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https://issues.apache.org/jira/browse/MAPREDUCE-4502?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13455909#comment-13455909
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Chris Douglas commented on MAPREDUCE-4502:
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bq. This seems to be good approach to deal with rack-level aggregation. Do you
have any results about the benchmark?
For reducing on key ranges, there's a paper in
[SOCC|http://www.socc2012.org/papers] on Sailfish. I don't have a link to that
paper, though there's a [tech
report|http://research.yahoo.com/files/yl-2012-003.pdf]. For the benchmark, we
were mostly handling cases without combiners; in our data, each combiner was
too effective to benefit from an intermediate level.
> Multi-level aggregation with combining the result of maps per node/rack
> -----------------------------------------------------------------------
>
> Key: MAPREDUCE-4502
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-4502
> Project: Hadoop Map/Reduce
> Issue Type: Improvement
> Components: applicationmaster, mrv2
> Reporter: Tsuyoshi OZAWA
> Assignee: Tsuyoshi OZAWA
> Attachments: speculative_draft.pdf
>
>
> The shuffle costs is expensive in Hadoop in spite of the existence of
> combiner, because the scope of combining is limited within only one MapTask.
> To solve this problem, it's a good way to aggregate the result of maps per
> node/rack by launch combiner.
> This JIRA is to implement the multi-level aggregation infrastructure,
> including combining per container(MAPREDUCE-3902 is related), coordinating
> containers by application master without breaking fault tolerance of jobs.
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