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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="theme-doc-markdown markdown"><header><h1>Apache Druid vs Spark</h1></header><p>Druid and Spark are complementary solutions as Druid can be used to accelerate OLAP queries in Spark.</p><p>Spark is a general cluster computing framework initially designed around the concept of Resilient Distributed Datasets (RDDs). |
| RDDs enable data reuse by persisting intermediate results |
| in memory and enable Spark to provide fast computations for iterative algorithms. |
| This is especially beneficial for certain work flows such as machine |
| learning, where the same operation may be applied over and over |
| again until some result is converged upon. The generality of Spark makes it very suitable as an engine to process (clean or transform) data. |
| Although Spark provides the ability to query data through Spark SQL, much like Hadoop, the query latencies are not specifically targeted to be interactive (sub-second).</p><p>Druid's focus is on extremely low latency queries, and is ideal for powering applications used by thousands of users, and where each query must |
| return fast enough such that users can interactively explore through data. Druid fully indexes all data, and can act as a middle layer between Spark and your application. |
| One typical setup seen in production is to process data in Spark, and load the processed data into Druid for faster access.</p><p>For more information about using Druid and Spark together, including benchmarks of the two systems, please see:</p><p><a href="https://www.linkedin.com/pulse/combining-druid-spark-interactive-flexible-analytics-scale-butani" target="_blank" rel="noopener noreferrer">https://www.linkedin.com/pulse/combining-druid-spark-interactive-flexible-analytics-scale-butani</a></p></div></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages"></nav></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. Except where otherwise noted, licensed under CC BY-SA 4.0. Apache Druid, Druid, and the Druid logo are either registered trademarks or trademarks of The Apache Software Foundation in the United States and other countries.</div></div></div></footer></div> |
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