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https://issues.apache.org/jira/browse/SPARK-4644?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14228308#comment-14228308
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Shixiong Zhu commented on SPARK-4644:
-------------------------------------
I disagree to use `broadcast join` because:
1. `broadcast join` is in Spark SQL. It's not convenient for people who only
want to use Spark Core. Some users (such as ALS in mllib) have already used
`join` of Spark Core, and I don't think forcing users to rewrite them with
Spark SQL is a good idea.
2. `broadcast join` assumes only one of two tables has skew keys. If both two
tables have skew keys, how to handle it?
I only know a little about Spark SQL. Please let me know if there is any
mistake.
> 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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