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docItemCol_VOVn"><div class="docItemContainer_Djhp"><article><nav class="theme-doc-breadcrumbs breadcrumbsContainer_Z_bl" aria-label="Breadcrumbs"><ul class="breadcrumbs" itemscope="" itemtype="https://schema.org/BreadcrumbList"><li class="breadcrumbs__item"><a aria-label="Home page" class="breadcrumbs__link" href="/"><svg viewBox="0 0 24 24" class="breadcrumbHomeIcon_YNFT"><path d="M10 19v-5h4v5c0 .55.45 1 1 1h3c.55 0 1-.45 1-1v-7h1.7c.46 0 .68-.57.33-.87L12.67 3.6c-.38-.34-.96-.34-1.34 0l-8.36 7.53c-.34.3-.13.87.33.87H5v7c0 .55.45 1 1 1h3c.55 0 1-.45 1-1z" fill="currentColor"></path></svg></a></li><li class="breadcrumbs__item"><span class="breadcrumbs__link">Ingestion</span><meta itemprop="position" content="1"></li><li class="breadcrumbs__item"><span class="breadcrumbs__link">Ingestion concepts</span><meta itemprop="position" content="2"></li><li itemscope="" itemprop="itemListElement" itemtype="https://schema.org/ListItem" class="breadcrumbs__item breadcrumbs__item--active"><span class="breadcrumbs__link" itemprop="name">Partitioning</span><meta itemprop="position" content="3"></li></ul></nav><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>Partitioning</h1></header><p>You can use segment partitioning and sorting within your Druid datasources to reduce the size of your data and increase performance.</p><p>One way to partition is to load data into separate datasources. This is a perfectly viable approach that works very well when the number of datasources does not lead to excessive per-datasource overheads.</p><p>This topic describes how to set up partitions within a single datasource. It does not cover how to use multiple datasources. See <a href="/docs/latest/querying/multitenancy">Multitenancy considerations</a> for more details on splitting data into separate datasources and potential operational considerations.</p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="time-chunk-partitioning">Time chunk partitioning<a href="#time-chunk-partitioning" class="hash-link" aria-label="Direct link to Time chunk partitioning" title="Direct link to Time chunk partitioning"></a></h2><p>Druid always partitions datasources by time into <em>time chunks</em>. Each time chunk contains one or more segments. This partitioning happens for all ingestion methods based on the <code>segmentGranularity</code> parameter in your ingestion spec <code>dataSchema</code> object.</p><p>Partitioning by time is important for two reasons:</p><ol><li>Queries that filter by <code>__time</code> (SQL) or <code>intervals</code> (native) are able to use time partitioning to prune the set of segments to consider.</li><li>Certain data management operations, such as overwriting and compacting existing data, acquire exclusive write locks on time partitions.</li><li>Each segment file is wholly contained within a time partition. Too-fine-grained partitioning may cause a large number |
| of small segments, which leads to poor performance.</li></ol><p>The most common choices to balance these considerations are <code>hour</code> and <code>day</code>. For streaming ingestion, <code>hour</code> is especially |
| common, because it allows compaction to follow ingestion with less of a time delay.</p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="secondary-partitioning">Secondary partitioning<a href="#secondary-partitioning" class="hash-link" aria-label="Direct link to Secondary partitioning" title="Direct link to Secondary partitioning"></a></h2><p>Druid can partition segments within a particular time chunk further depending upon options that vary based on the ingestion type you have chosen. In general, secondary partitioning on a particular dimension improves locality. This means that rows with the same value for that dimension are stored together, decreasing access time.</p><p>To achieve the best performance and smallest overall footprint, partition your data on a "natural" |
| dimension that you often use as a filter when possible. Such partitioning often improves compression and query performance. For example, some cases have yielded threefold storage size decreases.</p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="partitioning-and-sorting">Partitioning and sorting<a href="#partitioning-and-sorting" class="hash-link" aria-label="Direct link to Partitioning and sorting" title="Direct link to Partitioning and sorting"></a></h2><p>Partitioning and sorting work well together. If you do have a "natural" partitioning dimension, consider placing it first in the <code>dimensions</code> list of your <code>dimensionsSpec</code>. This way Druid sorts rows within each segment by that column. This sorting configuration frequently improves compression more than using partitioning alone.