[jira] [Commented] (OOZIE-2810) RM job was stuck when running with oozie

["Peter Cseh (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/OOZIE-2810?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15892730#comment-15892730 ] 

Peter Cseh commented on OOZIE-2810:
-----------------------------------

If you go to the RM's web interface on an address like http://localhost:8088/cluster you'll see a table at the top like this:

||Apps Submitted ||	Apps Pending	|| Apps Running ||	Apps Completed ||	Containers Running ||	Memory Used	|| Memory Total ||	Memory Reserved ||	VCores Used	 || VCores Total	|| VCores Reserved ||
|7	|0	|0	|7|	0	|0 B	|24 GB	|0 B	|0|	6	| 0|

If the VCores Used or Memory Used are too high, there will be no more space in the cluster to create a container. Provide more resources to the cluster or tweak the yarn properties to make it spawn smaller containers.



> RM job was stuck when running with oozie
> ----------------------------------------
>
>                 Key: OOZIE-2810
>                 URL: https://issues.apache.org/jira/browse/OOZIE-2810
>             Project: Oozie
>          Issue Type: Improvement
>          Components: action, core, HA, workflow
>    Affects Versions: 4.3.0
>         Environment: hadoop2.7.2,centos7*3
>            Reporter: yangsongjie
>              Labels: newbie
>             Fix For: 4.3.0
>
>   Original Estimate: 168h
>  Remaining Estimate: 168h
>
> I'm running a mapreduce wordcount job task on oozie. 2 jobs were submitted to the yarn, and then the monitoring tasks running upto 99% were stuck. Wordcount job has been 0%.
> When I kill off the monitor job, wordcount job runs smoothly.
> I use a cluster of 3 virtual machines, configuration is as follows:
>  Profile per VM: cores=2 memory=2048MB reserved=0GB usableMem=0GB disks=1
>  Num Container=3
>  Container Ram=640MB
>  Used Ram=1GB
>  Unused Ram=0GB
>  yarn.scheduler.minimum-allocation-mb=640
>  yarn.scheduler.maximum-allocation-mb=1920
>  yarn.nodemanager.resource.memory-mb=1920
>  mapreduce.map.memory.mb=640
>  mapreduce.map.java.opts=-Xmx512m
>  mapreduce.reduce.memory.mb=1280
>  mapreduce.reduce.java.opts=-Xmx1024m
>  yarn.app.mapreduce.am.resource.mb=640
>  yarn.app.mapreduce.am.command-opts=-Xmx512m
>  mapreduce.task.io.sort.mb=256
> Is there any way to solve this, to make two jobs run smoothly and finish at the same time ?



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