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https://issues.apache.org/jira/browse/SPARK-52516?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Joshua Kolash resolved SPARK-52516.
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Resolution: Duplicate
> 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
>
> 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 inÂ
> {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 classesÂ
> {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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