Kalle Jepsen created SPARK-7116:
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Summary: 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
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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