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https://issues.apache.org/jira/browse/YARN-3415?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14388262#comment-14388262
 ] 

Hadoop QA commented on YARN-3415:
---------------------------------

{color:red}-1 overall{color}.  Here are the results of testing the latest 
attachment 
  http://issues.apache.org/jira/secure/attachment/12708381/YARN-3415.001.patch
  against trunk revision b5a22e9.

    {color:red}-1 patch{color}.  The patch command could not apply the patch.

Console output: https://builds.apache.org/job/PreCommit-YARN-Build/7167//console

This message is automatically generated.

> Non-AM containers can be counted towards amResourceUsage of a fairscheduler 
> queue
> ---------------------------------------------------------------------------------
>
>                 Key: YARN-3415
>                 URL: https://issues.apache.org/jira/browse/YARN-3415
>             Project: Hadoop YARN
>          Issue Type: Bug
>          Components: fairscheduler
>    Affects Versions: 2.6.0
>            Reporter: Rohit Agarwal
>            Assignee: zhihai xu
>            Priority: Critical
>         Attachments: YARN-3415.000.patch, YARN-3415.001.patch
>
>
> We encountered this problem while running a spark cluster. The 
> amResourceUsage for a queue became artificially high and then the cluster got 
> deadlocked because the maxAMShare constrain kicked in and no new AM got 
> admitted to the cluster.
> I have described the problem in detail here: 
> https://github.com/apache/spark/pull/5233#issuecomment-87160289
> In summary - the condition for adding the container's memory towards 
> amResourceUsage is fragile. It depends on the number of live containers 
> belonging to the app. We saw that the spark AM went down without explicitly 
> releasing its requested containers and then one of those containers memory 
> was counted towards amResource.
> cc - [~sandyr]



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