[
https://issues.apache.org/jira/browse/SPARK-22285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wenchen Fan resolved SPARK-22285.
---------------------------------
Resolution: Fixed
Fix Version/s: 2.3.0
Issue resolved by pull request 19506
[https://github.com/apache/spark/pull/19506]
> Change implementation of ApproxCountDistinctForIntervals to
> TypedImperativeAggregate
> ------------------------------------------------------------------------------------
>
> Key: SPARK-22285
> URL: https://issues.apache.org/jira/browse/SPARK-22285
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
> Affects Versions: 2.3.0
> Reporter: Zhenhua Wang
> Fix For: 2.3.0
>
>
> The current implementation of `ApproxCountDistinctForIntervals` is
> `ImperativeAggregate`. The number of `aggBufferAttributes` is the number of
> total words in the hllppHelper array. Each hllppHelper has 52 words by
> default relativeSD.
> Since this aggregate function is used in equi-height histogram generation,
> and the number of buckets in histogram is usually hundreds, the number of
> `aggBufferAttributes` can easily reach tens of thousands or even more.
> This leads to a huge method in codegen and causes errors such as
> `org.codehaus.janino.JaninoRuntimeException: Code of method
> "apply(Lorg/apache/spark/sql/catalyst/InternalRow;)Lorg/apache/spark/sql/catalyst/expressions/UnsafeRow;"
> of class
> "org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection"
> grows beyond 64 KB`.
> Besides, huge generated methods also result in performance regression.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
