Yicong-Huang commented on code in PR #53992:
URL: https://github.com/apache/spark/pull/53992#discussion_r2779963075
##########
python/pyspark/worker.py:
##########
@@ -2847,10 +2788,19 @@ def read_udfs(pickleSer, infile, eval_type,
runner_conf):
or eval_type == PythonEvalType.SQL_MAP_PANDAS_ITER_UDF
)
# Arrow-optimized Python UDF takes a struct type argument as a Row
+ # When legacy pandas conversion is enabled, use "row" and convert
ndarray to list
struct_in_pandas = (
- "row" if eval_type == PythonEvalType.SQL_ARROW_BATCHED_UDF
else "dict"
+ "row"
+ if (
+ eval_type == PythonEvalType.SQL_ARROW_BATCHED_UDF
+ or runner_conf.use_legacy_pandas_udf_conversion
Review Comment:
Good catch! You're right.. the `or
runner_conf.use_legacy_pandas_udf_conversion` is redundant here. When
`use_legacy_pandas_udf_conversion=True` and `eval_type=SQL_ARROW_BATCHED_UDF`,
the earlier elif condition (line 2759) doesn't match, so it falls through to
the else branch and the or clause is unnecessary for that case.
And as you pointed out, it has an unintended side effect: if
`use_legacy_pandas_udf_conversion=True` while eval_type is something else
(e.g., `SQL_SCALAR_PANDAS_UDF`), it would incorrectly change `struct_in_pandas`
from "dict" to "row".
I've created a follow-up PR to fix this: #54212
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