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https://issues.apache.org/jira/browse/MAPREDUCE-2841?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14047843#comment-14047843
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Todd Lipcon commented on MAPREDUCE-2841:
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The patch required for the output collector is just this one:
https://github.com/intel-hadoop/nativetask/blob/native_output_collector/patch/hadoop-2.patch
In fact, this just provides the "automatic fallback" functionality. That
functionality is probably useful for all pluggable output collectors -- happy
to break it out to be distinct from the JIRA.
The only other diff in that patch is a trivial addition to the Text writable
implementation to allow setting a Text more easily from different serialization
formats. I don't think it makes sense to break it out to a separate JIRA, but
happy to do so if that makes things easier.
> 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
> Attachments: DESIGN.html, MAPREDUCE-2841.v1.patch,
> MAPREDUCE-2841.v2.patch, dualpivot-0.patch, dualpivotv20-0.patch,
> fb-shuffle.patch
>
>
> 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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