocms-assets.com/75153/1685637876-image7.png" title="Chart of real-time Mastodon usage"/></p><p>From the screenshot, we can identify mastodon.cloud as the most active server, though it produces only 1,800 messages in 10 minutes or three messages per second. The values in green indicate the change in values compared to the previous 10 minutes.</p><p>We can also create a chart showing the number of messages per minute for the last 10 minutes:</p><p><img alt="Time of day Mastodon usage" src="https://www.datocms-assets.com/75153/1685637945-image9.png" title="Time of day Mastodon usage"/></p><p>Based on this chart, we can see that we’re creating anywhere from 200–900 messages per second. Part of the reason lies in the fact that the Mastodon servers sometimes disconnect our listener, and at the moment, I have to manually reconnect.</p><p>Finally, we can look at the toot length by language:</p><p><img alt="Chart of toot length by language usage" src="https://www.datocms-assets.com/75153/1685637644-image2.png" title="Chart of toot length by language usage"/></p><p>We see much bigger ranges here than Simon saw in his analysis. He saw a maximum length of 200 characters, whereas we see some messages of up to 4,200 characters.</p><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>We hope you enjoyed following along as we explored this fun use case for <a target="_blank" rel="noopener noreferrer" href="https://startree.ai/resources/what-is-real-time-analytics">real-time analytics</a>. As you can see, even though we’re pulling the data from many of the popular Mastodon servers, it’s still not all that much data!</p><p>Give the code a try and let us know how it goes. If you have any questions, feel free to <a target="_blank" rel="noopener noreferrer" href="https://stree.ai/slack">join us on Slack</a>, where we’ll gladly do our best to help you out.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#the-existing-solution-kafka-connect-parquet-seaborn-and-duckdb">The Existing Solution: Kafka Connect, Parquet, Seaborn and DuckDB </a></li><li class="text-sm font-normal leading-tight"><a href="#going-real-time-with-apache-pinot">Going Real-Time with Apache Pinot™</a></li><li class="text-sm font-normal leading-tight"><a href="#setup">Setup</a></li><li class="text-sm font-normal leading-tight"><a href="#pinot-schema-and-table">Pinot Schema and Table</a></li><li class="text-sm font-normal leading-tight"><a href="#ingest-data-into-kafka">Ingest Data into Kafka</a></li><li class="text-sm font-normal leading-tight"><a href="#query-pinot">Query Pinot</a></li><li class="text-sm font-normal leading-tight"><a href="#streamlit">Streamlit</a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 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He then wrote a Kafka Connect configuration that consumes messages from Kafka and flushes them after every 1,000 messages into Apache Parquet files stored in an Amazon S3 bucket.\"}],[\"$\",\"p\",null,{\"children\":\"Finally, he queried those Parquet files using DuckDB and created some charts using the Seaborn library, as reflected in the architecture diagram below:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Flowchart of data collection to data processing\",\"src\":\"https://www.datocms-assets.com/75153/1685637607-image1.png\",\"title\":\"Flowchart of data collection to data processing\"}]}],[\"$\",\"p\",null,{\"children\":[\"Fig: \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://simonaubury.com/posts/202302_mastodon_duckdb/\",\"children\":\"Data Collection Architecture\"}]]}],[\"$\",\"p\",null,{\"children\":\"The awesome visualizations that Simon created make me wonder whether we can change what happens downstream of Kafka to make our queries even more real-time. Let’s find out!\"}],[\"$\",\"h2\",null,{\"id\":\"going-real-time-with-apache-pinot\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#going-real-time-with-apache-pinot\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Going Real-Time with Apache Pinot™\"]}],[\"$\",\"p\",null,{\"children\":[\"Now \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/resources/what-is-apache-pinot\",\"children\":\"Apache Pinot\"}],\" comes into the picture. Instead of using Kafka Connect to batch Mastodon toots into groups of 1,000 messages to generate Parquet files, we can stream the data immediately and directly, toot-by-toot into Pinot and then build a real-time dashboard using Streamlit:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Data collection in Mastodon, followed by processing in Apache Kafka, Apache Pinot, and Streamlit\",\"src\":\"https://www.datocms-assets.com/75153/1685637507-image4.png\",\"title\":\"Data collection in Mastodon, followed by processing in Apache Kafka, Apache Pinot, and Streamlit\"}]}],[\"$\",\"h2\",null,{\"id\":\"setup\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#setup\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Setup\"]}],[\"$\",\"p\",null,{\"children\":\"To follow along, first clone my fork of Simon’s GitHub repository:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"git\"}],\" clone git@github.com:mneedham/mastodon-stream.git\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token builtin class-name\",\"children\":\"cd\"}],\" mastodon-stream\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Then launch all of the components using Docker Compose:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker-compose\"}],\" up\\n\"]}]}]}],[\"$\",\"h2\",null,{\"id\":\"pinot-schema-and-table\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#pinot-schema-and-table\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Pinot Schema and Table\"]}],[\"$\",\"p\",null,{\"children\":\"Similar to what Simon did with DuckDB, we’ll ingest the Mastodon events into a table. Pinot tables have a schema that’s defined in a schema file.\"}],[\"$\",\"p\",null,{\"children\":\"To come up with a schema file, we need to know the structure of the ingested events. 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property\",\"children\":\"\\\"dateTimeFieldSpecs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"created_at\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token 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\",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1:MILLISECONDS\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Next up: our table config, shown below:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"mastodon\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"REALTIME\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"segmentsConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"timeColumnName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"created_at\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"timeType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MILLISECONDS\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schemaName\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"mastodon\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"replicasPerPartition\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"1\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tenants\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableIndexConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"loadMode\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"MMAP\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"streamConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"streamType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"kafka\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.type\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"lowLevel\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.topic.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"mastodon-topic\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.inputformat.avro.confluent.KafkaConfluentSchemaRegistryAvroMessageDecoder\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.factory.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.stream.kafka20.KafkaConsumerFactory\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.prop.format\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"AVRO\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.prop.schema.registry.rest.url\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://schema-registry:8081\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.prop.schema.registry.schema.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"mastodon-topic-value\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.broker.list\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"broker:9093\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.consumer.prop.auto.offset.reset\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"smallest\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"metadata\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"customConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"routing\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"instanceSelectorType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"strictReplicaGroup\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The following configs represent the most important ones for ingesting Apache Avro™ messages into Pinot:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.inputformat.avro.confluent.KafkaConfluentSchemaRegistryAvroMessageDecoder\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.prop.format\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"AVRO\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.prop.schema.registry.rest.url\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"http://schema-registry:8081\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.prop.schema.registry.schema.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"mastodon-topic-value\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The KafkaConfluentSchemaRegistryAvroMessageDecoder decoder calls the Schema Registry with the schema name to get back the schema that it will use to decode messages.\"}],[\"$\",\"p\",null,{\"children\":\"We can create the Pinot table by running the following command:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"docker\"}],\" run \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--network\"}],\" mastodon \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-v\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token constant environment\",\"children\":\"$$PWD\"}],\"/pinot:/config \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" apachepinot/pinot:0.12.0-arm64 AddTable \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-schemaFile\"}],\" /config/schema.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-tableConfigFile\"}],\" /config/table.json \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-controllerHost\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"pinot-controller\\\"\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-exec\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"We can then navigate to the table page of the Pinot UI:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/#/tenants/table/mastodon_REALTIME\",\"children\":\"http://localhost:9000/#/tenants/table/mastodon_REALTIME\"}]}],[\"$\",\"p\",null,{\"children\":\"Here, we’ll see the following:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Apache Pinot table config and schema\",\"src\":\"https://www.datocms-assets.com/75153/1685637837-image6.png\",\"title\":\"Apache Pinot table config and schema\"}]}],[\"$\",\"h2\",null,{\"id\":\"ingest-data-into-kafka\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#ingest-data-into-kafka\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Ingest Data into Kafka\"]}],[\"$\",\"p\",null,{\"children\":\"Now, we need to start ingesting data into Kafka. Simon created a script that accomplishes this for us, so we just need to indicate which Mastodon servers to query.\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"python mastodonlisten.py \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--baseURL\"}],\" https://data-folks.masto.host \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--public\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--enableKafka\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--quiet\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"python mastodonlisten.py \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--baseURL\"}],\" https://fosstodon.org/ \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--public\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--enableKafka\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--quiet\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"python mastodonlisten.py \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--baseURL\"}],\" https://mstdn.social/ \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--public\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--enableKafka\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"--quiet\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"We can then check the ingestion of messages with the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.confluent.io/platform/current/clients/kafkacat-usage.html\",\"children\":\"kcat\"}],\" command line tool:\"]}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"kcat \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-C\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-b\"}],\" localhost:9092 \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-t\"}],\" mastodon-topic \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"\\\\\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-s\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token variable assign-left\",\"children\":\"value\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\"avro \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-r\"}],\" http://localhost:8081 \",[\"$\",\"span\",null,{\"className\":\"token variable parameter\",\"children\":\"-e\"}],\"\\n\"]}]]}]}],[\"$\",\"h2\",null,{\"id\":\"query-pinot\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#query-pinot\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Query Pinot\"]}],[\"$\",\"p\",null,{\"children\":\"Now, let’s go to the Pinot UI to see what data we’ve got to play with:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"http://localhost:9000/\",\"children\":\"http://localhost:9000\"}]}],[\"$\",\"p\",null,{\"children\":\"We’ll see the following preview of the data in the mastodon table:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"SQL Editor, query response stats, and query result in Apache Pinot\",\"src\":\"https://www.datocms-assets.com/75153/1685637772-image5.png\",\"title\":\"SQL Editor, query response stats, and query result in Apache Pinot\"}]}],[\"$\",\"p\",null,{\"children\":\"We can then write a query to find the number of messages posted in the last five minutes:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-sql\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Num toots\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"distinct\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"username\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Num users\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"distinct\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"url\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Num urls\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" mastodon\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"where\"}],\" created_at\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"*\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1000\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"\u003e\"}],\" ago\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'PT1M'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"order\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"1\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"DESC\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\";\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Query results for toots, users, and urls\",\"src\":\"https://www.datocms-assets.com/75153/1685637909-image8.png\",\"title\":\"Query results for toots, users, and urls\"}]}],[\"$\",\"p\",null,{\"children\":\"We can also query Pinot via the Python client, which we can install by running the following:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-bash\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-bash\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"pip \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"install\"}],\" pinotdb\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":\"Once we’ve done that, let’s open the Python REPL and run the following code:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-python\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-python\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" pinotdb \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"import\"}],\" connect\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"import\"}],\" pandas \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"as\"}],\" pd\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"conn \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" connect\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"host\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'localhost'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" port\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"8099\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" path\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'/query/sql'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" scheme\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"'http'\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"curs \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" conn\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"cursor\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"st\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"header\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"Daily Mastodon Usage\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"query \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string triple-quoted-string\",\"children\":\"\\\"\\\"\\\"\\n\"}]]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string triple-quoted-string\",\"children\":\"select count(*) as \\\"Num toots\\\"\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string triple-quoted-string\",\"children\":\", count(distinct(username)) as \\\"Num users\\\"\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string triple-quoted-string\",\"children\":\", count(distinct(url)) as \\\"Num urls\\\"\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string triple-quoted-string\",\"children\":\"from mastodon\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string triple-quoted-string\",\"children\":\"where created_at*1000 \u003e ago('PT1M')\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"$\",\"span\",null,{\"className\":\"token string triple-quoted-string\",\"children\":\"order by 1 DESC;\\n\"}]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token string triple-quoted-string\",\"children\":\"\\\"\\\"\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"curs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"execute\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"query\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"df \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" pd\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"DataFrame\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"curs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" columns\",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],\"item\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"[\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"0\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"for\"}],\" item \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"in\"}],\" curs\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"description\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"]\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"This produces the resulting DataFrame:\"}],[\"$\",\"$L13\",null,{\"className\":\"language-js\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-js\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token maybe-class-name\",\"children\":\"Num\"}],\" toots \",[\"$\",\"span\",null,{\"className\":\"token maybe-class-name\",\"children\":\"Num\"}],\" users \",[\"$\",\"span\",null,{\"className\":\"token maybe-class-name\",\"children\":\"Num\"}],\" urls\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"0\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"552\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"173\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"192\"}],\"\\n\"]}]]}]}],[\"$\",\"h2\",null,{\"id\":\"streamlit\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#streamlit\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Streamlit\"]}],[\"$\",\"p\",null,{\"children\":\"Next, we’ll create a Streamlit dashboard to package up these queries. We’ll visualize the results using Plotly, which you can install using:\"}],[\"$\",\"p\",null,{\"children\":\"pip install streamlit plotly\"}],[\"$\",\"p\",null,{\"children\":[\"I’ve created a Streamlit app in the file \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/mneedham/mastodon-stream/blob/main/app.py\",\"children\":\"app.py\"}],\", which you can find in the GitHub repository. Let’s have a look at the kinds of visualizations that we can generate.\"]}],[\"$\",\"p\",null,{\"children\":[\"First, we’ll create metrics to show the number of toots, users, and URLs in the last \",[\"$\",\"em\",null,{\"children\":\"n\"}],\" minutes. \",[\"$\",\"em\",null,{\"children\":\"n\"}],\" will be configurable from the app as shown in the screenshot below:\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Chart of real-time Mastodon usage\",\"src\":\"https://www.datocms-assets.com/75153/1685637876-image7.png\",\"title\":\"Chart of real-time Mastodon usage\"}]}],[\"$\",\"p\",null,{\"children\":\"From the screenshot, we can identify mastodon.cloud as the most active server, though it produces only 1,800 messages in 10 minutes or three messages per second. The values in green indicate the change in values compared to the previous 10 minutes.\"}],[\"$\",\"p\",null,{\"children\":\"We can also create a chart showing the number of messages per minute for the last 10 minutes:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Time of day Mastodon usage\",\"src\":\"https://www.datocms-assets.com/75153/1685637945-image9.png\",\"title\":\"Time of day Mastodon usage\"}]}],[\"$\",\"p\",null,{\"children\":\"Based on this chart, we can see that we’re creating anywhere from 200–900 messages per second. Part of the reason lies in the fact that the Mastodon servers sometimes disconnect our listener, and at the moment, I have to manually reconnect.\"}],[\"$\",\"p\",null,{\"children\":\"Finally, we can look at the toot length by language:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Chart of toot length by language usage\",\"src\":\"https://www.datocms-assets.com/75153/1685637644-image2.png\",\"title\":\"Chart of toot length by language usage\"}]}],[\"$\",\"p\",null,{\"children\":\"We see much bigger ranges here than Simon saw in his analysis. He saw a maximum length of 200 characters, whereas we see some messages of up to 4,200 characters.\"}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":[\"We hope you enjoyed following along as we explored this fun use case for \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/resources/what-is-real-time-analytics\",\"children\":\"real-time analytics\"}],\". As you can see, even though we’re pulling the data from many of the popular Mastodon servers, it’s still not all that much data!\"]}],[\"$\",\"p\",null,{\"children\":[\"Give the code a try and let us know how it goes. If you have any questions, feel free to \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://stree.ai/slack\",\"children\":\"join us on Slack\"}],\", where we’ll gladly do our best to help you out.\"]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"The Existing Solution: Kafka Connect, Parquet, Seaborn and DuckDB \",\"url\":\"#the-existing-solution-kafka-connect-parquet-seaborn-and-duckdb\",\"depth\":2},{\"value\":\"Going Real-Time with Apache Pinot™\",\"url\":\"#going-real-time-with-apache-pinot\",\"depth\":2},{\"value\":\"Setup\",\"url\":\"#setup\",\"depth\":2},{\"value\":\"Pinot Schema and Table\",\"url\":\"#pinot-schema-and-table\",\"depth\":2},{\"value\":\"Ingest Data into Kafka\",\"url\":\"#ingest-data-into-kafka\",\"depth\":2},{\"value\":\"Query Pinot\",\"url\":\"#query-pinot\",\"depth\":2},{\"value\":\"Streamlit\",\"url\":\"#streamlit\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Real-Time Mastodon Usage with Apache Kafka, Apache Pinot, and Streamlit | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"The blog post discusses analyzing user activity and server popularity on Mastodon using Kafka Connect, Parquet, Seaborn, and DuckDB. 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diff --git a/blog/2023/06/01/real-time-mastodon-usage-with-apache-kafka-apache-pinot-and-streamlit.txt b/blog/2023/06/01/real-time-mastodon-usage-with-apache-kafka-apache-pinot-and-streamlit.txt
index 82172ae..f82b440 100644
--- a/blog/2023/06/01/real-time-mastodon-usage-with-apache-kafka-apache-pinot-and-streamlit.txt
+++ b/blog/2023/06/01/real-time-mastodon-usage-with-apache-kafka-apache-pinot-and-streamlit.txt
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The application team wanted to provide a rich analytical interface to these users so that not only can they see the current performance but also do custom slicing and dicing of data over a period of time. For example, compare their current campaign performance to one they ran two weeks back, do cohort analysis, and so on.</p><h3 id="why-was-the-existing-system-not-working"><a href="#why-was-the-existing-system-not-working" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Why was the existing system not working?</h3><p>Their existing tech stack was a mix of OSS and custom-built in-house code, which was both operationally difficult to manage and costly to maintain. Yet more importantly, it wasn’t able to meet the basic throughput and latency requirements required by the platform to sustain user growth as well as provide richer analytic capabilities in the product.</p><h3 id="the-problem-and-challenges"><a href="#the-problem-and-challenges" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>The Problem and Challenges?</h3><p>When the StarTree Sales Engineering team was engaged, the requirements were very clear:</p><ul><li>Throughput: Support 50+ QPS during POC and 200+ for production)</li><li>Latency: P95th latency of 2s, including query that needed aggregation of ~ 2 billion rows</li><li>Scalability: Ability to scale efficiently with future growth in QPS in a non-linear manner</li></ul><p>The biggest challenge was the size of data — 20+ TB and growing — and on top of that, a complex aggregation query driving the summary view for users so they can drill further in to get more details.</p><p>This particular query needed to aggregate close to 2 Billion records at read time and then would be fired for every active user interacting with the platform (so high concurrent QPS). In this case, despite applying all relevant indexes available in their existing system, out-of-the-box query performance was still in the 6-8 seconds range, which is expected given that bulk of the work for the query is happening in the aggregation phase and not during the filtering phase (indexing helps with this).</p><p>In other OLAP systems they explored, the only option available to handle this use case was doing ingestion time rollups. In other words, changing the data to higher granularity. However, this obviously means losing access to raw data and also potentially re-bootstrapping if new use cases come down the road that need raw data access.</p><p>This is exactly the type of scenario that the <a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/basics/indexing/star-tree-index">Star-Tree Index</a>, unique to Apache Pinot, is designed to address - handle large aggregation queries at scale that need sub-second performance. The best part is you can apply it anytime without any need to reprocess the data or plan any system downtime. (Segment reload to apply table config changes run as a background task in Apache Pinot.) In this specific case, the same query latencies with the star-tree index applied went down to 15 ms. This implicitly meant that with the same infrastructure footprint, StarTree was able to support ~70 QPS (Queries Per Second) vs < 1 QPS for this most complex query; while still keeping the raw data intact.</p><h3 id="data-size-and-infra-footprint-for-the-pilot"><a href="#data-size-and-infra-footprint-for-the-pilot" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Data Size and Infra Footprint for the Pilot: </h3><ul><li>Total # of records: ~2 Trillion</li><li>Data Size: ~20 TB</li><li>Capacity: 72 vCPUs across 9 Pinot servers (8 vCPU, 64GB per node).</li></ul><h3 id="impact-summary"><a href="#impact-summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Impact Summary:</h3><ul><li>99.76% reduction in latency vs. no Star-Tree Index (6.3 seconds to 15 ms)</li><li>99.99999% reduction in amount of data scanned/aggregated per query (> 1.8B docs to < 2,400)</li></ul><p><img alt="Visualization of the impact of start-tree index for an AdTech use case with Apache Pinot" src="https://www.datocms-assets.com/75153/1689174701-image1.png"/></p><h2 id="cybersecurity-use-case"><a href="#cybersecurity-use-case" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>CyberSecurity Use Case:</h2><p>A cybersecurity company that provides their customers with a real-time threat detection platform with AI, allowing them to analyze network flow logs in real-time with a sophisticated reporting/analytical UI. The initial landing page inside the customer portal is a summary view of everything the platform was monitoring in the user's environment and then provides the capability to drill down into specifics of each. For example, filter requests by a specific application or IP Address.</p><h3 id="why-was-the-existing-system-not-working-1"><a href="#why-was-the-existing-system-not-working-1" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Why was the existing system not working?</h3><p>Their existing tech stack was a mix of Athena/Presto, which couldn’t meet the throughput and latency requirements with growing data volume across their customers. Additionally, operational overhead around managing some of these systems in-house led to increased cost.