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https://issues.apache.org/jira/browse/MAPREDUCE-2841?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14175842#comment-14175842
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Todd Lipcon commented on MAPREDUCE-2841:
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Hey Nathan. I'm not adverse to putting it in branch-2. As of earlier this week
we are also now shipping this feature to our customers as an experimental
option, which should hopefully get some baking done. If the community is on
board, I'm happy to backport to branch-2 as well. Perhaps we can label it as
experimental in our next release, and stable in the release following if we get
some good feedback? Would be great to hear some results from testing at Y!.
> Task level native optimization
> ------------------------------
>
> Key: MAPREDUCE-2841
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-2841
> Project: Hadoop Map/Reduce
> Issue Type: Improvement
> Components: task
> Environment: x86-64 Linux/Unix
> Reporter: Binglin Chang
> Assignee: Sean Zhong
> Fix For: 3.0.0
>
> Attachments: DESIGN.html, MAPREDUCE-2841.v1.patch,
> MAPREDUCE-2841.v2.patch, MR-2841benchmarks.pdf, dualpivot-0.patch,
> dualpivotv20-0.patch, fb-shuffle.patch,
> hadoop-3.0-mapreduce-2841-2014-7-17.patch, micro-benchmark.txt,
> mr-2841-merge-2.txt, mr-2841-merge-3.patch, mr-2841-merge-4.patch,
> mr-2841-merge.txt
>
>
> I'm recently working on native optimization for MapTask based on JNI.
> The basic idea is that, add a NativeMapOutputCollector to handle k/v pairs
> emitted by mapper, therefore sort, spill, IFile serialization can all be done
> in native code, preliminary test(on Xeon E5410, jdk6u24) showed promising
> results:
> 1. Sort is about 3x-10x as fast as java(only binary string compare is
> supported)
> 2. IFile serialization speed is about 3x of java, about 500MB/s, if hardware
> CRC32C is used, things can get much faster(1G/
> 3. Merge code is not completed yet, so the test use enough io.sort.mb to
> prevent mid-spill
> This leads to a total speed up of 2x~3x for the whole MapTask, if
> IdentityMapper(mapper does nothing) is used
> There are limitations of course, currently only Text and BytesWritable is
> supported, and I have not think through many things right now, such as how to
> support map side combine. I had some discussion with somebody familiar with
> hive, it seems that these limitations won't be much problem for Hive to
> benefit from those optimizations, at least. Advices or discussions about
> improving compatibility are most welcome:)
> Currently NativeMapOutputCollector has a static method called canEnable(),
> which checks if key/value type, comparator type, combiner are all compatible,
> then MapTask can choose to enable NativeMapOutputCollector.
> This is only a preliminary test, more work need to be done. I expect better
> final results, and I believe similar optimization can be adopt to reduce task
> and shuffle too.
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