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Posted to issues@spark.apache.org by "Alex Duvall (JIRA)" <ji...@apache.org> on 2018/02/07 18:53:00 UTC
[jira] [Commented] (SPARK-15176) Job Scheduling Within Application
Suffers from Priority Inversion
[ https://issues.apache.org/jira/browse/SPARK-15176?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16355880#comment-16355880 ]
Alex Duvall commented on SPARK-15176:
-------------------------------------
Another interested party here - I'd find being able to limit max running tasks at a pool level extremely useful.
> Job Scheduling Within Application Suffers from Priority Inversion
> -----------------------------------------------------------------
>
> Key: SPARK-15176
> URL: https://issues.apache.org/jira/browse/SPARK-15176
> Project: Spark
> Issue Type: Bug
> Components: Scheduler
> Affects Versions: 1.6.1
> Reporter: Nick White
> Priority: Major
>
> Say I have two pools, and N cores in my cluster:
> * I submit a job to one, which has M >> N tasks
> * N of the M tasks are scheduled
> * I submit a job to the second pool - but none of its tasks get scheduled until a task from the other pool finishes!
> This can lead to unbounded denial-of-service for the second pool - regardless of `minShare` or `weight` settings. Ideally Spark would support a pre-emption mechanism, or an upper bound on a pool's resource usage.
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