</p><h3 id="the-problem-and-challenges-1"><a href="#the-problem-and-challenges-1" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>The Problem and Challenges?</h3><p>Some of the key requirements that StarTree Cloud cluster had to meet:</p><ul><li>Throughput: Up to 200 QPS (200 projected by end of year)</li><li>Latency: < 1 second P99</li><li>High ingestion rate: 300k events/sec</li><li>ROI: Provide better cost efficiencies</li></ul><p>Similar to Use case #1, the customer wanted to retain data at the lowest granularity (so no ingestion roll-ups), and given the time column granularity similar challenge with running the complex aggregation query to power off the summary view. Additionally, the requirement to get double-digit throughput(QPS) for the POC with the most efficient compute footprint made it quite challenging.</p><p>Given the overhead while doing complex aggregations, efficient filtering (indexes) wasn’t enough - in this case, with 3 * 4-core/32GB nodes query took more than 15 seconds. We immediately switched the table config to add star-tree index to the table config and do a segment reload, and the results were phenomenal — query latency was reduced to 10ms.</p><h3 id="data-size-and-infra-footprint-for-the-pilot-1"><a href="#data-size-and-infra-footprint-for-the-pilot-1" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Data Size and Infra Footprint for the Pilot: </h3><ul><li>Total # of records: ~8 Billion</li><li>Data Size: 500+ GB</li><li>Capacity: 12 vCPUs across 3 Pinot servers (4-core/32GB)</li></ul><h3 id="impact-summary-1"><a href="#impact-summary-1" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Impact Summary:</h3><ul><li>99.94% reduction in query latency (achieving 100 QPS for the same query with no extra hardware)</li><li>99.9998% reduction in data scanned/aggregated per query</li><li>Happy Customer 😃</li></ul><p><img alt="Visualization of the impact of star-tree index for a Cybersecurity use case with Apache Pinot" src="https://www.datocms-assets.com/75153/1689175033-image4.png"/></p><h2 id="multiplayer-game-leaderboard-use-case"><a href="#multiplayer-game-leaderboard-use-case" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Multiplayer Game Leaderboard Use Case</h2><p>A global leader in the interactive entertainment field has an A/B Testing / Experimentation use case that tracks players’ activities to measure the player engagement on the new features being rolled out.</p><h3 id="the-problem-and-challenges-2"><a href="#the-problem-and-challenges-2" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>The Problem and Challenges?</h3><p>Some of the key requirements that StarTree Cloud cluster had to meet:</p><ul><li>Throughput: = 200 QPS</li><li>Latencies: < 1 second P99</li><li>Ingestion rate: 50K events/sec</li></ul><p>Given the overhead while doing complex aggregations, efficient filtering (indexes) wasn’t enough - in this case, with 1 * 4-core/32GB nodes query took 163 milliseconds. After switching to a star-tree index, the query latency was reduced to 7ms (a reduction of 95.7%).</p><h3 id="data-size-and-infra-footprint-for-the-pilot-2"><a href="#data-size-and-infra-footprint-for-the-pilot-2" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Data Size and Infra Footprint for the Pilot: </h3><ul><li>Total # of records: ~34 Million</li><li>Data Size: 500+ GB</li><li>Capacity: 4 vCPUs - 1 Pinot server (4-cores / 32 GB)</li></ul><h3 id="impact-summary-2"><a href="#impact-summary-2" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Impact Summary:</h3><ul><li>95.70% improvement in query performance as a result of 99.9962% reduction in number of documents and entries scanned.</li></ul><p><img alt="Visualization of the impact of star-tree index for a Gaming use case with Apache Pinot" src="https://www.datocms-assets.com/75153/1689175176-image2.png"/></p><h2 id="quick-recap-star-tree-index-performance-improvements"><a href="#quick-recap-star-tree-index-performance-improvements" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Quick Recap: Star-Tree Index Performance Improvements</h2><p><img alt="Recap Table of the Impact that star-tree index had on three real-world customers using Apache Pinot™" src="https://www.datocms-assets.com/75153/1689175271-image3.png"/></p><ul><li>99.99% reduction in data scanned/aggregated per query</li><li>95 to 99% improvement in query performance</li></ul><p>Disk IO is the most expensive operation in query processing. The star-tree index reduces Disk IO significantly. Instead of scanning raw documents from the disk and computing aggregates on the fly, star-tree index scans pre-aggregated documents for the combination of dimensions specified in the query from the disk.</p><p>In part 1 of the series, we saw that the star-tree index reduced the disk reads by 99.999% from 584 Million entries (in case of an inverted index) to 2,045. Query latency came down 99.67% from 1,513 ms to 4 ms! This, in itself, was a HUGE benefit.</p><p>In addition to the drastic improvement in query latency, the memory and CPU usage decreased significantly, freeing up resources for taking up more concurrent workloads. The cumulative effect was the 126 x increase in QPS on this small 4 vCPU Pinot Server, as we saw in part 2 blog of this series.</p><p>And finally, in this part 3 of the blog series, we covered three real production use cases that have seen 95% to 99% improvement in query performance using Star-Tree Index.</p><h2 id="intrigued-by-what-youve-read"><a href="#intrigued-by-what-youve-read" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Intrigued by What You’ve Read?</h2><p>The next step is to load your data into an open-source <a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/basics/getting-started">Apache Pinot</a> cluster or, if you prefer, a fully-managed database-as-a-service (DBaaS). Sign up today for a <a target="_blank" rel="noopener noreferrer" href="https://startree.ai/saas-signup">StarTree Cloud account</a>, free for 30 days. If you have more questions, sign up for the <a target="_blank" rel="noopener noreferrer" href="https://communityinviter.com/apps/startreedata/startree-community">StarTree Community Slack</a>.</p><p><a target="_blank" rel="noopener noreferrer" href="https://startree.ai/saas-signup">GET STARTED ON STARTREE CLOUD</a></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#adtech-use-case">AdTech Use Case</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#why-was-the-existing-system-not-working">Why was the existing system not working?</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#the-problem-and-challenges">The Problem and Challenges?</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#data-size-and-infra-footprint-for-the-pilot">Data Size and Infra Footprint for the Pilot: </a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#impact-summary">Impact Summary:</a></li><li class="text-sm font-normal leading-tight"><a href="#cybersecurity-use-case">CyberSecurity Use Case:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#why-was-the-existing-system-not-working">Why was the existing system not working?</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#the-problem-and-challenges">The Problem and Challenges?</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#data-size-and-infra-footprint-for-the-pilot">Data Size and Infra Footprint for the Pilot: </a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#impact-summary">Impact Summary:</a></li><li class="text-sm font-normal leading-tight"><a href="#multiplayer-game-leaderboard-use-case">Multiplayer Game Leaderboard Use Case</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#the-problem-and-challenges">The Problem and Challenges?</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#data-size-and-infra-footprint-for-the-pilot">Data Size and Infra Footprint for the Pilot: </a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#impact-summary">Impact Summary:</a></li><li class="text-sm font-normal leading-tight"><a href="#quick-recap-star-tree-index-performance-improvements">Quick Recap: Star-Tree Index Performance Improvements</a></li><li class="text-sm font-normal leading-tight"><a href="#intrigued-by-what-youve-read">Intrigued by What You’ve Read?</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] 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This index has also been successful in cybersecurity threat detection and multiplayer game leaderboard tracking, resulting in improved query performance and cost savings. 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The application team wanted to provide a rich analytical interface to these users so that not only can they see the current performance but also do custom slicing and dicing of data over a period of time. For example, compare their current campaign performance to one they ran two weeks back, do cohort analysis, and so on.\"}],[\"$\",\"h3\",null,{\"id\":\"why-was-the-existing-system-not-working\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#why-was-the-existing-system-not-working\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Why was the existing system not working?\"]}],[\"$\",\"p\",null,{\"children\":\"Their existing tech stack was a mix of OSS and custom-built in-house code, which was both operationally difficult to manage and costly to maintain. Yet more importantly, it wasn’t able to meet the basic throughput and latency requirements required by the platform to sustain user growth as well as provide richer analytic capabilities in the product.\"}],[\"$\",\"h3\",null,{\"id\":\"the-problem-and-challenges\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#the-problem-and-challenges\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"The Problem and Challenges?\"]}],[\"$\",\"p\",null,{\"children\":\"When the StarTree Sales Engineering team was engaged, the requirements were very clear:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Throughput: Support 50+ QPS during POC and 200+ for production)\"}],[\"$\",\"li\",null,{\"children\":\"Latency: P95th latency of 2s, including query that needed aggregation of ~ 2 billion rows\"}],[\"$\",\"li\",null,{\"children\":\"Scalability: Ability to scale efficiently with future growth in QPS in a non-linear manner\"}]]}],[\"$\",\"p\",null,{\"children\":\"The biggest challenge was the size of data — 20+ TB and growing — and on top of that, a complex aggregation query driving the summary view for users so they can drill further in to get more details.\"}],[\"$\",\"p\",null,{\"children\":\"This particular query needed to aggregate close to 2 Billion records at read time and then would be fired for every active user interacting with the platform (so high concurrent QPS). In this case, despite applying all relevant indexes available in their existing system, out-of-the-box query performance was still in the 6-8 seconds range, which is expected given that bulk of the work for the query is happening in the aggregation phase and not during the filtering phase (indexing helps with this).\"}],[\"$\",\"p\",null,{\"children\":\"In other OLAP systems they explored, the only option available to handle this use case was doing ingestion time rollups. In other words, changing the data to higher granularity. However, this obviously means losing access to raw data and also potentially re-bootstrapping if new use cases come down the road that need raw data access.\"}],[\"$\",\"p\",null,{\"children\":[\"This is exactly the type of scenario that the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/basics/indexing/star-tree-index\",\"children\":\"Star-Tree Index\"}],\", unique to Apache Pinot, is designed to address - handle large aggregation queries at scale that need sub-second performance. The best part is you can apply it anytime without any need to reprocess the data or plan any system downtime. (Segment reload to apply table config changes run as a background task in Apache Pinot.) In this specific case, the same query latencies with the star-tree index applied went down to 15 ms. This implicitly meant that with the same infrastructure footprint, StarTree was able to support ~70 QPS (Queries Per Second) vs \u003c 1 QPS for this most complex query; while still keeping the raw data intact.\"]}],[\"$\",\"h3\",null,{\"id\":\"data-size-and-infra-footprint-for-the-pilot\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#data-size-and-infra-footprint-for-the-pilot\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Data Size and Infra Footprint for the Pilot: \"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Total # of records: ~2 Trillion\"}],[\"$\",\"li\",null,{\"children\":\"Data Size: ~20 TB\"}],[\"$\",\"li\",null,{\"children\":\"Capacity: 72 vCPUs across 9 Pinot servers (8 vCPU, 64GB per node).\"}]]}],[\"$\",\"h3\",null,{\"id\":\"impact-summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#impact-summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Impact Summary:\"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"99.76% reduction in latency vs. no Star-Tree Index (6.3 seconds to 15 ms)\"}],[\"$\",\"li\",null,{\"children\":\"99.99999% reduction in amount of data scanned/aggregated per query (\u003e 1.8B docs to \u003c 2,400)\"}]]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Visualization of the impact of start-tree index for an AdTech use case with Apache Pinot\",\"src\":\"https://www.datocms-assets.com/75153/1689174701-image1.png\"}]}],[\"$\",\"h2\",null,{\"id\":\"cybersecurity-use-case\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#cybersecurity-use-case\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"CyberSecurity Use Case:\"]}],[\"$\",\"p\",null,{\"children\":\"A cybersecurity company that provides their customers with a real-time threat detection platform with AI, allowing them to analyze network flow logs in real-time with a sophisticated reporting/analytical UI. The initial landing page inside the customer portal is a summary view of everything the platform was monitoring in the user's environment and then provides the capability to drill down into specifics of each. For example, filter requests by a specific application or IP Address.\"}],[\"$\",\"h3\",null,{\"id\":\"why-was-the-existing-system-not-working-1\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#why-was-the-existing-system-not-working-1\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Why was the existing system not working?\"]}],[\"$\",\"p\",null,{\"children\":\"Their existing tech stack was a mix of Athena/Presto, which couldn’t meet the throughput and latency requirements with growing data volume across their customers. Additionally, operational overhead around managing some of these systems in-house led to increased cost.\"}],[\"$\",\"h3\",null,{\"id\":\"the-problem-and-challenges-1\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#the-problem-and-challenges-1\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"The Problem and Challenges?\"]}],[\"$\",\"p\",null,{\"children\":\"Some of the key requirements that StarTree Cloud cluster had to meet:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Throughput: Up to 200 QPS (200 projected by end of year)\"}],[\"$\",\"li\",null,{\"children\":\"Latency: \u003c 1 second P99\"}],[\"$\",\"li\",null,{\"children\":\"High ingestion rate: 300k events/sec\"}],[\"$\",\"li\",null,{\"children\":\"ROI: Provide better cost efficiencies\"}]]}],[\"$\",\"p\",null,{\"children\":\"Similar to Use case #1, the customer wanted to retain data at the lowest granularity (so no ingestion roll-ups), and given the time column granularity similar challenge with running the complex aggregation query to power off the summary view. Additionally, the requirement to get double-digit throughput(QPS) for the POC with the most efficient compute footprint made it quite challenging.\"}],[\"$\",\"p\",null,{\"children\":\"Given the overhead while doing complex aggregations, efficient filtering (indexes) wasn’t enough - in this case, with 3 * 4-core/32GB nodes query took more than 15 seconds. We immediately switched the table config to add star-tree index to the table config and do a segment reload, and the results were phenomenal — query latency was reduced to 10ms.\"}],[\"$\",\"h3\",null,{\"id\":\"data-size-and-infra-footprint-for-the-pilot-1\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#data-size-and-infra-footprint-for-the-pilot-1\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Data Size and Infra Footprint for the Pilot: \"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Total # of records: ~8 Billion\"}],[\"$\",\"li\",null,{\"children\":\"Data Size: 500+ GB\"}],[\"$\",\"li\",null,{\"children\":\"Capacity: 12 vCPUs across 3 Pinot servers (4-core/32GB)\"}]]}],[\"$\",\"h3\",null,{\"id\":\"impact-summary-1\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#impact-summary-1\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Impact Summary:\"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"99.94% reduction in query latency (achieving 100 QPS for the same query with no extra hardware)\"}],[\"$\",\"li\",null,{\"children\":\"99.9998% reduction in data scanned/aggregated per query\"}],[\"$\",\"li\",null,{\"children\":\"Happy Customer 😃\"}]]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Visualization of the impact of star-tree index for a Cybersecurity use case with Apache Pinot\",\"src\":\"https://www.datocms-assets.com/75153/1689175033-image4.png\"}]}],[\"$\",\"h2\",null,{\"id\":\"multiplayer-game-leaderboard-use-case\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#multiplayer-game-leaderboard-use-case\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Multiplayer Game Leaderboard Use Case\"]}],[\"$\",\"p\",null,{\"children\":\"A global leader in the interactive entertainment field has an A/B Testing / Experimentation use case that tracks players’ activities to measure the player engagement on the new features being rolled out.\"}],[\"$\",\"h3\",null,{\"id\":\"the-problem-and-challenges-2\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#the-problem-and-challenges-2\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"The Problem and Challenges?\"]}],[\"$\",\"p\",null,{\"children\":\"Some of the key requirements that StarTree Cloud cluster had to meet:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Throughput: = 200 QPS\"}],[\"$\",\"li\",null,{\"children\":\"Latencies: \u003c 1 second P99\"}],[\"$\",\"li\",null,{\"children\":\"Ingestion rate: 50K events/sec\"}]]}],[\"$\",\"p\",null,{\"children\":\"Given the overhead while doing complex aggregations, efficient filtering (indexes) wasn’t enough - in this case, with 1 * 4-core/32GB nodes query took 163 milliseconds. After switching to a star-tree index, the query latency was reduced to 7ms (a reduction of 95.7%).\"}],[\"$\",\"h3\",null,{\"id\":\"data-size-and-infra-footprint-for-the-pilot-2\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#data-size-and-infra-footprint-for-the-pilot-2\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Data Size and Infra Footprint for the Pilot: \"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Total # of records: ~34 Million\"}],[\"$\",\"li\",null,{\"children\":\"Data Size: 500+ GB\"}],[\"$\",\"li\",null,{\"children\":\"Capacity: 4 vCPUs - 1 Pinot server (4-cores / 32 GB)\"}]]}],[\"$\",\"h3\",null,{\"id\":\"impact-summary-2\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#impact-summary-2\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Impact Summary:\"]}],[\"$\",\"ul\",null,{\"children\":[\"$\",\"li\",null,{\"children\":\"95.70% improvement in query performance as a result of 99.9962% reduction in number of documents and entries scanned.\"}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Visualization of the impact of star-tree index for a Gaming use case with Apache Pinot\",\"src\":\"https://www.datocms-assets.com/75153/1689175176-image2.png\"}]}],[\"$\",\"h2\",null,{\"id\":\"quick-recap-star-tree-index-performance-improvements\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#quick-recap-star-tree-index-performance-improvements\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Quick Recap: Star-Tree Index Performance Improvements\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Recap Table of the Impact that star-tree index had on three real-world customers using Apache Pinot™\",\"src\":\"https://www.datocms-assets.com/75153/1689175271-image3.png\"}]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"99.99% reduction in data scanned/aggregated per query\"}],[\"$\",\"li\",null,{\"children\":\"95 to 99% improvement in query performance\"}]]}],[\"$\",\"p\",null,{\"children\":\"Disk IO is the most expensive operation in query processing. The star-tree index reduces Disk IO significantly. Instead of scanning raw documents from the disk and computing aggregates on the fly, star-tree index scans pre-aggregated documents for the combination of dimensions specified in the query from the disk.\"}],[\"$\",\"p\",null,{\"children\":\"In part 1 of the series, we saw that the star-tree index reduced the disk reads by 99.999% from 584 Million entries (in case of an inverted index) to 2,045. Query latency came down 99.67% from 1,513 ms to 4 ms! This, in itself, was a HUGE benefit.\"}],[\"$\",\"p\",null,{\"children\":\"In addition to the drastic improvement in query latency, the memory and CPU usage decreased significantly, freeing up resources for taking up more concurrent workloads. The cumulative effect was the 126 x increase in QPS on this small 4 vCPU Pinot Server, as we saw in part 2 blog of this series.\"}],[\"$\",\"p\",null,{\"children\":\"And finally, in this part 3 of the blog series, we covered three real production use cases that have seen 95% to 99% improvement in query performance using Star-Tree Index.\"}],[\"$\",\"h2\",null,{\"id\":\"intrigued-by-what-youve-read\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#intrigued-by-what-youve-read\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Intrigued by What You’ve Read?\"]}],[\"$\",\"p\",null,{\"children\":[\"The next step is to load your data into an open-source \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/basics/getting-started\",\"children\":\"Apache Pinot\"}],\" cluster or, if you prefer, a fully-managed database-as-a-service (DBaaS). 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Real production use cases showed a 95% to 99% improvement in query performance using StarTree Cloud for real-time analytics.","image":"/static/images/twitter-card.png","url":"https://pinot.apache.org/blog/2023-07-12-star-tree-index-in-apache-pinot-part-3-understanding-the-impact-in-real-customer","author":[{"@type":"Person","name":"Sandeep Dabade"},{"@type":"Person","name":"Kulbir Nijjer"}]}</script><section class=" px-5 pt-10 md:px-[13.313rem] md:py-16"><div class="fixed bottom-8 right-8 hidden flex-col gap-3 md:hidden"><button aria-label="Scroll To Top" class="rounded-full bg-gray-200 p-2 text-gray-500 transition-all hover:bg-gray-300 dark:bg-gray-700 dark:text-gray-400 dark:hover:bg-gray-600"><svg class="h-5 w-5" viewBox="0 0 20 20" fill="currentColor"><path fill-rule="evenodd" d="M3.293 9.707a1 1 0 010-1.414l6-6a1 1 0 011.414 0l6 6a1 1 0 01-1.414 1.414L11 5.414V17a1 1 0 11-2 0V5.414L4.707 9.707a1 1 0 01-1.414 0z" clip-rule="evenodd"></path></svg></button></div><article class=""><div class="mx-auto lg:flex"><div class="lg:pr-12"><header class="pt-6 md:pr-10"><h1 class="text-4xl font-semibold">Star-Tree Index in Apache Pinot - Part 3 - Understanding the Impact in Real Customer Scenarios</h1><p class="pt-2 text-lg">By: <!-- -->Sandeep Dabade, Kulbir Nijjer</p><p class="py-2 text-sm">July 12th, 2023<!-- --> • <!-- -->8 min read</p></header><div class="flex flex-col lg:flex-row"><main class=""><div class="prose max-w-[45rem] pb-8 pt-10 dark:prose-invert"><p>In <a target="_blank" rel="noopener noreferrer" href="https://startree.ai/blog/star-tree-indexes-in-apache-pinot-part-1-understanding-the-impact-on-query-performance">part 1 of this blog series</a>, we looked at how a star-tree index brought down standalone query latency on a sizable dataset of ~633M records from 1,513ms to 4ms! — nearly 380x faster.</p><p>In <a target="_blank" rel="noopener noreferrer" href="https://startree.ai/blog/star-tree-indexes-in-apache-pinot-part-2-understanding-the-impact-during-high-concurrency">part 2 of this blog series</a>, we imitated a real production scenario by firing hundreds of concurrent queries using JMeter and showcased how using a star-tree index helped achieve a >95% drop in p90th / p95th / p99th latencies and 126x increase in Throughput.</p><p>In this part, we will cover some real customer stories that have seen 95% to 99% improvement in query performance using Star-Tree Index.</p><h2 id="adtech-use-case"><a href="#adtech-use-case" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>AdTech Use Case</h2><p>This was for a leading AdTech platform and a somewhat typical use case; users of the platform (advertisers, publishers, and influencers) wanted to see fresh metrics on how their activities (such as online content, ad, and email campaigns) were performing in real-time so they could tweak things as needed. The application team wanted to provide a rich analytical interface to these users so that not only can they see the current performance but also do custom slicing and dicing of data over a period of time. For example, compare their current campaign performance to one they ran two weeks back, do cohort analysis, and so on.</p><h3 id="why-was-the-existing-system-not-working"><a href="#why-was-the-existing-system-not-working" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Why was the existing system not working?</h3><p>Their existing tech stack was a mix of OSS and custom-built in-house code, which was both operationally difficult to manage and costly to maintain. Yet more importantly, it wasn’t able to meet the basic throughput and latency requirements required by the platform to sustain user growth as well as provide richer analytic capabilities in the product.</p><h3 id="the-problem-and-challenges"><a href="#the-problem-and-challenges" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>The Problem and Challenges?</h3><p>When the StarTree Sales Engineering team was engaged, the requirements were very clear:</p><ul><li>Throughput: Support 50+ QPS during POC and 200+ for production)</li><li>Latency: P95th latency of 2s, including query that needed aggregation of ~ 2 billion rows</li><li>Scalability: Ability to scale efficiently with future growth in QPS in a non-linear manner</li></ul><p>The biggest challenge was the size of data — 20+ TB and growing — and on top of that, a complex aggregation query driving the summary view for users so they can drill further in to get more details.</p><p>This particular query needed to aggregate close to 2 Billion records at read time and then would be fired for every active user interacting with the platform (so high concurrent QPS). In this case, despite applying all relevant indexes available in their existing system, out-of-the-box query performance was still in the 6-8 seconds range, which is expected given that bulk of the work for the query is happening in the aggregation phase and not during the filtering phase (indexing helps with this).</p><p>In other OLAP systems they explored, the only option available to handle this use case was doing ingestion time rollups. In other words, changing the data to higher granularity. However, this obviously means losing access to raw data and also potentially re-bootstrapping if new use cases come down the road that need raw data access.</p><p>This is exactly the type of scenario that the <a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/basics/indexing/star-tree-index">Star-Tree Index</a>, unique to Apache Pinot, is designed to address - handle large aggregation queries at scale that need sub-second performance. The best part is you can apply it anytime without any need to reprocess the data or plan any system downtime. (Segment reload to apply table config changes run as a background task in Apache Pinot.) In this specific case, the same query latencies with the star-tree index applied went down to 15 ms. This implicitly meant that with the same infrastructure footprint, StarTree was able to support ~70 QPS (Queries Per Second) vs < 1 QPS for this most complex query; while still keeping the raw data intact.</p><h3 id="data-size-and-infra-footprint-for-the-pilot"><a href="#data-size-and-infra-footprint-for-the-pilot" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Data Size and Infra Footprint for the Pilot: </h3><ul><li>Total # of records: ~2 Trillion</li><li>Data Size: ~20 TB</li><li>Capacity: 72 vCPUs across 9 Pinot servers (8 vCPU, 64GB per node).</li></ul><h3 id="impact-summary"><a href="#impact-summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Impact Summary:</h3><ul><li>99.76% reduction in latency vs. no Star-Tree Index (6.3 seconds to 15 ms)</li><li>99.99999% reduction in amount of data scanned/aggregated per query (> 1.8B docs to < 2,400)</li></ul><p><img alt="Visualization of the impact of start-tree index for an AdTech use case with Apache Pinot" src="https://www.datocms-assets.com/75153/1689174701-image1.png"/></p><h2 id="cybersecurity-use-case"><a href="#cybersecurity-use-case" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>CyberSecurity Use Case:</h2><p>A cybersecurity company that provides their customers with a real-time threat detection platform with AI, allowing them to analyze network flow logs in real-time with a sophisticated reporting/analytical UI. The initial landing page inside the customer portal is a summary view of everything the platform was monitoring in the user's environment and then provides the capability to drill down into specifics of each. For example, filter requests by a specific application or IP Address.</p><h3 id="why-was-the-existing-system-not-working-1"><a href="#why-was-the-existing-system-not-working-1" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Why was the existing system not working?</h3><p>Their existing tech stack was a mix of Athena/Presto, which couldn’t meet the throughput and latency requirements with growing data volume across their customers. Additionally, operational overhead around managing some of these systems in-house led to increased cost.</p><h3 id="the-problem-and-challenges-1"><a href="#the-problem-and-challenges-1" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>The Problem and Challenges?