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https://issues.apache.org/jira/browse/SPARK-9353?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14641856#comment-14641856
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Apache Spark commented on SPARK-9353:
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User 'andrewor14' has created a pull request for this issue:
https://github.com/apache/spark/pull/7668
> Standalone scheduling memory requirement incorrect if cores per executor is
> not set
> -----------------------------------------------------------------------------------
>
> Key: SPARK-9353
> URL: https://issues.apache.org/jira/browse/SPARK-9353
> Project: Spark
> Issue Type: Bug
> Components: Deploy
> Affects Versions: 1.5.0
> Reporter: Andrew Or
> Assignee: Andrew Or
>
> I tried to come up with a more succinct title.
> The issue only happens if `spark.executor.cores` is not set. Right now if we
> have a worker with 8G, and we set `spark.executor.memory` to 1G, then the
> executor launched on the worker can have at most 8 cores, even if the worker
> has more cores available.
> This is caused by the fix in SPARK-8881.
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