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Posted to issues@spark.apache.org by "Gaoxiang Liu (JIRA)" <ji...@apache.org> on 2016/10/13 01:15:20 UTC
[jira] [Comment Edited] (SPARK-16827) Stop reporting spill metrics
as shuffle metrics
[ https://issues.apache.org/jira/browse/SPARK-16827?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15570446#comment-15570446 ]
Gaoxiang Liu edited comment on SPARK-16827 at 10/13/16 1:14 AM:
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[~rxin], for this one, if I want to add spill time metrics, do you suggest I create a parent class DiskWriteMetrics, and ShuffleWriteMetrics and my new class (eg SpillWriteMetrics) inherit from it, and then pass parent class(DiskWriteMetrics) to UnsafeSorterSpillWriter https://github.com/facebook/FB-Spark/blob/fb-2.0/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java#L209 ?
Or do you suggest rename the ShuffleWriteMetrics class to something like WriteMetrics ?
was (Author: dreamworks007):
[~rxin], for this one, if I want to add spill metrics, do you suggest I create a parent class DiskWriteMetrics, and ShuffleWriteMetrics and my new class (eg SpillWriteMetrics) inherit from it, and then pass parent class(DiskWriteMetrics) to UnsafeSorterSpillWriter https://github.com/facebook/FB-Spark/blob/fb-2.0/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java#L209 ?
Or do you suggest rename the ShuffleWriteMetrics class to something like WriteMetrics ?
> 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: 2.1.0
>
>
> 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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