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https://issues.apache.org/jira/browse/MAPREDUCE-2841?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13093507#comment-13093507
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Binglin Chang commented on MAPREDUCE-2841:
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Hi, Yongqiang
bq. i may should make me more clear: we are trying to evaluate and compare the
c++ impl in HCE (and also this jira) and doing a pure java re-impl.
I think a pure java re-impl is possible, there are some tricks to slove memory
fragmentation issues, maybe not throughly, for example letting many adjacent
buckets share one MemoryBlock if partition number is too large, which is what I
will do in native implementation. And again, it's hard to support stream like
key/value serialization semantics, so the java re-impl has the same limitations
as native impl has.
But the low level unaligned memcmp & memcpy is hard to implement in java.
> 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
> Reporter: Binglin Chang
> Assignee: Binglin Chang
> Attachments: MAPREDUCE-2841.v1.patch, MAPREDUCE-2841.v2.patch,
> dualpivot-0.patch, dualpivotv20-0.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/s).
> 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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