zhengruifeng commented on code in PR #53992:
URL: https://github.com/apache/spark/pull/53992#discussion_r2777515521
##########
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:
@Yicong-Huang why adding `or runner_conf.use_legacy_pandas_udf_conversion`
here?
the `use_legacy_pandas_udf_conversion` is supposed to only take effect in
`SQL_ARROW_BATCHED_UDF`.
Suppose the eval type is `SQL_SCALAR_PANDAS_UDF` and the config
`use_legacy_pandas_udf_conversion` is true,
the `struct_in_pandas` was changed from `dict` -> `row`?
##########
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
+ )
+ else "dict"
+ )
+ ndarray_as_list = (
+ eval_type == PythonEvalType.SQL_ARROW_BATCHED_UDF
+ or runner_conf.use_legacy_pandas_udf_conversion
Review Comment:
ditto?
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