</h3><p>Some of the key requirements that StarTree Cloud cluster had to meet:</p><ul><li>Throughput: Up to 200 QPS (200 projected by end of year)</li><li>Latency: < 1 second P99</li><li>High ingestion rate: 300k events/sec</li><li>ROI: Provide better cost efficiencies</li></ul><p>Similar to Use case #1, the customer wanted to retain data at the lowest granularity (so no ingestion roll-ups), and given the time column granularity similar challenge with running the complex aggregation query to power off the summary view. Additionally, the requirement to get double-digit throughput(QPS) for the POC with the most efficient compute footprint made it quite challenging.</p><p>Given the overhead while doing complex aggregations, efficient filtering (indexes) wasn’t enough - in this case, with 3 * 4-core/32GB nodes query took more than 15 seconds. We immediately switched the table config to add star-tree index to the table config and do a segment reload, and the results were phenomenal — query latency was reduced to 10ms.</p><h3 id="data-size-and-infra-footprint-for-the-pilot-1"><a href="#data-size-and-infra-footprint-for-the-pilot-1" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Data Size and Infra Footprint for the Pilot: </h3><ul><li>Total # of records: ~8 Billion</li><li>Data Size: 500+ GB</li><li>Capacity: 12 vCPUs across 3 Pinot servers (4-core/32GB)</li></ul><h3 id="impact-summary-1"><a href="#impact-summary-1" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Impact Summary:</h3><ul><li>99.94% reduction in query latency (achieving 100 QPS for the same query with no extra hardware)</li><li>99.9998% reduction in data scanned/aggregated per query</li><li>Happy Customer 😃</li></ul><p><img alt="Visualization of the impact of star-tree index for a Cybersecurity use case with Apache Pinot" src="https://www.datocms-assets.com/75153/1689175033-image4.png"/></p><h2 id="multiplayer-game-leaderboard-use-case"><a href="#multiplayer-game-leaderboard-use-case" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Multiplayer Game Leaderboard Use Case</h2><p>A global leader in the interactive entertainment field has an A/B Testing / Experimentation use case that tracks players’ activities to measure the player engagement on the new features being rolled out.</p><h3 id="the-problem-and-challenges-2"><a href="#the-problem-and-challenges-2" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>The Problem and Challenges?</h3><p>Some of the key requirements that StarTree Cloud cluster had to meet:</p><ul><li>Throughput: = 200 QPS</li><li>Latencies: < 1 second P99</li><li>Ingestion rate: 50K events/sec</li></ul><p>Given the overhead while doing complex aggregations, efficient filtering (indexes) wasn’t enough - in this case, with 1 * 4-core/32GB nodes query took 163 milliseconds. After switching to a star-tree index, the query latency was reduced to 7ms (a reduction of 95.7%).</p><h3 id="data-size-and-infra-footprint-for-the-pilot-2"><a href="#data-size-and-infra-footprint-for-the-pilot-2" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Data Size and Infra Footprint for the Pilot: </h3><ul><li>Total # of records: ~34 Million</li><li>Data Size: 500+ GB</li><li>Capacity: 4 vCPUs - 1 Pinot server (4-cores / 32 GB)</li></ul><h3 id="impact-summary-2"><a href="#impact-summary-2" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Impact Summary:</h3><ul><li>95.70% improvement in query performance as a result of 99.9962% reduction in number of documents and entries scanned.</li></ul><p><img alt="Visualization of the impact of star-tree index for a Gaming use case with Apache Pinot" src="https://www.datocms-assets.com/75153/1689175176-image2.png"/></p><h2 id="quick-recap-star-tree-index-performance-improvements"><a href="#quick-recap-star-tree-index-performance-improvements" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Quick Recap: Star-Tree Index Performance Improvements</h2><p><img alt="Recap Table of the Impact that star-tree index had on three real-world customers using Apache Pinot™" src="https://www.datocms-assets.com/75153/1689175271-image3.png"/></p><ul><li>99.99% reduction in data scanned/aggregated per query</li><li>95 to 99% improvement in query performance</li></ul><p>Disk IO is the most expensive operation in query processing. The star-tree index reduces Disk IO significantly. Instead of scanning raw documents from the disk and computing aggregates on the fly, star-tree index scans pre-aggregated documents for the combination of dimensions specified in the query from the disk.</p><p>In part 1 of the series, we saw that the star-tree index reduced the disk reads by 99.999% from 584 Million entries (in case of an inverted index) to 2,045. Query latency came down 99.67% from 1,513 ms to 4 ms! This, in itself, was a HUGE benefit.</p><p>In addition to the drastic improvement in query latency, the memory and CPU usage decreased significantly, freeing up resources for taking up more concurrent workloads. The cumulative effect was the 126 x increase in QPS on this small 4 vCPU Pinot Server, as we saw in part 2 blog of this series.</p><p>And finally, in this part 3 of the blog series, we covered three real production use cases that have seen 95% to 99% improvement in query performance using Star-Tree Index.</p><h2 id="intrigued-by-what-youve-read"><a href="#intrigued-by-what-youve-read" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Intrigued by What You’ve Read?</h2><p>The next step is to load your data into an open-source <a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/basics/getting-started">Apache Pinot</a> cluster or, if you prefer, a fully-managed database-as-a-service (DBaaS). Sign up today for a <a target="_blank" rel="noopener noreferrer" href="https://startree.ai/saas-signup">StarTree Cloud account</a>, free for 30 days. If you have more questions, sign up for the <a target="_blank" rel="noopener noreferrer" href="https://communityinviter.com/apps/startreedata/startree-community">StarTree Community Slack</a>.</p><p><a target="_blank" rel="noopener noreferrer" href="https://startree.ai/saas-signup">GET STARTED ON STARTREE CLOUD</a></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#adtech-use-case">AdTech Use Case</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#why-was-the-existing-system-not-working">Why was the existing system not working?</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#the-problem-and-challenges">The Problem and Challenges?</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#data-size-and-infra-footprint-for-the-pilot">Data Size and Infra Footprint for the Pilot: </a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#impact-summary">Impact Summary:</a></li><li class="text-sm font-normal leading-tight"><a href="#cybersecurity-use-case">CyberSecurity Use Case:</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#why-was-the-existing-system-not-working">Why was the existing system not working?</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#the-problem-and-challenges">The Problem and Challenges?</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#data-size-and-infra-footprint-for-the-pilot">Data Size and Infra Footprint for the Pilot: </a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#impact-summary">Impact Summary:</a></li><li class="text-sm font-normal leading-tight"><a href="#multiplayer-game-leaderboard-use-case">Multiplayer Game Leaderboard Use Case</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#the-problem-and-challenges">The Problem and Challenges?</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#data-size-and-infra-footprint-for-the-pilot">Data Size and Infra Footprint for the Pilot: </a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#impact-summary">Impact Summary:</a></li><li class="text-sm font-normal leading-tight"><a href="#quick-recap-star-tree-index-performance-improvements">Quick Recap: Star-Tree Index Performance Improvements</a></li><li class="text-sm font-normal leading-tight"><a href="#intrigued-by-what-youve-read">Intrigued by What You’ve Read?</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] 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Apache Pinot, Pinot, Apache, the Apache feather logo, and the Apache Pinot project logo are registered trademarks of The Apache Software Foundation. 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This index has also been successful in cybersecurity threat detection and multiplayer game leaderboard tracking, resulting in improved query performance and cost savings. 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The application team wanted to provide a rich analytical interface to these users so that not only can they see the current performance but also do custom slicing and dicing of data over a period of time. For example, compare their current campaign performance to one they ran two weeks back, do cohort analysis, and so on.\"}],[\"$\",\"h3\",null,{\"id\":\"why-was-the-existing-system-not-working\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#why-was-the-existing-system-not-working\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Why was the existing system not working?\"]}],[\"$\",\"p\",null,{\"children\":\"Their existing tech stack was a mix of OSS and custom-built in-house code, which was both operationally difficult to manage and costly to maintain. Yet more importantly, it wasn’t able to meet the basic throughput and latency requirements required by the platform to sustain user growth as well as provide richer analytic capabilities in the product.\"}],[\"$\",\"h3\",null,{\"id\":\"the-problem-and-challenges\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#the-problem-and-challenges\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"The Problem and Challenges?\"]}],[\"$\",\"p\",null,{\"children\":\"When the StarTree Sales Engineering team was engaged, the requirements were very clear:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Throughput: Support 50+ QPS during POC and 200+ for production)\"}],[\"$\",\"li\",null,{\"children\":\"Latency: P95th latency of 2s, including query that needed aggregation of ~ 2 billion rows\"}],[\"$\",\"li\",null,{\"children\":\"Scalability: Ability to scale efficiently with future growth in QPS in a non-linear manner\"}]]}],[\"$\",\"p\",null,{\"children\":\"The biggest challenge was the size of data — 20+ TB and growing — and on top of that, a complex aggregation query driving the summary view for users so they can drill further in to get more details.\"}],[\"$\",\"p\",null,{\"children\":\"This particular query needed to aggregate close to 2 Billion records at read time and then would be fired for every active user interacting with the platform (so high concurrent QPS). In this case, despite applying all relevant indexes available in their existing system, out-of-the-box query performance was still in the 6-8 seconds range, which is expected given that bulk of the work for the query is happening in the aggregation phase and not during the filtering phase (indexing helps with this).\"}],[\"$\",\"p\",null,{\"children\":\"In other OLAP systems they explored, the only option available to handle this use case was doing ingestion time rollups. In other words, changing the data to higher granularity. However, this obviously means losing access to raw data and also potentially re-bootstrapping if new use cases come down the road that need raw data access.\"}],[\"$\",\"p\",null,{\"children\":[\"This is exactly the type of scenario that the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/basics/indexing/star-tree-index\",\"children\":\"Star-Tree Index\"}],\", unique to Apache Pinot, is designed to address - handle large aggregation queries at scale that need sub-second performance. The best part is you can apply it anytime without any need to reprocess the data or plan any system downtime. (Segment reload to apply table config changes run as a background task in Apache Pinot.) In this specific case, the same query latencies with the star-tree index applied went down to 15 ms. This implicitly meant that with the same infrastructure footprint, StarTree was able to support ~70 QPS (Queries Per Second) vs \u003c 1 QPS for this most complex query; while still keeping the raw data intact.\"]}],[\"$\",\"h3\",null,{\"id\":\"data-size-and-infra-footprint-for-the-pilot\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#data-size-and-infra-footprint-for-the-pilot\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Data Size and Infra Footprint for the Pilot: \"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Total # of records: ~2 Trillion\"}],[\"$\",\"li\",null,{\"children\":\"Data Size: ~20 TB\"}],[\"$\",\"li\",null,{\"children\":\"Capacity: 72 vCPUs across 9 Pinot servers (8 vCPU, 64GB per node).\"}]]}],[\"$\",\"h3\",null,{\"id\":\"impact-summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#impact-summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Impact Summary:\"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"99.76% reduction in latency vs. no Star-Tree Index (6.3 seconds to 15 ms)\"}],[\"$\",\"li\",null,{\"children\":\"99.99999% reduction in amount of data scanned/aggregated per query (\u003e 1.8B docs to \u003c 2,400)\"}]]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Visualization of the impact of start-tree index for an AdTech use case with Apache Pinot\",\"src\":\"https://www.datocms-assets.com/75153/1689174701-image1.png\"}]}],[\"$\",\"h2\",null,{\"id\":\"cybersecurity-use-case\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#cybersecurity-use-case\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"CyberSecurity Use Case:\"]}],[\"$\",\"p\",null,{\"children\":\"A cybersecurity company that provides their customers with a real-time threat detection platform with AI, allowing them to analyze network flow logs in real-time with a sophisticated reporting/analytical UI. The initial landing page inside the customer portal is a summary view of everything the platform was monitoring in the user's environment and then provides the capability to drill down into specifics of each. For example, filter requests by a specific application or IP Address.\"}],[\"$\",\"h3\",null,{\"id\":\"why-was-the-existing-system-not-working-1\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#why-was-the-existing-system-not-working-1\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Why was the existing system not working?\"]}],[\"$\",\"p\",null,{\"children\":\"Their existing tech stack was a mix of Athena/Presto, which couldn’t meet the throughput and latency requirements with growing data volume across their customers. Additionally, operational overhead around managing some of these systems in-house led to increased cost.\"}],[\"$\",\"h3\",null,{\"id\":\"the-problem-and-challenges-1\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#the-problem-and-challenges-1\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"The Problem and Challenges?\"]}],[\"$\",\"p\",null,{\"children\":\"Some of the key requirements that StarTree Cloud cluster had to meet:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Throughput: Up to 200 QPS (200 projected by end of year)\"}],[\"$\",\"li\",null,{\"children\":\"Latency: \u003c 1 second P99\"}],[\"$\",\"li\",null,{\"children\":\"High ingestion rate: 300k events/sec\"}],[\"$\",\"li\",null,{\"children\":\"ROI: Provide better cost efficiencies\"}]]}],[\"$\",\"p\",null,{\"children\":\"Similar to Use case #1, the customer wanted to retain data at the lowest granularity (so no ingestion roll-ups), and given the time column granularity similar challenge with running the complex aggregation query to power off the summary view. Additionally, the requirement to get double-digit throughput(QPS) for the POC with the most efficient compute footprint made it quite challenging.\"}],[\"$\",\"p\",null,{\"children\":\"Given the overhead while doing complex aggregations, efficient filtering (indexes) wasn’t enough - in this case, with 3 * 4-core/32GB nodes query took more than 15 seconds. We immediately switched the table config to add star-tree index to the table config and do a segment reload, and the results were phenomenal — query latency was reduced to 10ms.\"}],[\"$\",\"h3\",null,{\"id\":\"data-size-and-infra-footprint-for-the-pilot-1\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#data-size-and-infra-footprint-for-the-pilot-1\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Data Size and Infra Footprint for the Pilot: \"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Total # of records: ~8 Billion\"}],[\"$\",\"li\",null,{\"children\":\"Data Size: 500+ GB\"}],[\"$\",\"li\",null,{\"children\":\"Capacity: 12 vCPUs across 3 Pinot servers (4-core/32GB)\"}]]}],[\"$\",\"h3\",null,{\"id\":\"impact-summary-1\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#impact-summary-1\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Impact Summary:\"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"99.94% reduction in query latency (achieving 100 QPS for the same query with no extra hardware)\"}],[\"$\",\"li\",null,{\"children\":\"99.9998% reduction in data scanned/aggregated per query\"}],[\"$\",\"li\",null,{\"children\":\"Happy Customer 😃\"}]]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Visualization of the impact of star-tree index for a Cybersecurity use case with Apache Pinot\",\"src\":\"https://www.datocms-assets.com/75153/1689175033-image4.png\"}]}],[\"$\",\"h2\",null,{\"id\":\"multiplayer-game-leaderboard-use-case\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#multiplayer-game-leaderboard-use-case\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Multiplayer Game Leaderboard Use Case\"]}],[\"$\",\"p\",null,{\"children\":\"A global leader in the interactive entertainment field has an A/B Testing / Experimentation use case that tracks players’ activities to measure the player engagement on the new features being rolled out.\"}],[\"$\",\"h3\",null,{\"id\":\"the-problem-and-challenges-2\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#the-problem-and-challenges-2\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"The Problem and Challenges?\"]}],[\"$\",\"p\",null,{\"children\":\"Some of the key requirements that StarTree Cloud cluster had to meet:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Throughput: = 200 QPS\"}],[\"$\",\"li\",null,{\"children\":\"Latencies: \u003c 1 second P99\"}],[\"$\",\"li\",null,{\"children\":\"Ingestion rate: 50K events/sec\"}]]}],[\"$\",\"p\",null,{\"children\":\"Given the overhead while doing complex aggregations, efficient filtering (indexes) wasn’t enough - in this case, with 1 * 4-core/32GB nodes query took 163 milliseconds. After switching to a star-tree index, the query latency was reduced to 7ms (a reduction of 95.7%).\"}],[\"$\",\"h3\",null,{\"id\":\"data-size-and-infra-footprint-for-the-pilot-2\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#data-size-and-infra-footprint-for-the-pilot-2\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Data Size and Infra Footprint for the Pilot: \"]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Total # of records: ~34 Million\"}],[\"$\",\"li\",null,{\"children\":\"Data Size: 500+ GB\"}],[\"$\",\"li\",null,{\"children\":\"Capacity: 4 vCPUs - 1 Pinot server (4-cores / 32 GB)\"}]]}],[\"$\",\"h3\",null,{\"id\":\"impact-summary-2\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#impact-summary-2\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Impact Summary:\"]}],[\"$\",\"ul\",null,{\"children\":[\"$\",\"li\",null,{\"children\":\"95.70% improvement in query performance as a result of 99.9962% reduction in number of documents and entries scanned.\"}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Visualization of the impact of star-tree index for a Gaming use case with Apache Pinot\",\"src\":\"https://www.datocms-assets.com/75153/1689175176-image2.png\"}]}],[\"$\",\"h2\",null,{\"id\":\"quick-recap-star-tree-index-performance-improvements\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#quick-recap-star-tree-index-performance-improvements\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Quick Recap: Star-Tree Index Performance Improvements\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Recap Table of the Impact that star-tree index had on three real-world customers using Apache Pinot™\",\"src\":\"https://www.datocms-assets.com/75153/1689175271-image3.png\"}]}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"99.99% reduction in data scanned/aggregated per query\"}],[\"$\",\"li\",null,{\"children\":\"95 to 99% improvement in query performance\"}]]}],[\"$\",\"p\",null,{\"children\":\"Disk IO is the most expensive operation in query processing. The star-tree index reduces Disk IO significantly. Instead of scanning raw documents from the disk and computing aggregates on the fly, star-tree index scans pre-aggregated documents for the combination of dimensions specified in the query from the disk.\"}],[\"$\",\"p\",null,{\"children\":\"In part 1 of the series, we saw that the star-tree index reduced the disk reads by 99.999% from 584 Million entries (in case of an inverted index) to 2,045. Query latency came down 99.67% from 1,513 ms to 4 ms! This, in itself, was a HUGE benefit.\"}],[\"$\",\"p\",null,{\"children\":\"In addition to the drastic improvement in query latency, the memory and CPU usage decreased significantly, freeing up resources for taking up more concurrent workloads. The cumulative effect was the 126 x increase in QPS on this small 4 vCPU Pinot Server, as we saw in part 2 blog of this series.\"}],[\"$\",\"p\",null,{\"children\":\"And finally, in this part 3 of the blog series, we covered three real production use cases that have seen 95% to 99% improvement in query performance using Star-Tree Index.\"}],[\"$\",\"h2\",null,{\"id\":\"intrigued-by-what-youve-read\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#intrigued-by-what-youve-read\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Intrigued by What You’ve Read?\"]}],[\"$\",\"p\",null,{\"children\":[\"The next step is to load your data into an open-source \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/basics/getting-started\",\"children\":\"Apache Pinot\"}],\" cluster or, if you prefer, a fully-managed database-as-a-service (DBaaS). Sign up today for a \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/saas-signup\",\"children\":\"StarTree Cloud account\"}],\", free for 30 days. If you have more questions, sign up for the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://communityinviter.com/apps/startreedata/startree-community\",\"children\":\"StarTree Community Slack\"}],\".\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/saas-signup\",\"children\":\"GET STARTED ON STARTREE CLOUD\"}]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L13\",null,{\"chapters\":[{\"value\":\"AdTech Use Case\",\"url\":\"#adtech-use-case\",\"depth\":2},{\"value\":\"Why was the existing system not working?\",\"url\":\"#why-was-the-existing-system-not-working\",\"depth\":3},{\"value\":\"The Problem and Challenges?\",\"url\":\"#the-problem-and-challenges\",\"depth\":3},{\"value\":\"Data Size and Infra Footprint for the Pilot: \",\"url\":\"#data-size-and-infra-footprint-for-the-pilot\",\"depth\":3},{\"value\":\"Impact Summary:\",\"url\":\"#impact-summary\",\"depth\":3},{\"value\":\"CyberSecurity Use Case:\",\"url\":\"#cybersecurity-use-case\",\"depth\":2},{\"value\":\"Why was the existing system not working?\",\"url\":\"#why-was-the-existing-system-not-working\",\"depth\":3},{\"value\":\"The Problem and Challenges?\",\"url\":\"#the-problem-and-challenges\",\"depth\":3},{\"value\":\"Data Size and Infra Footprint for the Pilot: \",\"url\":\"#data-size-and-infra-footprint-for-the-pilot\",\"depth\":3},{\"value\":\"Impact Summary:\",\"url\":\"#impact-summary\",\"depth\":3},{\"value\":\"Multiplayer Game Leaderboard Use Case\",\"url\":\"#multiplayer-game-leaderboard-use-case\",\"depth\":2},{\"value\":\"The Problem and Challenges?\",\"url\":\"#the-problem-and-challenges\",\"depth\":3},{\"value\":\"Data Size and Infra Footprint for the Pilot: \",\"url\":\"#data-size-and-infra-footprint-for-the-pilot\",\"depth\":3},{\"value\":\"Impact Summary:\",\"url\":\"#impact-summary\",\"depth\":3},{\"value\":\"Quick Recap: Star-Tree Index Performance Improvements\",\"url\":\"#quick-recap-star-tree-index-performance-improvements\",\"depth\":2},{\"value\":\"Intrigued by What You’ve Read?\",\"url\":\"#intrigued-by-what-youve-read\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Star-Tree Index in Apache Pinot - Part 3 - Understanding the Impact in Real Customer Scenarios | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"The blog post discusses how implementing a star-tree index significantly improved query performance for an AdTech platform by reducing latency. 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diff --git a/blog/2023/08/04/segment-compaction-for-upsert-enabled-tables-in-apache-pinot-3f30657aa077.html b/blog/2023/08/04/segment-compaction-for-upsert-enabled-tables-in-apache-pinot-3f30657aa077.html
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</span><span class="code-line"> <span class="token punctuation">}</span>
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-</span></code></pre></div><p>All the configs above (excluding schedule) determine which completed segments are selected for compaction.</p><p>bufferTimePeriod is the amount of time that has elapsed since the segment was consuming. In the example above, this has been set to “7d” which means that any segment that was completed over 7 days ago may be eligible for compaction. However, if you want to ensure that segments are compacted without any additional delay this config can be set to “0d”.</p><p>invalidRecordsThresholdPercent is a limit to the amount of older records allowed in the completed segment represented as a percentage of the total number of records in the segment (i.e. old records / total records). In the example above, this has been set to “30” which means that if more than 30% of the records in the completed segment are old, then the segment may be selected for compaction. As segment compaction is an expensive operation, it is not recommended to set this config (or invalidRecordsThresholdCount) too close to 1. This config is optional on the condition that invalidRecordsThresholdCount has been set and can be used in conjunction with invalidRecordsThresholdCount.</p><p>invalidRecordsThresholdCount is also a limit similar to invalidRecordsThresholdPercent, but allows you to express the threshold as a record count. In the example above, this has been set to “100000” which means that if the segment contains more than 100K records then it may be selected for compaction.</p><h2 id="example-use-case"><a href="#example-use-case" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Example Use Case</h2><p>I’ve created a data set that includes 24M records. The data set contains 240K unique keys that have each been duplicated 100 times.</p><p><img alt="alt" src="https://miro.medium.com/v2/resize:fit:4800/0*gEyUq_Tp4Fycgp2r"/></p><p>After ingesting the data set there are 6 segments (5 completed segments + 1 consuming segment) with a total estimated size of 22.8MB. Submitting the query “set skipUpsert=true; select count(*) from transcript_upsert” before compaction produces the following query result.</p><p><img alt="alt" src="https://miro.medium.com/v2/resize:fit:1064/0*GE3P1fqAcsr0Xs5A"/></p><p>After the compaction tasks are complete, the Minion Task Manager UI reports the following.</p><p><img alt="alt" src="https://miro.medium.com/v2/resize:fit:2000/0*SMxDZNndFwpoeNMI"/></p><p>Segment compaction generates a task for each segment to be compacted. 5 tasks were generated in this case because 90% of the records (3.6–4.5M records) are old in all 5 of the completed segments, therefore exceeding the configured thresholds. If a completed segment only contains old records, it is deleted immediately and a task isn’t generated to compact it.</p><p><img alt="alt" src="https://miro.medium.com/v2/resize:fit:1068/0*LB1itt-wCohpz42i"/></p><p>Submitting the query again we now see that count matches the set of 240K unique keys.</p><p><img alt="alt" src="https://miro.medium.com/v2/resize:fit:2000/0*cmx4zxoMsD4-tR_u"/></p><p>Once compaction has completed and the original segments have been replaced with their compacted counterparts we see that the total number of segments remained the same, but the total estimated size dropped to only 2.77MB! Since compaction can yield very small segments, one improvement would be to merge smaller segments into larger ones as this would improve query latency.</p><h2 id="conclusion"><a href="#conclusion" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Conclusion</h2><p>In this brief overview of Segment Compaction I covered the problem it addresses, how you can configure it, and demonstrated its ability to reclaim storage space. I’d like to thank Ankit Sultana, Seunghyun Lee, and especially Jackie Jiang for their feedback and support throughout the design and development stages. If you have any questions or feedback, I’m available on the Apache Pinot Slack.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#context-and-configuration">Context and Configuration</a></li><li class="text-sm font-normal leading-tight"><a href="#example-use-case">Example Use Case</a></li><li class="text-sm font-normal leading-tight"><a href="#conclusion">Conclusion</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap 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In this post, I will briefly discuss the problem segment compaction addresses, how to configure it, and what it looks like in action. If you’re unfamiliar with Pinot’s Upsert features, I recommend reviewing \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://dev.startree.ai/docs/pinot/recipes/upserts-full\",\"children\":\"Full Upserts in Pinot\"}],\" to get started and \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/basics/data-import/upsert\",\"children\":\"Stream Ingestion with Upsert\"}],\" for more information.\"]}],[\"$\",\"h2\",null,{\"id\":\"context-and-configuration\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#context-and-configuration\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Context and Configuration\"]}],[\"$\",\"p\",null,{\"children\":\"As Pinot’s Upsert stores all versions of the record ingested into immutable segments on disk, older records unnecessarily consume valuable storage space when they’re no longer used in query results. Pinot’s Segment Compaction reclaims this valuable storage space by introducing a periodic process that replaces the completed segments with compacted segments which only contain the latest version of the records. I recommend reviewing the Minion documentation if you’re unfamiliar with Pinot’s ability to run periodic processes.\"}],[\"$\",\"p\",null,{\"children\":\"With task scheduling enabled and an available Minion, you can configure segment compaction by adding the following to your table’s config.