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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 SQL-on-Hadoop</h1></header><p>SQL-on-Hadoop engines provide an |
| execution engine for various data formats and data stores, and |
| many can be made to push down computations down to Druid, while providing a SQL interface to Druid.</p><p>For a direct comparison between the technologies and when to only use one or the other, things basically comes down to your |
| product requirements and what the systems were designed to do.</p><p>Druid was designed to</p><ol><li>be an always on service</li><li>ingest data in real-time</li><li>handle slice-n-dice style ad-hoc queries</li></ol><p>SQL-on-Hadoop engines generally sidestep Map/Reduce, instead querying data directly from HDFS or, in some cases, other storage systems. |
| Some of these engines (including Impala and Presto) can be co-located with HDFS data nodes and coordinate with them to achieve data locality for queries. |
| What does this mean? We can talk about it in terms of three general areas</p><ol><li>Queries</li><li>Data Ingestion</li><li>Query Flexibility</li></ol><h3 class="anchor anchorWithStickyNavbar_LWe7" id="queries">Queries<a href="#queries" class="hash-link" aria-label="Direct link to Queries" title="Direct link to Queries"></a></h3><p>Druid segments stores data in a custom column format. Segments are scanned directly as part of queries and each Druid server |
| calculates a set of results that are eventually merged at the Broker level. This means the data that is transferred between servers |
| are queries and results, and all computation is done internally as part of the Druid servers.</p><p>Most SQL-on-Hadoop engines are responsible for query planning and execution for underlying storage layers and storage formats. |
| They are processes that stay on even if there is no query running (eliminating the JVM startup costs from Hadoop MapReduce). |
| Some (Impala/Presto) SQL-on-Hadoop engines have daemon processes that can be run where the data is stored, virtually eliminating network transfer costs. There is still |
| some latency overhead (e.g. serialization/deserialization time) associated with pulling data from the underlying storage layer into the computation layer. We are unaware of exactly |
| how much of a performance impact this makes.</p><h3 class="anchor anchorWithStickyNavbar_LWe7" id="data-ingestion">Data Ingestion<a href="#data-ingestion" class="hash-link" aria-label="Direct link to Data Ingestion" title="Direct link to Data Ingestion"></a></h3><p>Druid is built to allow for real-time ingestion of data. You can ingest data and query it immediately upon ingestion, |
| the latency between how quickly the event is reflected in the data is dominated by how long it takes to deliver the event to Druid.</p><p>SQL-on-Hadoop, being based on data in HDFS or some other backing store, are limited in their data ingestion rates by the |
| rate at which that backing store can make data available. Generally, the backing store is the biggest bottleneck for |
| how quickly data can become available.</p><h3 class="anchor anchorWithStickyNavbar_LWe7" id="query-flexibility">Query Flexibility<a href="#query-flexibility" class="hash-link" aria-label="Direct link to Query Flexibility" title="Direct link to Query Flexibility"></a></h3><p>Druid's query language is fairly low level and maps to how Druid operates internally. Although Druid can be combined with a high level query |
| planner to support most SQL queries and analytic SQL queries (minus joins among large tables), |
| base Druid is less flexible than SQL-on-Hadoop solutions for generic processing.</p><p>SQL-on-Hadoop support SQL style queries with full joins.</p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="druid-vs-parquet">Druid vs Parquet<a href="#druid-vs-parquet" class="hash-link" aria-label="Direct link to Druid vs Parquet" title="Direct link to Druid vs Parquet"></a></h2><p>Parquet is a column storage format that is designed to work with SQL-on-Hadoop engines. Parquet doesn't have a query execution engine, and instead |
| relies on external sources to pull data out of it.</p><p>Druid's storage format is highly optimized for linear scans. Although Druid has support for nested data, Parquet's storage format is much |
| more hierarchical, and is more designed for binary chunking. In theory, this should lead to faster scans in Druid.</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="#queries" class="table-of-contents__link toc-highlight">Queries</a></li><li><a href="#data-ingestion" class="table-of-contents__link toc-highlight">Data Ingestion</a></li><li><a href="#query-flexibility" class="table-of-contents__link toc-highlight">Query Flexibility</a></li><li><a href="#druid-vs-parquet" class="table-of-contents__link toc-highlight">Druid vs Parquet</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. 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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