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https://issues.apache.org/jira/browse/SPARK-4383?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14311064#comment-14311064
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Kay Ousterhout commented on SPARK-4383:
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This issue was mostly fixed by the fix for 4939. The specific issue discussed
in the description has been fixed. If a task set has, say, node-local and
rack-local tasks, it's still the case that none of the rack-local tasks will be
scheduled until all of the node-local ones have been scheduled, but with
Davies' fix for 4939, one all of the node-local tasks have been scheduled,
we'll immediately start scheduling the rack-local ones rather than waiting for
a timer to expire.
> Delay scheduling doesn't work right when jobs have tasks with different
> locality levels
> ---------------------------------------------------------------------------------------
>
> Key: SPARK-4383
> URL: https://issues.apache.org/jira/browse/SPARK-4383
> Project: Spark
> Issue Type: Bug
> Components: Scheduler
> Affects Versions: 1.0.2, 1.1.0
> Reporter: Kay Ousterhout
> Fix For: 1.3.0
>
>
> Copied from mailing list discussion:
> Now our application will load data from hdfs in the same spark cluster, it
> will get NODE_LOCAL and RACK_LOCAL level tasks during loading stage, if the
> tasks in loading stage have same locality level, ether NODE_LOCAL or
> RACK_LOCAL it works fine.
> But if the tasks in loading stage get mixed locality level, such as 3
> NODE_LOCAL tasks, and 2 RACK_LOCAL tasks, then the TaskSetManager of loading
> stage will submit the 3 NODE_LOCAL tasks as soon as resources were offered,
> then wait for spark.locality.wait.node, which was set to 30 minutes, the 2
> RACK_LOCAL tasks will wait 30 minutes even though resources are available.
> Fixing this is quite tricky -- do we need to track the locality level
> individually for each task?
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