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