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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">Querying</span><meta itemprop="position" content="1"></li><li class="breadcrumbs__item"><span class="breadcrumbs__link">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">Multitenancy</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>Multitenancy considerations</h1></header><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 class="anchor anchorWithStickyNavbar_LWe7" id="shared-datasources-or-datasource-per-tenant">Shared datasources or datasource-per-tenant?<a href="#shared-datasources-or-datasource-per-tenant" class="hash-link" aria-label="Direct link to Shared datasources or datasource-per-tenant?" title="Direct link to Shared datasources or datasource-per-tenant?"></a></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 class="anchor anchorWithStickyNavbar_LWe7" id="partitioning-shared-datasources">Partitioning shared datasources<a href="#partitioning-shared-datasources" class="hash-link" aria-label="Direct link to Partitioning shared datasources" title="Direct link to Partitioning shared datasources"></a></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="/docs/34.0.0/ingestion/hadoop#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 class="anchor anchorWithStickyNavbar_LWe7" id="customizing-data-distribution">Customizing data distribution<a href="#customizing-data-distribution" class="hash-link" aria-label="Direct link to Customizing data distribution" title="Direct link to Customizing data distribution"></a></h2> |
| <p>Druid additionally supports multitenancy by providing configurable means of distributing data. Druid's Historical processes |
| can be configured into <a href="/docs/34.0.0/operations/rule-configuration">tiers</a>, and <a href="/docs/34.0.0/operations/rule-configuration">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 class="anchor anchorWithStickyNavbar_LWe7" id="supporting-high-query-concurrency">Supporting high query concurrency<a href="#supporting-high-query-concurrency" class="hash-link" aria-label="Direct link to Supporting high query concurrency" title="Direct link to Supporting high query concurrency"></a></h2> |
| <p>Druid uses a <a href="/docs/34.0.0/design/segments">segment</a> as its fundamental unit of computation. Processes scan segments in parallel and a given process can scan <code>druid.processing.numThreads</code> concurrently. You can add more cores to a cluster to process more data in parallel and increase performance. Size your Druid segments such that any computation over any given segment should complete in at most 500ms. Use the <a href="/docs/34.0.0/operations/metrics#historical"><code>query/segment/time</code></a> metric to monitor computation times.</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/34.0.0/querying/query-context-reference">query context reference</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/34.0.0/design/router">Router</a> |
| process that can route queries to different Brokers based on various query parameters (datasource, interval, etc.).</p></div></article><nav class="pagination-nav docusaurus-mt-lg" aria-label="Docs pages"><a class="pagination-nav__link pagination-nav__link--prev" href="/docs/34.0.0/querying/nested-columns"><div class="pagination-nav__sublabel">Previous</div><div class="pagination-nav__label">Nested columns</div></a><a class="pagination-nav__link pagination-nav__link--next" href="/docs/34.0.0/querying/caching"><div class="pagination-nav__sublabel">Next</div><div class="pagination-nav__label">Query caching</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="#shared-datasources-or-datasource-per-tenant" class="table-of-contents__link toc-highlight">Shared datasources or datasource-per-tenant?</a></li><li><a href="#partitioning-shared-datasources" class="table-of-contents__link toc-highlight">Partitioning shared datasources</a></li><li><a href="#customizing-data-distribution" class="table-of-contents__link toc-highlight">Customizing data distribution</a></li><li><a href="#supporting-high-query-concurrency" class="table-of-contents__link toc-highlight">Supporting high query concurrency</a></li></ul></div></div></div></div></main></div></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="footer__logo themedComponent_mlkZ themedComponent--light_NVdE"><img src="/img/favicon.png" class="footer__logo themedComponent_mlkZ themedComponent--dark_xIcU"></div><div class="footer__copyright">Copyright © 2025 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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