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https://issues.apache.org/jira/browse/SPARK-16827?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15571007#comment-15571007
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Gaoxiang Liu commented on SPARK-16827:
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I am quite new to spark, but I think I am getting some confusion on the
requirement now -
[~sitalke...@gmail.com] , [~chobrian] , could you please give some comment here
as for the requirement based on what [~rxin] said ?
> Stop reporting spill metrics as shuffle metrics
> -----------------------------------------------
>
> Key: SPARK-16827
> URL: https://issues.apache.org/jira/browse/SPARK-16827
> Project: Spark
> Issue Type: Bug
> Components: Shuffle, Spark Core
> Affects Versions: 2.0.0
> Reporter: Sital Kedia
> Assignee: Brian Cho
> Labels: performance
> Fix For: 2.0.2, 2.1.0
>
>
> One of our hive job which looks like this -
> {code}
> SELECT userid
> FROM table1 a
> JOIN table2 b
> ON a.ds = '2016-07-15'
> AND b.ds = '2016-07-15'
> AND a.source_id = b.id
> {code}
> After upgrade to Spark 2.0 the job is significantly slow. Digging a little
> into it, we found out that one of the stages produces excessive amount of
> shuffle data. Please note that this is a regression from Spark 1.6. Stage 2
> of the job which used to produce 32KB shuffle data with 1.6, now produces
> more than 400GB with Spark 2.0. We also tried turning off whole stage code
> generation but that did not help.
> PS - Even if the intermediate shuffle data size is huge, the job still
> produces accurate output.
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