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https://issues.apache.org/jira/browse/MAPREDUCE-1220?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12832838#action_12832838
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Tom White commented on MAPREDUCE-1220:
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bq. Most of the effort involved teasing out the framework in the MapTask and
ReduceTask to allow several components such as MapOutputBuffer,
ReduceValuesIterator etc. to be used as 'pluggable' components.
Interesting. MAPREDUCE-326 has a proposal for making these components
pluggable, which might make the work of this JIRA simpler.
> 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: Arun C Murthy
> Fix For: 0.22.0
>
> Attachments: MAPREDUCE-1220_yhadoop20.patch
>
>
> 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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