[jira] [Updated] (SPARK-7116) Intermediate RDD cached but never unpersisted

["Michael Armbrust (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-7116?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Michael Armbrust updated SPARK-7116:
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    Target Version/s: 1.4.0

> 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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