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https://issues.apache.org/jira/browse/SPARK-61237?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon resolved SPARK-61237.
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Resolution: Fixed
Issue resolved by pull request 419
[https://github.com/apache/spark-connect-rust/pull/419]
> Release the relation of a checkpointed DataFrame on the server when its last
> DataFrame is dropped, as PySpark does
> ------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-61237
> URL: https://issues.apache.org/jira/browse/SPARK-61237
> Project: Spark
> Issue Type: Bug
> Components: Connect
> Affects Versions: connect-rust-4.3.0
> Reporter: Hyukjin Kwon
> Assignee: Hyukjin Kwon
> Priority: Major
> Labels: pull-request-available
> Fix For: connect-rust-4.3.0
>
>
> checkpoint() and localCheckpoint() in the Rust client (and the Python
> drop-in, which calls the same core code) return a DataFrame over a
> CachedRemoteRelation, but nothing ever sent
> RemoveCachedRemoteRelationCommand, so every checkpoint stayed on the server,
> with its data, until the session ended.
> Repro: a DataFrame from localCheckpoint() is dropped, then its relation is
> still readable on the server (count succeeds), while official PySpark 4.3.0
> gives CONNECT_INVALID_PLAN.DATAFRAME_NOT_FOUND once the checkpoint and its
> derived DataFrames are dropped and garbage-collected.
> Fix: the checkpoint plan owns a reference that removes the relation when the
> last plan of it is dropped, as CachedRemoteRelation.__del__ does in PySpark.
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