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| id | title |
|---|---|
| caching | Query caching |
Apache Druid supports query result caching at both the segment and whole-query result level. Cache data can be stored in the local JVM heap or in an external distributed key/value store. In all cases, the Druid cache is a query result cache. The only difference is whether the result is a partial result for a particular segment, or the result for an entire query. In both cases, the cache is invalidated as soon as any underlying data changes; it will never return a stale result.
Segment-level caching allows the cache to be leveraged even when some of the underling segments are mutable and undergoing real-time ingestion. In this case, Druid will potentially cache query results for immutable historical segments, while re-computing results for the real-time segments on each query. Whole-query result level caching is not useful in this scenario, since it would be continuously invalidated.
Segment-level caching does require Druid to merge the per-segment results on each query, even when they are served from the cache. For this reason, whole-query result level caching can be more efficient if invalidation due to real-time ingestion is not an issue.
Using and populating cache
All caches have a pair of parameters that control the behavior of how individual queries interact with the cache, a 'use' cache parameter, and a 'populate' cache parameter. These settings must be enabled at the service level via runtime properties to utilize cache, but can be controlled on a per query basis by setting them on the query context. The 'use' parameter obviously controls if a query will utilize cached results. The 'populate' parameter controls if a query will update cached results. These are separate parameters to allow queries on uncommon data to utilize cached results without polluting the cache with results that are unlikely to be re-used by other queries, for example large reports or very old data.
Query caching on Brokers
Brokers support both segment-level and whole-query result level caching. Segment-level caching is controlled by the
parameters useCache and populateCache. Whole-query result level caching is controlled by the parameters
useResultLevelCache and populateResultLevelCache and runtime properties
druid.broker.cache.*.
Enabling segment-level caching on the Broker can yield faster results than if query caches were enabled on Historicals for small
clusters. This is the recommended setup for smaller production clusters (< 5 servers). Populating segment-level caches on
the Broker is not recommended for large production clusters, since when the property druid.broker.cache.populateCache is
set to true (and query context parameter populateCache is not set to false), results from Historicals are returned
on a per segment basis, and Historicals will not be able to do any local result merging. This impairs the ability of the
Druid cluster to scale well.
Query caching on Historicals
Historicals only support segment-level caching. Segment-level caching is controlled by the query context
parameters useCache and populateCache and runtime properties
druid.historical.cache.*.
Larger production clusters should enable segment-level cache population on Historicals only (not on Brokers) to avoid having to use Brokers to merge all query results. Enabling cache population on the Historicals instead of the Brokers enables the Historicals to do their own local result merging and puts less strain on the Brokers.
Query caching on Ingestion Tasks
Task executor processes such as the Peon or the experimental Indexer only support segment-level caching. Segment-level
caching is controlled by the query context parameters useCache and populateCache
and runtime properties druid.realtime.cache.*.
Larger production clusters should enable segment-level cache population on task execution processes only (not on Brokers) to avoid having to use Brokers to merge all query results. Enabling cache population on the task execution processes instead of the Brokers enables the task execution processes to do their own local result merging and puts less strain on the Brokers.
Note that the task executor processes only support caches that keep their data locally, such as the caffeine cache.
This restriction exists because the cache stores results at the level of intermediate partial segments generated by the
ingestion tasks. These intermediate partial segments will not necessarily be identical across task replicas, so
remote cache types such as memcached will be ignored by task executor processes.