uros-b commented on code in PR #56754:
URL: https://github.com/apache/spark/pull/56754#discussion_r3470718370
##########
python/pyspark/worker.py:
##########
@@ -3621,30 +3636,6 @@ def mapper(a):
return f(keys, vals, state)
- elif eval_type == PythonEvalType.SQL_GROUPED_AGG_PANDAS_ITER_UDF:
- # We assume there is only one UDF here because grouped agg doesn't
- # support combining multiple UDFs.
- assert num_udfs == 1
-
- arg_offsets, f = udfs[0]
-
- # Convert to iterator of pandas Series:
- # - Iterator[pd.Series] for single column
- # - Iterator[Tuple[pd.Series, ...]] for multiple columns
- def mapper(batch_iter):
- # batch_iter is Iterator[Tuple[pd.Series, ...]] where each tuple
represents one batch
- # Convert to Iterator[pd.Series] or Iterator[Tuple[pd.Series,
...]] based on arg_offsets
- if len(arg_offsets) == 1:
- # Single column: Iterator[Tuple[pd.Series, ...]] ->
Iterator[pd.Series]
- series_iter = (batch_series[arg_offsets[0]] for batch_series
in batch_iter)
- else:
- # Multiple columns: Iterator[Tuple[pd.Series, ...]] ->
- # Iterator[Tuple[pd.Series, ...]]
- series_iter = (
- tuple(batch_series[o] for o in arg_offsets) for
batch_series in batch_iter
- )
- return f(series_iter)
-
else:
Review Comment:
Note: no test exercises useLargeVarTypes=true for
SQL_GROUPED_AGG_PANDAS_ITER (none existed before either, so not a regression in
coverage). Given the above delta, a targeted test would be worthwhile.
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