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From "Todd Lipcon (Commented) (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MAPREDUCE-2841) Task level native optimization
Date Thu, 15 Dec 2011 06:49:31 GMT

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Todd Lipcon commented on MAPREDUCE-2841:
----------------------------------------

I chatted recently with the MR guys over at Facebook. They have another implementation here
which they've been working on that gives similar gains, while staying all in java. The approach
is something like the following:
- map output collector collects into small buffers, each sized something close to L3 cache
- when any buffer is full, sort it but don't spill it
- when enough buffers are collected to fill io.sort.mb, merge them from memory to disk

This fixes the cache locality issues that everyone has identified, but doesn't require native
code. It's up on their github here: https://github.com/facebook/hadoop-20/blob/master/src/mapred/org/apache/hadoop/mapred/BlockMapOutputBuffer.java

Maybe Yongqiang can comment more on this approach? Dmytro has given permission offline for
us to work from the code on their github and contribute it on trunk (they may not have time
to contribute it in the nearterm)

I think a short term goal for this area we could attack would be to make the map output collector
implementation pluggable. Then people can experiment more freely with different collector
implementations. I don't have time for it - just throwing it out there as a thought.
                
> 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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