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Posted to issues@spark.apache.org by "Hyukjin Kwon (Jira)" <ji...@apache.org> on 2019/08/30 01:31:00 UTC
[jira] [Resolved] (SPARK-28843) Set OMP_NUM_THREADS to executor
cores reduce Python memory consumption
[ https://issues.apache.org/jira/browse/SPARK-28843?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-28843.
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Fix Version/s: 3.0.0
Resolution: Fixed
Issue resolved by pull request 25545
[https://github.com/apache/spark/pull/25545]
> Set OMP_NUM_THREADS to executor cores reduce Python memory consumption
> ----------------------------------------------------------------------
>
> Key: SPARK-28843
> URL: https://issues.apache.org/jira/browse/SPARK-28843
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
> Affects Versions: 2.3.3, 3.0.0, 2.4.3
> Reporter: Ryan Blue
> Assignee: Ryan Blue
> Priority: Major
> Labels: release-notes
> Fix For: 3.0.0
>
>
> While testing hardware with more cores, we found that the amount of memory required by PySpark applications increased and tracked the problem to importing numpy. The numpy issue is [https://github.com/numpy/numpy/issues/10455]
> NumPy uses OpenMP that starts a thread pool with the number of cores on the machine (and does not respect cgroups). When we set this lower we see a significant reduction in memory consumption.
> This parallelism setting should be set to the number of cores allocated to the executor, not the number of cores available.
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