zhengruifeng commented on code in PR #53035:
URL: https://github.com/apache/spark/pull/53035#discussion_r2525599083
##########
python/pyspark/sql/pandas/functions.py:
##########
@@ -301,6 +303,66 @@ def calculate(iterator: Iterator[pa.Array]) ->
Iterator[pa.Array]:
Therefore, mutating the input arrays is not allowed and will cause
incorrect results.
For the same reason, users should also not rely on the index of
the input arrays.
+ * Iterator of Arrays to Scalar
+ `Iterator[pyarrow.Array]` -> `Any`
+
+ The function takes an iterator of `pyarrow.Array` and returns a scalar
value. This is
+ useful for grouped aggregations where the UDF can process all batches
for a group
+ iteratively, which is more memory-efficient than loading all data at
once. The returned
+ scalar can be a python primitive type, a numpy data type, or a
`pyarrow.Scalar` instance.
+
+ >>> import pandas as pd
+ >>> from typing import Iterator
+ >>> @arrow_udf("double")
+ ... def arrow_mean(it: Iterator[pa.Array]) -> float:
+ ... sum_val = 0.0
+ ... cnt = 0
+ ... for v in it:
+ ... assert isinstance(v, pa.Array)
+ ... sum_val += pa.compute.sum(v).as_py()
+ ... cnt += len(v)
+ ... return sum_val / cnt
+ ...
+ >>> df = spark.createDataFrame(
+ ... [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)], ("id",
"v"))
+ >>> df.groupby("id").agg(arrow_mean(df['v'])).show() # doctest: +SKIP
Review Comment:
do not skip the doctests
##########
python/pyspark/sql/pandas/functions.py:
##########
@@ -301,6 +303,66 @@ def calculate(iterator: Iterator[pa.Array]) ->
Iterator[pa.Array]:
Therefore, mutating the input arrays is not allowed and will cause
incorrect results.
For the same reason, users should also not rely on the index of
the input arrays.
+ * Iterator of Arrays to Scalar
+ `Iterator[pyarrow.Array]` -> `Any`
+
+ The function takes an iterator of `pyarrow.Array` and returns a scalar
value. This is
+ useful for grouped aggregations where the UDF can process all batches
for a group
+ iteratively, which is more memory-efficient than loading all data at
once. The returned
+ scalar can be a python primitive type, a numpy data type, or a
`pyarrow.Scalar` instance.
+
+ >>> import pandas as pd
+ >>> from typing import Iterator
+ >>> @arrow_udf("double")
+ ... def arrow_mean(it: Iterator[pa.Array]) -> float:
+ ... sum_val = 0.0
+ ... cnt = 0
+ ... for v in it:
+ ... assert isinstance(v, pa.Array)
+ ... sum_val += pa.compute.sum(v).as_py()
+ ... cnt += len(v)
+ ... return sum_val / cnt
+ ...
+ >>> df = spark.createDataFrame(
+ ... [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)], ("id",
"v"))
+ >>> df.groupby("id").agg(arrow_mean(df['v'])).show() # doctest: +SKIP
+ +---+---------------+
+ | id|arrow_mean(v) |
+ +---+---------------+
+ | 1| 1.5|
+ | 2| 6.0|
+ +---+---------------+
+
+ * Iterator of Multiple Arrays to Scalar
+ `Iterator[Tuple[pyarrow.Array, ...]]` -> `Any`
+
+ The function takes an iterator of a tuple of multiple `pyarrow.Array`
and returns a
+ scalar value. This is useful for grouped aggregations with multiple
input columns.
+
+ >>> from typing import Iterator, Tuple
+ >>> import numpy as np
+ >>> @arrow_udf("double")
+ ... def arrow_weighted_mean(it: Iterator[Tuple[pa.Array, pa.Array]])
-> float:
+ ... weighted_sum = 0.0
+ ... weight = 0.0
+ ... for v, w in it:
+ ... assert isinstance(v, pa.Array)
+ ... assert isinstance(w, pa.Array)
+ ... weighted_sum += np.dot(v, w)
+ ... weight += pa.compute.sum(w).as_py()
+ ... return weighted_sum / weight
+ ...
+ >>> df = spark.createDataFrame(
+ ... [(1, 1.0, 1.0), (1, 2.0, 2.0), (2, 3.0, 1.0), (2, 5.0, 2.0),
(2, 10.0, 3.0)],
+ ... ("id", "v", "w"))
+ >>> df.groupby("id").agg(arrow_weighted_mean(df["v"], df["w"])).show()
# doctest: +SKIP
+ +---+---------------------------------+
+ | id|arrow_weighted_mean(v, w) |
+ +---+---------------------------------+
+ | 1| 1.6666666666666667|
+ | 2| 7.166666666666667|
Review Comment:
```suggestion
| 1| 1.6666666666666...|
| 2| 7.166666666666...|
```
##########
python/pyspark/sql/pandas/functions.py:
##########
@@ -301,6 +303,66 @@ def calculate(iterator: Iterator[pa.Array]) ->
Iterator[pa.Array]:
Therefore, mutating the input arrays is not allowed and will cause
incorrect results.
For the same reason, users should also not rely on the index of
the input arrays.
+ * Iterator of Arrays to Scalar
+ `Iterator[pyarrow.Array]` -> `Any`
+
+ The function takes an iterator of `pyarrow.Array` and returns a scalar
value. This is
+ useful for grouped aggregations where the UDF can process all batches
for a group
+ iteratively, which is more memory-efficient than loading all data at
once. The returned
+ scalar can be a python primitive type, a numpy data type, or a
`pyarrow.Scalar` instance.
