Igor Amelin created SPARK-35262:
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Summary: Memory leak when dataset is being persisted
Key: SPARK-35262
URL: https://issues.apache.org/jira/browse/SPARK-35262
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 3.1.1
Reporter: Igor Amelin
If a Java- or Scala-application with SparkSession runs a long time and persists
a lot of datasets, it can crash because of a memory leak.
I've noticed the following. When we have a dataset and persist it, the
SparkSession used to load that dataset is cloned in CacheManager, and this
clone is added as a listener to `listenersPlusTimers` in `ListenerBus`. But
this clone isn't removed from the list of listeners after that, e.g.
unpersisting the dataset. If we persist a lot of datasets, the SparkSession is
cloned and added to `ListenerBus` many times. This leads to a memory leak since
the `listenersPlusTimers` list become very large.
I've found out that the SparkSession is cloned is CacheManager when the
parameters `spark.sql.sources.bucketing.autoBucketedScan.enabled` and
`spark.sql.adaptive.enabled` are true. The first one is true by default, and
this default behavior leads to the problem. When auto bucketed scan is
disabled, the SparkSession isn't cloned, and there are no duplicates in
ListenerBus, so the memory leak doesn't occur.
Here is a small Java application to reproduce the memory leak:
[https://github.com/iamelin/spark-memory-leak]
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