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[SPARK-56781][PYTHON] Refactor SQL_GROUPED_AGG_PANDAS_UDF #55808

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[SPARK-56781][PYTHON] Refactor SQL_GROUPED_AGG_PANDAS_UDF #55808

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73 changes: 48 additions & 25 deletions python/pyspark/worker.py
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Original file line number Diff line number Diff line change
Expand Up @@ -726,21 +726,6 @@ def verify_element(result):
return lambda k, v, s: [(wrapped(k, v, s), return_type)]


def wrap_grouped_agg_pandas_udf(f, args_offsets, kwargs_offsets, return_type, runner_conf):
func, args_kwargs_offsets = wrap_kwargs_support(f, args_offsets, kwargs_offsets)

def wrapped(*series):
import pandas as pd

result = func(*series)
return pd.Series([result])

return (
args_kwargs_offsets,
lambda *a: (wrapped(*a), return_type),
)


def wrap_grouped_agg_pandas_iter_udf(f, args_offsets, kwargs_offsets, return_type, runner_conf):
func, args_kwargs_offsets = wrap_kwargs_support(f, args_offsets, kwargs_offsets)

Expand Down Expand Up @@ -1031,11 +1016,8 @@ def read_single_udf(pickleSer, udf_info, eval_type, runner_conf, udf_index):
elif eval_type == PythonEvalType.SQL_COGROUPED_MAP_ARROW_UDF:
argspec = inspect.getfullargspec(chained_func) # signature was lost when wrapping it
return func, args_offsets, return_type, len(argspec.args)
elif eval_type == PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF:
return wrap_grouped_agg_pandas_udf(
func, args_offsets, kwargs_offsets, return_type, runner_conf
)
elif eval_type in (
PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF,
PythonEvalType.SQL_GROUPED_AGG_ARROW_UDF,
PythonEvalType.SQL_GROUPED_AGG_ARROW_ITER_UDF,
):
Expand Down Expand Up @@ -2239,14 +2221,14 @@ def read_udfs(pickleSer, udf_info_list, eval_type, runner_conf, eval_conf):
PythonEvalType.SQL_GROUPED_MAP_PANDAS_ITER_UDF,
PythonEvalType.SQL_GROUPED_MAP_ARROW_UDF,
PythonEvalType.SQL_GROUPED_MAP_ARROW_ITER_UDF,
PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF,
PythonEvalType.SQL_GROUPED_AGG_ARROW_UDF,
PythonEvalType.SQL_GROUPED_AGG_ARROW_ITER_UDF,
):
ser = ArrowStreamGroupSerializer(write_start_stream=True)
elif eval_type == PythonEvalType.SQL_WINDOW_AGG_ARROW_UDF:
ser = ArrowStreamGroupSerializer(write_start_stream=True)
elif eval_type in (
PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF,
PythonEvalType.SQL_GROUPED_AGG_PANDAS_ITER_UDF,
PythonEvalType.SQL_WINDOW_AGG_PANDAS_UDF,
):
Expand Down Expand Up @@ -2572,6 +2554,50 @@ def grouped_func(
# profiling is not supported for UDF
return grouped_func, None, ser, ser

if eval_type == PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF:
import pyarrow as pa
import pandas as pd

col_names = ["_%d" % i for i in range(len(udfs))]
output_schema = StructType(
[StructField(name, rt) for name, (_, _, _, rt) in zip(col_names, udfs)]
)

def grouped_func(
split_index: int, data: Iterator["GroupedBatch"]
) -> Iterator[pa.RecordBatch]:
for group in data:
batch_list = list(group)
if not batch_list:
continue
table = pa.Table.from_batches(batch_list).combine_chunks()
all_series = ArrowBatchTransformer.to_pandas(
table,
timezone=runner_conf.timezone,
prefer_int_ext_dtype=runner_conf.prefer_int_ext_dtype,
)
results = [
udf_func(
*[all_series[o] for o in args_offsets],
**{k: all_series[v] for k, v in kwargs_offsets.items()},
)
for udf_func, args_offsets, kwargs_offsets, _ in udfs
]
result_series = [pd.Series([r]) for r in results]
yield PandasToArrowConversion.convert(
result_series,
output_schema,
timezone=runner_conf.timezone,
safecheck=runner_conf.safecheck,
arrow_cast=True,
prefers_large_types=runner_conf.use_large_var_types,
assign_cols_by_name=runner_conf.assign_cols_by_name,
int_to_decimal_coercion_enabled=runner_conf.int_to_decimal_coercion_enabled,
)

# profiling is not supported for UDF
return grouped_func, None, ser, ser

if eval_type == PythonEvalType.SQL_WINDOW_AGG_ARROW_UDF:
import pyarrow as pa

Expand Down Expand Up @@ -3523,13 +3549,10 @@ def mapper(batch_iter):
)
return f(series_iter)

elif eval_type in (
PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF,
PythonEvalType.SQL_WINDOW_AGG_PANDAS_UDF,
):
elif eval_type == PythonEvalType.SQL_WINDOW_AGG_PANDAS_UDF:
import pandas as pd

# For SQL_GROUPED_AGG_PANDAS_UDF and SQL_WINDOW_AGG_PANDAS_UDF,
# For SQL_WINDOW_AGG_PANDAS_UDF,
# convert iterator of batch tuples to concatenated pandas Series
def mapper(batch_iter):
# batch_iter is Iterator[Tuple[pd.Series, ...]] where each tuple represents one batch
Expand Down