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https://issues.apache.org/jira/browse/CRUNCH-213?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13678828#comment-13678828
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Gabriel Reid commented on CRUNCH-213:
-------------------------------------
Josh, thanks for looking at it.
1) Yep, thanks for catching that on hadoop-2. It is indeed an issue with the
generation of the TaskAttemptContext, and I'll upload a patch in a couple of
minutes.
2) The default join strategy caches the values for a single key at a time in
memory from the left table, not the whole table, so replicating the left side
won't result in more memory being used for this caching. The general intention
is that the left table is (much) smaller than the right table, so by
replicating the left table and sharding the right table, the size of the
shuffle stays nearly the same, whereas replicating the right table would make
the shuffle much larger (depending on the proportions of the left and right
table of course).
3) Correct, there's no default sampling-based strategy yet. Like you thought, I
wanted to keep this functionality possible, but not worry about it specifically
yet.
> Add sharded join functionality
> ------------------------------
>
> Key: CRUNCH-213
> URL: https://issues.apache.org/jira/browse/CRUNCH-213
> Project: Crunch
> Issue Type: New Feature
> Reporter: Gabriel Reid
> Assignee: Gabriel Reid
> Attachments: CRUNCH-213.patch, CRUNCH-213.patch
>
>
> Performing joins where a large proportion of the values on one or both sides
> of the join are mapped to a single key can result in poor performance, as one
> (or a small number) of reducers end up handling most of the joining work,
> leaving the rest of the cluster idle.
> Sharded joining should be added to allow splitting up join keys, thereby
> distributing values mapped to a single key over multiple reducer partitions.
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