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https://issues.apache.org/jira/browse/SPARK-1065?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14094981#comment-14094981
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Davies Liu commented on SPARK-1065:
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[~frol], I think broadcast the RDD object is already done by that PR.
But the serialized closure will still be sent to JVM by py4j. After using
broadcast for large datasets, the serialized closure should not be too huge, so
I guess it will not be a big issue.
> PySpark runs out of memory with large broadcast variables
> ---------------------------------------------------------
>
> Key: SPARK-1065
> URL: https://issues.apache.org/jira/browse/SPARK-1065
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 0.7.3, 0.8.1, 0.9.0
> Reporter: Josh Rosen
> Assignee: Davies Liu
>
> PySpark's driver components may run out of memory when broadcasting large
> variables (say 1 gigabyte).
> Because PySpark's broadcast is implemented on top of Java Spark's broadcast
> by broadcasting a pickled Python as a byte array, we may be retaining
> multiple copies of the large object: a pickled copy in the JVM and a
> deserialized copy in the Python driver.
> The problem could also be due to memory requirements during pickling.
> PySpark is also affected by broadcast variables not being garbage collected.
> Adding an unpersist() method to broadcast variables may fix this:
> https://github.com/apache/incubator-spark/pull/543.
> As a first step to fixing this, we should write a failing test to reproduce
> the error.
> This was discovered by [~sandy]: ["trouble with broadcast variables on
> pyspark"|http://apache-spark-user-list.1001560.n3.nabble.com/trouble-with-broadcast-variables-on-pyspark-tp1301.html].
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