nit

Author: zhengruifeng

core/src/main/scala/org/apache/spark/api/python/PythonRunner.scala

@@ -69,6 +69,7 @@ private[spark] object PythonEvalType {
 
   // Arrow UDFs
   val SQL_SCALAR_ARROW_UDF = 250
+  val SQL_SCALAR_ARROW_ITER_UDF = 251
 
   val SQL_TABLE_UDF = 300
   val SQL_ARROW_TABLE_UDF = 301
@@ -96,7 +97,10 @@ private[spark] object PythonEvalType {
     case SQL_TRANSFORM_WITH_STATE_PYTHON_ROW_UDF => "SQL_TRANSFORM_WITH_STATE_PYTHON_ROW_UDF"
     case SQL_TRANSFORM_WITH_STATE_PYTHON_ROW_INIT_STATE_UDF =>
       "SQL_TRANSFORM_WITH_STATE_PYTHON_ROW_INIT_STATE_UDF"
+
+    // Arrow UDFs
     case SQL_SCALAR_ARROW_UDF => "SQL_SCALAR_ARROW_UDF"
+    case SQL_SCALAR_ARROW_ITER_UDF => "SQL_SCALAR_ARROW_ITER_UDF"
   }
 }
 

python/pyspark/sql/connect/udf.py

@@ -278,6 +278,7 @@ def register(
                 PythonEvalType.SQL_SCALAR_PANDAS_UDF,
                 PythonEvalType.SQL_SCALAR_ARROW_UDF,
                 PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF,
+                PythonEvalType.SQL_SCALAR_ARROW_ITER_UDF,
                 PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF,
             ]:
                 raise PySparkTypeError(

python/pyspark/sql/pandas/_typing/__init__.pyi

@@ -62,6 +62,7 @@ GroupedMapUDFTransformWithStateInitStateType = Literal[214]
 
 # Arrow UDFs
 ArrowScalarUDFType = Literal[250]
+ArrowScalarIterUDFType = Literal[251]
 
 class ArrowVariadicScalarToScalarFunction(Protocol):
     def __call__(self, *_: pyarrow.Array) -> pyarrow.Array: ...
@@ -135,6 +136,11 @@ ArrowScalarToScalarFunction = Union[
     ],
 ]
 
+ArrowScalarIterFunction = Union[
+    Callable[[Iterable[pyarrow.Array]], Iterable[pyarrow.Array]],
+    Callable[[Tuple[pyarrow.Array, ...]], Iterable[pyarrow.Array]],
+]
+
 class PandasVariadicScalarToScalarFunction(Protocol):
     def __call__(self, *_: DataFrameOrSeriesLike_) -> DataFrameOrSeriesLike_: ...
 

python/pyspark/sql/pandas/functions.py

@@ -46,6 +46,8 @@ class ArrowUDFType:
 
     SCALAR = PythonEvalType.SQL_SCALAR_ARROW_UDF
 
+    SCALAR_ITER = PythonEvalType.SQL_SCALAR_ARROW_ITER_UDF
+
 
 def arrow_udf(f=None, returnType=None, functionType=None):
     return vectorized_udf(f, returnType, functionType, "arrow")
@@ -451,6 +453,7 @@ def calculate(iterator: Iterator[pd.Series]) -> Iterator[pd.Series]:
         )
     if kind == "arrow" and eval_type not in [
         PythonEvalType.SQL_SCALAR_ARROW_UDF,
+        PythonEvalType.SQL_SCALAR_ARROW_ITER_UDF,
         None,
     ]:  # None means it should infer the type from type hints.
         raise PySparkTypeError(

python/pyspark/sql/pandas/functions.pyi

@@ -37,6 +37,8 @@ from pyspark.sql.pandas._typing import (
     PandasScalarUDFType,
     ArrowScalarToScalarFunction,
     ArrowScalarUDFType,
+    ArrowScalarIterFunction,
+    ArrowScalarIterUDFType,
 )
 
 from pyspark import since as since  # noqa: F401
@@ -51,6 +53,7 @@ class PandasUDFType:
 
 class ArrowUDFType:
     SCALAR: ArrowScalarUDFType
+    SCALAR_ITER: ArrowScalarIterUDFType
 
