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https://issues.apache.org/jira/browse/MAPREDUCE-1220?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Greg Roelofs updated MAPREDUCE-1220:
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Attachment: MR-1220.v1b.sshot-03-jobdetailshistory.jsp.png
MR-1220.v1b.sshot-03-jobdetailshistory.jsp.png
screenshot of the jobdetailshistory page (uber-job complete)
This was taken before the setup and cleanup tasks were moved inside UberTask.
The UI didn't get updated for that, IIRC, so I think it shows lots of zeros on
the top and bottom lines now. (Another TODO item...)
Note also the wrappable column heading, courtesy of "<wbr>" pseudo-tags.
(Browsers optionally break there, but cut-and-paste doesn't pick up a spurious
space. Highly recommended for other fat table cells such as counter names and
types, job names, hostnames, etc. I should file a separate JIRA...)
> Implement an in-cluster LocalJobRunner
> --------------------------------------
>
> Key: MAPREDUCE-1220
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-1220
> Project: Hadoop Map/Reduce
> Issue Type: New Feature
> Components: client, jobtracker
> Reporter: Arun C Murthy
> Assignee: Greg Roelofs
> Attachments: MAPREDUCE-1220_yhadoop20.patch,
> MR-1220.v1.trunk-hadoop-common.Progress-dumper.patch.txt,
> MR-1220.v10e-v11c-v12b.ytrunk-hadoop-mapreduce.delta.patch.txt,
> MR-1220.v13.ytrunk-hadoop-mapreduce.delta.patch.txt,
> MR-1220.v14b.ytrunk-hadoop-mapreduce.delta.patch.txt,
> MR-1220.v15.ytrunk-hadoop-mapreduce.delta.patch.txt,
> MR-1220.v1b.sshot-02-jobdetails.jsp.png,
> MR-1220.v1b.sshot-03-jobdetailshistory.jsp.png,
> MR-1220.v2.trunk-hadoop-mapreduce.patch.txt,
> MR-1220.v2.trunk-hadoop-mapreduce.patch.txt,
> MR-1220.v2b.sshot-01-jobtracker.jsp.png,
> MR-1220.v6.ytrunk-hadoop-mapreduce.patch.txt,
> MR-1220.v7.ytrunk-hadoop-mapreduce.delta.patch.txt,
> MR-1220.v8b.ytrunk-hadoop-mapreduce.delta.patch.txt,
> MR-1220.v9c.ytrunk-hadoop-mapreduce.delta.patch.txt
>
>
> Currently very small map-reduce jobs suffer from latency issues due to
> overheads in Hadoop Map-Reduce such as scheduling, jvm startup etc. We've
> periodically tried to optimize all parts of framework to achieve lower
> latencies.
> I'd like to turn the problem around a little bit. I propose we allow very
> small jobs to run as a single task job with multiple maps and reduces i.e.
> similar to our current implementation of the LocalJobRunner. Thus, under
> certain conditions (maybe user-set configuration, or if input data is small
> i.e. less a DFS blocksize) we could launch a special task which will run all
> maps in a serial manner, followed by the reduces. This would really help
> small jobs achieve significantly smaller latencies, thanks to lesser
> scheduling overhead, jvm startup, lack of shuffle over the network etc.
> This would be a huge benefit, especially on large clusters, to small Hive/Pig
> queries.
> Thoughts?
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