\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-json code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"task\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"taskTypeConfigsMap\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"UpsertCompactionTask\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schedule\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"0 */5 * ? * *\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"bufferTimePeriod\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"7d\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"invalidRecordsThresholdPercent\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"30\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"invalidRecordsThresholdCount\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"100000\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"All the configs above (excluding schedule) determine which completed segments are selected for compaction.\"}],[\"$\",\"p\",null,{\"children\":\"bufferTimePeriod is the amount of time that has elapsed since the segment was consuming. In the example above, this has been set to “7d” which means that any segment that was completed over 7 days ago may be eligible for compaction. However, if you want to ensure that segments are compacted without any additional delay this config can be set to “0d”.\"}],[\"$\",\"p\",null,{\"children\":\"invalidRecordsThresholdPercent is a limit to the amount of older records allowed in the completed segment represented as a percentage of the total number of records in the segment (i.e. old records / total records). In the example above, this has been set to “30” which means that if more than 30% of the records in the completed segment are old, then the segment may be selected for compaction. As segment compaction is an expensive operation, it is not recommended to set this config (or invalidRecordsThresholdCount) too close to 1. This config is optional on the condition that invalidRecordsThresholdCount has been set and can be used in conjunction with invalidRecordsThresholdCount.\"}],[\"$\",\"p\",null,{\"children\":\"invalidRecordsThresholdCount is also a limit similar to invalidRecordsThresholdPercent, but allows you to express the threshold as a record count. In the example above, this has been set to “100000” which means that if the segment contains more than 100K records then it may be selected for compaction.\"}],[\"$\",\"h2\",null,{\"id\":\"example-use-case\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#example-use-case\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Example Use Case\"]}],[\"$\",\"p\",null,{\"children\":\"I’ve created a data set that includes 24M records. The data set contains 240K unique keys that have each been duplicated 100 times.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"alt\",\"src\":\"https://miro.medium.com/v2/resize:fit:4800/0*gEyUq_Tp4Fycgp2r\"}]}],[\"$\",\"p\",null,{\"children\":\"After ingesting the data set there are 6 segments (5 completed segments + 1 consuming segment) with a total estimated size of 22.8MB. Submitting the query “set skipUpsert=true; select count(*) from transcript_upsert” before compaction produces the following query result.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"alt\",\"src\":\"https://miro.medium.com/v2/resize:fit:1064/0*GE3P1fqAcsr0Xs5A\"}]}],[\"$\",\"p\",null,{\"children\":\"After the compaction tasks are complete, the Minion Task Manager UI reports the following.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"alt\",\"src\":\"https://miro.medium.com/v2/resize:fit:2000/0*SMxDZNndFwpoeNMI\"}]}],[\"$\",\"p\",null,{\"children\":\"Segment compaction generates a task for each segment to be compacted. 5 tasks were generated in this case because 90% of the records (3.6–4.5M records) are old in all 5 of the completed segments, therefore exceeding the configured thresholds. If a completed segment only contains old records, it is deleted immediately and a task isn’t generated to compact it.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"alt\",\"src\":\"https://miro.medium.com/v2/resize:fit:1068/0*LB1itt-wCohpz42i\"}]}],[\"$\",\"p\",null,{\"children\":\"Submitting the query again we now see that count matches the set of 240K unique keys.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"alt\",\"src\":\"https://miro.medium.com/v2/resize:fit:2000/0*cmx4zxoMsD4-tR_u\"}]}],[\"$\",\"p\",null,{\"children\":\"Once compaction has completed and the original segments have been replaced with their compacted counterparts we see that the total number of segments remained the same, but the total estimated size dropped to only 2.77MB! Since compaction can yield very small segments, one improvement would be to merge smaller segments into larger ones as this would improve query latency.\"}],[\"$\",\"h2\",null,{\"id\":\"conclusion\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#conclusion\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Conclusion\"]}],[\"$\",\"p\",null,{\"children\":\"In this brief overview of Segment Compaction I covered the problem it addresses, how you can configure it, and demonstrated its ability to reclaim storage space. I’d like to thank Ankit Sultana, Seunghyun Lee, and especially Jackie Jiang for their feedback and support throughout the design and development stages. If you have any questions or feedback, I’m available on the Apache Pinot Slack.\"}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"Context and Configuration\",\"url\":\"#context-and-configuration\",\"depth\":2},{\"value\":\"Example Use Case\",\"url\":\"#example-use-case\",\"depth\":2},{\"value\":\"Conclusion\",\"url\":\"#conclusion\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Segment Compaction for Upsert Enabled Tables in Apache Pinot | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"The blog post discusses the feature contribution of segment compaction to Apache Pinot project, addressing the issue of older records consuming unnecessary storage space. 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+</span></code></pre></div><p>All the configs above (excluding schedule) determine which completed segments are selected for compaction.</p><p>bufferTimePeriod is the amount of time that has elapsed since the segment was consuming. In the example above, this has been set to “7d” which means that any segment that was completed over 7 days ago may be eligible for compaction. However, if you want to ensure that segments are compacted without any additional delay this config can be set to “0d”.</p><p>invalidRecordsThresholdPercent is a limit to the amount of older records allowed in the completed segment represented as a percentage of the total number of records in the segment (i.e. old records / total records). In the example above, this has been set to “30” which means that if more than 30% of the records in the completed segment are old, then the segment may be selected for compaction. As segment compaction is an expensive operation, it is not recommended to set this config (or invalidRecordsThresholdCount) too close to 1. This config is optional on the condition that invalidRecordsThresholdCount has been set and can be used in conjunction with invalidRecordsThresholdCount.</p><p>invalidRecordsThresholdCount is also a limit similar to invalidRecordsThresholdPercent, but allows you to express the threshold as a record count. In the example above, this has been set to “100000” which means that if the segment contains more than 100K records then it may be selected for compaction.</p><h2 id="example-use-case"><a href="#example-use-case" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Example Use Case</h2><p>I’ve created a data set that includes 24M records. The data set contains 240K unique keys that have each been duplicated 100 times.</p><p><img alt="alt" src="https://miro.medium.com/v2/resize:fit:4800/0*gEyUq_Tp4Fycgp2r"/></p><p>After ingesting the data set there are 6 segments (5 completed segments + 1 consuming segment) with a total estimated size of 22.8MB. Submitting the query “set skipUpsert=true; select count(*) from transcript_upsert” before compaction produces the following query result.</p><p><img alt="alt" src="https://miro.medium.com/v2/resize:fit:1064/0*GE3P1fqAcsr0Xs5A"/></p><p>After the compaction tasks are complete, the Minion Task Manager UI reports the following.</p><p><img alt="alt" src="https://miro.medium.com/v2/resize:fit:2000/0*SMxDZNndFwpoeNMI"/></p><p>Segment compaction generates a task for each segment to be compacted. 5 tasks were generated in this case because 90% of the records (3.6–4.5M records) are old in all 5 of the completed segments, therefore exceeding the configured thresholds. If a completed segment only contains old records, it is deleted immediately and a task isn’t generated to compact it.</p><p><img alt="alt" src="https://miro.medium.com/v2/resize:fit:1068/0*LB1itt-wCohpz42i"/></p><p>Submitting the query again we now see that count matches the set of 240K unique keys.</p><p><img alt="alt" src="https://miro.medium.com/v2/resize:fit:2000/0*cmx4zxoMsD4-tR_u"/></p><p>Once compaction has completed and the original segments have been replaced with their compacted counterparts we see that the total number of segments remained the same, but the total estimated size dropped to only 2.77MB! Since compaction can yield very small segments, one improvement would be to merge smaller segments into larger ones as this would improve query latency.</p><h2 id="conclusion"><a href="#conclusion" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Conclusion</h2><p>In this brief overview of Segment Compaction I covered the problem it addresses, how you can configure it, and demonstrated its ability to reclaim storage space. I’d like to thank Ankit Sultana, Seunghyun Lee, and especially Jackie Jiang for their feedback and support throughout the design and development stages. If you have any questions or feedback, I’m available on the Apache Pinot Slack.</p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#context-and-configuration">Context and Configuration</a></li><li class="text-sm font-normal leading-tight"><a href="#example-use-case">Example Use Case</a></li><li class="text-sm font-normal leading-tight"><a href="#conclusion">Conclusion</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap 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I recommend reviewing the Minion documentation if you’re unfamiliar with Pinot’s ability to run periodic processes.\"}],[\"$\",\"p\",null,{\"children\":\"With task scheduling enabled and an available Minion, you can configure segment compaction by adding the following to your table’s config.\"}],[\"$\",\"$L13\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-json code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"task\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"taskTypeConfigsMap\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"UpsertCompactionTask\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"schedule\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"0 */5 * ? * *\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"bufferTimePeriod\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"7d\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"invalidRecordsThresholdPercent\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"30\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"invalidRecordsThresholdCount\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"100000\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"All the configs above (excluding schedule) determine which completed segments are selected for compaction.\"}],[\"$\",\"p\",null,{\"children\":\"bufferTimePeriod is the amount of time that has elapsed since the segment was consuming. In the example above, this has been set to “7d” which means that any segment that was completed over 7 days ago may be eligible for compaction. However, if you want to ensure that segments are compacted without any additional delay this config can be set to “0d”.\"}],[\"$\",\"p\",null,{\"children\":\"invalidRecordsThresholdPercent is a limit to the amount of older records allowed in the completed segment represented as a percentage of the total number of records in the segment (i.e. old records / total records). In the example above, this has been set to “30” which means that if more than 30% of the records in the completed segment are old, then the segment may be selected for compaction. As segment compaction is an expensive operation, it is not recommended to set this config (or invalidRecordsThresholdCount) too close to 1. This config is optional on the condition that invalidRecordsThresholdCount has been set and can be used in conjunction with invalidRecordsThresholdCount.\"}],[\"$\",\"p\",null,{\"children\":\"invalidRecordsThresholdCount is also a limit similar to invalidRecordsThresholdPercent, but allows you to express the threshold as a record count. In the example above, this has been set to “100000” which means that if the segment contains more than 100K records then it may be selected for compaction.\"}],[\"$\",\"h2\",null,{\"id\":\"example-use-case\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#example-use-case\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Example Use Case\"]}],[\"$\",\"p\",null,{\"children\":\"I’ve created a data set that includes 24M records. The data set contains 240K unique keys that have each been duplicated 100 times.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"alt\",\"src\":\"https://miro.medium.com/v2/resize:fit:4800/0*gEyUq_Tp4Fycgp2r\"}]}],[\"$\",\"p\",null,{\"children\":\"After ingesting the data set there are 6 segments (5 completed segments + 1 consuming segment) with a total estimated size of 22.8MB. Submitting the query “set skipUpsert=true; select count(*) from transcript_upsert” before compaction produces the following query result.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"alt\",\"src\":\"https://miro.medium.com/v2/resize:fit:1064/0*GE3P1fqAcsr0Xs5A\"}]}],[\"$\",\"p\",null,{\"children\":\"After the compaction tasks are complete, the Minion Task Manager UI reports the following.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"alt\",\"src\":\"https://miro.medium.com/v2/resize:fit:2000/0*SMxDZNndFwpoeNMI\"}]}],[\"$\",\"p\",null,{\"children\":\"Segment compaction generates a task for each segment to be compacted. 5 tasks were generated in this case because 90% of the records (3.6–4.5M records) are old in all 5 of the completed segments, therefore exceeding the configured thresholds. If a completed segment only contains old records, it is deleted immediately and a task isn’t generated to compact it.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"alt\",\"src\":\"https://miro.medium.com/v2/resize:fit:1068/0*LB1itt-wCohpz42i\"}]}],[\"$\",\"p\",null,{\"children\":\"Submitting the query again we now see that count matches the set of 240K unique keys.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"alt\",\"src\":\"https://miro.medium.com/v2/resize:fit:2000/0*cmx4zxoMsD4-tR_u\"}]}],[\"$\",\"p\",null,{\"children\":\"Once compaction has completed and the original segments have been replaced with their compacted counterparts we see that the total number of segments remained the same, but the total estimated size dropped to only 2.77MB! Since compaction can yield very small segments, one improvement would be to merge smaller segments into larger ones as this would improve query latency.\"}],[\"$\",\"h2\",null,{\"id\":\"conclusion\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#conclusion\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Conclusion\"]}],[\"$\",\"p\",null,{\"children\":\"In this brief overview of Segment Compaction I covered the problem it addresses, how you can configure it, and demonstrated its ability to reclaim storage space. I’d like to thank Ankit Sultana, Seunghyun Lee, and especially Jackie Jiang for their feedback and support throughout the design and development stages. If you have any questions or feedback, I’m available on the Apache Pinot Slack.\"}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of Contents\"}]}],[\"$\",\"$L14\",null,{\"chapters\":[{\"value\":\"Context and Configuration\",\"url\":\"#context-and-configuration\",\"depth\":2},{\"value\":\"Example Use Case\",\"url\":\"#example-use-case\",\"depth\":2},{\"value\":\"Conclusion\",\"url\":\"#conclusion\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Segment Compaction for Upsert Enabled Tables in Apache Pinot | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"The blog post discusses the feature contribution of segment compaction to Apache Pinot project, addressing the issue of older records consuming unnecessary storage space. 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-</span></code></pre></div><p><code><field-names-1 and 2></code> are a comma-separated list of fields you wish to encode with CLP.</p><p>You can read the design <a target="_blank" rel="noopener noreferrer" href="https://docs.google.com/document/d/1nHZb37re4mUwEA258x3a2pgX13EWLWMJ0uLEDk1dUyU/edit">document</a> for more details into why and how this feature was implemented.</p><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>Apache Pinot’s evolution is expressly due to the humans behind the code, and in reaching 1.0 release status it is proper and fitting to give credit to the open source project’s key committers. Since its early days, over <a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/graphs/contributors">three hundred contributors</a> have produced more than 1.3 million source lines of code (SLOC).</p><p><img alt="Image test" src="https://pinot.apache.org/blogs/apache-pinot-1-0-name-cloud.png"/></p><p>The introduction of Apache Pinot 1.0 represents an exceptional stride forward in real-time online analytical processing (OLAP) capabilities, marking a watershed moment in the evolution of real-time analytics systems. This release redefines the limits of what can be achieved in the realm of instant data analysis, presenting a game-changing solution for organizations seeking high throughput and low latency in their OLAP queries. If you would like to get started with Apache Pinot 1.0, you can check out the documentation, and download it now.</p><h2 id="resources"><a href="#resources" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Resources</h2><p>If you want to try out Apache Pinot, the following resources will help you get started:</p><p><a target="_blank" rel="noopener noreferrer" href="https://pinot.apache.org/download/">Download page</a></p><p><a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/getting-started">Getting started</a></p><p><a target="_blank" rel="noopener noreferrer" href="https://communityinviter.com/apps/apache-pinot/apache-pinot">Join our Slack channel</a></p><p><a target="_blank" rel="noopener noreferrer" href="https://www.meetup.com/apache-pinot">See our upcoming events</a></p><p><a target="_blank" rel="noopener noreferrer" href="https://twitter.com/ApachePinot">Follow us on social media</a></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#what-makes-a-10-release">What Makes a “1.0 Release?”</a></li><li class="text-sm font-normal leading-tight"><a href="#join-support">Join Support</a></li><li class="text-sm font-normal leading-tight"><a href="#upsert-improvements">Upsert Improvements</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#segment-compaction-for-upsert-tables">Segment Compaction for Upsert Tables</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#delete-support-for-upsert-tables">DELETE Support for Upsert Tables</a></li><li class="text-sm font-normal leading-tight"><a href="#null-value-support">NULL Value Support</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#aggregations">Aggregations</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#group-by">Group By</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#sorting">Sorting</a></li><li class="text-sm font-normal leading-tight"><a href="#index-pluggability">Index Pluggability</a></li><li class="text-sm font-normal leading-tight"><a href="#apache-pinot-spark-3-connector-and-passing-pinot-options">Apache Pinot Spark 3 Connector and Passing Pinot Options</a></li><li class="text-sm font-normal leading-tight"><a href="#petabyte-scale-log-storage-and-search-in-pinot-with-clp">Petabyte-Scale Log Storage and Search in Pinot with CLP</a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li><li class="text-sm font-normal leading-tight"><a href="#resources">Resources</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 2.426-2.068l.002-.002m6.452 5.605c-.445-.793-1.032-1.395-1.76-1.808a4.72 4.72 0 0 0-2.362-.618c-.847 0-1.602.211-2.33.635-.728.423-1.315 1.038-1.76 1.841-.445.804-.668 1.749-.668 2.835 0 1.087.221 2.032.668 2.835.445.804 1.032 1.417 1.76 1.842a4.557 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1.243h18c0-.588-.4-1.1-.97-1.243v.002"></path><path d="M13.5 23a2.5 2.5 0 1 0 0-5 2.5 2.5 0 0 0 0 5M8 5a1 1 0 1 0 0-2 1 1 0 0 0 0 2M12 8a1 1 0 1 0 0-2 1 1 0 0 0 0 2M16 2a1 1 0 1 0 0-2 1 1 0 0 0 0 2"></path></g><defs><clipPath id="logo_svg__a"><path fill="#fff" d="M0 0h120v48H0z"></path></clipPath></defs></svg></div><div class="flex flex-wrap gap-x-16 gap-y-5 py-8 md:pl-24 md:pr-[21.625rem]"> <div><h5 class="mb-4 text-lg font-semibold">Resources</h5><div class="flex justify-between gap-x-10"><div class="flex flex-col"><a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/" class="block py-1 text-gray-600 hover:text-gray-900">Docs</a><a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/getting-started" class="block py-1 text-gray-600 hover:text-gray-900">Getting Started</a><a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/integrations/thirdeye" class="block py-1 text-gray-600 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Back then it was developed at a single company with a single use case in mind: to power “who viewed my profile?” Over the ensuing decade the Apache Pinot community expanded to be embraced by many other organizations, and those organizations have expanded its capabilities to address new use cases. Apache Pinot in 2023 is continuously evolving to address emerging needs in the real-time analytics community. Let’s look at how much innovation has gone into Apache Pinot over the years:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Upserts — data-in-motion tends to stay in motion, and one of the cornerstone capabilities of Apache Pinot is upsert support to handle upsert mutations in real-time.\"}],[\"$\",\"li\",null,{\"children\":\"Query-time Native JOINs — it was important to get this right, so that they were performant and scalable, allowing high QPS. This we will discuss in more detail below.\"}],[\"$\",\"li\",null,{\"children\":\"Pluggable architecture — a broad user base requires the ability to extend the database with new customizable index types, routing strategies and storage options\"}],[\"$\",\"li\",null,{\"children\":\"Handling Semi-Structured/Unstructured Data — Pinot can easily index JSON and text data types at scale.\"}],[\"$\",\"li\",null,{\"children\":\"Improving ANSI SQL Compliance — to that end, we’ve added better NULL handling, window functions, and as stated above, the capability for native JOINs.\"}]]}],[\"$\",\"p\",null,{\"children\":\"With all of these features and capabilities, Apache Pinot moves farther and farther from mere database status, and becomes more of a complete platform that can tackle entire new classes of use cases that were beyond its capabilities in earlier days.\"}],[\"$\",\"p\",null,{\"children\":\"First let’s look at what Apache Pinot 1.0 itself is delivering. The first foundational pillar of what makes something worthy of a “1.0” release is software quality. Over the past year, since September 2022, engineers across the Apache Pinot community have closed over 300 issues to provide new features, optimize performance, expand test coverage, and squash bugs.\"}],[\"$\",\"p\",null,{\"children\":[\"Features are also a key thing that makes a new release worthy of “1.0” status. 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While this query engine has been available within Apache Pinot already (since release 0.11.0), with the release of Apache Pinot 1.0 this feature is functionally complete.\"}],[\"$\",\"p\",null,{\"children\":[\"(While you can read more below, check out the accompanying blog by Apache Pinot PMC Neha Pawar about using query-time JOINs \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/blog/query-time-joins-in-apache-pinot-1-0\",\"children\":\"here\"}],\").\"]}],[\"$\",\"p\",null,{\"children\":[\"This post is a summary of the high points, but you can find a full list of everything included in the release notes. And if you’d like a \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://youtu.be/2cwRHM4J7kI?si=hEtl6W2eNlMkWqag\",\"children\":\"video treatment of many of the main features in 1.0\"}],\", including some helpful animations, watch here:\"]}],[\"$\",\"$L13\",null,{\"src\":\"https://www.youtube.com/embed/2cwRHM4J7kI\",\"title\":\"YouTube video player\"}],[\"$\",\"p\",null,{\"children\":\"Otherwise, let’s have a look at some of the highlighted changes:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Join Support - Part of the Multi-Stage Query Engine\"}],[\"$\",\"li\",null,{\"children\":\"Improved Upserts - Deletion and Compaction Support\"}],[\"$\",\"li\",null,{\"children\":\"Encode User-Specified Compressed Log Processor (CLP) During Ingestion\"}],[\"$\",\"li\",null,{\"children\":\"NULL Support\"}],[\"$\",\"li\",null,{\"children\":\"Pluggable Index Types [Index Service Provider Interface (SPI)]\"}],[\"$\",\"li\",null,{\"children\":\"Improved Pinot-Spark Integration - Spark3 Compatibility\"}]]}],[\"$\",\"h2\",null,{\"id\":\"join-support\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#join-support\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Join Support\"]}],[\"$\",\"p\",null,{\"children\":\"Apache Pinot 1.0 introduces native query-time JOIN support equipping Pinot to handle a broad spectrum of JOIN scenarios providing full coverage from user-facing analytics all the way up to ad hoc analytics. Underpinning this innovation is the multi-stage query engine, introduced a year ago, which efficiently manages complex analytical queries, including JOIN operations. This engine alleviates computational burdens by offloading tasks from brokers to a dedicated intermediate compute stage. Additionally, a new planner supporting full SQL semantics enhances Pinot's analytical capabilities.\"}],[\"$\",\"p\",null,{\"children\":\"JOIN optimization strategies play a pivotal role in Apache Pinot 1.0. These include predicate push-down to individual tables and using indexing and pruning to reduce scanning which speeds up query processing, smart data layout considerations to minimize data shuffling, and query hints for fine-tuning JOIN operations. With support for all JOIN types and three JOIN algorithms, including broadcast join, shuffle distributed hash join, and lookup join, Apache Pinot delivers versatility and scalability. By significantly reducing query latency and simplifying architecture, Apache Pinot 1.0 is a game-changer for real-time OLAP systems.\"}],[\"$\",\"p\",null,{\"children\":[\"For more detailed information on JOINs, please visit this blog \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/blog/query-time-joins-in-apache-pinot-1-0\",\"children\":\"post\"}],\".\"]}],[\"$\",\"p\",null,{\"children\":\"Discover How Uber is using Joins in Apache Pinot For a real-world use case, Uber is already using the new join capabilities of Apache Pinot at scale in production. You can watch this video to learn more.\"}],[\"$\",\"$L13\",null,{\"src\":\"https://www.youtube.com/embed/z4Chhref1BM\",\"title\":\"YouTube video player\"}],[\"$\",\"h2\",null,{\"id\":\"upsert-improvements\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#upsert-improvements\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Upsert Improvements\"]}],[\"$\",\"p\",null,{\"children\":\"Support for upserts is one of the key capabilities Apache Pinot offers that differentiates it from other real-time analytics databases. It is a vital feature when real-time streaming data is prone to frequent updates. 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However, if you want to ensure that segments are compacted without any additional delay this config can be set to “0d”.\"}],[\"$\",\"p\",null,{\"children\":\"invalidRecordsThresholdPercent is an optional limit to the amount of older records allowed in the completed segment represented as a percentage of the total number of records in the segment (i.e. old records / total records). In the example, this property is set to “30” which means that if more than 30% of the records in the completed segment are old, then the segment may be selected for compaction.\"}],[\"$\",\"p\",null,{\"children\":\"invalidRecordsThresholdCount is also a limit similar to the previous property, but allows you to express the threshold as a record count. In the example above, this property is set to “100000” which means that if the segment contains more than 100K records then it may be selected for compaction.\"}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://robert-zych.medium.com/segment-compaction-for-upsert-enabled-tables-in-apache-pinot-3f30657aa077\",\"children\":\"Read more\"}],\" about the design of this feature.\"]}],[\"$\",\"h3\",null,{\"id\":\"delete-support-for-upsert-tables\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#delete-support-for-upsert-tables\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"DELETE Support for Upsert Tables\"]}],[\"$\",\"p\",null,{\"children\":\"Apache Pinot upsert tables now support deleting records. Supporting delete with upsert avoids the need for the user to explicitly filter out invalid records in the query. SELECT _FROM table WHERE deleted_column != true becomes as simple as SELECT _ FROM table. Pinot will only return the latest non-deleted records from the table. This feature opens up the support to ingest Change Data Capture (CDC) data like Debezium where the changes from a source (typically, mutable) will contain DELETE events.\"}],[\"$\",\"p\",null,{\"children\":[\"Deletes itself is implemented as a soft-delete in Apache Pinot with a dedicated boolean column that serves as a delete marker for the record. Pinot automatically filters out records that are marked in this column. For more details, please see the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/basics/data-import/upsert#delete-column\",\"children\":\"documentation\"}],\".