Yicong-Huang commented on code in PR #54125:
URL: https://github.com/apache/spark/pull/54125#discussion_r2800826789
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python/pyspark/sql/pandas/serializers.py:
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@@ -816,17 +626,24 @@ def dump_stream(self, iterator, stream):
"""
import pyarrow as pa
- def create_batches():
- for packed in iterator:
- if len(packed) == 2 and isinstance(packed[1], pa.DataType):
- # single array UDF in a projection
- arrs = [self._create_array(packed[0], packed[1],
self._arrow_cast)]
- else:
- # multiple array UDFs in a projection
- arrs = [self._create_array(t[0], t[1], self._arrow_cast)
for t in packed]
- yield pa.RecordBatch.from_arrays(arrs, ["_%d" % i for i in
range(len(arrs))])
+ def create_batch(
+ packed: Union[
+ Tuple["pa.Array", "pa.DataType"],
+ List[Tuple["pa.Array", "pa.DataType"]],
Review Comment:
Same as above — added a local `normalize` helper so `create_batch` always
receives a list of `(arr, type)` tuples. Unifying the callsites to always
produce the multi-UDF form will be done when we extract the logic to each eval
type.
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