ueshin commented on code in PR #38624:
URL: https://github.com/apache/spark/pull/38624#discussion_r1396553305
##########
python/pyspark/sql/pandas/group_ops.py:
##########
@@ -495,6 +623,104 @@ def applyInPandas(
jdf = self._gd1._jgd.flatMapCoGroupsInPandas(self._gd2._jgd,
udf_column._jc.expr())
return DataFrame(jdf, self._gd1.session)
+ def applyInArrow(
+ self, func: "ArrowCogroupedMapFunction", schema: Union[StructType, str]
+ ) -> "DataFrame":
+ """
+ Applies a function to each cogroup using Arrow and returns the result
+ as a `DataFrame`.
+
+ The function should take two `pyarrow.Table`\\s and return another
+ `pyarrow.Table`. Alternatively, the user can pass a function that takes
+ a tuple of `pyarrow.Scalar` grouping key(s) and the two
`pyarrow.Table`\\s.
+ For each side of the cogroup, all columns are passed together as a
+ `pyarrow.Table` to the user-function and the returned `pyarrow.Table`
are combined as
+ a :class:`DataFrame`.
+
+ The `schema` should be a :class:`StructType` describing the schema of
the returned
+ `pandas.DataFrame`. The column labels of the returned
`pandas.DataFrame` must either match
+ the field names in the defined schema if specified as strings, or
match the
+ field data types by position if not strings, e.g. integer indices.
+ The length of the returned `pyarrow.Table` can be arbitrary.
+
+ .. versionadded:: 3.4.0
Review Comment:
should be `4.0.0`?
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