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Setting limits for Query Results

Controlling the size of GetAggregatesQuery queries can be very handy when there is a need to limit the number of resulting locations. A typical example is the possibility to control big queries hitting a cluster at a data node level, allowing thus to limit / reduce the traffic when using distribution. It can also act as a safeguard to prevent queries from consuming too much memory.

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Configurable limits on Query Results

Limiting queries results can be achieved using the IQueriesResultLimit context value. We distinguish two types of limit:
  • intermediateLimit: It defines the limit number of point locations for a single intermediate result (i.e. retrieval).
  • transientLimit: It defines the transient limit resulting from the accumulation of all the intermediate results within a single query.
Note that the default transient and intermediate result limit amount to 100,000 and 1,000,000 point locations respectively, and can be obtained by calling QueriesResultLimit#defaultLimit().
Query results limit property can be enabled through Atoti context values, 
pivot.getContext().set(IQueriesResultLimit.class, QueriesResultLimit.defaultLimit());
or in the cube description 
StartBuilding.cube("tweets")
    .withSingleLevelDimensions("sender_id")
    .withDimension("time")
    .withHierarchy("time")
    .withLevel("year")
    .withLevel("month")
    .withLevel("day")
    .withSharedContextValue(QueriesResultLimit.withLimit(10_000, 1_000_000))
or through query context values
Not defining a limit for queries is equivalent to using QueriesResultLimit#withoutLimit().
Exceeding the configured limit will result in a RetrievalResultSizeException, aborting the execution of all operations involved in the query.

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Miscellaneous

When setting a limit for queries results, there is a couple of points to keep in mind:
  • Intermediate queries results may exceed the configured limit even though the final result does not. A typical example is a copper join including factless points that will be removed from the final result.
  • In a distributed setup, if a data node exceeds the configured limit then the initial query will fail (partial results from other data nodes won’t be accounted for).
  • In a distributed polymorphic setup, expect the replication to produce additional locations and in some cases introduce points without underlying contribution.