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https://issues.apache.org/jira/browse/SPARK-7116?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14532160#comment-14532160
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Apache Spark commented on SPARK-7116:
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User 'ksonj' has created a pull request for this issue:
https://github.com/apache/spark/pull/5973
> Intermediate RDD cached but never unpersisted
> ---------------------------------------------
>
> Key: SPARK-7116
> URL: https://issues.apache.org/jira/browse/SPARK-7116
> Project: Spark
> Issue Type: Improvement
> Components: PySpark, SQL
> Affects Versions: 1.3.1
> Reporter: Kalle Jepsen
>
> In
> https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUdfs.scala#L233
> an intermediate RDD is cached, but never unpersisted. It shows up in the
> 'Storage' section of the Web UI, but cannot be removed. There's already a
> comment in the source, suggesting to 'clean up'. If that cleanup is more
> involved than simply calling `unpersist`, it probably exceeds my current
> Scala skills.
> Why that is a problem:
> I'm adding a constant column to a DataFrame of about 20M records resulting
> from an inner join with {{df.withColumn(colname, ud_func())}} , where
> {{ud_func}} is simply a wrapped {{lambda: 1}}. Before and after applying the
> UDF the DataFrame takes up ~430MB in the cache. The cached intermediate RDD
> however takes up ~10GB(!) of storage, and I know of no way to uncache it.
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