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https://issues.apache.org/jira/browse/SPARK-61140?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon reassigned SPARK-61140:
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Assignee: Hyukjin Kwon
> Send the renames of DataFrame.withColumnsRenamed in their order, in the Rust
> core and the Python client, as PySpark does
> ------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-61140
> URL: https://issues.apache.org/jira/browse/SPARK-61140
> 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
>
>
> PySpark's DataFrame.withColumnsRenamed sends its renames in the order of
> their dict (the repeated renames of WithColumnsRenamed), and the server
> applies them in that order. So df.withColumnsRenamed({"id": "a", "a": "b"})
> has the columns ["b"], and df.withColumnsRenamed({"a": "b", "id": "a"}) has
> ["a"].
> The Rust core of spark-connect-rust keeps the renames of a WithColumnsRenamed
> plan in a HashMap. So it sends them in the order of their hashes, not in the
> order given, and the columns of such renames depend on that order. Apache
> Spark's test_ordering_of_with_columns_renamed fails on the client: ['a'] !=
> ['b'].
> The core should keep the renames in their order, and send them in it, as
> PySpark does.
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