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https://issues.apache.org/jira/browse/SPARK-9310?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14640368#comment-14640368
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Sean Owen commented on SPARK-9310:
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I don't think there's enough information here -- or else this is a question for
user@. This is also vs a fairly old version. Can you try a newer release?
> Spark shuffle performance degrades significantly with an increased number of
> tasks
> ----------------------------------------------------------------------------------
>
> Key: SPARK-9310
> URL: https://issues.apache.org/jira/browse/SPARK-9310
> Project: Spark
> Issue Type: Bug
> Components: Shuffle
> Affects Versions: 1.2.0
> Environment: 2 node cluster - CDH 5.3.2 on CentOS
> Reporter: Jem Tucker
> Labels: performance
>
> When running a large number of complex stages on high volumes of data shuffle
> duration increased by a factor of 3 when the parallelism was increased by a
> factor of 5 from 2000 to 10000.
> In both cases tasks run for over a minute (to process approximately 2MB of
> data with initial parallelisation) so I ruled out any task overhead that
> could be causing this.
> Monitoring IO and network traffic showed that neither were at more than 10%
> of their potential max during shuffles and CPU utilization seemed worryingly
> low as well, neither are we experiencing a concerning level of garbage
> collection.
> Is performance of shuffles expected to be so heavily influenced by the number
> of tasks? If so, is there an effective way to tune the number of partitions
> at run-time for different inputs?
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