Task level native optimization
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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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