\"]}],[\"$\",\"h2\",null,{\"id\":\"null-value-support\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#null-value-support\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"NULL Value Support\"]}],[\"$\",\"p\",null,{\"children\":\"This feature enables Postgres compatible NULL semantics in Apache Pinot queries. The NULL semantics are important for usability for full SQL compatibility which many BI applications like Tableau rely upon when invoking queries to render dashboards. Previously in Pinot, we could not represent NULL. The workaround was to use special values like Integer.MIN_VALUE to represent NULL. Now Pinot 1.0 has full support to represent NULL values. By adding NULL support, Pinot 1.0 has increased the Tableau certification pass rate by 90%.\"}],[\"$\",\"p\",null,{\"children\":\"Here are some examples of how NULLs will work in Pinot 1.0.\"}],[\"$\",\"h3\",null,{\"id\":\"aggregations\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#aggregations\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Aggregations\"]}],[\"$\",\"p\",null,{\"children\":\"Given the following table below, aggregating columns with NULL values will have this behavior.\"}],[\"$\",\"div\",null,{\"className\":\"w-full overflow-x-auto\",\"children\":[\"$\",\"table\",null,{\"children\":[[\"$\",\"thead\",null,{\"children\":[\"$\",\"tr\",null,{\"children\":[[\"$\",\"th\",null,{\"children\":\"col1\"}],[\"$\",\"th\",null,{\"children\":\"col2\"}]]}]}],[\"$\",\"tbody\",null,{\"children\":[[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"1\"}],[\"$\",\"td\",null,{\"children\":\"NULL\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"2\"}],[\"$\",\"td\",null,{\"children\":\"NULL\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"3\"}],[\"$\",\"td\",null,{\"children\":\"1\"}]]}]]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Since col1 does not contain NULL values, all the values are included in the aggregation.\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"col1\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token comment\",\"children\":\"-- returns 6\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"col1\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token comment\",\"children\":\"-- returns 3\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"In the select statement below, the NULL values in col2 are not included in the aggregation.\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"col2\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token comment\",\"children\":\"-- returns 1\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"col2\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token comment\",\"children\":\"-- returns 1\"}],\"\\n\"]}]]}]}],[\"$\",\"h3\",null,{\"id\":\"group-by\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#group-by\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Group By\"]}],[\"$\",\"p\",null,{\"children\":\"Pinot now supports grouping by NULL. In the example below, we are grouping by col1 which contains a NULL value. Given the table below, grouping by columns with NULL value will have this behavior.\"}],[\"$\",\"div\",null,{\"className\":\"w-full overflow-x-auto\",\"children\":[\"$\",\"table\",null,{\"children\":[[\"$\",\"thead\",null,{\"children\":[\"$\",\"tr\",null,{\"children\":[\"$\",\"th\",null,{\"children\":\"col1\"}]}]}],[\"$\",\"tbody\",null,{\"children\":[[\"$\",\"tr\",null,{\"children\":[\"$\",\"td\",null,{\"children\":\"a\"}]}],[\"$\",\"tr\",null,{\"children\":[\"$\",\"td\",null,{\"children\":\"NULL\"}]}],[\"$\",\"tr\",null,{\"children\":[\"$\",\"td\",null,{\"children\":\"b\"}]}],[\"$\",\"tr\",null,{\"children\":[\"$\",\"td\",null,{\"children\":\"a\"}]}]]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The following select statement will output the following result.\"}],[\"$\",\"p\",null,{\"children\":\"select col1, count(*) from table group by col1\"}],[\"$\",\"div\",null,{\"className\":\"w-full overflow-x-auto\",\"children\":[\"$\",\"table\",null,{\"children\":[[\"$\",\"thead\",null,{\"children\":[\"$\",\"tr\",null,{\"children\":[[\"$\",\"th\",null,{\"children\":\"col1\"}],[\"$\",\"th\",null,{\"children\":\"count()\"}]]}]}],[\"$\",\"tbody\",null,{\"children\":[[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"a\"}],[\"$\",\"td\",null,{\"children\":\"2\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"b\"}],[\"$\",\"td\",null,{\"children\":\"1\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"NULL\"}],[\"$\",\"td\",null,{\"children\":\"1\"}]]}]]}]]}]}],[\"$\",\"h3\",null,{\"id\":\"sorting\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#sorting\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Sorting\"]}],[\"$\",\"p\",null,{\"children\":\"Pinot now allows you to specify the location of NULL values when sorting records. The default is to act as though NULLs are larger than non-NULLs.\"}],[\"$\",\"p\",null,{\"children\":\"Given this list of values, sorting them will result in the following.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"values: 1, 2, 3, NULL\"}]}],[\"$\",\"p\",null,{\"children\":\"Example 1:\"}],[\"$\",\"p\",null,{\"children\":\"NULL values sort BEFORE all non-NULL values.\"}],[\"$\",\"p\",null,{\"children\":\"SQL:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" col \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"table\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"order\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" col NULLS \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FIRST\"}],\"\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"RESULT: NULL, 1, 2, 3\"}]}],[\"$\",\"p\",null,{\"children\":\"Example 2:\"}],[\"$\",\"p\",null,{\"children\":\"NULL values sort AFTER all non-NULL values.\"}],[\"$\",\"p\",null,{\"children\":\"SQL:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" col \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"table\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"order\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" col \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ASC\"}],\" NULLS \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"LAST\"}],\"\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"RESULT: 1, 2, 3, NULL\"}]}],[\"$\",\"p\",null,{\"children\":\"Example 3:\"}],[\"$\",\"p\",null,{\"children\":\"Default behavior is NULL LAST.\"}],[\"$\",\"p\",null,{\"children\":\"SQL:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" col \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"table\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"order\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" col\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"RESULT: 1, 2, 3, NULL\"}]}],[\"$\",\"h2\",null,{\"id\":\"index-pluggability\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#index-pluggability\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Index Pluggability\"]}],[\"$\",\"p\",null,{\"children\":\"Today, Pinot supports multiple index types, like forward index, bloom filter, and range index. Before Pinot 1.0, index types were all statically defined, which means that in order to create a new index type, you’d need to rebuild Pinot from source. Ideally that shouldn’t be the case.\"}],[\"$\",\"p\",null,{\"children\":[\"To increase speed of development, \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/apache/pinot/issues/10183\",\"children\":\"Index Service Provider Interface (SPI)\"}],\", or index-spi, reduces friction by adding the ability to include new index types at runtime in Pinot. This opens the ability of adding third party indexes by including an external jar in the classpath and adding some configuration. This opens up Pinot indexing to lower-friction innovation from the community.\"]}],[\"$\",\"p\",null,{\"children\":\"For now, SPI-accessible indexes are limited to single field index types. Features like the star-tree index or other multi-column approaches are not yet supported.\"}],[\"$\",\"h2\",null,{\"id\":\"apache-pinot-spark-3-connector-and-passing-pinot-options\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#apache-pinot-spark-3-connector-and-passing-pinot-options\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Apache Pinot Spark 3 Connector and Passing Pinot Options\"]}],[\"$\",\"p\",null,{\"children\":[\"Apache Spark users can now take advantage of Pinot’s ability to provide high scalability, low latency, and high concurrency within the context of a Spark 3 cluster using the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/apache/pinot/blob/master/pinot-connectors/pinot-spark-3-connector/README.md\",\"children\":\"Apache Pinot Spark 3 Connector\"}],\".\"]}],[\"$\",\"p\",null,{\"children\":\"This connector supports Apache Spark (2.x and 3.x) as a processor to create and push segment files to the database and can read realtime, offline, and hybrid tables from Pinot.\"}],[\"$\",\"p\",null,{\"children\":\"Now you can merge your streaming and batch datasets together in Spark to provide a full view of real-time and historical data for your machine learning algorithms and feature stores.\"}],[\"$\",\"p\",null,{\"children\":\"Performance Features\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Distributed, parallel scan\"}],[\"$\",\"li\",null,{\"children\":\"Streaming reads using gRPC (optional)\"}],[\"$\",\"li\",null,{\"children\":\"Column and filter push down to optimize performance\"}],[\"$\",\"li\",null,{\"children\":\"Support for Pinot’s Query Options that include: maxExecutionThreads, enableNullHandling, skipUpsert, etc.\"}]]}],[\"$\",\"p\",null,{\"children\":\"Usability Features\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"SQL support instead of PQL\"}],[\"$\",\"li\",null,{\"children\":\"Overlap between realtime and offline segments is queried exactly once for hybrid tables\"}],[\"$\",\"li\",null,{\"children\":[\"Schema discovery - If schema is not specified, the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/apache/pinot/blob/master/pinot-connectors/pinot-spark-3-connector/documentation/read_model.md\",\"children\":\"connector reads the table schema\"}],\" from the Pinot controller, and then converts to the Spark schema.\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"Here is an example that reads a Pinot table, by setting the format to “pinot” spark will automatically load the Pinot connector and read the “airlinesStats” table. The queryOptions property allows you to provide \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/users/user-guide-query/query-options\",\"children\":\"Pinot Query Options\"}],\".\"]}],[\"$\",\"$L14\",null,{\"className\":\"language-scala\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-scala\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"val\"}],\" data \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" spark\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"read\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"format\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"pinot\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"option\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"table\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"airlineStats\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"option\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"tableType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"offline\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"option\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"queryOptions\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"enableNullHandling=true,maxExecutionThreads=1\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"load\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"sql\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"SELECT * FROM airlineStats WHERE DEST = ‘SFO’\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"data\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"show\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"100\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}]]}]}],[\"$\",\"h2\",null,{\"id\":\"petabyte-scale-log-storage-and-search-in-pinot-with-clp\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#petabyte-scale-log-storage-and-search-in-pinot-with-clp\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Petabyte-Scale Log Storage and Search in Pinot with CLP\"]}],[\"$\",\"p\",null,{\"children\":[\"Compressed Log Processor (CLP) is a tool capable of losslessly compressing text logs and searching them in their compressed state. It achieves a better compression ratio than general purpose compressors alone, while retaining the ability to search the compressed log events without incurring the performance penalty of fully decompressing them. Part of CLP’s algorithm was deployed within \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://www.uber.com/blog/reducing-logging-cost-by-two-orders-of-magnitude-using-clp/\",\"children\":\"Uber\"}],\" to compress unstructured Spark logs, as they are generated, achieving an unprecedented compression of 169×.\"]}],[\"$\",\"p\",null,{\"children\":\"Log events generated as JSON objects with user-defined schemas, meaning each event may have different keys. Such user-defined schemas make these events challenging to store in a table with a set schema. With Log Storage and Search in Pinot with CLP, users would be able to:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Store their log events losslessly (without dropping fields)\"}],[\"$\",\"li\",null,{\"children\":\"Store their logs with some amount of compression\"}],[\"$\",\"li\",null,{\"children\":\"Query their logs efficiently\"}]]}],[\"$\",\"p\",null,{\"children\":\"The CLP ingestion pipeline can be used on log events from a stream, such as JSON log events ingested from Kafka. The plugin takes two inputs: a JSON record and a list of fields to encode with CLP.\"}],[\"$\",\"p\",null,{\"children\":\"The fields to encode can be configured as shown:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" ...\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableIndexConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" ...\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"streamConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" ...\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.inputformat.clplog.CLPLogMessageDecoder\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.prop.fieldsForClpEncoding\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"\u003cfield-name-1\u003e,\u003cfield-name-2\u003e\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"code\",null,{\"children\":\"\u003cfield-names-1 and 2\u003e\"}],\" are a comma-separated list of fields you wish to encode with CLP.\"]}],[\"$\",\"p\",null,{\"children\":[\"You can read the design \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.google.com/document/d/1nHZb37re4mUwEA258x3a2pgX13EWLWMJ0uLEDk1dUyU/edit\",\"children\":\"document\"}],\" for more details into why and how this feature was implemented.\"]}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":[\"Apache Pinot’s evolution is expressly due to the humans behind the code, and in reaching 1.0 release status it is proper and fitting to give credit to the open source project’s key committers. Since its early days, over \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/apache/pinot/graphs/contributors\",\"children\":\"three hundred contributors\"}],\" have produced more than 1.3 million source lines of code (SLOC).\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Image test\",\"src\":\"https://pinot.apache.org/blogs/apache-pinot-1-0-name-cloud.png\"}]}],[\"$\",\"p\",null,{\"children\":\"The introduction of Apache Pinot 1.0 represents an exceptional stride forward in real-time online analytical processing (OLAP) capabilities, marking a watershed moment in the evolution of real-time analytics systems. This release redefines the limits of what can be achieved in the realm of instant data analysis, presenting a game-changing solution for organizations seeking high throughput and low latency in their OLAP queries. 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noreferrer\",\"href\":\"https://communityinviter.com/apps/apache-pinot/apache-pinot\",\"children\":\"Join our Slack channel\"}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://www.meetup.com/apache-pinot\",\"children\":\"See our upcoming events\"}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://twitter.com/ApachePinot\",\"children\":\"Follow us on social media\"}]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of 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Options\",\"url\":\"#apache-pinot-spark-3-connector-and-passing-pinot-options\",\"depth\":2},{\"value\":\"Petabyte-Scale Log Storage and Search in Pinot with CLP\",\"url\":\"#petabyte-scale-log-storage-and-search-in-pinot-with-clp\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2},{\"value\":\"Resources\",\"url\":\"#resources\",\"depth\":2}]}]]}]}]]}]}]]}]]\n"])</script><script>self.__next_f.push([1,"6:[[\"$\",\"meta\",\"0\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"1\",{\"charSet\":\"utf-8\"}],[\"$\",\"title\",\"2\",{\"children\":\"Announcing Apache Pinot 1.0™ | Apache Pinot™\"}],[\"$\",\"meta\",\"3\",{\"name\":\"description\",\"content\":\"Introducing Apache Pinot 1.0 Release\"}],[\"$\",\"meta\",\"4\",{\"name\":\"robots\",\"content\":\"index, follow\"}],[\"$\",\"meta\",\"5\",{\"name\":\"googlebot\",\"content\":\"index, follow, max-video-preview:-1, max-image-preview:large, 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+</span></code></pre></div><p><code><field-names-1 and 2></code> are a comma-separated list of fields you wish to encode with CLP.</p><p>You can read the design <a target="_blank" rel="noopener noreferrer" href="https://docs.google.com/document/d/1nHZb37re4mUwEA258x3a2pgX13EWLWMJ0uLEDk1dUyU/edit">document</a> for more details into why and how this feature was implemented.</p><h2 id="summary"><a href="#summary" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Summary</h2><p>Apache Pinot’s evolution is expressly due to the humans behind the code, and in reaching 1.0 release status it is proper and fitting to give credit to the open source project’s key committers. Since its early days, over <a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/graphs/contributors">three hundred contributors</a> have produced more than 1.3 million source lines of code (SLOC).</p><p><img alt="Image test" src="https://pinot.apache.org/blogs/apache-pinot-1-0-name-cloud.png"/></p><p>The introduction of Apache Pinot 1.0 represents an exceptional stride forward in real-time online analytical processing (OLAP) capabilities, marking a watershed moment in the evolution of real-time analytics systems. This release redefines the limits of what can be achieved in the realm of instant data analysis, presenting a game-changing solution for organizations seeking high throughput and low latency in their OLAP queries. If you would like to get started with Apache Pinot 1.0, you can check out the documentation, and download it now.</p><h2 id="resources"><a href="#resources" aria-hidden="true" tabindex="-1"><span class="icon icon-link"></span></a>Resources</h2><p>If you want to try out Apache Pinot, the following resources will help you get started:</p><p><a target="_blank" rel="noopener noreferrer" href="https://pinot.apache.org/download/">Download page</a></p><p><a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/getting-started">Getting started</a></p><p><a target="_blank" rel="noopener noreferrer" href="https://communityinviter.com/apps/apache-pinot/apache-pinot">Join our Slack channel</a></p><p><a target="_blank" rel="noopener noreferrer" href="https://www.meetup.com/apache-pinot">See our upcoming events</a></p><p><a target="_blank" rel="noopener noreferrer" href="https://twitter.com/ApachePinot">Follow us on social media</a></p></div></main></div></div><aside class="mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full"><section class="sticky top-0 mb-4 w-[15.375rem]"><div class="flex flex-col space-y-1.5 pb-3"><h3 class="text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100">Table of Contents</h3></div><nav class="flex items-center self-start" aria-label="Table of Contents"><ol class="list-none space-y-3"><li class="text-sm leading-tight font-bold"><a href="#what-makes-a-10-release">What Makes a “1.0 Release?”</a></li><li class="text-sm font-normal leading-tight"><a href="#join-support">Join Support</a></li><li class="text-sm font-normal leading-tight"><a href="#upsert-improvements">Upsert Improvements</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#segment-compaction-for-upsert-tables">Segment Compaction for Upsert Tables</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#delete-support-for-upsert-tables">DELETE Support for Upsert Tables</a></li><li class="text-sm font-normal leading-tight"><a href="#null-value-support">NULL Value Support</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#aggregations">Aggregations</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#group-by">Group By</a></li><li class="text-sm leading-tight ml-6 font-normal"><a href="#sorting">Sorting</a></li><li class="text-sm font-normal leading-tight"><a href="#index-pluggability">Index Pluggability</a></li><li class="text-sm font-normal leading-tight"><a href="#apache-pinot-spark-3-connector-and-passing-pinot-options">Apache Pinot Spark 3 Connector and Passing Pinot Options</a></li><li class="text-sm font-normal leading-tight"><a href="#petabyte-scale-log-storage-and-search-in-pinot-with-clp">Petabyte-Scale Log Storage and Search in Pinot with CLP</a></li><li class="text-sm font-normal leading-tight"><a href="#summary">Summary</a></li><li class="text-sm font-normal leading-tight"><a href="#resources">Resources</a></li></ol></nav></section></aside></div></article></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div class="flex-shrink-0"><svg xmlns="http://www.w3.org/2000/svg" width="120" height="48" fill="none"><g fill="#C7154A" clip-path="url(#logo_svg__a)"><path d="M42.99 18.448c1.032-.553 2.21-.831 3.535-.831 1.542 0 2.938.38 4.187 1.14 1.248.76 2.236 1.841 2.965 3.241.728 1.402 1.091 3.025 1.091 4.872s-.363 3.482-1.091 4.903c-.729 1.424-1.717 2.525-2.965 3.307-1.25.782-2.645 1.173-4.187 1.173-1.325 0-2.493-.271-3.503-.815-1.01-.543-1.83-1.226-2.46-2.053v14.612H36V17.912h4.562v2.606c.586-.825 1.395-1.515 2.426-2.068l.002-.002m6.452 5.605c-.445-.793-1.032-1.395-1.76-1.808a4.72 4.72 0 0 0-2.362-.618c-.847 0-1.602.211-2.33.635-.728.423-1.315 1.038-1.76 1.841-.445.804-.668 1.749-.668 2.835 0 1.087.221 2.032.668 2.835.445.804 1.032 1.417 1.76 1.842a4.557 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1.243h18c0-.588-.4-1.1-.97-1.243v.002"></path><path d="M13.5 23a2.5 2.5 0 1 0 0-5 2.5 2.5 0 0 0 0 5M8 5a1 1 0 1 0 0-2 1 1 0 0 0 0 2M12 8a1 1 0 1 0 0-2 1 1 0 0 0 0 2M16 2a1 1 0 1 0 0-2 1 1 0 0 0 0 2"></path></g><defs><clipPath id="logo_svg__a"><path fill="#fff" d="M0 0h120v48H0z"></path></clipPath></defs></svg></div><div class="flex flex-wrap gap-x-16 gap-y-5 py-8 md:pl-24 md:pr-[21.625rem]"> <div><h5 class="mb-4 text-lg font-semibold">Resources</h5><div class="flex justify-between gap-x-10"><div class="flex flex-col"><a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/" class="block py-1 text-gray-600 hover:text-gray-900">Docs</a><a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/getting-started" class="block py-1 text-gray-600 hover:text-gray-900">Getting Started</a><a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/integrations/thirdeye" class="block py-1 text-gray-600 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Back then it was developed at a single company with a single use case in mind: to power “who viewed my profile?” Over the ensuing decade the Apache Pinot community expanded to be embraced by many other organizations, and those organizations have expanded its capabilities to address new use cases. Apache Pinot in 2023 is continuously evolving to address emerging needs in the real-time analytics community. Let’s look at how much innovation has gone into Apache Pinot over the years:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Upserts — data-in-motion tends to stay in motion, and one of the cornerstone capabilities of Apache Pinot is upsert support to handle upsert mutations in real-time.\"}],[\"$\",\"li\",null,{\"children\":\"Query-time Native JOINs — it was important to get this right, so that they were performant and scalable, allowing high QPS. This we will discuss in more detail below.\"}],[\"$\",\"li\",null,{\"children\":\"Pluggable architecture — a broad user base requires the ability to extend the database with new customizable index types, routing strategies and storage options\"}],[\"$\",\"li\",null,{\"children\":\"Handling Semi-Structured/Unstructured Data — Pinot can easily index JSON and text data types at scale.\"}],[\"$\",\"li\",null,{\"children\":\"Improving ANSI SQL Compliance — to that end, we’ve added better NULL handling, window functions, and as stated above, the capability for native JOINs.\"}]]}],[\"$\",\"p\",null,{\"children\":\"With all of these features and capabilities, Apache Pinot moves farther and farther from mere database status, and becomes more of a complete platform that can tackle entire new classes of use cases that were beyond its capabilities in earlier days.\"}],[\"$\",\"p\",null,{\"children\":\"First let’s look at what Apache Pinot 1.0 itself is delivering. The first foundational pillar of what makes something worthy of a “1.0” release is software quality. Over the past year, since September 2022, engineers across the Apache Pinot community have closed over 300 issues to provide new features, optimize performance, expand test coverage, and squash bugs.\"}],[\"$\",\"p\",null,{\"children\":[\"Features are also a key thing that makes a new release worthy of “1.0” status. 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While this query engine has been available within Apache Pinot already (since release 0.11.0), with the release of Apache Pinot 1.0 this feature is functionally complete.\"}],[\"$\",\"p\",null,{\"children\":[\"(While you can read more below, check out the accompanying blog by Apache Pinot PMC Neha Pawar about using query-time JOINs \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/blog/query-time-joins-in-apache-pinot-1-0\",\"children\":\"here\"}],\").\"]}],[\"$\",\"p\",null,{\"children\":[\"This post is a summary of the high points, but you can find a full list of everything included in the release notes. And if you’d like a \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://youtu.be/2cwRHM4J7kI?si=hEtl6W2eNlMkWqag\",\"children\":\"video treatment of many of the main features in 1.0\"}],\", including some helpful animations, watch here:\"]}],[\"$\",\"$L13\",null,{\"src\":\"https://www.youtube.com/embed/2cwRHM4J7kI\",\"title\":\"YouTube video player\"}],[\"$\",\"p\",null,{\"children\":\"Otherwise, let’s have a look at some of the highlighted changes:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Join Support - Part of the Multi-Stage Query Engine\"}],[\"$\",\"li\",null,{\"children\":\"Improved Upserts - Deletion and Compaction Support\"}],[\"$\",\"li\",null,{\"children\":\"Encode User-Specified Compressed Log Processor (CLP) During Ingestion\"}],[\"$\",\"li\",null,{\"children\":\"NULL Support\"}],[\"$\",\"li\",null,{\"children\":\"Pluggable Index Types [Index Service Provider Interface (SPI)]\"}],[\"$\",\"li\",null,{\"children\":\"Improved Pinot-Spark Integration - Spark3 Compatibility\"}]]}],[\"$\",\"h2\",null,{\"id\":\"join-support\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#join-support\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Join Support\"]}],[\"$\",\"p\",null,{\"children\":\"Apache Pinot 1.0 introduces native query-time JOIN support equipping Pinot to handle a broad spectrum of JOIN scenarios providing full coverage from user-facing analytics all the way up to ad hoc analytics. Underpinning this innovation is the multi-stage query engine, introduced a year ago, which efficiently manages complex analytical queries, including JOIN operations. This engine alleviates computational burdens by offloading tasks from brokers to a dedicated intermediate compute stage. Additionally, a new planner supporting full SQL semantics enhances Pinot's analytical capabilities.\"}],[\"$\",\"p\",null,{\"children\":\"JOIN optimization strategies play a pivotal role in Apache Pinot 1.0. These include predicate push-down to individual tables and using indexing and pruning to reduce scanning which speeds up query processing, smart data layout considerations to minimize data shuffling, and query hints for fine-tuning JOIN operations. With support for all JOIN types and three JOIN algorithms, including broadcast join, shuffle distributed hash join, and lookup join, Apache Pinot delivers versatility and scalability. By significantly reducing query latency and simplifying architecture, Apache Pinot 1.0 is a game-changer for real-time OLAP systems.\"}],[\"$\",\"p\",null,{\"children\":[\"For more detailed information on JOINs, please visit this blog \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://startree.ai/blog/query-time-joins-in-apache-pinot-1-0\",\"children\":\"post\"}],\".\"]}],[\"$\",\"p\",null,{\"children\":\"Discover How Uber is using Joins in Apache Pinot For a real-world use case, Uber is already using the new join capabilities of Apache Pinot at scale in production. You can watch this video to learn more.\"}],[\"$\",\"$L13\",null,{\"src\":\"https://www.youtube.com/embed/z4Chhref1BM\",\"title\":\"YouTube video player\"}],[\"$\",\"h2\",null,{\"id\":\"upsert-improvements\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#upsert-improvements\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Upsert Improvements\"]}],[\"$\",\"p\",null,{\"children\":\"Support for upserts is one of the key capabilities Apache Pinot offers that differentiates it from other real-time analytics databases. It is a vital feature when real-time streaming data is prone to frequent updates. 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However, if you want to ensure that segments are compacted without any additional delay this config can be set to “0d”.\"}],[\"$\",\"p\",null,{\"children\":\"invalidRecordsThresholdPercent is an optional limit to the amount of older records allowed in the completed segment represented as a percentage of the total number of records in the segment (i.e. old records / total records). In the example, this property is set to “30” which means that if more than 30% of the records in the completed segment are old, then the segment may be selected for compaction.\"}],[\"$\",\"p\",null,{\"children\":\"invalidRecordsThresholdCount is also a limit similar to the previous property, but allows you to express the threshold as a record count. In the example above, this property is set to “100000” which means that if the segment contains more than 100K records then it may be selected for compaction.