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https://issues.apache.org/jira/browse/SPARK-55224?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-55224:
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Labels: pull-request-available (was: )
> Use Spark DataType as ground truth in Pandas-Arrow serialization
> ----------------------------------------------------------------
>
> Key: SPARK-55224
> URL: https://issues.apache.org/jira/browse/SPARK-55224
> Project: Spark
> Issue Type: Improvement
> Components: PySpark
> Affects Versions: 4.2.0
> Reporter: Yicong Huang
> Priority: Major
> Labels: pull-request-available
>
> Currently, PySpark's Pandas serializers pass both {{spark_type}} and
> {{arrow_type}} through the serialization pipeline. This creates confusion
> about which type is authoritative. Analysis shows {{spark_type}} (from user's
> UDF return type) is always the source, and {{arrow_type}} is derived via
> {{to_arrow_type()}} which is lightweight.
> This ticket proposes refactoring {{_create_batch}} and {{_create_array}} to
> take only {{spark_type}} and derive {{arrow_type}} internally.
> {code:python}
> # Before: caller pre-computes arrow_type
> arrow_return_type = to_arrow_type(return_type, ...)
> yield (result, arrow_return_type, return_type)
> # After: only pass spark_type
> yield (result, return_type)
> {code}
> {code:python}
> # Before: _create_array takes arrow_type as primary
> def _create_array(self, series, arrow_type, spark_type=None,
> arrow_cast=False):
> dt = spark_type or from_arrow_type(arrow_type) # confusing fallback
> # After: spark_type is ground truth
> def _create_array(self, series, spark_type, *, arrow_cast=False,
> prefers_large_types=False):
> arrow_type = to_arrow_type(spark_type, ...) if spark_type else None
> {code}
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