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<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.html">Multitenancy considerations</a> for more details on splitting data into separate datasources and potential operational considerations.</p>
<h2><a class="anchor" aria-hidden="true" id="time-chunk-partitioning"></a><a href="#time-chunk-partitioning" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Time chunk partitioning</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><a class="anchor" aria-hidden="true" id="secondary-partitioning"></a><a href="#secondary-partitioning" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Secondary partitioning</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 &quot;natural&quot;
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><a class="anchor" aria-hidden="true" id="partitioning-and-sorting"></a><a href="#partitioning-and-sorting" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Partitioning and sorting</h2>
<p>Partitioning and sorting work well together. If you do have a &quot;natural&quot; 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.html#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.html#secondary-timestamps">secondary timestamp</a> as the &quot;real&quot; timestamp.</p>
<h2><a class="anchor" aria-hidden="true" id="how-to-configure-partitioning"></a><a href="#how-to-configure-partitioning" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>How to configure partitioning</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.html#reindex">reindexing</a> or <a href="/docs/latest/data-management/compaction.html">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.html">Native batch</a></td><td>Configured using <a href="/docs/latest/ingestion/native-batch.html#partitionsspec"><code>partitionsSpec</code></a> inside the <code>tuningConfig</code>.</td></tr>
<tr><td><a href="/docs/latest/multi-stage-query/index.html">SQL</a></td><td>Configured using <a href="/docs/latest/multi-stage-query/concepts.html#partitioning"><code>PARTITIONED BY</code></a> and <a href="/docs/latest/multi-stage-query/concepts.html#clustering"><code>CLUSTERED BY</code></a>.</td></tr>
<tr><td><a href="/docs/latest/ingestion/hadoop.html">Hadoop</a></td><td>Configured using <a href="/docs/latest/ingestion/hadoop.html#partitionsspec"><code>partitionsSpec</code></a> inside the <code>tuningConfig</code>.</td></tr>
<tr><td><a href="/docs/latest/development/extensions-core/kafka-ingestion.html">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.html#reindex">reindex</a> or <a href="/docs/latest/data-management/compaction.html">compact</a> to repartition after initial ingestion.</td></tr>
<tr><td><a href="/docs/latest/development/extensions-core/kinesis-ingestion.html">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.html#reindex">reindex</a> or <a href="/docs/latest/data-management/compaction.html">compact</a> to repartition after initial ingestion.</td></tr>
</tbody>
</table>
<h2><a class="anchor" aria-hidden="true" id="learn-more"></a><a href="#learn-more" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Learn more</h2>
<p>See the following topics for more information:</p>
<ul>
<li><a href="/docs/latest/ingestion/native-batch.html#partitionsspec"><code>partitionsSpec</code></a> for more detail on partitioning with Native Batch ingestion.</li>
<li><a href="/docs/latest/data-management/update.html#reindex">Reindexing</a> and <a href="/docs/latest/data-management/compaction.html">Compaction</a> for information on how to repartition existing data in Druid.</li>
</ul>
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