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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-22285) Change implementation of ApproxCountDistinctForIntervals to TypedImperativeAggregate
Date Mon, 16 Oct 2017 09:03:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-22285?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-22285:
------------------------------------

    Assignee: Apache Spark

> 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
>            Assignee: Apache Spark
>
> 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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