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https://issues.apache.org/jira/browse/MAPREDUCE-3568?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13173738#comment-13173738
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Hadoop QA commented on MAPREDUCE-3568:
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-1 overall. Here are the results of testing the latest attachment
http://issues.apache.org/jira/secure/attachment/12508170/MAPREDUCE-3568-20111220.txt
against trunk revision .
+1 @author. The patch does not contain any @author tags.
+1 tests included. The patch appears to include 9 new or modified tests.
+1 javadoc. The javadoc tool did not generate any warning messages.
+1 javac. The applied patch does not increase the total number of javac
compiler warnings.
+1 eclipse:eclipse. The patch built with eclipse:eclipse.
+1 findbugs. The patch does not introduce any new Findbugs (version 1.3.9)
warnings.
+1 release audit. The applied patch does not increase the total number of
release audit warnings.
-1 core tests. The patch failed these unit tests:
org.apache.hadoop.mapreduce.v2.app.TestRMContainerAllocator
org.apache.hadoop.mapreduce.v2.app.job.impl.TestJobImpl
+1 contrib tests. The patch passed contrib unit tests.
Test results:
https://builds.apache.org/job/PreCommit-MAPREDUCE-Build/1487//testReport/
Console output:
https://builds.apache.org/job/PreCommit-MAPREDUCE-Build/1487//console
This message is automatically generated.
> Optimize Job's progress calculations in MR AM
> ---------------------------------------------
>
> Key: MAPREDUCE-3568
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-3568
> Project: Hadoop Map/Reduce
> Issue Type: Sub-task
> Components: mr-am, mrv2, performance
> Affects Versions: 0.23.0
> Reporter: Vinod Kumar Vavilapalli
> Assignee: Vinod Kumar Vavilapalli
> Fix For: 0.23.1
>
> Attachments: MAPREDUCE-3568-20111215.1.txt,
> MAPREDUCE-3568-20111220.txt
>
>
> Besides catering to client requests, Job progress is calculated in every
> heartbeat to the RM so as to print the MR AM's progress. Today the map and
> reduce progresses are calculated by looking up of each task in a big map
> while we can simply make do with a scan and aggregate. With large number of
> tasks, this can make a difference.
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