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https://issues.apache.org/jira/browse/SPARK-4352?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14567016#comment-14567016
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Saisai Shao commented on SPARK-4352:
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Hi [~sandyr], thanks a lot for your suggestion. IIUC the algorithm you describe
is trying to make the executor request be proportional to the node preference,
say your desired tasks on cluster are 3 : 3 : 2 : 1, so you're trying to
allocate the executors following this, but I'm curious about algorithm on 7
and 18 situation, what you describe is:
requests for 5 executors with nodes = <a, b, c, d>
requests for 2 executors with nodes = <a, b, c>
that is 7 : 7 : 7 : 5
is it better like this:
requests for 2 executors with nodes = <a, b, c, d>
requests for 2 executors with nodes = <a, b, c>
requests for 3 executors with nodes = <a, b>
here is 7 : 7 : 4 : 2
Also for 18 situation, why not:
requests for 6 executors with nodes = <a, b, c, d>
requests for 6 executors with nodes = <a, b, c>
requests for 6 executors with nodes = <a, b>
Would you please help to explain it, maybe I missed in some places:).
> Incorporate locality preferences in dynamic allocation requests
> ---------------------------------------------------------------
>
> Key: SPARK-4352
> URL: https://issues.apache.org/jira/browse/SPARK-4352
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core, YARN
> Affects Versions: 1.2.0
> Reporter: Sandy Ryza
> Assignee: Saisai Shao
> Priority: Critical
> Attachments: Supportpreferrednodelocationindynamicallocation.pdf
>
>
> Currently, achieving data locality in Spark is difficult unless an
> application takes resources on every node in the cluster.
> preferredNodeLocalityData provides a sort of hacky workaround that has been
> broken since 1.0.
> With dynamic executor allocation, Spark requests executors in response to
> demand from the application. When this occurs, it would be useful to look at
> the pending tasks and communicate their location preferences to the cluster
> resource manager.
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