Yicong-Huang commented on code in PR #53952:
URL: https://github.com/apache/spark/pull/53952#discussion_r2730516018
##########
python/pyspark/sql/conversion.py:
##########
@@ -63,17 +63,18 @@ class ArrowBatchTransformer:
"""
@staticmethod
- def flatten_struct(batch: "pa.RecordBatch") -> "pa.RecordBatch":
+ def flatten_struct(batch: "pa.RecordBatch", column_index: int = 0) ->
"pa.RecordBatch":
"""
- Flatten a single struct column into a RecordBatch.
+ Flatten a struct column at given index into a RecordBatch.
Used by:
- ArrowStreamUDFSerializer.load_stream
- GroupArrowUDFSerializer.load_stream
+ - ArrowStreamArrowUDTFSerializer.load_stream
"""
import pyarrow as pa
- struct = batch.column(0)
+ struct = batch.column(column_index)
return pa.RecordBatch.from_arrays(struct.flatten(),
schema=pa.schema(struct.type))
Review Comment:
I see your point. can we treat this as an intermediate state, to merge and
unblock later refactoring? At the end of the sequence of refactoring I can make
sure to clean up those too easy transformers.
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