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https://issues.apache.org/jira/browse/SPARK-55168?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-55168:
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Labels: pull-request-available (was: )
> Refactor GroupArrowUDFSerializer to use ArrowBatchTransformer.flatten_struct
> ----------------------------------------------------------------------------
>
> Key: SPARK-55168
> URL: https://issues.apache.org/jira/browse/SPARK-55168
> Project: Spark
> Issue Type: Sub-task
> Components: PySpark
> Affects Versions: 4.2.0
> Reporter: Yicong Huang
> Priority: Major
> Labels: pull-request-available
>
> Currently, {{GroupArrowUDFSerializer.load_stream}} has an inline
> {{process_group}} function that duplicates the flatten_struct logic already
> extracted in {{ArrowBatchTransformer}}:
> {code:python}
> # Current code in GroupArrowUDFSerializer.load_stream (line 1021-1024)
> def process_group(batches: "Iterator[pa.RecordBatch]"):
> for batch in batches:
> struct = batch.column(0)
> yield pa.RecordBatch.from_arrays(struct.flatten(),
> schema=pa.schema(struct.type))
> {code}
> This is identical to {{ArrowBatchTransformer.flatten_struct}}. We should
> reuse the existing transformer:
> {code:python}
> # Proposed change
> def load_stream(self, stream):
> ...
> if dataframes_in_group == 1:
> batch_iter = map(ArrowBatchTransformer.flatten_struct,
> ArrowStreamSerializer.load_stream(self, stream))
> yield batch_iter
> ...
> {code}
> This continues Phase 1 of SPARK-55159 by eliminating duplicated
> transformation logic and improving code reuse.
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