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href="/docs/0.20.0/comparisons/druid-vs-spark.html">Apache Druid vs Spark</a></li><li class="navListItem"><a class="navItem" href="/docs/0.20.0/comparisons/druid-vs-sql-on-hadoop.html">Apache Druid vs SQL-on-Hadoop</a></li><li class="navListItem"><a class="navItem" href="/docs/0.20.0/design/auth.html">Authentication and Authorization</a></li><li class="navListItem"><a class="navItem" href="/docs/0.20.0/design/broker.html">Broker</a></li><li class="navListItem"><a class="navItem" href="/docs/0.20.0/design/coordinator.html">Coordinator Process</a></li><li class="navListItem"><a class="navItem" href="/docs/0.20.0/design/historical.html">Historical Process</a></li><li class="navListItem"><a class="navItem" href="/docs/0.20.0/design/indexer.html">Indexer Process</a></li><li class="navListItem"><a class="navItem" href="/docs/0.20.0/design/indexing-service.html">Indexing Service</a></li><li class="navListItem"><a class="navItem" href="/docs/0.20.0/design/middlemanager.html">MiddleManager 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| </script></nav></div><div class="container mainContainer docsContainer"><div class="wrapper"><div class="post"><header class="postHeader"><a class="edit-page-link button" href="https://github.com/apache/druid/edit/master/docs/querying/multitenancy.md" target="_blank" rel="noreferrer noopener">Edit</a><h1 id="__docusaurus" class="postHeaderTitle">Multitenancy considerations</h1></header><article><div><span><!-- |
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| <p>Apache Druid is often used to power user-facing data applications, where multitenancy is an important requirement. This |
| document outlines Druid's multitenant storage and querying features.</p> |
| <h2><a class="anchor" aria-hidden="true" id="shared-datasources-or-datasource-per-tenant"></a><a href="#shared-datasources-or-datasource-per-tenant" 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>Shared datasources or datasource-per-tenant?</h2> |
| <p>A datasource is the Druid equivalent of a database table. Multitenant workloads can either use a separate datasource |
| for each tenant, or can share one or more datasources between tenants using a "tenant_id" dimension. When deciding |
| which path to go down, consider that each path has pros and cons.</p> |
| <p>Pros of datasources per tenant:</p> |
| <ul> |
| <li>Each datasource can have its own schema, its own backfills, its own partitioning rules, and its own data loading |
| and expiration rules.</li> |
| <li>Queries can be faster since there will be fewer segments to examine for a typical tenant's query.</li> |
| <li>You get the most flexibility.</li> |
| </ul> |
| <p>Pros of shared datasources:</p> |
| <ul> |
| <li>Each datasource requires its own JVMs for realtime indexing.</li> |
| <li>Each datasource requires its own YARN resources for Hadoop batch jobs.</li> |
| <li>Each datasource requires its own segment files on disk.</li> |
| <li>For these reasons it can be wasteful to have a very large number of small datasources.</li> |
| </ul> |
| <p>One compromise is to use more than one datasource, but a smaller number than tenants. For example, you could have some |
| tenants with partitioning rules A and some with partitioning rules B; you could use two datasources and split your |
| tenants between them.</p> |
| <h2><a class="anchor" aria-hidden="true" id="partitioning-shared-datasources"></a><a href="#partitioning-shared-datasources" 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 shared datasources</h2> |
| <p>If your multitenant cluster uses shared datasources, most of your queries will likely filter on a "tenant_id" |
| dimension. These sorts of queries perform best when data is well-partitioned by tenant. There are a few ways to |
| accomplish this.</p> |
| <p>With batch indexing, you can use <a href="../ingestion/hadoop.html#single-dimension-range-partitioning">single-dimension partitioning</a> |
| to partition your data by tenant_id. Druid always partitions by time first, but the secondary partition within each |
| time bucket will be on tenant_id.</p> |
| <p>With realtime indexing, you'd do this by tweaking the stream you send to Druid. For example, if you're using Kafka then |
| you can have your Kafka producer partition your topic by a hash of tenant_id.</p> |
| <h2><a class="anchor" aria-hidden="true" id="customizing-data-distribution"></a><a href="#customizing-data-distribution" 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>Customizing data distribution</h2> |
| <p>Druid additionally supports multitenancy by providing configurable means of distributing data. Druid's Historical processes |
| can be configured into <a href="/docs/0.20.0/operations/rule-configuration.html">tiers</a>, and <a href="/docs/0.20.0/operations/rule-configuration.html">rules</a> |