</p><p>Note that Druid always sorts rows within a segment by timestamp first, even before the first dimension listed in your <code>dimensionsSpec</code>. This sorting can preclude the efficacy of dimension sorting. To work around this limitation if necessary, set your <code>queryGranularity</code> equal to <code>segmentGranularity</code> in your <a href="/docs/latest/ingestion/ingestion-spec#granularityspec"><code>granularitySpec</code></a>. Druid will set all timestamps within the segment to the same value, letting you identify a <a href="/docs/latest/ingestion/schema-design#secondary-timestamps">secondary timestamp</a> as the "real" timestamp.</p><h2 class="anchor anchorWithStickyNavbar_LWe7" id="how-to-configure-partitioning">How to configure partitioning<a href="#how-to-configure-partitioning" class="hash-link" aria-label="Direct link to How to configure partitioning" title="Direct link to How to configure partitioning"></a></h2><p>Not all ingestion methods support an explicit partitioning configuration, and not all have equivalent levels of flexibility. If you are doing initial ingestion through a less-flexible method like |
| Kafka), you can use <a href="/docs/latest/data-management/update#reindex">reindexing</a> or <a href="/docs/latest/data-management/compaction">compaction</a> to repartition your data after initial ingestion. This is a powerful technique you can use to optimally partition any data older than a certain time threshold while you continuously add new data from a stream.</p><p>The following table shows how each ingestion method handles partitioning:</p><table><thead><tr><th>Method</th><th>How it works</th></tr></thead><tbody><tr><td><a href="/docs/latest/ingestion/native-batch">Native batch</a></td><td>Configured using <a href="/docs/latest/ingestion/native-batch#partitionsspec"><code>partitionsSpec</code></a> inside the <code>tuningConfig</code>.</td></tr><tr><td><a href="/docs/latest/multi-stage-query/">SQL</a></td><td>Configured using <a href="/docs/latest/multi-stage-query/concepts#partitioning"><code>PARTITIONED BY</code></a> and <a href="/docs/latest/multi-stage-query/concepts#clustering"><code>CLUSTERED BY</code></a>.</td></tr><tr><td><a href="/docs/latest/ingestion/hadoop">Hadoop</a></td><td>Configured using <a href="/docs/latest/ingestion/hadoop#partitionsspec"><code>partitionsSpec</code></a> inside the <code>tuningConfig</code>.</td></tr><tr><td><a href="/docs/latest/development/extensions-core/kafka-ingestion">Kafka indexing service</a></td><td>Kafka topic partitioning defines how Druid partitions the datasource. You can also <a href="/docs/latest/data-management/update#reindex">reindex</a> or <a href="/docs/latest/data-management/compaction">compact</a> to repartition after initial ingestion.</td></tr><tr><td><a href="/docs/latest/development/extensions-core/kinesis-ingestion">Kinesis indexing service</a></td><td>Kinesis stream sharding defines how Druid partitions the datasource. You can also <a href="/docs/latest/data-management/update#reindex">reindex</a> or <a href="/docs/latest/data-management/compaction">compact</a> to repartition after initial ingestion.</td></tr></tbody></table><h2 class="anchor anchorWithStickyNavbar_LWe7" id="learn-more">Learn more<a href="#learn-more" class="hash-link" aria-label="Direct link to Learn more" title="Direct link to Learn more"></a></h2><p>See the following topics for more information:</p><ul><li><a href="/docs/latest/ingestion/native-batch#partitionsspec"><code>partitionsSpec</code></a> for more detail on partitioning with Native Batch ingestion.</li><li><a href="/docs/latest/data-management/update#reindex">Reindexing</a> and <a href="/docs/latest/data-management/compaction">Compaction</a> for information on how to repartition existing data in Druid.</li></ul></div></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages"><a class="pagination-nav__link pagination-nav__link--prev" href="/docs/latest/ingestion/rollup"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Rollup</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/docs/latest/ingestion/tasks"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">Task reference</div></a></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="#time-chunk-partitioning" class="table-of-contents__link toc-highlight">Time chunk partitioning</a></li><li><a href="#secondary-partitioning" class="table-of-contents__link toc-highlight">Secondary partitioning</a></li><li><a href="#partitioning-and-sorting" class="table-of-contents__link toc-highlight">Partitioning and sorting</a></li><li><a href="#how-to-configure-partitioning" class="table-of-contents__link toc-highlight">How to configure partitioning</a></li><li><a href="#learn-more" class="table-of-contents__link toc-highlight">Learn more</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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