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https://issues.apache.org/jira/browse/SPARK-33000?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17214904#comment-17214904
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Nicholas Chammas commented on SPARK-33000:
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Thanks for the pointer!
No need for a new ticket. I can adjust the title of this ticket and open a PR
for the doc update.
Separate question: Do you know why there is such a restriction on when this
configuration needs to be set? It's surprising since you can set the checkpoint
directory after job submission.
> cleanCheckpoints config does not clean all checkpointed RDDs on shutdown
> ------------------------------------------------------------------------
>
> Key: SPARK-33000
> URL: https://issues.apache.org/jira/browse/SPARK-33000
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.4.6
> Reporter: Nicholas Chammas
> Priority: Minor
>
> Maybe it's just that the documentation needs to be updated, but I found this
> surprising:
> {code:python}
> $ pyspark
> ...
> >>> spark.conf.set('spark.cleaner.referenceTracking.cleanCheckpoints', 'true')
> >>> spark.sparkContext.setCheckpointDir('/tmp/spark/checkpoint/')
> >>> a = spark.range(10)
> >>> a.checkpoint()
> DataFrame[id: bigint]
>
> >>> exit(){code}
> The checkpoint data is left behind in {{/tmp/spark/checkpoint/}}. I expected
> Spark to clean it up on shutdown.
> The documentation for {{spark.cleaner.referenceTracking.cleanCheckpoints}}
> says:
> {quote}Controls whether to clean checkpoint files if the reference is out of
> scope.
> {quote}
> When Spark shuts down, everything goes out of scope, so I'd expect all
> checkpointed RDDs to be cleaned up.
> For the record, I see the same behavior in both the Scala and Python REPLs.
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