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

["Binglin Chang (Commented) (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MAPREDUCE-2841?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13169965#comment-13169965 ] 

Binglin Chang commented on MAPREDUCE-2841:
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bq. Dual-pivot quicksort causes the exact same number of comparisons as ordinary quicksort. However, it should have fewer swaps (0.80 times as many). 
Unfortunately, the main overhead of current sort in Hadoop comes from comparison, the swaps just swap two integer index, I thinks that's why Dual-pivot quicksort don't show any improvements. As Todd's results in MAPREDUCE-3235 and I experienced in this issue, the main overhead for the current Hadoop sort implementation is cache miss, nearly all comparison operations cause 2 random memory access in a huge memory area(typically X00MB). 
So it's not language differentes, just implementation differentes, to get better performance, we can:
Add index in MAPREDUCE-3235, or use partition bucket based sort.

I port DualPivotQuickSort java code to C++ and tested it on my intel i5 macbookpro, with terasort 10bytes key type and word key type in RandomTextWriter.

TeraSort input data 50MB 500000 key/value pair
11/12/15 12:18:16 INFO qsort time: 0.23108s
11/12/15 12:18:16 INFO std::sort time: 0.18266s
11/12/15 12:18:17 INFO DualPivotQuicksort time: 0.17167s

About 6% faster, I think sorting an array of ints can get much better results, cause compare two inplace ints is much faster than campare two indexed binary string.

Some updates about my work, I almost finished whole native mapTask, and part of native reduce task.
As for native MapTask with C++ RecordReader, Mapper, Partitioner, MapOutputCollector, a native MapTask now can process 250MB(47MB compressed) terasort input data in just 1.6s, comparing this with the earlier test results(14s for java, 7s for java with NativeMapOutputCollector), it is a huge speed up, and it can be further optimized.


                
> 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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