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https://issues.apache.org/jira/browse/SPARK-60920?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-60920:
-----------------------------------
Labels: pull-request-available (was: )
> Make reader/writer setters mutate in place like PySpark in the Spark Connect
> Rust drop-in
> -----------------------------------------------------------------------------------------
>
> Key: SPARK-60920
> URL: https://issues.apache.org/jira/browse/SPARK-60920
> Project: Spark
> Issue Type: Bug
> Components: Connect
> Affects Versions: connect-rust-4.3.0
> Reporter: L. C. Hsieh
> Priority: Major
> Labels: pull-request-available
> Fix For: connect-rust-4.3.0
>
>
> In the spark-connect-rust Python drop-in, {{DataFrameReader}},
> {{DataStreamReader}}, {{DataFrameWriter}}, {{DataFrameWriterV2}},
> {{DataStreamWriter}} and {{MergeIntoWriter}} are consuming builders. Each
> setter ({{format}}, {{schema}}, {{option}}, {{options}}, {{mode}},
> {{partitionBy}}, {{withSchemaEvolution}}, ...) takes the inner Rust builder
> and returns a new wrapper, so the original Python object is left empty.
> In PySpark these setters change {{self}} and return it. So the common PySpark
> pattern
> {code:python}
> r = spark.read
> r.option("a", "b")
> r.format("jdbc").load()
> {code}
> fails with {{RuntimeError: DataFrameReader already consumed}}. The vendored
> pandas-on-Spark {{read_sql_table}} calls {{reader.option(...)}},
> {{reader.schema(...)}} and {{reader.options(...)}} on one reader and ignores
> the return values, so it hits this error.
> It also fails in these cases:
> * reusing a reader or writer after a {{load()}} / {{save()}}, which PySpark
> allows
> * reusing one after a format method that failed argument validation, e.g.
> {{csv(schema=...)}} with an invalid schema, since the reader was taken before
> the arguments were checked
> * a {{MergeIntoWriter}} clause ({{whenMatched()}} etc.). PySpark's clause
> actions append to the parent writer and return that same writer, and a clause
> can be used more than once.
> The drop-in should mirror PySpark: setters mutate in place and return the
> same object, and terminal methods leave the object reusable.
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