[jira] [Commented] (MAPREDUCE-2841) Task level native optimization

["Todd Lipcon (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MAPREDUCE-2841?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14119174#comment-14119174 ] 

Todd Lipcon commented on MAPREDUCE-2841:
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FYI, I'm running terasort validations/perf tests on a cluster now. If there are any specific configurations that you'd like to see for terasort on a 5-node cluster, please let me know. (eg HDFS block sizes, MR task memory configurations, etc). I did my best to pick some reasonable settings that kept the machine resources saturated, but want to make sure everyone's input is heard.

> 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, hadoop-3.0-mapreduce-2841-2014-7-17.patch, micro-benchmark.txt
>
>
> 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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