[jira] [Updated] (SPARK-52516) Memory Leak with coalesce foreachpartitions and v2 datasources

["ASF GitHub Bot (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-52516?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

ASF GitHub Bot updated SPARK-52516:
-----------------------------------
    Labels: pull-request-available  (was: )

> Memory Leak with coalesce foreachpartitions and v2 datasources
> --------------------------------------------------------------
>
>                 Key: SPARK-52516
>                 URL: https://issues.apache.org/jira/browse/SPARK-52516
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core
>    Affects Versions: 3.4.3
>            Reporter: Joshua Kolash
>            Priority: Major
>              Labels: pull-request-available
>
> Doing the following should not leak any significant amount of memory.
> {code:java}
> sparkSession.sql("select * from icebergcatalog.db.table").coalesce(4).foreachPartition( 
>     (iterator) -> { while (iterator.hasNext()) iterator.next(); }
> ); {code}
> Some of the details of this are contained in this thread here
> [https://github.com/apache/iceberg/issues/13297]
> In summary there is a bug where adding a heavy reference in 
> {code:java}
> context.addTaskCompletionListener{code}
> can lead to an OOM as the callback is preventing garbage collection of those heavy references. In particular doing a coalesce piles up "sub-tasks" such that they cannot be cleaned up until the coalesce task completes.
> This same issue manifested in 2 different scala classes 
> {code:java}
> sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceRDD.scala
> sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/parquet/ParquetPartitionReaderFactory.scala {code}
> Iceberg is affected by the first but using the v2 parquet readers are affected by the 2nd.
> The proposed solution is to use a delegate class to de-reference the heavy objects on iterator exhaustion or close. Which only requires changes local to those classes without any public api changes.
> The proposed changes were tested on spark 3.4.X but not on 4.0.0 But I believe 4.0.0 is likely impacted.



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