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From "Chris Douglas (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (MAPREDUCE-2841) Task level native optimization
Date Sun, 28 Aug 2011 11:49:37 GMT

     [ https://issues.apache.org/jira/browse/MAPREDUCE-2841?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Chris Douglas updated MAPREDUCE-2841:
-------------------------------------

    Attachment: dualpivotv20-0.patch
                dualpivot-0.patch

bq. Java 7 uses a different sort algorithm that is often 2x as fast as Java 6 for objects
(dual pivot quicksort) and a faster mergesort implementation too for arrays (TimSort)

As Binglin points out, that's easy to test given a rote translation. I ported the code here
mechanically:

http://permalink.gmane.org/gmane.comp.java.openjdk.core-libs.devel/2628

----

The goal of the last few implementations was a predictable memory footprint. Per-partition
buckets are obviously faster to sort, but record skew and internal fragmentation in the tracking
structures (and overhead in Java objects) motivated packing records into a single buffer.
Can you summarize how the memory management works in the current patch?

It's exciting to see such a significant gain, here.

> 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, 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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