\"}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://robert-zych.medium.com/segment-compaction-for-upsert-enabled-tables-in-apache-pinot-3f30657aa077\",\"children\":\"Read more\"}],\" about the design of this feature.\"]}],[\"$\",\"h3\",null,{\"id\":\"delete-support-for-upsert-tables\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#delete-support-for-upsert-tables\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"DELETE Support for Upsert Tables\"]}],[\"$\",\"p\",null,{\"children\":\"Apache Pinot upsert tables now support deleting records. Supporting delete with upsert avoids the need for the user to explicitly filter out invalid records in the query. SELECT _FROM table WHERE deleted_column != true becomes as simple as SELECT _ FROM table. Pinot will only return the latest non-deleted records from the table. This feature opens up the support to ingest Change Data Capture (CDC) data like Debezium where the changes from a source (typically, mutable) will contain DELETE events.\"}],[\"$\",\"p\",null,{\"children\":[\"Deletes itself is implemented as a soft-delete in Apache Pinot with a dedicated boolean column that serves as a delete marker for the record. Pinot automatically filters out records that are marked in this column. For more details, please see the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/basics/data-import/upsert#delete-column\",\"children\":\"documentation\"}],\".\"]}],[\"$\",\"h2\",null,{\"id\":\"null-value-support\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#null-value-support\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"NULL Value Support\"]}],[\"$\",\"p\",null,{\"children\":\"This feature enables Postgres compatible NULL semantics in Apache Pinot queries. The NULL semantics are important for usability for full SQL compatibility which many BI applications like Tableau rely upon when invoking queries to render dashboards. Previously in Pinot, we could not represent NULL. The workaround was to use special values like Integer.MIN_VALUE to represent NULL. Now Pinot 1.0 has full support to represent NULL values. By adding NULL support, Pinot 1.0 has increased the Tableau certification pass rate by 90%.\"}],[\"$\",\"p\",null,{\"children\":\"Here are some examples of how NULLs will work in Pinot 1.0.\"}],[\"$\",\"h3\",null,{\"id\":\"aggregations\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#aggregations\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Aggregations\"]}],[\"$\",\"p\",null,{\"children\":\"Given the following table below, aggregating columns with NULL values will have this behavior.\"}],[\"$\",\"div\",null,{\"className\":\"w-full overflow-x-auto\",\"children\":[\"$\",\"table\",null,{\"children\":[[\"$\",\"thead\",null,{\"children\":[\"$\",\"tr\",null,{\"children\":[[\"$\",\"th\",null,{\"children\":\"col1\"}],[\"$\",\"th\",null,{\"children\":\"col2\"}]]}]}],[\"$\",\"tbody\",null,{\"children\":[[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"1\"}],[\"$\",\"td\",null,{\"children\":\"NULL\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"2\"}],[\"$\",\"td\",null,{\"children\":\"NULL\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"3\"}],[\"$\",\"td\",null,{\"children\":\"1\"}]]}]]}]]}]}],[\"$\",\"p\",null,{\"children\":\"Since col1 does not contain NULL values, all the values are included in the aggregation.\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"col1\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token comment\",\"children\":\"-- returns 6\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"col1\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token comment\",\"children\":\"-- returns 3\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":\"In the select statement below, the NULL values in col2 are not included in the aggregation.\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"sum\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"col2\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token comment\",\"children\":\"-- returns 1\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token function\",\"children\":\"count\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],\"col2\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token comment\",\"children\":\"-- returns 1\"}],\"\\n\"]}]]}]}],[\"$\",\"h3\",null,{\"id\":\"group-by\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#group-by\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Group By\"]}],[\"$\",\"p\",null,{\"children\":\"Pinot now supports grouping by NULL. In the example below, we are grouping by col1 which contains a NULL value. Given the table below, grouping by columns with NULL value will have this behavior.\"}],[\"$\",\"div\",null,{\"className\":\"w-full overflow-x-auto\",\"children\":[\"$\",\"table\",null,{\"children\":[[\"$\",\"thead\",null,{\"children\":[\"$\",\"tr\",null,{\"children\":[\"$\",\"th\",null,{\"children\":\"col1\"}]}]}],[\"$\",\"tbody\",null,{\"children\":[[\"$\",\"tr\",null,{\"children\":[\"$\",\"td\",null,{\"children\":\"a\"}]}],[\"$\",\"tr\",null,{\"children\":[\"$\",\"td\",null,{\"children\":\"NULL\"}]}],[\"$\",\"tr\",null,{\"children\":[\"$\",\"td\",null,{\"children\":\"b\"}]}],[\"$\",\"tr\",null,{\"children\":[\"$\",\"td\",null,{\"children\":\"a\"}]}]]}]]}]}],[\"$\",\"p\",null,{\"children\":\"The following select statement will output the following result.\"}],[\"$\",\"p\",null,{\"children\":\"select col1, count(*) from table group by col1\"}],[\"$\",\"div\",null,{\"className\":\"w-full overflow-x-auto\",\"children\":[\"$\",\"table\",null,{\"children\":[[\"$\",\"thead\",null,{\"children\":[\"$\",\"tr\",null,{\"children\":[[\"$\",\"th\",null,{\"children\":\"col1\"}],[\"$\",\"th\",null,{\"children\":\"count()\"}]]}]}],[\"$\",\"tbody\",null,{\"children\":[[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"a\"}],[\"$\",\"td\",null,{\"children\":\"2\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"b\"}],[\"$\",\"td\",null,{\"children\":\"1\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"NULL\"}],[\"$\",\"td\",null,{\"children\":\"1\"}]]}]]}]]}]}],[\"$\",\"h3\",null,{\"id\":\"sorting\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#sorting\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Sorting\"]}],[\"$\",\"p\",null,{\"children\":\"Pinot now allows you to specify the location of NULL values when sorting records. The default is to act as though NULLs are larger than non-NULLs.\"}],[\"$\",\"p\",null,{\"children\":\"Given this list of values, sorting them will result in the following.\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"values: 1, 2, 3, NULL\"}]}],[\"$\",\"p\",null,{\"children\":\"Example 1:\"}],[\"$\",\"p\",null,{\"children\":\"NULL values sort BEFORE all non-NULL values.\"}],[\"$\",\"p\",null,{\"children\":\"SQL:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" col \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"table\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"order\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" col NULLS \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"FIRST\"}],\"\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"RESULT: NULL, 1, 2, 3\"}]}],[\"$\",\"p\",null,{\"children\":\"Example 2:\"}],[\"$\",\"p\",null,{\"children\":\"NULL values sort AFTER all non-NULL values.\"}],[\"$\",\"p\",null,{\"children\":\"SQL:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" col \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"table\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"order\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" col \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"ASC\"}],\" NULLS \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"LAST\"}],\"\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"RESULT: 1, 2, 3, NULL\"}]}],[\"$\",\"p\",null,{\"children\":\"Example 3:\"}],[\"$\",\"p\",null,{\"children\":\"Default behavior is NULL LAST.\"}],[\"$\",\"p\",null,{\"children\":\"SQL:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-sql\",\"children\":[\"$\",\"code\",null,{\"className\":\"language-sql code-highlight\",\"children\":[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"select\"}],\" col \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"from\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"table\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"order\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"by\"}],\" col\\n\"]}]}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"code\",null,{\"children\":\"RESULT: 1, 2, 3, NULL\"}]}],[\"$\",\"h2\",null,{\"id\":\"index-pluggability\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#index-pluggability\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Index Pluggability\"]}],[\"$\",\"p\",null,{\"children\":\"Today, Pinot supports multiple index types, like forward index, bloom filter, and range index. Before Pinot 1.0, index types were all statically defined, which means that in order to create a new index type, you’d need to rebuild Pinot from source. Ideally that shouldn’t be the case.\"}],[\"$\",\"p\",null,{\"children\":[\"To increase speed of development, \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/apache/pinot/issues/10183\",\"children\":\"Index Service Provider Interface (SPI)\"}],\", or index-spi, reduces friction by adding the ability to include new index types at runtime in Pinot. This opens the ability of adding third party indexes by including an external jar in the classpath and adding some configuration. This opens up Pinot indexing to lower-friction innovation from the community.\"]}],[\"$\",\"p\",null,{\"children\":\"For now, SPI-accessible indexes are limited to single field index types. Features like the star-tree index or other multi-column approaches are not yet supported.\"}],[\"$\",\"h2\",null,{\"id\":\"apache-pinot-spark-3-connector-and-passing-pinot-options\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#apache-pinot-spark-3-connector-and-passing-pinot-options\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Apache Pinot Spark 3 Connector and Passing Pinot Options\"]}],[\"$\",\"p\",null,{\"children\":[\"Apache Spark users can now take advantage of Pinot’s ability to provide high scalability, low latency, and high concurrency within the context of a Spark 3 cluster using the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/apache/pinot/blob/master/pinot-connectors/pinot-spark-3-connector/README.md\",\"children\":\"Apache Pinot Spark 3 Connector\"}],\".\"]}],[\"$\",\"p\",null,{\"children\":\"This connector supports Apache Spark (2.x and 3.x) as a processor to create and push segment files to the database and can read realtime, offline, and hybrid tables from Pinot.\"}],[\"$\",\"p\",null,{\"children\":\"Now you can merge your streaming and batch datasets together in Spark to provide a full view of real-time and historical data for your machine learning algorithms and feature stores.\"}],[\"$\",\"p\",null,{\"children\":\"Performance Features\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Distributed, parallel scan\"}],[\"$\",\"li\",null,{\"children\":\"Streaming reads using gRPC (optional)\"}],[\"$\",\"li\",null,{\"children\":\"Column and filter push down to optimize performance\"}],[\"$\",\"li\",null,{\"children\":\"Support for Pinot’s Query Options that include: maxExecutionThreads, enableNullHandling, skipUpsert, etc.\"}]]}],[\"$\",\"p\",null,{\"children\":\"Usability Features\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"SQL support instead of PQL\"}],[\"$\",\"li\",null,{\"children\":\"Overlap between realtime and offline segments is queried exactly once for hybrid tables\"}],[\"$\",\"li\",null,{\"children\":[\"Schema discovery - If schema is not specified, the \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/apache/pinot/blob/master/pinot-connectors/pinot-spark-3-connector/documentation/read_model.md\",\"children\":\"connector reads the table schema\"}],\" from the Pinot controller, and then converts to the Spark schema.\"]}]]}],[\"$\",\"p\",null,{\"children\":[\"Here is an example that reads a Pinot table, by setting the format to “pinot” spark will automatically load the Pinot connector and read the “airlinesStats” table. The queryOptions property allows you to provide \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/users/user-guide-query/query-options\",\"children\":\"Pinot Query Options\"}],\".\"]}],[\"$\",\"$L14\",null,{\"className\":\"language-scala\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-scala\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token keyword\",\"children\":\"val\"}],\" data \",[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\"=\"}],\" spark\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"read\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"format\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"pinot\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"option\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"table\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"airlineStats\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"option\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"tableType\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"offline\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"option\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"queryOptions\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"enableNullHandling=true,maxExecutionThreads=1\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"load\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"sql\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"SELECT * FROM airlineStats WHERE DEST = ‘SFO’\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\"\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\"data\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\".\"}],\"show\",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"(\"}],[\"$\",\"span\",null,{\"className\":\"token number\",\"children\":\"100\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\")\"}],\"\\n\"]}]]}]}],[\"$\",\"h2\",null,{\"id\":\"petabyte-scale-log-storage-and-search-in-pinot-with-clp\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#petabyte-scale-log-storage-and-search-in-pinot-with-clp\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Petabyte-Scale Log Storage and Search in Pinot with CLP\"]}],[\"$\",\"p\",null,{\"children\":[\"Compressed Log Processor (CLP) is a tool capable of losslessly compressing text logs and searching them in their compressed state. It achieves a better compression ratio than general purpose compressors alone, while retaining the ability to search the compressed log events without incurring the performance penalty of fully decompressing them. Part of CLP’s algorithm was deployed within \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://www.uber.com/blog/reducing-logging-cost-by-two-orders-of-magnitude-using-clp/\",\"children\":\"Uber\"}],\" to compress unstructured Spark logs, as they are generated, achieving an unprecedented compression of 169×.\"]}],[\"$\",\"p\",null,{\"children\":\"Log events generated as JSON objects with user-defined schemas, meaning each event may have different keys. Such user-defined schemas make these events challenging to store in a table with a set schema. With Log Storage and Search in Pinot with CLP, users would be able to:\"}],[\"$\",\"ul\",null,{\"children\":[[\"$\",\"li\",null,{\"children\":\"Store their log events losslessly (without dropping fields)\"}],[\"$\",\"li\",null,{\"children\":\"Store their logs with some amount of compression\"}],[\"$\",\"li\",null,{\"children\":\"Query their logs efficiently\"}]]}],[\"$\",\"p\",null,{\"children\":\"The CLP ingestion pipeline can be used on log events from a stream, such as JSON log events ingested from Kafka. The plugin takes two inputs: a JSON record and a list of fields to encode with CLP.\"}],[\"$\",\"p\",null,{\"children\":\"The fields to encode can be configured as shown:\"}],[\"$\",\"$L14\",null,{\"className\":\"language-json\",\"children\":[\"$\",\"code\",null,{\"className\":\"code-highlight language-json\",\"children\":[[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" ...\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"tableIndexConfig\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" ...\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"streamConfigs\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"{\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":\" ...\\n\"}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.class.name\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"org.apache.pinot.plugin.inputformat.clplog.CLPLogMessageDecoder\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\",\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token property\",\"children\":\"\\\"stream.kafka.decoder.prop.fieldsForClpEncoding\\\"\"}],[\"$\",\"span\",null,{\"className\":\"token operator\",\"children\":\":\"}],\" \",[\"$\",\"span\",null,{\"className\":\"token string\",\"children\":\"\\\"\u003cfield-name-1\u003e,\u003cfield-name-2\u003e\\\"\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[\" \",[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}],[\"$\",\"span\",null,{\"className\":\"code-line\",\"children\":[[\"$\",\"span\",null,{\"className\":\"token punctuation\",\"children\":\"}\"}],\"\\n\"]}]]}]}],[\"$\",\"p\",null,{\"children\":[[\"$\",\"code\",null,{\"children\":\"\u003cfield-names-1 and 2\u003e\"}],\" are a comma-separated list of fields you wish to encode with CLP.\"]}],[\"$\",\"p\",null,{\"children\":[\"You can read the design \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.google.com/document/d/1nHZb37re4mUwEA258x3a2pgX13EWLWMJ0uLEDk1dUyU/edit\",\"children\":\"document\"}],\" for more details into why and how this feature was implemented.\"]}],[\"$\",\"h2\",null,{\"id\":\"summary\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#summary\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Summary\"]}],[\"$\",\"p\",null,{\"children\":[\"Apache Pinot’s evolution is expressly due to the humans behind the code, and in reaching 1.0 release status it is proper and fitting to give credit to the open source project’s key committers. Since its early days, over \",[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://github.com/apache/pinot/graphs/contributors\",\"children\":\"three hundred contributors\"}],\" have produced more than 1.3 million source lines of code (SLOC).\"]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"img\",null,{\"alt\":\"Image test\",\"src\":\"https://pinot.apache.org/blogs/apache-pinot-1-0-name-cloud.png\"}]}],[\"$\",\"p\",null,{\"children\":\"The introduction of Apache Pinot 1.0 represents an exceptional stride forward in real-time online analytical processing (OLAP) capabilities, marking a watershed moment in the evolution of real-time analytics systems. This release redefines the limits of what can be achieved in the realm of instant data analysis, presenting a game-changing solution for organizations seeking high throughput and low latency in their OLAP queries. If you would like to get started with Apache Pinot 1.0, you can check out the documentation, and download it now.\"}],[\"$\",\"h2\",null,{\"id\":\"resources\",\"children\":[[\"$\",\"a\",null,{\"href\":\"#resources\",\"aria-hidden\":\"true\",\"tabIndex\":\"-1\",\"children\":[\"$\",\"span\",null,{\"className\":\"icon icon-link\"}]}],\"Resources\"]}],[\"$\",\"p\",null,{\"children\":\"If you want to try out Apache Pinot, the following resources will help you get started:\"}],[\"$\",\"p\",null,{\"children\":[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://pinot.apache.org/download/\",\"children\":\"Download page\"}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://docs.pinot.apache.org/getting-started\",\"children\":\"Getting started\"}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://communityinviter.com/apps/apache-pinot/apache-pinot\",\"children\":\"Join our Slack channel\"}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://www.meetup.com/apache-pinot\",\"children\":\"See our upcoming events\"}]}],[\"$\",\"p\",null,{\"children\":[\"$\",\"a\",null,{\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"href\":\"https://twitter.com/ApachePinot\",\"children\":\"Follow us on social media\"}]}]]}]}]}]]}],[\"$\",\"aside\",null,{\"className\":\"mt-10 hidden border-l-2 pl-5 lg:sticky lg:top-1 lg:block lg:h-full\",\"children\":[\"$\",\"section\",null,{\"className\":\"sticky top-0 mb-4 w-[15.375rem]\",\"children\":[[\"$\",\"div\",null,{\"className\":\"flex flex-col space-y-1.5 pb-3\",\"children\":[\"$\",\"h3\",null,{\"className\":\"text-sm font-semibold leading-snug text-neutral-500 dark:text-neutral-100\",\"children\":\"Table of 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content="Download Apache Pinot"/><meta property="og:url" content="https://pinot.apache.org/download"/><meta property="og:site_name" content="Apache Pinot™"/><meta property="og:locale" content="en_US"/><meta property="og:image" content="https://pinot.apache.org/static/images/twitter-card.png"/><meta property="og:type" content="website"/><meta name="twitter:card" content="summary_large_image"/><meta name="twitter:title" content="Download | Apache Pinot™"/><meta name="twitter:description" content="Realtime distributed OLAP datastore"/><meta name="twitter:image" content="https://pinot.apache.org/static/images/twitter-card.png"/><meta name="next-size-adjust"/><script src="/_next/static/chunks/polyfills-c67a75d1b6f99dc8.js" crossorigin="" noModule=""></script></head><body class="bg-white text-black antialiased dark:bg-gray-950 dark:text-white"><script>!function(){try{var d=document.documentElement,c=d.classList;c.remove('light','dark');var 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class="font-semibold">Version</div><div>1.1.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>March 25th, 2024</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This release comes with several features, SQL /UI/Perf enhancements Bugfixes across areas ranging from Multistage Query Engine to Ingestion, Storage format, SQL support, etc.</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>1.0.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>September 12th, 2023</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">Apache Pinot Release 1.0.0</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.12.1</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>March 12th, 2023</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">Apache Pinot Release 0.12.1</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.12.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>January 19th, 2023</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">Apache Pinot Release 0.12.0</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.11.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>September 2nd, 2022</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">Apache Pinot 0.11.0 has introduced many new features to extend the query abilities, e.g. the Multi-Stage query engine enables Pinot to do distributed joins, more sql syntax(DML support), query functions and indexes(Text index, Timestamp index) supported for new use cases. And as always, more integrations with other systems(E.g. Spark3, Flink).</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.10.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>March 19th, 2022</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This release introduces some new great features, performance enhancements, UI improvments, and bug fixes which are described in details in the following sections.</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.9.3</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>December 24th, 2021</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This is a bug fixing release contains: Update Log4j to 2.17.0 to address CVE-2021-45105 (#7933).</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.9.2</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>December 15th, 2021</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This is a bug fixing release contains: Upgrade log4j to 2.16.0 to fix CVE-2021-45046 (#7903), upgrade swagger-ui to 3.23.11 to fix CVE-2019-17495 (#7902), fix the bug that RealtimeToOfflineTask failed to progress with large time bucket gaps (#7814).</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.9.1</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>December 12th, 2021</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This release fixes the major issue of CVE-2021-44228 and a major bug fixing of pinot admin exit code issue #7798.</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.9.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>November 12th, 2021</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This release introduces a new features: Segment Merge and Rollup to simplify users day to day operational work. A new metrics plugin is added to support dropwizard. As usual, new functionalities and many UI/ Performance improvements.</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.8.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>August 12th, 2021</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This release introduced several awesome new features, including compatibility tests, enhanced complex type and Json support, partial upsert support, and new stream ingestion plugins (AWS Kinesis, Apache Pulsar).</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.7.1</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>April 7th, 2021</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This release introduced several awesome new features, including JSON index, lookup-based join support, geospatial support, TLS support for pinot connections, and various performance optimizations and improvements.</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.6.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>November 6th, 2020</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This release introduced some excellent new features, including upsert, tiered storage, pinot-spark-connector, support of having clause, more validations on table config and schema</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.5.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>September 3rd, 2020</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This release includes many new features on Pinot ingestion and connectors, query capabilities and admin functions.</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.4.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>June 2nd, 2020</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This release introduced various new features, including the theta-sketch based distinct count aggregation function, an S3 filesystem plugin, a unified star-tree index implementation, deprecation of TimeFieldSpec in favor of DateTimeFieldSpec, etc.</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.3.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>March 25th, 2020</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">The reason behind the architectural change from the previous release (0.2.0) and this release (0.3.0), is the possibility of extending Apache Pinot.</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.2.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>November 11th, 2019</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">Added support for Kafka 2.0</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.1.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>March 8th, 2019</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This is the first official release of Apache Pinot.</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div></div><div class="relative w-full overflow-auto"><table class="caption-bottom text-sm hidden w-full md:table"><thead class="[&_tr]:border-b border-b-2 border-neutral-400 font-semibold"><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted"><th class="h-12 px-4 text-left [&:has([role=checkbox])]:pr-0 align-middle text-lg font-semibold text-stone-900 dark:text-gray-100">Version</th><th class="h-12 px-4 text-left [&:has([role=checkbox])]:pr-0 align-middle text-lg font-semibold text-stone-900 dark:text-gray-100">Date</th><th class="h-12 px-4 text-left [&:has([role=checkbox])]:pr-0 align-middle text-lg font-semibold text-stone-900 dark:text-gray-100">Official source</th><th class="h-12 px-4 text-left [&:has([role=checkbox])]:pr-0 align-middle text-lg font-semibold text-stone-900 dark:text-gray-100">Binary</th><th class="h-12 px-4 text-left [&:has([role=checkbox])]:pr-0 align-middle text-lg font-semibold text-stone-900 dark:text-gray-100">Release notes</th></tr></thead><tbody class="[&_tr:last-child]:border-0"><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">1.1.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">March 25th, 2024</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This release comes with several features, SQL /UI/Perf enhancements Bugfixes across areas ranging from Multistage Query Engine to Ingestion, Storage format, SQL support, etc.</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">1.0.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">September 12th, 2023</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">Apache Pinot Release 1.0.0</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.12.1</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">March 12th, 2023</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">Apache Pinot Release 0.12.1</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.12.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">January 19th, 2023</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">Apache Pinot Release 0.12.0</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.11.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">September 2nd, 2022</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">Apache Pinot 0.11.0 has introduced many new features to extend the query abilities, e.g. the Multi-Stage query engine enables Pinot to do distributed joins, more sql syntax(DML support), query functions and indexes(Text index, Timestamp index) supported for new use cases. And as always, more integrations with other systems(E.g. Spark3, Flink).</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.10.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">March 19th, 2022</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This release introduces some new great features, performance enhancements, UI improvments, and bug fixes which are described in details in the following sections.</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.9.3</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">December 24th, 2021</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This is a bug fixing release contains: Update Log4j to 2.17.0 to address CVE-2021-45105 (#7933).