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| <h1 id="multitenancy-considerations">Multitenancy Considerations</h1> |
| |
| <p>Apache Druid (incubating) 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 id="shared-datasources-or-datasource-per-tenant">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 id="partitioning-shared-datasources">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-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 have a couple of options.</p> |
| |
| <ol> |
| <li>Partition on tenant_id upfront. You'd do this by tweaking the stream you send to Druid. If you're using Kafka then |
| you can have your Kafka producer partition your topic by a hash of tenant_id. If you're using Tranquility then you can |
| define a custom <a href="http://static.druid.io/tranquility/api/latest/#com.metamx.tranquility.partition.Partitioner">Partitioner</a>.</li> |
| <li>Reindex your older data periodically. You can do this with the <a href="../ingestion/hadoop.html#datasource">"dataSource" input spec</a>. |
| You can use this in concert with single-dimension partitioning to repartition your data.</li> |
| </ol> |
| |
| <h2 id="customizing-data-distribution">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="../operations/rule-configuration.html">tiers</a>, and <a href="../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 id="supporting-high-query-concurrency">Supporting high query concurrency</h2> |
| |
| <p>Druid's fundamental unit of computation is a <a href="../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="../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="../development/router.html">Router</a> |
| process that can route queries to different Brokers based on various query parameters (datasource, interval, etc.). </p> |
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