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https://issues.apache.org/jira/browse/SPARK-4644?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14229512#comment-14229512
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Shixiong Zhu commented on SPARK-4644:
-------------------------------------
I don't think solving things like `groupByKey` is valuable. If people want to
cache values of a key by themselves in `groupByKey`, our optimization of
`groupByKey` is useless, OOM happens in the user side. If they don't, they can
always use `reduceByKey` to solve their problems.
`join` is different from `groupByKey` because people have no alternative
solution.
{quote}
we could provide an interface similar to ExternalAppendOnlyMap but which
returns an Iterator[(K, Iterable[V])] pairs
{quote}
If I understand correctly, the iterator should be {noformat}Iterator[(K,
Iterable[LEFT], Iterable[RIGHT])]{noformat}. It should collect the values of
the same key from both LEFT and RIGHT.
> Implement skewed join
> ---------------------
>
> Key: SPARK-4644
> URL: https://issues.apache.org/jira/browse/SPARK-4644
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Reporter: Shixiong Zhu
> Attachments: Skewed Join Design Doc.pdf
>
>
> Skewed data is not rare. For example, a book recommendation site may have
> several books which are liked by most of the users. Running ALS on such
> skewed data will raise a OutOfMemory error, if some book has too many users
> which cannot be fit into memory. To solve it, we propose a skewed join
> implementation.
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