[jira] [Commented] (YARN-10702) Add cluster metric for amount of CPU used by RM Event Processor

["Jim Brennan (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/YARN-10702?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17314086#comment-17314086 ] 

Jim Brennan commented on YARN-10702:
------------------------------------

Patch 005 adds a configuration property for this:
{noformat}
<property>
  <description>
    Resource manager dispatcher thread cpu monitor sampling rate.
    Units are samples per minute.  This controls how often to sample
    the cpu utilization of the resource manager dispatcher thread.
    The cpu utilization is displayed on the RM UI as scheduler busy %.
    Set this to zero to disable the dispatcher thread monitor.  Defaults
    to 60 samples per minute.
  </description>
  <name>yarn.dispatcher.cpu-monitor.samples-per-min</name>
  <value>60</value>
</property>
 {noformat}
If it is disabled by setting this property to zero, the UI shows "N/A" for the Scheduler Busy value, to distinguish it from 0, which is a valid avg cpu usage for the thread on a lightly loaded cluster.

> Add cluster metric for amount of CPU used by RM Event Processor
> ---------------------------------------------------------------
>
>                 Key: YARN-10702
>                 URL: https://issues.apache.org/jira/browse/YARN-10702
>             Project: Hadoop YARN
>          Issue Type: Sub-task
>          Components: yarn
>    Affects Versions: 2.10.1, 3.4.0
>            Reporter: Jim Brennan
>            Assignee: Jim Brennan
>            Priority: Minor
>         Attachments: Scheduler-Busy.png, YARN-10702.001.patch, YARN-10702.002.patch, YARN-10702.003.patch, YARN-10702.004.patch, YARN-10702.005.patch, simon-scheduler-busy.png
>
>
> Add a cluster metric to track the cpu usage of the ResourceManager Event Processing thread.   This lets us know when the critical path of the RM is running out of headroom.
> This feature was originally added for us internally by [~nroberts] and we've been running with it on production clusters for nearly four years.



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