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https://issues.apache.org/jira/browse/SPARK-61003?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon resolved SPARK-61003.
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Resolution: Fixed
Issue resolved by pull request 205
[https://github.com/apache/spark-connect-rust/pull/205]
> Register UDTFs on the server with spark.udtf.register of the Python client as
> PySpark does
> ------------------------------------------------------------------------------------------
>
> Key: SPARK-61003
> URL: https://issues.apache.org/jira/browse/SPARK-61003
> 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
>
>
> In PySpark Connect, {{spark.udtf.register(name, f)}} registers the UDTF {{f}}
> under {{name}} on the server, with a {{register_table_function}} command of
> its {{CommonInlineUserDefinedTableFunction}} (its handler, return type, eval
> type and determinism), so that SQL can call it: {{spark.sql("SELECT * FROM
> name(2)")}}. It raises {{CANNOT_REGISTER_UDTF}} for an {{f}} that is not a
> UDTF, and {{INVALID_UDTF_EVAL_TYPE}} for one of another eval type.
> The Rust-backed Python client only renames the UDTF object, so the server
> does not know the name: for a registered UDTF, {{spark.sql("SELECT * FROM
> name(2)")}} raises {{UNRESOLVABLE_TABLE_VALUED_FUNCTION}} where PySpark 4.3.0
> returns its rows. It also takes a handler class rather than a UDTF. Register
> UDTFs as PySpark does.
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