| can be set that determines which segments go into which tiers. One use case of this is that recent data tends to be accessed |
| more frequently than older data. Tiering enables more recent segments to be hosted on more powerful hardware for better performance. |
| A second copy of recent segments can be replicated on cheaper hardware (a different tier), and older segments can also be |
| stored on this tier.</p> |
| <h2><a class="anchor" aria-hidden="true" id="supporting-high-query-concurrency"></a><a href="#supporting-high-query-concurrency" 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>Supporting high query concurrency</h2> |
| <p>Druid's fundamental unit of computation is a <a href="/docs/0.20.0/design/segments.html">segment</a>. Processes scan segments in parallel and a |
| given process can scan <code>druid.processing.numThreads</code> concurrently. To |
| process more data in parallel and increase performance, more cores can be added to a cluster. Druid segments |
| should be sized such that any computation over any given segment should complete in at most 500ms.</p> |
| <p>Druid internally stores requests to scan segments in a priority queue. If a given query requires scanning |
| more segments than the total number of available processors in a cluster, and many similarly expensive queries are concurrently |
| running, we don't want any query to be starved out. Druid's internal processing logic will scan a set of segments from one query and release resources as soon as the scans complete. |
| This allows for a second set of segments from another query to be scanned. By keeping segment computation time very small, we ensure |
| that resources are constantly being yielded, and segments pertaining to different queries are all being processed.</p> |
| <p>Druid queries can optionally set a <code>priority</code> flag in the <a href="/docs/0.20.0/querying/query-context.html">query context</a>. Queries known to be |
| slow (download or reporting style queries) can be de-prioritized and more interactive queries can have higher priority.</p> |
| <p>Broker processes can also be dedicated to a given tier. For example, one set of Broker processes can be dedicated to fast interactive queries, |
| and a second set of Broker processes can be dedicated to slower reporting queries. Druid also provides a <a href="/docs/0.20.0/design/router.html">Router</a> |
| process that can route queries to different Brokers based on various query parameters (datasource, interval, etc.).</p> |
| </span></div></article></div><div class="docs-prevnext"><a class="docs-prev button" href="/docs/0.20.0/querying/multi-value-dimensions.html"><span class="arrow-prev">← </span><span>Multi-value dimensions</span></a><a class="docs-next button" href="/docs/0.20.0/querying/caching.html"><span>Query caching</span><span class="arrow-next"> →</span></a></div></div></div><nav class="onPageNav"><ul class="toc-headings"><li><a href="#shared-datasources-or-datasource-per-tenant">Shared datasources or datasource-per-tenant?</a></li><li><a href="#partitioning-shared-datasources">Partitioning shared datasources</a></li><li><a href="#customizing-data-distribution">Customizing data distribution</a></li><li><a href="#supporting-high-query-concurrency">Supporting high query concurrency</a></li></ul></nav></div><footer class="nav-footer druid-footer" id="footer"><div class="container"><div class="text-center"><p><a href="/technology">Technology</a> · <a href="/use-cases">Use Cases</a> · <a href="/druid-powered">Powered by Druid</a> · <a href="/docs/0.20.0/latest">Docs</a> · <a href="/community/">Community</a> · <a href="/downloads.html">Download</a> · <a href="/faq">FAQ</a></p></div><div class="text-center"><a title="Join the user group" href="https://groups.google.com/forum/#!forum/druid-user" target="_blank"><span class="fa fa-comments"></span></a> · <a title="Follow Druid" href="https://twitter.com/druidio" target="_blank"><span class="fab fa-twitter"></span></a> · <a title="Download via Apache" href="https://www.apache.org/dyn/closer.cgi?path=/incubator/druid/{{ site.druid_versions[0].versions[0].version }}/apache-druid-{{ site.druid_versions[0].versions[0].version }}-bin.tar.gz" target="_blank"><span class="fas fa-feather"></span></a> · <a title="GitHub" href="https://github.com/apache/druid" target="_blank"><span class="fab fa-github"></span></a></div><div class="text-center license">Copyright © 2019 <a href="https://www.apache.org/" target="_blank">Apache Software Foundation</a>.<br/>Except where otherwise noted, licensed under <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0</a>.<br/>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></footer></div><script type="text/javascript" src="https://cdn.jsdelivr.net/docsearch.js/1/docsearch.min.js"></script><script> |
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