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https://issues.apache.org/jira/browse/SPARK-1065?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14094965#comment-14094965
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Vlad Frolov commented on SPARK-1065:
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[~davies] Will your PR take into account this fix: [SPARK-2521] Broadcast RDD
object (instead of sending it along with every task)
https://github.com/apache/spark/commit/7b8cd175254d42c8e82f0aa8eb4b7f3508d8fde2
?
"The patch uses broadcast to send RDD objects and the closures to executors"
> 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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