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https://issues.apache.org/jira/browse/DRILL-143?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16458866#comment-16458866
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ASF GitHub Bot commented on DRILL-143:
--------------------------------------

Github user paul-rogers commented on the issue:

    https://github.com/apache/drill/pull/1239
  
    Just to be clear, I have no objection Drill enforcing its own cgroup limits.
    
    My point is rather that CPU limits must be integrated with YARN, via the 
DoY config file, so that the user specifies CPU limits in one place and that 
limit is the same one passed to YARN for container allocation and to whichever 
code is doing cgroup enforcement. By default, it will be YARN if cgroups are 
enabled in YARN, as they can be for Apache YARN.
    
    It is then fine to have an additional option to enable self-enforcement in 
Drill for those odd cases where users cannot or choose not to use versions of 
YARN that do the work.
    
    The problem would be if the user has to configure CPU in two places: in a 
shell script to enable self-enforcement, and in the DoY config to tell YARN the 
container size. This goes against the ease-of-use experience DoY was designed 
to provide. (That is, getting multiple to work consistently correct is very 
hard if you're just trying to get Drill to run and are not a Drill internals 
expert.)


> Support CGROUPs resource management
> -----------------------------------
>
>                 Key: DRILL-143
>                 URL: https://issues.apache.org/jira/browse/DRILL-143
>             Project: Apache Drill
>          Issue Type: New Feature
>            Reporter: Jacques Nadeau
>            Assignee: Kunal Khatua
>            Priority: Major
>              Labels: doc-impacting
>             Fix For: 1.14.0
>
>         Attachments: 253ce178-ddeb-e482-cd64-44ab7284ad1c.sys.drill
>
>
> For the purpose of playing nice on clusters that don't have YARN, we should 
> write up configuration and scripts to allows users to run Drill next to 
> existing workloads without sharing resources.



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