Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/19592#discussion_r147784119
--- Diff: python/pyspark/worker.py ---
@@ -105,8 +105,14 @@ def read_single_udf(pickleSer, infile, eval_type):
elif eval_type == PythonEvalType.SQL_PANDAS_GROUPED_UDF:
# a groupby apply udf has already been wrapped under apply()
return arg_offsets, row_func
- else:
+ elif eval_type == PythonEvalType.SQL_BATCHED_UDF:
return arg_offsets, wrap_udf(row_func, return_type)
+ elif eval_type == PythonEvalType.SQL_BATCHED_OPT_UDF:
--- End diff --
Would it be possible to do this type of wrapping in `BatchEvalPython`, and
remove the need to add another eval_type? If so then you could just the
true/false result as is and not have to add anything in python. I think that
would reduce the scope of this and simplify things a bit.
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