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https://issues.apache.org/jira/browse/YARN-3997?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15180328#comment-15180328
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Karthik Kambatla commented on YARN-3997:
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Sorry for the delay in following up on this. I am looking to take a
comprehensive look at preemption in FairScheduler.
[~damagebo], [~MindTheGap], [~ilanas], [~umiron] - can any of you comment on if
FairScheduler is preempting any containers at all? Is it possible that
containers are being preempted, but not all on the same node? I wonder if this
is just another manifestation of YARN-2154?
> An Application requesting multiple core containers can't preempt running
> application made of single core containers
> -------------------------------------------------------------------------------------------------------------------
>
> Key: YARN-3997
> URL: https://issues.apache.org/jira/browse/YARN-3997
> Project: Hadoop YARN
> Issue Type: Sub-task
> Components: fairscheduler
> Affects Versions: 2.7.1
> Environment: Ubuntu 14.04, Hadoop 2.7.1, Physical Machines
> Reporter: Dan Shechter
> Assignee: Arun Suresh
> Priority: Critical
>
> When our cluster is configured with preemption, and is fully loaded with an
> application consuming 1-core containers, it will not kill off these
> containers when a new application kicks in requesting containers with a size
> > 1, for example 4 core containers.
> When the "second" application attempts to us 1-core containers as well,
> preemption proceeds as planned and everything works properly.
> It is my assumption, that the fair-scheduler, while recognizing it needs to
> kill off some container to make room for the new application, fails to find a
> SINGLE container satisfying the request for a 4-core container (since all
> existing containers are 1-core containers), and isn't "smart" enough to
> realize it needs to kill off 4 single-core containers (in this case) on a
> single node, for the new application to be able to proceed...
> The exhibited affect is that the new application is hung indefinitely and
> never gets the resources it requires.
> This can easily be replicated with any yarn application.
> Our "goto" scenario in this case is running pyspark with 1-core executors
> (containers) while trying to launch h20.ai framework which INSISTS on having
> at least 4 cores per container.
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