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From "Shixiong Zhu (JIRA)" <>
Subject [jira] [Commented] (SPARK-4644) Implement skewed join
Date Mon, 01 Dec 2014 08:01:12 GMT


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.

we could provide an interface similar to ExternalAppendOnlyMap but which returns an Iterator[(K,
Iterable[V])] pairs
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:
>             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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