+
+ >>> import pandas as pd
+ >>> from typing import Iterator
+ >>> @arrow_udf("double")
+ ... def arrow_mean(it: Iterator[pa.Array]) -> float:
+ ... sum_val = 0.0
+ ... cnt = 0
+ ... for v in it:
+ ... assert isinstance(v, pa.Array)
+ ... sum_val += pa.compute.sum(v).as_py()
+ ... cnt += len(v)
+ ... return sum_val / cnt
+ ...
+ >>> df = spark.createDataFrame(
+ ... [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)], ("id",
"v"))
+ >>> df.groupby("id").agg(arrow_mean(df['v'])).show() # doctest: +SKIP
+ +---+---------------+
+ | id|arrow_mean(v) |
+ +---+---------------+
+ | 1| 1.5|
+ | 2| 6.0|
+ +---+---------------+
+
+ * Iterator of Multiple Arrays to Scalar
+ `Iterator[Tuple[pyarrow.Array, ...]]` -> `Any`
+
+ The function takes an iterator of a tuple of multiple `pyarrow.Array`
and returns a
+ scalar value. This is useful for grouped aggregations with multiple
input columns.
+
+ >>> from typing import Iterator, Tuple
+ >>> import numpy as np
+ >>> @arrow_udf("double")
+ ... def arrow_weighted_mean(it: Iterator[Tuple[pa.Array, pa.Array]])
-> float:
+ ... weighted_sum = 0.0
+ ... weight = 0.0
+ ... for v, w in it:
+ ... assert isinstance(v, pa.Array)
+ ... assert isinstance(w, pa.Array)
+ ... weighted_sum += np.dot(v, w)
+ ... weight += pa.compute.sum(w).as_py()
+ ... return weighted_sum / weight
+ ...
+ >>> df = spark.createDataFrame(
+ ... [(1, 1.0, 1.0), (1, 2.0, 2.0), (2, 3.0, 1.0), (2, 5.0, 2.0),
(2, 10.0, 3.0)],
+ ... ("id", "v", "w"))
+ >>> df.groupby("id").agg(arrow_weighted_mean(df["v"], df["w"])).show()
# doctest: +SKIP
Review Comment:
ditto
##########
python/pyspark/sql/pandas/serializers.py:
##########
@@ -1200,6 +1200,72 @@ def __repr__(self):
return "ArrowStreamAggArrowUDFSerializer"
+# Serializer for SQL_GROUPED_AGG_ARROW_ITER_UDF
+class ArrowStreamAggArrowIterUDFSerializer(ArrowStreamArrowUDFSerializer):
Review Comment:
we should consolidate it with `ArrowStreamAggArrowUDFSerializer`: make
`ArrowStreamAggArrowUDFSerializer` output the iterator and adjust the wrapper
of `SQL_GROUPED_AGG_ARROW_UDF` and `SQL_WINDOW_AGG_ARROW_UDF`
##########
python/pyspark/sql/pandas/functions.py:
##########
@@ -301,6 +303,66 @@ def calculate(iterator: Iterator[pa.Array]) ->
Iterator[pa.Array]:
Therefore, mutating the input arrays is not allowed and will cause
incorrect results.
For the same reason, users should also not rely on the index of
the input arrays.
+ * Iterator of Arrays to Scalar
+ `Iterator[pyarrow.Array]` -> `Any`
+
+ The function takes an iterator of `pyarrow.Array` and returns a scalar
value. This is
+ useful for grouped aggregations where the UDF can process all batches
for a group
+ iteratively, which is more memory-efficient than loading all data at
once. The returned
+ scalar can be a python primitive type, a numpy data type, or a
`pyarrow.Scalar` instance.
+
+ >>> import pandas as pd
Review Comment:
pandas is not used?
##########
python/pyspark/sql/pandas/serializers.py:
##########
@@ -1200,6 +1200,72 @@ def __repr__(self):
return "ArrowStreamAggArrowUDFSerializer"
+# Serializer for SQL_GROUPED_AGG_ARROW_ITER_UDF
+class ArrowStreamAggArrowIterUDFSerializer(ArrowStreamArrowUDFSerializer):
+ def __init__(
+ self,
+ timezone,
+ safecheck,
+ assign_cols_by_name,
+ arrow_cast,
+ ):
+ super().__init__(
+ timezone=timezone,
+ safecheck=safecheck,
+ assign_cols_by_name=False,
+ arrow_cast=True,
+ )
+ self._timezone = timezone
+ self._safecheck = safecheck
+ self._assign_cols_by_name = assign_cols_by_name
+ self._arrow_cast = arrow_cast
+
+ def load_stream(self, stream):
+ """
+ Yield column iterators instead of concatenating batches.
+ Each group yields a structure where indexing by column offset gives an
iterator of arrays.
+ """
+ import pyarrow as pa
+
+ dataframes_in_group = None
+
+ while dataframes_in_group is None or dataframes_in_group > 0:
+ dataframes_in_group = read_int(stream)
+
+ if dataframes_in_group == 1:
+ batches = list(ArrowStreamSerializer.load_stream(self, stream))
Review Comment:
we should not load all batches in a group, this new API is designed to
process each group in an incremental approach
##########
python/pyspark/sql/pandas/typehints.py:
##########
@@ -226,6 +234,41 @@ def infer_arrow_eval_type(
if is_iterator_array:
return ArrowUDFType.SCALAR_ITER
+ # Iterator[Tuple[pa.Array, ...]] -> Any
Review Comment:
let's move the new inference after `pa.Array, ... -> Any`
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