</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.9.2</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">December 15th, 2021</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This is a bug fixing release contains: Upgrade log4j to 2.16.0 to fix CVE-2021-45046 (#7903), upgrade swagger-ui to 3.23.11 to fix CVE-2019-17495 (#7902), fix the bug that RealtimeToOfflineTask failed to progress with large time bucket gaps (#7814).</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.9.1</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">December 12th, 2021</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This release fixes the major issue of CVE-2021-44228 and a major bug fixing of pinot admin exit code issue #7798.</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.9.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">November 12th, 2021</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This release introduces a new features: Segment Merge and Rollup to simplify users day to day operational work. A new metrics plugin is added to support dropwizard. As usual, new functionalities and many UI/ Performance improvements.</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.8.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">August 12th, 2021</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This release introduced several awesome new features, including compatibility tests, enhanced complex type and Json support, partial upsert support, and new stream ingestion plugins (AWS Kinesis, Apache Pulsar).</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.7.1</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">April 7th, 2021</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This release introduced several awesome new features, including JSON index, lookup-based join support, geospatial support, TLS support for pinot connections, and various performance optimizations and improvements.</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.6.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">November 6th, 2020</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This release introduced some excellent new features, including upsert, tiered storage, pinot-spark-connector, support of having clause, more validations on table config and schema</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.5.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">September 3rd, 2020</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This release includes many new features on Pinot ingestion and connectors, query capabilities and admin functions.</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.4.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">June 2nd, 2020</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This release introduced various new features, including the theta-sketch based distinct count aggregation function, an S3 filesystem plugin, a unified star-tree index implementation, deprecation of TimeFieldSpec in favor of DateTimeFieldSpec, etc.</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.3.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">March 25th, 2020</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">The reason behind the architectural change from the previous release (0.2.0) and this release (0.3.0), is the possibility of extending Apache Pinot.</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.2.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">November 11th, 2019</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">Added support for Kafka 2.0</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.1.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">March 8th, 2019</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This is the first official release of Apache Pinot.</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr></tbody></table></div></div></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div 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0-.286-.02-1.264-.02-2.283-3.34.734-4.036-1.466-4.036-1.466-.537-1.426-1.332-1.793-1.332-1.793-1.094-.754.08-.754.08-.754 1.212.082 1.849 1.263 1.849 1.263 1.073 1.874 2.803 1.345 3.5 1.019.098-.795.417-1.345.755-1.65-2.665-.285-5.468-1.345-5.468-6.07 0-1.345.477-2.445 1.232-3.3-.119-.306-.537-1.57.12-3.26 0 0 1.014-.326 3.3 1.263.98-.27 1.989-.407 3.003-.408 1.014 0 2.048.143 3.002.408 2.287-1.59 3.301-1.263 3.301-1.263.657 1.69.239 2.954.12 3.26.775.855 1.232 1.955 1.232 3.3 0 4.725-2.803 5.764-5.488 6.07.438.387.815 1.12.815 2.281 0 1.65-.02 2.975-.02 3.382 0 .326.22.713.816.59C20.56 22.347 24 17.744 24 12.305 24.02 5.5 18.63 0 12.01 0" clip-rule="evenodd"></path></g><defs><clipPath id="github_svg__a"><path fill="#fff" d="M0 0h24v24H0z"></path></clipPath></defs></svg>3.5k</a><button class="inline-flex items-center justify-center whitespace-nowrap font-medium ring-offset-background transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50 text-primary-foreground hover:bg-vine-120 rounded-md bg-vine-100 px-6 py-2 text-base"><a target="_blank" rel="noopener noreferrer" href="https://docs.pinot.apache.org/basics/getting-started">Get Started</a></button></div></header><main><section><header class="p-8 text-center md:p-0 md:pt-16"><h1 class="mb-6 text-3xl font-bold md:text-5xl">Apache Pinot™ Download</h1><p class="text-lg md:mb-12">Be sure to verify your downloads by these<!-- --> <a class="custom-link" href="https://www.apache.org/info/verification">procedures</a> <!-- -->using these<!-- --> <a class="custom-link" href="https://www.apache.org/dyn/closer.lua/pinot/KEYS">KEYS</a> <!-- -->for any Apache release.</p></header><div class="p-4 pt-0 md:px-16 md:pb-16"><div class="md:hidden"><hr class="my-4 border-neutral-400"/><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>1.1.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>March 25th, 2024</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This release comes with several features, SQL /UI/Perf enhancements Bugfixes across areas ranging from Multistage Query Engine to Ingestion, Storage format, SQL support, etc.</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>1.0.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>September 12th, 2023</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">Apache Pinot Release 1.0.0</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.12.1</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>March 12th, 2023</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">Apache Pinot Release 0.12.1</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.12.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>January 19th, 2023</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">Apache Pinot Release 0.12.0</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.11.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>September 2nd, 2022</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">Apache Pinot 0.11.0 has introduced many new features to extend the query abilities, e.g. the Multi-Stage query engine enables Pinot to do distributed joins, more sql syntax(DML support), query functions and indexes(Text index, Timestamp index) supported for new use cases. And as always, more integrations with other systems(E.g. Spark3, Flink).</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.10.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>March 19th, 2022</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This release introduces some new great features, performance enhancements, UI improvments, and bug fixes which are described in details in the following sections.</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.9.3</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>December 24th, 2021</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This is a bug fixing release contains: Update Log4j to 2.17.0 to address CVE-2021-45105 (#7933).</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.9.2</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>December 15th, 2021</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This is a bug fixing release contains: Upgrade log4j to 2.16.0 to fix CVE-2021-45046 (#7903), upgrade swagger-ui to 3.23.11 to fix CVE-2019-17495 (#7902), fix the bug that RealtimeToOfflineTask failed to progress with large time bucket gaps (#7814).</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.9.1</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>December 12th, 2021</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This release fixes the major issue of CVE-2021-44228 and a major bug fixing of pinot admin exit code issue #7798.</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.9.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>November 12th, 2021</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This release introduces a new features: Segment Merge and Rollup to simplify users day to day operational work. A new metrics plugin is added to support dropwizard. As usual, new functionalities and many UI/ Performance improvements.</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.8.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>August 12th, 2021</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This release introduced several awesome new features, including compatibility tests, enhanced complex type and Json support, partial upsert support, and new stream ingestion plugins (AWS Kinesis, Apache Pulsar).</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.7.1</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>April 7th, 2021</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This release introduced several awesome new features, including JSON index, lookup-based join support, geospatial support, TLS support for pinot connections, and various performance optimizations and improvements.</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.6.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>November 6th, 2020</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This release introduced some excellent new features, including upsert, tiered storage, pinot-spark-connector, support of having clause, more validations on table config and schema</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.5.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>September 3rd, 2020</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This release includes many new features on Pinot ingestion and connectors, query capabilities and admin functions.</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.4.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>June 2nd, 2020</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This release introduced various new features, including the theta-sketch based distinct count aggregation function, an S3 filesystem plugin, a unified star-tree index implementation, deprecation of TimeFieldSpec in favor of DateTimeFieldSpec, etc.</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.3.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>March 25th, 2020</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">The reason behind the architectural change from the previous release (0.2.0) and this release (0.3.0), is the possibility of extending Apache Pinot.</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.2.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>November 11th, 2019</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">Added support for Kafka 2.0</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div><div class="mb-8 last:mb-0"><div class="flex w-72 justify-between"><div class="flex flex-col"><div class="font-semibold">Version</div><div>0.1.0</div></div><div class="flex flex-col"><div class="font-semibold">Date</div><div>March 8th, 2019</div></div></div><div class="my-2"><div class="font-semibold">Official source</div><div class="flex space-x-2 font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Binary</div><div class="flex space-x-2 align-top font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></div><div class="my-2"><div class="font-semibold">Release notes</div><p class="leading-relaxed">This is the first official release of Apache Pinot.</p><a href="#releaseNotes" class="align-top font-semibold text-green-700 hover:text-green-900">See all notes</a></div><hr class="my-4 border-neutral-400"/></div></div><div class="relative w-full overflow-auto"><table class="caption-bottom text-sm hidden w-full md:table"><thead class="[&_tr]:border-b border-b-2 border-neutral-400 font-semibold"><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted"><th class="h-12 px-4 text-left [&:has([role=checkbox])]:pr-0 align-middle text-lg font-semibold text-stone-900 dark:text-gray-100">Version</th><th class="h-12 px-4 text-left [&:has([role=checkbox])]:pr-0 align-middle text-lg font-semibold text-stone-900 dark:text-gray-100">Date</th><th class="h-12 px-4 text-left [&:has([role=checkbox])]:pr-0 align-middle text-lg font-semibold text-stone-900 dark:text-gray-100">Official source</th><th class="h-12 px-4 text-left [&:has([role=checkbox])]:pr-0 align-middle text-lg font-semibold text-stone-900 dark:text-gray-100">Binary</th><th class="h-12 px-4 text-left [&:has([role=checkbox])]:pr-0 align-middle text-lg font-semibold text-stone-900 dark:text-gray-100">Release notes</th></tr></thead><tbody class="[&_tr:last-child]:border-0"><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">1.1.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">March 25th, 2024</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.1.0/apache-pinot-1.1.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This release comes with several features, SQL /UI/Perf enhancements Bugfixes across areas ranging from Multistage Query Engine to Ingestion, Storage format, SQL support, etc.</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">1.0.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">September 12th, 2023</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-1.0.0/apache-pinot-1.0.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">Apache Pinot Release 1.0.0</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.12.1</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">March 12th, 2023</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.1/apache-pinot-0.12.1-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">Apache Pinot Release 0.12.1</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.12.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">January 19th, 2023</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.12.0/apache-pinot-0.12.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">Apache Pinot Release 0.12.0</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.11.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">September 2nd, 2022</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.11.0/apache-pinot-0.11.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">Apache Pinot 0.11.0 has introduced many new features to extend the query abilities, e.g. the Multi-Stage query engine enables Pinot to do distributed joins, more sql syntax(DML support), query functions and indexes(Text index, Timestamp index) supported for new use cases. And as always, more integrations with other systems(E.g. Spark3, Flink).</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.10.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">March 19th, 2022</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.10.0/apache-pinot-0.10.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This release introduces some new great features, performance enhancements, UI improvments, and bug fixes which are described in details in the following sections.</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.9.3</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">December 24th, 2021</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.3/apache-pinot-0.9.3-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This is a bug fixing release contains: Update Log4j to 2.17.0 to address CVE-2021-45105 (#7933).</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.9.2</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">December 15th, 2021</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.2/apache-pinot-0.9.2-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This is a bug fixing release contains: Upgrade log4j to 2.16.0 to fix CVE-2021-45046 (#7903), upgrade swagger-ui to 3.23.11 to fix CVE-2019-17495 (#7902), fix the bug that RealtimeToOfflineTask failed to progress with large time bucket gaps (#7814).</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.9.1</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">December 12th, 2021</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.1/apache-pinot-0.9.1-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This release fixes the major issue of CVE-2021-44228 and a major bug fixing of pinot admin exit code issue #7798.</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.9.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">November 12th, 2021</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.9.0/apache-pinot-0.9.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This release introduces a new features: Segment Merge and Rollup to simplify users day to day operational work. A new metrics plugin is added to support dropwizard. As usual, new functionalities and many UI/ Performance improvements.</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.8.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">August 12th, 2021</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://www.apache.org/dyn/closer.lua/pinot/apache-pinot-0.8.0/apache-pinot-0.8.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This release introduced several awesome new features, including compatibility tests, enhanced complex type and Json support, partial upsert support, and new stream ingestion plugins (AWS Kinesis, Apache Pulsar).</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.7.1</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">April 7th, 2021</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.7.1/apache-pinot-incubating-0.7.1-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This release introduced several awesome new features, including JSON index, lookup-based join support, geospatial support, TLS support for pinot connections, and various performance optimizations and improvements.</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.6.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">November 6th, 2020</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.6.0/apache-pinot-incubating-0.6.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This release introduced some excellent new features, including upsert, tiered storage, pinot-spark-connector, support of having clause, more validations on table config and schema</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.5.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">September 3rd, 2020</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.5.0/apache-pinot-incubating-0.5.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This release includes many new features on Pinot ingestion and connectors, query capabilities and admin functions.</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.4.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">June 2nd, 2020</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.4.0/apache-pinot-incubating-0.4.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This release introduced various new features, including the theta-sketch based distinct count aggregation function, an S3 filesystem plugin, a unified star-tree index implementation, deprecation of TimeFieldSpec in favor of DateTimeFieldSpec, etc.</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.3.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">March 25th, 2020</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.3.0/apache-pinot-incubating-0.3.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">The reason behind the architectural change from the previous release (0.2.0) and this release (0.3.0), is the possibility of extending Apache Pinot.</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.2.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">November 11th, 2019</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.2.0/apache-pinot-incubating-0.2.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">Added support for Kafka 2.0</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr><tr class="border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted border-t border-neutral-400"><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">0.1.0</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top text-lg">March 8th, 2019</td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-src.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-src.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-src.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 [&:has([role=checkbox])]:pr-0 align-top"><div class="flex space-x-2 align-top text-lg font-semibold"><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-bin.tar.gz?action=download" class="text-green-700 hover:text-green-900">Download</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-bin.tar.gz.sha512?action=download" class="text-green-700 hover:text-green-900">SHA512</a><span>/</span><a target="_blank" rel="noopener noreferrer" href="https://archive.apache.org/dist/incubator/pinot/apache-pinot-incubating-0.1.0/apache-pinot-incubating-0.1.0-bin.tar.gz.asc?action=download" class="text-green-700 hover:text-green-900">ASC</a></div></td><td class="p-4 align-middle [&:has([role=checkbox])]:pr-0"><p class="text-lg">This is the first official release of Apache Pinot.</p><a target="_blank" rel="noopener noreferrer" href="https://github.com/apache/pinot/releases" class="align-top text-lg font-semibold text-green-700 hover:text-green-900">See all notes</a></td></tr></tbody></table></div></div></section></main><footer class="border-t bg-sky-100 px-5 py-10 md:px-[6.75rem] md:pb-10 md:pt-16"><div class="mx-auto flex max-w-7xl flex-wrap justify-between"><div 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Impact Summary:\",\"url\":\"#5-impact-summary\",\"depth\":2},{\"value\":\"Key Benefits of the Star-Tree Index:\",\"url\":\"#key-benefits-of-the-star-tree-index\",\"depth\":3}],\"structuredData\":{\"@context\":\"https://schema.org\",\"@type\":\"BlogPosting\",\"headline\":\"StarTree Indexes in Apache Pinot Part-1 - Understanding the Impact on Query Performance\",\"datePublished\":\"2023-05-16T00:00:00.000Z\",\"dateModified\":\"2023-05-16T00:00:00.000Z\",\"description\":\"The blog post explains the star-tree index in Apache Pinot and its benefits compared to traditional materialized views. 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Consumer Record Lag\",\"date\":\"2023-03-30T00:00:00.000Z\",\"tags\":[\"Pinot\",\"Data\",\"Analytics\",\"User-Facing Analytics\",\"consumer record lag\",\"kafka\"],\"summary\":\"This post will explore a new API endpoint that lets you check how much Pinot is lagging when ingesting from Apache Kafka.\",\"authors\":[\"needham\"],\"type\":\"Blog\",\"readingTime\":{\"text\":\"5 min read\",\"minutes\":4.57,\"time\":274200,\"words\":914},\"slug\":\"2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag\",\"customSlug\":\"2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag\",\"path\":\"blog/2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag\",\"customPath\":\"blog/2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag\",\"filePath\":\"blog/2023-03-30-Apache-Pinot-0-12-Consumer-Record-Lag.mdx\",\"toc\":[{\"value\":\"Why do we need this?\",\"url\":\"#why-do-we-need-this\",\"depth\":2},{\"value\":\"Worked Example\",\"url\":\"#worked-example\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}],\"structuredData\":{\"@context\":\"https://schema.org\",\"@type\":\"BlogPosting\",\"headline\":\"Apache Pinot™ 0.12 - 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Unfortunately, this approach results in scanning data and time value conversion work that takes a long time at large data volumes. 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Inserts from SQL\",\"datePublished\":\"2022-11-17T00:00:00.000Z\",\"dateModified\":\"2022-11-17T00:00:00.000Z\",\"description\":\"Explore the INSERT INTO clause, which makes ingesting batch data into Pinot as easy as writing a SQL query.\",\"image\":\"/static/images/twitter-card.png\",\"url\":\"https://pinot.apache.org/blog/2022-11-17-Apache Pinot-Inserts-from-SQL\"}},{\"title\":\"Apache Pinot™ 0.11 - How do I see my indexes?\",\"date\":\"2022-11-08T00:00:00.000Z\",\"tags\":[\"Pinot\",\"Data\",\"Analytics\",\"User-Facing Analytics\",\"Indexes\"],\"summary\":\"How you can work out which indexes are currently defined on a Pinot table\",\"authors\":[\"needham\"],\"type\":\"Blog\",\"readingTime\":{\"text\":\"4 min read\",\"minutes\":3.79,\"time\":227400,\"words\":758},\"slug\":\"2022/11/08/Apache Pinot-How-do-I-see-my-indexes\",\"customSlug\":\"2022/11/08/Apache Pinot-How-do-I-see-my-indexes\",\"path\":\"blog/2022/11/08/Apache Pinot-How-do-I-see-my-indexes\",\"customPath\":\"blog/2022/11/08/Apache Pinot-How-do-I-see-my-indexes\",\"filePath\":\"blog/2022-11-08-Apache Pinot-How-do-I-see-my-indexes.mdx\",\"toc\":[{\"value\":\"Spinning up Pinot\",\"url\":\"#spinning-up-pinot\",\"depth\":2},{\"value\":\"Indexing Config\",\"url\":\"#indexing-config\",\"depth\":2},{\"value\":\"Viewing Indexes\",\"url\":\"#viewing-indexes\",\"depth\":2},{\"value\":\"Adding an Index\",\"url\":\"#adding-an-index\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}],\"structuredData\":{\"@context\":\"https://schema.org\",\"@type\":\"BlogPosting\",\"headline\":\"Apache Pinot™ 0.11 - How do I see my indexes?\",\"datePublished\":\"2022-11-08T00:00:00.000Z\",\"dateModified\":\"2022-11-08T00:00:00.000Z\",\"description\":\"How you can work out which indexes are currently defined on a Pinot table\",\"image\":\"/static/images/twitter-card.png\",\"url\":\"https://pinot.apache.org/blog/2022-11-08-Apache Pinot-How-do-I-see-my-indexes\",\"author\":[{\"@type\":\"Person\",\"name\":\"Mark Needham\"}]}},{\"title\":\"GapFill Function For Time-Series Datasets In Pinot\",\"date\":\"2022-08-02T00:00:00.000Z\",\"tags\":[\"Pinot\",\"Data\",\"Analytics\",\"User-Facing Analytics\",\"interpolation\",\"gapfilling\"],\"summary\":\"Gapfilling functions in Pinot to provide the on-the-fly interpolation (filling the missing data) functionality to better handle time-series data.\",\"authors\":[\"sun\",\"velusamy\"],\"type\":\"Blog\",\"readingTime\":{\"text\":\"9 min read\",\"minutes\":8.59,\"time\":515400,\"words\":1718},\"slug\":\"2022/08/02/GapFill-Function-For-Time-Series-Datasets-In-Pinot\",\"customSlug\":\"2022/08/02/GapFill-Function-For-Time-Series-Datasets-In-Pinot\",\"path\":\"blog/2022/08/02/GapFill-Function-For-Time-Series-Datasets-In-Pinot\",\"customPath\":\"blog/2022/08/02/GapFill-Function-For-Time-Series-Datasets-In-Pinot\",\"filePath\":\"blog/2022-08-02-GapFill-Function-For-Time-Series-Datasets-In-Pinot.mdx\",\"toc\":[{\"value\":\"Processing time series data in Pinot\",\"url\":\"#processing-time-series-data-in-pinot\",\"depth\":2},{\"value\":\"Sample Dataset:\",\"url\":\"#sample-dataset\",\"depth\":3},{\"value\":\"Interpolating missing data\",\"url\":\"#interpolating-missing-data\",\"depth\":2},{\"value\":\"Gapfill Query with a Use Case:\",\"url\":\"#gapfill-query-with-a-use-case\",\"depth\":2},{\"value\":\"Query Syntax:\",\"url\":\"#query-syntax\",\"depth\":3},{\"value\":\"Query components:\",\"url\":\"#query-components\",\"depth\":3},{\"value\":\"Query Workflow\",\"url\":\"#query-workflow\",\"depth\":3},{\"value\":\"Other Supported Query Scenarios:\",\"url\":\"#other-supported-query-scenarios\",\"depth\":3},{\"value\":\"How does it work?\",\"url\":\"#how-does-it-work\",\"depth\":2},{\"value\":\"Request Flow\",\"url\":\"#request-flow\",\"depth\":3},{\"value\":\"Execution Plan\",\"url\":\"#execution-plan\",\"depth\":3},{\"value\":\"Time and Space complexity:\",\"url\":\"#time-and-space-complexity\",\"depth\":3},{\"value\":\"Challenges\",\"url\":\"#challenges\",\"depth\":3},{\"value\":\"The Future Work\",\"url\":\"#the-future-work\",\"depth\":2}],\"structuredData\":{\"@context\":\"https://schema.org\",\"@type\":\"BlogPosting\",\"headline\":\"GapFill Function For Time-Series Datasets In Pinot\",\"datePublished\":\"2022-08-02T00:00:00.000Z\",\"dateModified\":\"2022-08-02T00:00:00.000Z\",\"description\":\"Gapfilling functions in Pinot to provide the on-the-fly interpolation (filling the missing data) functionality to better handle time-series data.\",\"image\":\"/static/images/twitter-card.png\",\"url\":\"https://pinot.apache.org/blog/2022-08-02-GapFill-Function-For-Time-Series-Datasets-In-Pinot\"}},{\"title\":\"How to Ingest Streaming Data from Kafka to Apache Pinot™\",\"date\":\"2022-05-30T00:00:00.000Z\",\"tags\":[\"Pinot\",\"Data\",\"Analytics\",\"User-Facing Analytics\",\"kafka\",\"streaming\",\"json\"],\"summary\":\"The blog post explains how to use Apache Kafka topics in Apache Pinot to ingest streaming data, with step-by-step instructions provided for installation and setup. It focuses on ingesting Wikipedia events into Kafka and connecting it to Pinot to create a real-time table. The post highlights Pinot's capabilities in ingesting and transforming JSON data into OLAP tables and encourages reader engagement through the community Slack.\",\"authors\":[\"herman\"],\"type\":\"Blog\",\"readingTime\":{\"text\":\"9 min read\",\"minutes\":8.935,\"time\":536100,\"words\":1787},\"slug\":\"2023/05/30/how-to-ingest-streaming-data-from-kafka-to-apache-pinot\",\"customSlug\":\"2023/05/30/how-to-ingest-streaming-data-from-kafka-to-apache-pinot\",\"path\":\"blog/2023/05/30/how-to-ingest-streaming-data-from-kafka-to-apache-pinot\",\"customPath\":\"blog/2023/05/30/how-to-ingest-streaming-data-from-kafka-to-apache-pinot\",\"filePath\":\"blog/2023-05-30-how-to-ingest-streaming-data-from-kafka-to-apache-pinot.mdx\",\"toc\":[{\"value\":\"The obligatory “What is Apache Pinot and StarTree?” section\",\"url\":\"#the-obligatory-what-is-apache-pinot-and-startree-section\",\"depth\":3},{\"value\":\"How to install Kafka alongside Pinot \",\"url\":\"#how-to-install-kafka-alongside-pinot\",\"depth\":2},{\"value\":\"Prerequisite\",\"url\":\"#prerequisite\",\"depth\":3},{\"value\":\"Step 1: Install Kafka on your Pinot Docker image\",\"url\":\"#step-1-install-kafka-on-your-pinot-docker-image\",\"depth\":3},{\"value\":\"Step 2: Install Kafka on the Docker container\",\"url\":\"#step-2-install-kafka-on-the-docker-container\",\"depth\":3},{\"value\":\"Step 3: Ingest data into Kafka\",\"url\":\"#step-3-ingest-data-into-kafka\",\"depth\":3},{\"value\":\"Step 4: Connect Kafka to Pinot\",\"url\":\"#step-4-connect-kafka-to-pinot\",\"depth\":3},{\"value\":\"Conclusion\",\"url\":\"#conclusion\",\"depth\":2}],\"structuredData\":{\"@context\":\"https://schema.org\",\"@type\":\"BlogPosting\",\"headline\":\"How to Ingest Streaming Data from Kafka to Apache Pinot™\",\"datePublished\":\"2022-05-30T00:00:00.000Z\",\"dateModified\":\"2022-05-30T00:00:00.000Z\",\"description\":\"The blog post explains how to use Apache Kafka topics in Apache Pinot to ingest streaming data, with step-by-step instructions provided for installation and setup. 