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https://issues.apache.org/jira/browse/MAPREDUCE-1220?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13004001#comment-13004001
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Greg Roelofs commented on MAPREDUCE-1220:
-----------------------------------------

If you've been watching the commits go by on Owen's yahoo-merge branch, about 
five months' worth of work on this was included.  Unfortunately, I screwed up 
my most recent push to Yahoo's internal git repo, and as a result, every 
internal/temporary/debug commit was exposed, which amounts to a lot of noise (> 
3 dozen extra commits).

I'll post the ~8 "real" patches corresponding to all of that here, along with a 
dump() method for Progress (in common), which was useful for debugging and may 
be needed again.  There are also a few screenshots, but I'll probably need to 
scrub some internal hostnames before posting those.

> 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.v2.trunk-hadoop-mapreduce.patch.txt, 
> MR-1220.v2.trunk-hadoop-mapreduce.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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