Details

    • Type: Improvement
    • Status: Resolved
    • Priority: Major
    • Resolution: Fixed
    • Affects Version/s: None
    • Fix Version/s: 3.0.0-alpha1
    • Component/s: task
    • Labels:
      None
    • Environment:

      x86-64 Linux/Unix

    • Hadoop Flags:
      Reviewed
    • Release Note:
      Hide
      Adds a native implementation of the map output collector. The native library will build automatically with -Pnative. Users may choose the new collector on a job-by-job basis by setting mapreduce.job.map.output.collector.class=org.apache.hadoop.mapred.
      nativetask.NativeMapOutputCollectorDelegator in their job configuration. For shuffle-intensive jobs this may provide speed-ups of 30% or more.
      Show
      Adds a native implementation of the map output collector. The native library will build automatically with -Pnative. Users may choose the new collector on a job-by-job basis by setting mapreduce.job.map.output.collector.class=org.apache.hadoop.mapred. nativetask.NativeMapOutputCollectorDelegator in their job configuration. For shuffle-intensive jobs this may provide speed-ups of 30% or more.
    • Tags:
      optimization task

      Description

      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.

        Attachments

        1. DESIGN.html
          42 kB
          Binglin Chang
        2. dualpivot-0.patch
          5 kB
          Chris Douglas
        3. dualpivotv20-0.patch
          4 kB
          Chris Douglas
        4. fb-shuffle.patch
          76 kB
          Todd Lipcon
        5. hadoop-3.0-mapreduce-2841-2014-7-17.patch
          3.50 MB
          Sean Zhong
        6. MAPREDUCE-2841.v1.patch
          180 kB
          Binglin Chang
        7. MAPREDUCE-2841.v2.patch
          190 kB
          Binglin Chang
        8. micro-benchmark.txt
          13 kB
          Todd Lipcon
        9. MR-2841benchmarks.pdf
          213 kB
          Todd Lipcon
        10. mr-2841-merge.txt
          2.72 MB
          Todd Lipcon
        11. mr-2841-merge-2.txt
          2.70 MB
          Sean Zhong
        12. mr-2841-merge-3.patch
          2.73 MB
          Sean Zhong
        13. mr-2841-merge-4.patch
          2.68 MB
          Sean Zhong

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              • Assignee:
                clockfly Sean Zhong
                Reporter:
                decster Binglin Chang
              • Votes:
                4 Vote for this issue
                Watchers:
                82 Start watching this issue

                Dates

                • Created:
                  Updated:
                  Resolved: