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https://issues.apache.org/jira/browse/YARN-7467?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16509074#comment-16509074
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zhuqi commented on YARN-7467:
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[~templedf] , thanks for your comment, i should improve my code of course.
* I only work with memory because the original computeShares() function, only
use the memory type to compute, i want to match it, the MEMORY here is the
resouce type:
ComputeFairShares.computeShares(schedulables, totalResources, MEMORY)
* I use ceiling because i test the original one and compare it to my change, i
find the original one is actual the ceiling result. For example, in the
original one, the total fairshare in a queue is 8G, there are 3 runnable apps
in the queue, each one will have 2731M, but reasonable result is 2730, so i
add the ceiling to match the original one.
* I try to confirm the ceiling result again today, in my test cluster there is
768G = 786432M , and i add 7 apps use one queue, each one fairshare is 112348,
but 786432 / 7 = 112347.42 if not use the ceiling the result is 112347, here is
the test result:
!image-2018-06-12-10-02-25-724.png!
Thanks.
> FSLeafQueue unnecessarily calls ComputeFairShares.computeShare() to calculate
> fair share for apps
> -------------------------------------------------------------------------------------------------
>
> Key: YARN-7467
> URL: https://issues.apache.org/jira/browse/YARN-7467
> Project: Hadoop YARN
> Issue Type: Improvement
> Components: fairscheduler
> Affects Versions: 3.1.0
> Reporter: Daniel Templeton
> Assignee: Daniel Templeton
> Priority: Critical
>
> All apps have the same weight, the same max share (unbounded), and the same
> min share (none). There's no reason to call {{computeShares()}} at all.
> Just divide the resources by the number of apps.
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