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https://issues.apache.org/jira/browse/AMBARI-5607?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14020871#comment-14020871
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Michael Harp commented on AMBARI-5607:
--------------------------------------
Update from Steve Ratay;
"It seems a little better. It's taking about 1 minute to start updating the
NodeManager metrics. But after the ResourceManager reports 0 applications
running, the NodeManagers are still reporting containers allocated for at least
another 2-3 minutes. Viewpoint is calling the ResourceManager and NodeManager
APIs in Ambari one immediately after the other. However, the number of
containers allocated being reported by the ResourceManager is never even close
to what you get if you add up the NodeManagers. When I use the YARN UI
directly, these numbers always appear to match up and I see much more frequent
updates of the data"
> Yarn Nodemanager Metrics only update every few minutes
> ------------------------------------------------------
>
> Key: AMBARI-5607
> URL: https://issues.apache.org/jira/browse/AMBARI-5607
> Project: Ambari
> Issue Type: Bug
> Affects Versions: 1.5.1
> Environment: sles11sp1
> Reporter: Michael Harp
> Assignee: Dmytro Sen
> Fix For: 1.6.1
>
>
> Yarn Nodemanager Metrics take far too long between updates.
> To demonstrate:
> Run Terasort or anything that runs mapreduce:
> {code}
> hdfs dfs -mkdir -p benchmarks/terasort
> hadoop jar /usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar teragen
> -Dmapred.map.tasks=72 -Dmapred.reduce.tasks=36 1000000
> benchmarks/terasort/input
> hadoop jar /usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar terasort
> -Dmapred.map.tasks=72 -Dmapred.reduce.tasks=36 benchmarks/terasort/input
> benchmarks/terasort/output
> hdfs dfs -rm -R -skipTrash benchmarks/terasort
> {code}
> Then repeatedly probe the API at:
> {noformat}https://<server>:8081/api/v1/clusters/c1/services/YARN/components/NODEMANAGER?fields=host_components/metrics/yarn{noformat}
> It usually takes 2-3 minutes to see the metrics update, very repeatable.
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