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+ border-transparent opacity-30">ARM64</button></div><div class="table w-full whitespace-pre-wrap p-4 font-[Source_Code_Pro] leading-loose"><div class="table-row"><span class="table-cell select-none pr-1 text-gray-400">1</span><span class="table-cell">docker run -p 9000:9000 \</span></div><div class="table-row"><span class="table-cell select-none pr-1 text-gray-400">2</span><span class="table-cell">apachepinot/pinot:1.1.0 \</span></div><div class="table-row"><span class="table-cell select-none pr-1 text-gray-400">3</span><span class="table-cell">QuickStart -type hybrid</span></div></div></div><button class="inline-flex items-center justify-center whitespace-nowrap text-sm font-medium ring-offset-background transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50 bg-background hover:bg-accent hover:text-accent-foreground h-9 rounded-md px-3 m-7 w-16 border-2 border-vine-100 text-vine-100">copy</button></div></aside></div></section><section aria-labelledby="blog-section-title" class="md:mx-auto md:max-w-screen-outerLiveArea"><h4 id="blog-section-title" class="m-12 text-center text-[2rem] font-bold leading-10 lg:mt-24">Pinot Blog</h4><div class="grid grid-cols-1 gap-[1.875rem] md:grid-cols-3 lg:px-24"><a aria-label="Announcing Apache Pinot 1.0™" class="px-5 sm:px-0" href="/blog/2023/09/19/Annoucing-Apache-Pinot-1-0"><div class="rounded-lg border bg-card text-card-foreground shadow-sm h-full border-neutral-500 p-5"><div class="flex flex-col space-y-1.5 h-full justify-between p-0"><div><h3 class="tracking-tight pb-2 text-xl font-semibold leading-8 text-stone-900">Announcing Apache Pinot 1.0™</h3><p class="text-sm leading-[1.1rem] text-stone-900">Hubert Dulay, Mayank Shrivastava, Neha Pawar</p></div><p class="text-sm text-muted-foreground leading-[1.1rem]">September 19th, 2023<!-- --> • <!-- -->13 min read</p></div></div></a><a aria-label="Segment Compaction for Upsert Enabled Tables in Apache Pinot" class="px-5 sm:px-0" href="/blog/2023/08/04/segment-compaction-for-upsert-enabled-tables-in-apache-pinot-3f30657aa077"><div class="rounded-lg border bg-card text-card-foreground shadow-sm h-full border-neutral-500 p-5"><div class="flex flex-col space-y-1.5 h-full justify-between p-0"><div><h3 class="tracking-tight pb-2 text-xl font-semibold leading-8 text-stone-900">Segment Compaction for Upsert Enabled Tables in Apache Pinot</h3><p class="text-sm leading-[1.1rem] text-stone-900">Robert Zych</p></div><p class="text-sm text-muted-foreground leading-[1.1rem]">August 4th, 2023<!-- --> • <!-- -->4 min read</p></div></div></a><a aria-label="Star-Tree Index in Apache Pinot - Part 3 - Understanding the Impact in Real Customer Scenarios" class="px-5 sm:px-0" href="/blog/2023/07/12/star-tree-index-in-apache-pinot-part-3-understanding-the-impact-in-real-customer"><div class="rounded-lg border bg-card text-card-foreground shadow-sm h-full 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The post provides instructions on ingesting Apache Avro messages into Pinot, creating a Pinot table, and querying the data.\",\"authors\":[\"needham\"],\"type\":\"Blog\",\"readingTime\":{\"text\":\"7 min read\",\"minutes\":6.755,\"time\":405300,\"words\":1351},\"slug\":\"2023/06/01/real-time-mastodon-usage-with-apache-kafka-apache-pinot-and-streamlit\",\"customSlug\":\"2023/06/01/real-time-mastodon-usage-with-apache-kafka-apache-pinot-and-streamlit\",\"path\":\"blog/2023/06/01/real-time-mastodon-usage-with-apache-kafka-apache-pinot-and-streamlit\",\"customPath\":\"blog/2023/06/01/real-time-mastodon-usage-with-apache-kafka-apache-pinot-and-streamlit\",\"filePath\":\"blog/2023-06-01-real-time-mastodon-usage-with-apache-kafka-apache-pinot-and-streamlit.mdx\",\"toc\":[{\"value\":\"The Existing Solution: Kafka Connect, Parquet, Seaborn and DuckDB \",\"url\":\"#the-existing-solution-kafka-connect-parquet-seaborn-and-duckdb\",\"depth\":2},{\"value\":\"Going Real-Time with Apache Pinot™\",\"url\":\"#going-real-time-with-apache-pinot\",\"depth\":2},{\"value\":\"Setup\",\"url\":\"#setup\",\"depth\":2},{\"value\":\"Pinot Schema and Table\",\"url\":\"#pinot-schema-and-table\",\"depth\":2},{\"value\":\"Ingest Data into Kafka\",\"url\":\"#ingest-data-into-kafka\",\"depth\":2},{\"value\":\"Query Pinot\",\"url\":\"#query-pinot\",\"depth\":2},{\"value\":\"Streamlit\",\"url\":\"#streamlit\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}],\"structuredData\":{\"@context\":\"https://schema.org\",\"@type\":\"BlogPosting\",\"headline\":\"Real-Time Mastodon Usage with Apache Kafka, Apache Pinot, and Streamlit\",\"datePublished\":\"2023-06-01T00:00:00.000Z\",\"dateModified\":\"2023-06-01T00:00:00.000Z\",\"description\":\"The blog post discusses analyzing user activity and server popularity on Mastodon using Kafka Connect, Parquet, Seaborn, and DuckDB. 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It also mentions the availability of CDC connectors in Debezium for various streaming systems.\",\"authors\":[\"dulay\"],\"type\":\"Blog\",\"readingTime\":{\"text\":\"10 min read\",\"minutes\":9.105,\"time\":546300,\"words\":1821},\"slug\":\"2023/05/23/change-data-capture-with-apache-pinot-how-does-it-work\",\"customSlug\":\"2023/05/23/change-data-capture-with-apache-pinot-how-does-it-work\",\"path\":\"blog/2023/05/23/change-data-capture-with-apache-pinot-how-does-it-work\",\"customPath\":\"blog/2023/05/23/change-data-capture-with-apache-pinot-how-does-it-work\",\"filePath\":\"blog/2023-05-23-change-data-capture-with-apache-pinot-how-does-it-work.mdx\",\"toc\":[{\"value\":\"Pre-Image, Post-Image, or Diffs?\",\"url\":\"#pre-image-post-image-or-diffs\",\"depth\":2},{\"value\":\"How to Capture Change Data with Debezium\",\"url\":\"#how-to-capture-change-data-with-debezium\",\"depth\":2},{\"value\":\"Debezium Data Format\",\"url\":\"#debezium-data-format\",\"depth\":2},{\"value\":\"Materialized Views\",\"url\":\"#materialized-views\",\"depth\":2},{\"value\":\"Upsert in Apache Pinot\",\"url\":\"#upsert-in-apache-pinot\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}],\"structuredData\":{\"@context\":\"https://schema.org\",\"@type\":\"BlogPosting\",\"headline\":\"Change Data Capture with Apache Pinot - How Does It Work?\",\"datePublished\":\"2023-05-23T00:00:00.000Z\",\"dateModified\":\"2023-05-23T00:00:00.000Z\",\"description\":\"This blog post discusses the use of Change Data Capture (CDC) in Apache Pinot and the data format used in Debezium for efficient querying and analytics. 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By implementing a star-tree index, query performance significantly improved, reducing query latency from 1,513 ms to just 4 ms and drastically reducing disk reads by 99.999%.\",\"authors\":[\"dabade\"],\"type\":\"Blog\",\"readingTime\":{\"text\":\"7 min read\",\"minutes\":6.25,\"time\":375000,\"words\":1250},\"slug\":\"2023/05/16/star-tree-indexes-in-apache-pinot-part-1-understanding-the-impact-on-query-performance\",\"customSlug\":\"2023/05/16/star-tree-indexes-in-apache-pinot-part-1-understanding-the-impact-on-query-performance\",\"path\":\"blog/2023/05/16/star-tree-indexes-in-apache-pinot-part-1-understanding-the-impact-on-query-performance\",\"customPath\":\"blog/2023/05/16/star-tree-indexes-in-apache-pinot-part-1-understanding-the-impact-on-query-performance\",\"filePath\":\"blog/2023-05-16-star-tree-indexes-in-apache-pinot-part-1-understanding-the-impact-on-query-performance.mdx\",\"toc\":[{\"value\":\"1. The Dataset:\",\"url\":\"#1-the-dataset\",\"depth\":2},{\"value\":\"Schema:\",\"url\":\"#schema\",\"depth\":3},{\"value\":\"2. Query Pattern\",\"url\":\"#2-query-pattern\",\"depth\":2},{\"value\":\"Star-Tree Index Config:\",\"url\":\"#star-tree-index-config\",\"depth\":3},{\"value\":\"4. Query Results and Stats\",\"url\":\"#4-query-results-and-stats\",\"depth\":2},{\"value\":\"Iteration #1: w/o any Apache Pinot optimizations:\",\"url\":\"#iteration-1-wo-any-apache-pinot-optimizations\",\"depth\":3},{\"value\":\"Iteration #2: w/ Inverted Index \",\"url\":\"#iteration-2-w-inverted-index\",\"depth\":3},{\"value\":\"Iteration #3: w/ Star-Tree Index: \",\"url\":\"#iteration-3-w-star-tree-index\",\"depth\":3},{\"value\":\"Comparison:\",\"url\":\"#comparison\",\"depth\":3},{\"value\":\"5. Impact Summary:\",\"url\":\"#5-impact-summary\",\"depth\":2},{\"value\":\"Key Benefits of the Star-Tree Index:\",\"url\":\"#key-benefits-of-the-star-tree-index\",\"depth\":3}],\"structuredData\":{\"@context\":\"https://schema.org\",\"@type\":\"BlogPosting\",\"headline\":\"StarTree Indexes in Apache Pinot Part-1 - Understanding the Impact on Query Performance\",\"datePublished\":\"2023-05-16T00:00:00.000Z\",\"dateModified\":\"2023-05-16T00:00:00.000Z\",\"description\":\"The blog post explains the star-tree index in Apache Pinot and its benefits compared to traditional materialized views. 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Consumer Record Lag\",\"date\":\"2023-03-30T00:00:00.000Z\",\"tags\":[\"Pinot\",\"Data\",\"Analytics\",\"User-Facing Analytics\",\"consumer record lag\",\"kafka\"],\"summary\":\"This post will explore a new API endpoint that lets you check how much Pinot is lagging when ingesting from Apache Kafka.\",\"authors\":[\"needham\"],\"type\":\"Blog\",\"readingTime\":{\"text\":\"5 min read\",\"minutes\":4.57,\"time\":274200,\"words\":914},\"slug\":\"2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag\",\"customSlug\":\"2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag\",\"path\":\"blog/2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag\",\"customPath\":\"blog/2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag\",\"filePath\":\"blog/2023-03-30-Apache-Pinot-0-12-Consumer-Record-Lag.mdx\",\"toc\":[{\"value\":\"Why do we need this?\",\"url\":\"#why-do-we-need-this\",\"depth\":2},{\"value\":\"Worked Example\",\"url\":\"#worked-example\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}],\"structuredData\":{\"@context\":\"https://schema.org\",\"@type\":\"BlogPosting\",\"headline\":\"Apache Pinot™ 0.12 - 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Deduplication on Real-Time Tables\",\"datePublished\":\"2023-01-29T00:00:00.000Z\",\"dateModified\":\"2023-01-29T00:00:00.000Z\",\"description\":\"Learn about the deduplication for the real-time tables feature in Apache Pinot\",\"image\":\"/static/images/twitter-card.png\",\"url\":\"https://pinot.apache.org/blog/2023-01-29-Apache-Pinot-Deduplication-on-Real-Time-Tables\"}},{\"title\":\"Apache Pinot™ 0.11 - Pausing Real-Time Ingestion\",\"date\":\"2022-11-28T00:00:00.000Z\",\"tags\":[\"Pinot\",\"Data\",\"Analytics\",\"User-Facing Analytics\",\"pause\",\"resume\",\"real-time ingestion\"],\"summary\":\"Learn about a feature that lets you pause and resume real-time data ingestion in Apache Pinot\",\"authors\":[\"needham\"],\"type\":\"Blog\",\"readingTime\":{\"text\":\"7 min read\",\"minutes\":6.475,\"time\":388500,\"words\":1295},\"slug\":\"2022/11/28/Apache-Pinot-Pausing-Real-Time-Ingestion\",\"customSlug\":\"2022/11/28/Apache-Pinot-Pausing-Real-Time-Ingestion\",\"path\":\"blog/2022/11/28/Apache-Pinot-Pausing-Real-Time-Ingestion\",\"customPath\":\"blog/2022/11/28/Apache-Pinot-Pausing-Real-Time-Ingestion\",\"filePath\":\"blog/2022-11-28-Apache-Pinot-Pausing-Real-Time-Ingestion.mdx\",\"toc\":[{\"value\":\"How does real-time ingestion work?\",\"url\":\"#how-does-real-time-ingestion-work\",\"depth\":2},{\"value\":\"Why do we need to pause and resume ingestion?\",\"url\":\"#why-do-we-need-to-pause-and-resume-ingestion\",\"depth\":2},{\"value\":\"Data Generation\",\"url\":\"#data-generation\",\"depth\":2},{\"value\":\"Pinot Schema/Table Config\",\"url\":\"#pinot-schematable-config\",\"depth\":2},{\"value\":\"The Pause/Resume Flow\",\"url\":\"#the-pauseresume-flow\",\"depth\":2},{\"value\":\"Summary\",\"url\":\"#summary\",\"depth\":2}],\"structuredData\":{\"@context\":\"https://schema.org\",\"@type\":\"BlogPosting\",\"headline\":\"Apache Pinot™ 0.11 - 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Unfortunately, this approach results in scanning data and time value conversion work that takes a long time at large data volumes. 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decoding="async" data-nimg="1" style="color:transparent;width:auto;height:auto" src="/static/images/stories/walmart.svg"/><p class="text-base leading-relaxed text-black">Walmart leverages Apache Pinot to power real-time analytics, optimizing inventory management, enhancing customer experiences, and ensuring efficient processing of vast data on their platform, leading to improved operations and customer satisfaction.</p></div><div class="inline-flex flex-col items-start justify-start gap-4 rounded bg-sky-100 px-5 py-8 md:px-8"><img alt="Slack logo" loading="lazy" width="80" height="80" decoding="async" data-nimg="1" style="color:transparent;width:auto;height:auto" src="/static/images/stories/slack.svg"/><p class="text-base leading-relaxed text-black">Slack harnesses Apache Pinot for real-time analytics, improving search and discovery features. This empowers users to find information quickly, enhancing collaboration and productivity.</p></div><div class="inline-flex flex-col items-start justify-start gap-4 rounded bg-sky-100 px-5 py-8 md:px-8"><img alt="Linkedin logo" loading="lazy" width="80" height="80" decoding="async" data-nimg="1" style="color:transparent;width:auto;height:auto" src="/static/images/stories/linkedin.svg"/><p class="text-base leading-relaxed text-black">LinkedIn utilizes Apache Pinot for real-time analytics, enabling faster, data-driven decisions and delivering personalized content and recommendations to users, enhancing engagement and user experience.</p></div><div class="inline-flex flex-col items-start justify-start gap-4 rounded bg-sky-100 px-5 py-8 md:px-8"><img alt="Stripe logo" loading="lazy" width="80" height="80" decoding="async" data-nimg="1" style="color:transparent;width:auto;height:auto" src="/static/images/stories/stripe.svg"/><p class="text-base leading-relaxed text-black">Stripe harnesses Apache Pinot for real-time data analytics, optimizing payment processing, fraud detection, and enhancing user experiences. This ensures secure and efficient transactions on their platform, benefiting both businesses and customers.</p></div><div class="inline-flex flex-col items-start justify-start gap-4 rounded bg-sky-100 px-5 py-8 md:px-8"><img alt="Wix logo" loading="lazy" width="80" height="80" decoding="async" data-nimg="1" style="color:transparent;width:auto;height:auto" src="/static/images/stories/wix.svg"/><p class="text-base leading-relaxed text-black">Wix employs Apache Pinot for real-time analytics, improving website performance insights and user engagement tracking. This enhances website building and hosting experiences for millions of users.</p></div><div class="inline-flex flex-col items-start justify-start gap-4 rounded bg-sky-100 px-5 py-8 md:px-8"><img alt="Uber logo" loading="lazy" width="80" height="80" decoding="async" data-nimg="1" style="color:transparent;width:auto;height:auto" src="/static/images/stories/uber.svg"/><p class="text-base leading-relaxed text-black">Uber utilizes Apache Pinot for real-time data processing, enabling efficient dispatching, dynamic pricing, and tracking millions of rides. This enhances the overall user experience and operational efficiency.</p></div></div></div><h3 class="p-4 pt-14 text-center text-2xl font-semibold md:pb-10">Companies Using Apache Pinot</h3><div class="container mx-auto md:mb-28 md:px-24 md:pb-4"><div class="hidden sm:block"><div class="flex flex-wrap justify-center gap-10"><button class="text-base leading-snug border-b-4 border-crimson-100 font-semibold">All</button><button class="text-base leading-snug font-normal">Retail</button><button class="text-base leading-snug font-normal">Media/Comms</button><button class="text-base leading-snug font-normal">Fintech</button><button class="text-base leading-snug font-normal">Cloud Native/SaaS</button><button class="text-base leading-snug font-normal">Food/Logistics</button><button class="text-base leading-snug font-normal">Other</button></div></div><div class="sm:hidden"><select title="Filter by type" class="mt-1 block w-full rounded-md border-gray-300 shadow-sm focus:border-indigo-300 focus:ring focus:ring-indigo-200 focus:ring-opacity-50 sm:text-sm "><option value="all" selected="">All</option><option value="retail">Retail</option><option value="media">Media/Comms</option><option value="fintech">Fintech</option><option value="cloud">Cloud Native/SaaS</option><option value="food">Food/Logistics</option><option value="other">Other</option></select></div><div class="grid grid-cols-3 gap-6 py-6 lg:grid-cols-6"><div><picture><img src="/static/images/companies/retail/seven_eleven.svg" alt="7 Eleven"/></picture></div><div><picture><img src="/static/images/companies/retail/etsy.svg" alt="Etsy"/></picture></div><div><picture><img src="/static/images/companies/retail/guitar_center.svg" alt="Guitar Center"/></picture></div><div><picture><img src="/static/images/companies/retail/myntra.svg" alt="Myntra"/></picture></div><div><picture><img src="/static/images/companies/retail/target.svg" alt="Target"/></picture></div><div><picture><img 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Unlimited"/></picture></div><div><picture><img src="/static/images/companies/media/weibo.svg" alt="Weibo"/></picture></div><div><picture><img src="/static/images/companies/media/sorvn.svg" alt="Sovrn"/></picture></div><div><picture><img src="/static/images/companies/media/bliss_point_media.svg" alt="Bliss Point Media"/></picture></div><div><picture><img src="/static/images/companies/media/webex.svg" alt="Webex"/></picture></div><div><picture><img src="/static/images/companies/media/jio_saavn.svg" alt="Jio Saavn"/></picture></div><div><picture><img src="/static/images/companies/media/dazn.svg" alt="DAZN"/></picture></div><div><picture><img src="/static/images/companies/media/roku.svg" alt="roku"/></picture></div><div><picture><img src="/static/images/companies/media/promoted_ai.svg" alt="PromotedAI"/></picture></div><div><picture><img src="/static/images/companies/media/media_net.svg" alt="MediaNet"/></picture></div><div><picture><img src="/static/images/companies/fintech/citibank.svg" 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decoding="async" data-nimg="1" style="color:transparent;width:auto;height:auto" src="/static/images/stories/walmart.svg"/><p class="text-base leading-relaxed text-black">Walmart leverages Apache Pinot to power real-time analytics, optimizing inventory management, enhancing customer experiences, and ensuring efficient processing of vast data on their platform, leading to improved operations and customer satisfaction.</p></div><div class="inline-flex flex-col items-start justify-start gap-4 rounded bg-sky-100 px-5 py-8 md:px-8"><img alt="Slack logo" loading="lazy" width="80" height="80" decoding="async" data-nimg="1" style="color:transparent;width:auto;height:auto" src="/static/images/stories/slack.svg"/><p class="text-base leading-relaxed text-black">Slack harnesses Apache Pinot for real-time analytics, improving search and discovery features. This empowers users to find information quickly, enhancing collaboration and productivity.</p></div><div class="inline-flex flex-col items-start justify-start gap-4 rounded bg-sky-100 px-5 py-8 md:px-8"><img alt="Linkedin logo" loading="lazy" width="80" height="80" decoding="async" data-nimg="1" style="color:transparent;width:auto;height:auto" src="/static/images/stories/linkedin.svg"/><p class="text-base leading-relaxed text-black">LinkedIn utilizes Apache Pinot for real-time analytics, enabling faster, data-driven decisions and delivering personalized content and recommendations to users, enhancing engagement and user experience.</p></div><div class="inline-flex flex-col items-start justify-start gap-4 rounded bg-sky-100 px-5 py-8 md:px-8"><img alt="Stripe logo" loading="lazy" width="80" height="80" decoding="async" data-nimg="1" style="color:transparent;width:auto;height:auto" src="/static/images/stories/stripe.svg"/><p class="text-base leading-relaxed text-black">Stripe harnesses Apache Pinot for real-time data analytics, optimizing payment processing, fraud detection, and enhancing user experiences. This ensures secure and efficient transactions on their platform, benefiting both businesses and customers.</p></div><div class="inline-flex flex-col items-start justify-start gap-4 rounded bg-sky-100 px-5 py-8 md:px-8"><img alt="Wix logo" loading="lazy" width="80" height="80" decoding="async" data-nimg="1" style="color:transparent;width:auto;height:auto" src="/static/images/stories/wix.svg"/><p class="text-base leading-relaxed text-black">Wix employs Apache Pinot for real-time analytics, improving website performance insights and user engagement tracking. This enhances website building and hosting experiences for millions of users.</p></div><div class="inline-flex flex-col items-start justify-start gap-4 rounded bg-sky-100 px-5 py-8 md:px-8"><img alt="Uber logo" loading="lazy" width="80" height="80" decoding="async" data-nimg="1" style="color:transparent;width:auto;height:auto" src="/static/images/stories/uber.svg"/><p class="text-base leading-relaxed text-black">Uber utilizes Apache Pinot for real-time data processing, enabling efficient dispatching, dynamic pricing, and tracking millions of rides. This enhances the overall user experience and operational efficiency.</p></div></div></div><h3 class="p-4 pt-14 text-center text-2xl font-semibold md:pb-10">Companies Using Apache Pinot</h3><div class="container mx-auto md:mb-28 md:px-24 md:pb-4"><div class="hidden sm:block"><div class="flex flex-wrap justify-center gap-10"><button class="text-base leading-snug border-b-4 border-crimson-100 font-semibold">All</button><button class="text-base leading-snug font-normal">Retail</button><button class="text-base leading-snug font-normal">Media/Comms</button><button class="text-base leading-snug font-normal">Fintech</button><button class="text-base leading-snug font-normal">Cloud Native/SaaS</button><button class="text-base leading-snug font-normal">Food/Logistics</button><button class="text-base leading-snug font-normal">Other</button></div></div><div class="sm:hidden"><select title="Filter by type" class="mt-1 block w-full rounded-md border-gray-300 shadow-sm focus:border-indigo-300 focus:ring focus:ring-indigo-200 focus:ring-opacity-50 sm:text-sm "><option value="all" selected="">All</option><option value="retail">Retail</option><option value="media">Media/Comms</option><option value="fintech">Fintech</option><option value="cloud">Cloud Native/SaaS</option><option value="food">Food/Logistics</option><option value="other">Other</option></select></div><div class="grid grid-cols-3 gap-6 py-6 lg:grid-cols-6"><div><picture><img src="/static/images/companies/retail/seven_eleven.svg" alt="7 Eleven"/></picture></div><div><picture><img src="/static/images/companies/retail/etsy.svg" alt="Etsy"/></picture></div><div><picture><img src="/static/images/companies/retail/guitar_center.svg" alt="Guitar Center"/></picture></div><div><picture><img src="/static/images/companies/retail/myntra.svg" alt="Myntra"/></picture></div><div><picture><img src="/static/images/companies/retail/target.svg" alt="Target"/></picture></div><div><picture><img src="/static/images/companies/retail/walmart.svg" alt="Walmart"/></picture></div><div><picture><img src="/static/images/companies/retail/cora.svg" alt="Cora"/></picture></div><div><picture><img src="/static/images/companies/media/adbeat.svg" alt="AdBeat"/></picture></div><div><picture><img src="/static/images/companies/media/linkedin.svg" alt="LinkedIn"/></picture></div><div><picture><img src="/static/images/companies/media/constant_contact.svg" alt="Constant Contact"/></picture></div><div><picture><img src="/static/images/companies/media/cricket.svg" alt="Cricket"/></picture></div><div><picture><img src="/static/images/companies/media/inmobi.svg" alt="Inmobi"/></picture></div><div><picture><img src="/static/images/companies/media/publicis_sapient.svg" alt="Publicis Sapient"/></picture></div><div><picture><img src="/static/images/companies/media/reelevant.svg" alt="Reelevant"/></picture></div><div><picture><img src="/static/images/companies/media/scale_unlimited.svg" alt="ConstantScale Unlimited"/></picture></div><div><picture><img src="/static/images/companies/media/weibo.svg" alt="Weibo"/></picture></div><div><picture><img src="/static/images/companies/media/sorvn.svg" alt="Sovrn"/></picture></div><div><picture><img src="/static/images/companies/media/bliss_point_media.svg" alt="Bliss Point Media"/></picture></div><div><picture><img src="/static/images/companies/media/webex.svg" alt="Webex"/></picture></div><div><picture><img src="/static/images/companies/media/jio_saavn.svg" alt="Jio Saavn"/></picture></div><div><picture><img src="/static/images/companies/media/dazn.svg" alt="DAZN"/></picture></div><div><picture><img src="/static/images/companies/media/roku.svg" alt="roku"/></picture></div><div><picture><img src="/static/images/companies/media/promoted_ai.svg" alt="PromotedAI"/></picture></div><div><picture><img src="/static/images/companies/media/media_net.svg" alt="MediaNet"/></picture></div><div><picture><img src="/static/images/companies/fintech/citibank.svg" alt="Citi"/></picture></div><div><picture><img src="/static/images/companies/fintech/wepay.svg" alt="WePay"/></picture></div><div><picture><img src="/static/images/companies/fintech/fi_money.svg" alt="Fi Money"/></picture></div><div><picture><img src="/static/images/companies/fintech/stripe.svg" alt="Stripe"/></picture></div><div><picture><img src="/static/images/companies/fintech/synpulse.svg" alt="Synpulse"/></picture></div><div><picture><img src="/static/images/companies/fintech/cred.svg" alt="Cred"/></picture></div><div><picture><img src="/static/images/companies/fintech/tradeweb.svg" alt="Tradeweb"/></picture></div><div><picture><img src="/static/images/companies/fintech/goldman_sachs.svg" alt="Goldman Sachs"/></picture></div><div><picture><img src="/static/images/companies/fintech/phone_pe.svg" alt="PhonePe"/></picture></div><div><picture><img src="/static/images/companies/fintech/10x_banking.svg" alt="10x Banking"/></picture></div><div><picture><img src="/static/images/companies/fintech/robinhood.svg" alt="Robinhood"/></picture></div><div><picture><img src="/static/images/companies/fintech/pine_labs.svg" alt="Pine Labs"/></picture></div><div><picture><img src="/static/images/companies/cloud/slack.svg" alt="Slack"/></picture></div><div><picture><img src="/static/images/companies/cloud/zoho.svg" alt="Zoho"/></picture></div><div><picture><img src="/static/images/companies/cloud/bettermode.svg" alt="Bettermode"/></picture></div><div><picture><img src="/static/images/companies/cloud/dialpad.svg" alt="Dialpadai"/></picture></div><div><picture><img src="/static/images/companies/cloud/wix.svg" alt="Wix"/></picture></div><div><picture><img src="/static/images/companies/cloud/expedia.svg" alt="Expedia"/></picture></div><div><picture><img src="/static/images/companies/cloud/hrt.svg" alt="HRT"/></picture></div><div><picture><img src="/static/images/companies/cloud/momentive.svg" alt="Momentive"/></picture></div><div><picture><img src="/static/images/companies/cloud/hubspot.svg" alt="Hubspot"/></picture></div><div><picture><img src="/static/images/companies/cloud/phenom.svg" alt="Phenom"/></picture></div><div><picture><img src="/static/images/companies/cloud/zuora.svg" alt="Zuora"/></picture></div><div><picture><img src="/static/images/companies/food/uber.svg" alt="Uber"/></picture></div><div><picture><img src="/static/images/companies/food/just_eat.svg" alt="Just Eat"/></picture></div><div><picture><img src="/static/images/companies/food/roadie.svg" alt="Roadie"/></picture></div><div><picture><img src="/static/images/companies/food/blinkit.svg" alt="BlinkIt"/></picture></div><div><picture><img src="/static/images/companies/food/cloud_kitchens.svg" alt="CLoudKitchens"/></picture></div><div><picture><img src="/static/images/companies/food/door_dash.svg" alt="DoorDash"/></picture></div><div><picture><img src="/static/images/companies/food/careem.svg" alt="Careem"/></picture></div><div><picture><img src="/static/images/companies/food/ola.svg" alt="Ola"/></picture></div><div><picture><img src="/static/images/companies/other/eero.svg" alt="Eero"/></picture></div><div><picture><img src="/static/images/companies/other/confluera.svg" alt="confluera"/></picture></div><div><picture><img src="/static/images/companies/other/hyundai.svg" alt="Hyundai"/></picture></div><div><picture><img src="/static/images/companies/other/startree.svg" alt="starTree"/></picture></div><div><picture><img src="/static/images/companies/other/defined_ai.svg" alt="DefinedAi"/></picture></div><div><picture><img src="/static/images/companies/other/traceable.svg" alt="Traceable"/></picture></div><div><picture><img src="/static/images/companies/other/nvidia.svg" alt="Nvidia"/></picture></div><div><picture><img src="/static/images/companies/other/kloudfuse.svg" alt="Kloudfuse"/></picture></div><div><picture><img src="/static/images/companies/other/mixmode.svg" alt="MixMode"/></picture></div><div><picture><img src="/static/images/companies/other/rippling.svg" alt="Rippling"/></picture></div><div><picture><img src="/static/images/companies/other/morgan_morgan.svg" alt="Morgan & Morgan"/></picture></div><div><picture><img src="/static/images/companies/other/moveworks.svg" alt="Moveworks"/></picture></div><div><picture><img src="/static/images/companies/other/tuoagente.svg" alt="TuoAgente"/></picture></div><div><picture><img src="/static/images/companies/other/link_labs.svg" alt="Link Labs"/></picture></div><div><picture><img src="/static/images/companies/other/vedantu.svg" alt="Vedantu"/></picture></div><div><picture><img src="/static/images/companies/other/boond_manager.svg" alt="Boond Manager"/></picture></div><div><picture><img src="/static/images/companies/other/you_gov.svg" alt="YouGov"/></picture></div><div><picture><img src="/static/images/companies/other/mobileum.svg" alt="Mobileum"/></picture></div><div><picture><img src="/static/images/companies/other/hire_ez.svg" 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