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https://issues.apache.org/jira/browse/MAPREDUCE-4892?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13566182#comment-13566182
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Bikas Saha commented on MAPREDUCE-4892:
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I meant unnecessary computations for nodes that are not going to produce more
splits in every iteration. The code will try to group any remaining blocks and
fail to produce a meaningful split. And it will do this again and again.
In the example, we could end up generating 5, 1, 3 or 4, 1, 4 splits by trying
to be completely node local. Or we could go 3, 1, 3 and 2 rack-local. Upon
scheduling the rack-local option would start 3, 3, 3 if there are 3 slots per
node. The extra node local ones would have to wait for nodes 1 and 3 to finish
the first wave before they can run. Or wait for delay scheduling to kick in and
get scheduled on node 2. Which of these will be faster is not obvious.
In any case, we can debate this endlessly. I have attached an alternative patch
which iterates multiple times over nodes until it all blocks have been grouped
or no node if left with more than minsize of blocks, while trying to keep
number of iterations low.
> CombineFileInputFormat node input split can be skewed on small clusters
> -----------------------------------------------------------------------
>
> Key: MAPREDUCE-4892
> URL: https://issues.apache.org/jira/browse/MAPREDUCE-4892
> Project: Hadoop Map/Reduce
> Issue Type: Bug
> Reporter: Bikas Saha
> Assignee: Bikas Saha
> Fix For: 3.0.0
>
> Attachments: MAPREDUCE-4892.1.alt.patch, MAPREDUCE-4892.1.patch
>
>
> The CombineFileInputFormat split generation logic tries to group blocks by
> node in order to create splits. It iterates through the nodes and creates
> splits on them until there aren't enough blocks left on a node that can be
> grouped into a valid split. If the first few nodes have a lot of blocks on
> them then they can end up getting a disproportionately large share of the
> total number of splits created. This can result in poor locality of maps.
> This problem is likely to happen on small clusters where its easier to create
> a skew in the distribution of blocks on nodes.
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