 @overload
 def arrow_udf(
@@ -71,6 +74,24 @@ def arrow_udf(
     *, returnType: DataTypeOrString, functionType: ArrowScalarUDFType
 ) -> Callable[[ArrowScalarToScalarFunction], UserDefinedFunctionLike]: ...
 @overload
+def arrow_udf(
+    f: ArrowScalarIterFunction,
+    returnType: Union[AtomicDataTypeOrString, ArrayType],
+    functionType: ArrowScalarIterUDFType,
+) -> UserDefinedFunctionLike: ...
+@overload
+def arrow_udf(
+    f: Union[AtomicDataTypeOrString, ArrayType], returnType: ArrowScalarIterUDFType
+) -> Callable[[ArrowScalarIterFunction], UserDefinedFunctionLike]: ...
+@overload
+def arrow_udf(
+    *, returnType: Union[AtomicDataTypeOrString, ArrayType], functionType: ArrowScalarIterUDFType
+) -> Callable[[ArrowScalarIterFunction], UserDefinedFunctionLike]: ...
+@overload
+def arrow_udf(
+    f: Union[AtomicDataTypeOrString, ArrayType], *, functionType: ArrowScalarIterUDFType
+) -> Callable[[ArrowScalarIterFunction], UserDefinedFunctionLike]: ...
+@overload
 def pandas_udf(
     f: PandasScalarToScalarFunction,
     returnType: Union[AtomicDataTypeOrString, ArrayType],

python/pyspark/sql/pandas/typehints.py

@@ -26,6 +26,7 @@
         PandasScalarIterUDFType,
         PandasGroupedAggUDFType,
         ArrowScalarUDFType,
+        ArrowScalarIterUDFType,
     )
 
 
@@ -36,6 +37,7 @@ def infer_eval_type(
     "PandasScalarIterUDFType",
     "PandasGroupedAggUDFType",
     "ArrowScalarUDFType",
+    "ArrowScalarIterUDFType",
 ]:
     """
     Infers the evaluation type in :class:`pyspark.util.PythonEvalType` from
@@ -110,6 +112,21 @@ def infer_eval_type(
         )
     )
 
+    # Iterator[Tuple[pa.Array, ...] -> Iterator[pa.Array]
+    is_iterator_tuple_array = (
+        len(parameters_sig) == 1
+        and check_iterator_annotation(  # Iterator
+            parameters_sig[0],
+            parameter_check_func=lambda a: check_tuple_annotation(  # Tuple
+                a,
+                parameter_check_func=lambda ta: (ta == Ellipsis or ta == pa.Array),
+            ),
+        )
+        and check_iterator_annotation(
+            return_annotation, parameter_check_func=lambda a: a == pa.Array
+        )
+    )
+
     # Iterator[Series, Frame or Union[DataFrame, Series]] -> Iterator[Series or Frame]
     is_iterator_series_or_frame = (
         len(parameters_sig) == 1
@@ -128,6 +145,18 @@ def infer_eval_type(
         )
     )
 
+    # Iterator[pa.Array] -> Iterator[pa.Array]
+    is_iterator_array = (
+        len(parameters_sig) == 1
+        and check_iterator_annotation(
+            parameters_sig[0],
+            parameter_check_func=lambda a: (a == pd.Series or a == pa.Array),
+        )
+        and check_iterator_annotation(
+            return_annotation, parameter_check_func=lambda a: a == pa.Array
+        )
+    )
+
     # Series, Frame or Union[DataFrame, Series], ... -> Any
     is_series_or_frame_agg = all(
         a == pd.Series
@@ -152,6 +181,8 @@ def infer_eval_type(
         return ArrowUDFType.SCALAR
     elif is_iterator_tuple_series_or_frame or is_iterator_series_or_frame:
         return PandasUDFType.SCALAR_ITER
+    elif is_iterator_tuple_array or is_iterator_array:
+        return ArrowUDFType.SCALAR_ITER
     elif is_series_or_frame_agg:
         return PandasUDFType.GROUPED_AGG
     else: