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https://issues.apache.org/jira/browse/SPARK-21082?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16048270#comment-16048270
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Sean Owen commented on SPARK-21082:
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I don't see how this would interact with, for example, data locality
considerations. You can't actually estimate how much memory a task would take
anyway.
> Consider Executor's memory usage when scheduling task
> ------------------------------------------------------
>
> Key: SPARK-21082
> URL: https://issues.apache.org/jira/browse/SPARK-21082
> Project: Spark
> Issue Type: Improvement
> Components: Scheduler, Spark Core
> Affects Versions: 2.2.1
> Reporter: DjvuLee
>
> Spark Scheduler do not consider the memory usage during dispatch tasks, this
> can lead to Executor OOM if the RDD is cached sometimes, because Spark can
> not estimate the memory usage enough well(especially when the RDD type is not
> flatten), scheduler may dispatch so many tasks on one Executor.
> We can offer a configuration for user to decide whether scheduler will
> consider the memory usage.
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