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class="search-icon" tabindex="0"></span><input type="search" id="search_input_react" placeholder="Loading..." aria-label="Search" class="navbar__search-input search-bar" disabled=""></div></div></div></div><div role="presentation" class="navbar-sidebar__backdrop"></div></nav><div id="__docusaurus_skipToContent_fallback" class="main-wrapper mainWrapper_z2l0 docsWrapper_BCFX"><button aria-label="Scroll back to top" class="clean-btn theme-back-to-top-button backToTopButton_sjWU" type="button"></button><div class="docPage__5DB"><main class="docMainContainer_gTbr docMainContainerEnhanced_Uz_u"><div class="container padding-top--md padding-bottom--lg"><div class="row"><div class="col docItemCol_VOVn"><div class="docItemContainer_Djhp"><article><div class="tocCollapsible_ETCw theme-doc-toc-mobile tocMobile_ITEo"><button type="button" class="clean-btn tocCollapsibleButton_TO0P">On this page</button></div><div class="theme-doc-markdown markdown"><header><h1>Apache Druid vs Redshift</h1></header><h3 class="anchor anchorWithStickyNavbar_LWe7" id="how-does-druid-compare-to-redshift">How does Druid compare to Redshift?<a href="#how-does-druid-compare-to-redshift" class="hash-link" aria-label="Direct link to How does Druid compare to Redshift?" title="Direct link to How does Druid compare to Redshift?"></a></h3><p>In terms of drawing a differentiation, Redshift started out as ParAccel (Actian), which Amazon is licensing and has since heavily modified.</p><p>Aside from potential performance differences, there are some functional differences:</p><h3 class="anchor anchorWithStickyNavbar_LWe7" id="real-time-data-ingestion">Real-time data ingestion<a href="#real-time-data-ingestion" class="hash-link" aria-label="Direct link to Real-time data ingestion" title="Direct link to Real-time data ingestion"></a></h3><p>Because Druid is optimized to provide insight against massive quantities of streaming data; it is able to load and aggregate data in real-time.</p><p>Generally traditional data warehouses including column stores work only with batch ingestion and are not optimal for streaming data in regularly.</p><h3 class="anchor anchorWithStickyNavbar_LWe7" id="druid-is-a-read-oriented-analytical-data-store">Druid is a read oriented analytical data store<a href="#druid-is-a-read-oriented-analytical-data-store" class="hash-link" aria-label="Direct link to Druid is a read oriented analytical data store" title="Direct link to Druid is a read oriented analytical data store"></a></h3><p>Druid’s write semantics are not as fluid and does not support full joins (we support large table to small table joins). Redshift provides full SQL support including joins and insert/update statements.</p><h3 class="anchor anchorWithStickyNavbar_LWe7" id="data-distribution-model">Data distribution model<a href="#data-distribution-model" class="hash-link" aria-label="Direct link to Data distribution model" title="Direct link to Data distribution model"></a></h3><p>Druid’s data distribution is segment-based and leverages a highly available "deep" storage such as S3 or HDFS. Scaling up (or down) does not require massive copy actions or downtime; in fact, losing any number of Historical processes does not result in data loss because new Historical processes can always be brought up by reading data from "deep" storage.</p><p>To contrast, ParAccel’s data distribution model is hash-based. Expanding the cluster requires re-hashing the data across the nodes, making it difficult to perform without taking downtime. Amazon’s Redshift works around this issue with a multi-step process:</p><ul><li>set cluster into read-only mode</li><li>copy data from cluster to new cluster that exists in parallel</li><li>redirect traffic to new cluster</li></ul><h3 class="anchor anchorWithStickyNavbar_LWe7" id="replication-strategy">Replication strategy<a href="#replication-strategy" class="hash-link" aria-label="Direct link to Replication strategy" title="Direct link to Replication strategy"></a></h3><p>Druid employs segment-level data distribution meaning that more processes can be added and rebalanced without having to perform a staged swap. The replication strategy also makes all replicas available for querying. Replication is done automatically and without any impact to performance.</p><p>ParAccel’s hash-based distribution generally means that replication is conducted via hot spares. This puts a numerical limit on the number of nodes you can lose without losing data, and this replication strategy often does not allow the hot spare to help share query load.</p><h3 class="anchor anchorWithStickyNavbar_LWe7" id="indexing-strategy">Indexing strategy<a href="#indexing-strategy" class="hash-link" aria-label="Direct link to Indexing strategy" title="Direct link to Indexing strategy"></a></h3><p>Along with column oriented structures, Druid uses indexing structures to speed up query execution when a filter is provided. Indexing structures do increase storage overhead (and make it more difficult to allow for mutation), but they also significantly speed up queries.</p><p>ParAccel does not appear to employ indexing strategies.</p></div></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages"></nav></div></div><div class="col col--3"><div class="tableOfContents_bqdL thin-scrollbar theme-doc-toc-desktop"><ul class="table-of-contents table-of-contents__left-border"><li><a href="#how-does-druid-compare-to-redshift" class="table-of-contents__link toc-highlight">How does Druid compare to Redshift?</a></li><li><a href="#real-time-data-ingestion" class="table-of-contents__link toc-highlight">Real-time data ingestion</a></li><li><a href="#druid-is-a-read-oriented-analytical-data-store" class="table-of-contents__link toc-highlight">Druid is a read oriented analytical data store</a></li><li><a href="#data-distribution-model" class="table-of-contents__link toc-highlight">Data distribution model</a></li><li><a href="#replication-strategy" class="table-of-contents__link toc-highlight">Replication strategy</a></li><li><a href="#indexing-strategy" class="table-of-contents__link toc-highlight">Indexing strategy</a></li></ul></div></div></div></div></main></div></div><footer class="footer"><div class="container container-fluid"><div class="footer__bottom text--center"><div class="margin-bottom--sm"><img src="/img/favicon.png" class="themedImage_ToTc themedImage--light_HNdA footer__logo"><img src="/img/favicon.png" class="themedImage_ToTc themedImage--dark_i4oU footer__logo"></div><div class="footer__copyright">Copyright © 2023 Apache Software Foundation. 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