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https://issues.apache.org/jira/browse/SPARK-21082?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16048821#comment-16048821
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Sean Owen commented on SPARK-21082:
-----------------------------------
This doesn't address my question about locality. I think this is a non-starter
until you have a more comprehensive suggestion for how this does or doesn't
interact with considerations like data locality, and available CPU. Spark
already tries to balance tasks, and it's never going to perfectly balance them.
> 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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