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From "Binglin Chang (JIRA)" <j...@apache.org>
Subject [jira] [Created] (MAPREDUCE-2841) Task level native optimization
Date Sat, 13 Aug 2011 07:30:27 GMT
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


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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