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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2017/10/16 09:03:00 UTC
[jira] [Commented] (SPARK-22285) Change implementation of
ApproxCountDistinctForIntervals to TypedImperativeAggregate
[ https://issues.apache.org/jira/browse/SPARK-22285?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16205599#comment-16205599 ]
Apache Spark commented on SPARK-22285:
--------------------------------------
User 'wzhfy' has created a pull request for this issue:
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
>
> 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.
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