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From "Xianyin Xin (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (YARN-3652) A SchedulerMetrics may be need for evaluating the scheduler's performance
Date Fri, 15 May 2015 14:52:00 GMT

    [ https://issues.apache.org/jira/browse/YARN-3652?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14545607#comment-14545607
] 

Xianyin Xin commented on YARN-3652:
-----------------------------------

Thanks for comments, [~sunilg].
{quote}
1. *Throughput* : Are you mentioning about #events processed over a period of time? If so,
how can we set the timeline by which throughput is calculated (configurable?)?
A clear indicator from this will be like we can predict possible end timeline for the pending
events in dispatcher queue. Adding throughput with #no of pending events may give much more
better indication about RM overload.
{quote}
In fact the first comes in my mind is the #containers allocated by scheduler per second, because
the containers allocation what users care and the node update event is the most important
scheduler event. The rate of processing events is also a nice indicator, just as you comment.

{quote}
2. However there are many events coming to scheduler, if possible a filter for the events
based on events type may be helpful to give an accuracy for throughout and scheduling delay.
{quote}
+1 for the idea. Besides, the #events processed by scheduler per second is large, so the indexes
based on this is volatile. We may consider some method to smooth the fluctuate, like making
sampling or statistics.

> A SchedulerMetrics may be need for evaluating the scheduler's performance
> -------------------------------------------------------------------------
>
>                 Key: YARN-3652
>                 URL: https://issues.apache.org/jira/browse/YARN-3652
>             Project: Hadoop YARN
>          Issue Type: Improvement
>          Components: resourcemanager, scheduler
>            Reporter: Xianyin Xin
>
> As discussed in YARN-3630, a {{SchedulerMetrics}} may be need for evaluating the scheduler's
performance. The performance indexes includes #events waiting for being handled by scheduler,
the throughput, the scheduling delay and/or other indicators.



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