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https://issues.apache.org/jira/browse/SPARK-979?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14708739#comment-14708739
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Apache Spark commented on SPARK-979:
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User 'tdas' has created a pull request for this issue:
https://github.com/apache/spark/pull/8387
> Add some randomization to scheduler to better balance in-memory partition
> distributions
> ---------------------------------------------------------------------------------------
>
> Key: SPARK-979
> URL: https://issues.apache.org/jira/browse/SPARK-979
> Project: Spark
> Issue Type: Improvement
> Reporter: Reynold Xin
> Assignee: Kay Ousterhout
> Fix For: 1.0.0
>
>
> The Spark scheduler is very deterministic, which causes problems for the
> following workload (in serial order on a cluster with a small number of
> nodes):
> cache rdd 1 with 1 partition
> cache rdd 2 with 1 partition
> cache rdd 3 with 1 partition
> ....
> After a while, only executor 1 will have data in memory, and eventually
> leading to evicting in-memory blocks to disk while all other executors are
> empty.
> We can solve this problem by adding some randomization to the cluster
> scheduling, or by adding memory aware scheduling (which is much harder to
> do).
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