Yicong-Huang opened a new pull request, #57800:
URL: https://github.com/apache/spark/pull/57800
### What changes were proposed in this pull request?
Tighten the `mapInPandas` (`SQL_MAP_PANDAS_ITER_UDF`) return-value contract
in `worker.py` to require a strict `Iterator`, matching `mapInArrow`
(`SQL_MAP_ARROW_ITER_UDF`).
Previously the runtime check was `isinstance(result, Iterator) or
hasattr(result, "__iter__")`, which accepted any iterable (e.g. a returned
`list`). It is now `isinstance(result, Iterator)`; a non-`Iterator` iterable is
rejected with `UDF_RETURN_TYPE` ("iterator of
pandas.DataFrame"/"pandas.Series"). The comment explaining why
`verify_return_type` was not reused is removed accordingly.
### Why are the changes needed?
The declared signature has always been `PandasMapIterFunction =
Callable[[Iterator[DataFrameLike]], Iterator[DataFrameLike]]`, and the
`DataFrame.mapInPandas` docstring states the function "outputs an iterator of
pandas.DataFrames". The runtime, however, leniently accepted any iterable,
diverging from the documented contract. This aligns the runtime with the
declared `Iterator[...]` signature, mirroring the same tightening already done
for `mapInArrow` in SPARK-56612. It also unblocks reusing `verify_return_type`
at the `mapInPandas` site with `Iterator[...]` (SPARK-58598).
### Does this PR introduce _any_ user-facing change?
No. Minor tightening: a UDF returning a non-`Iterator` iterable (e.g.
`list`) is now rejected with `UDF_RETURN_TYPE`, aligning the runtime with the
documented `Iterator[...]` signature. This mirrors the `mapInArrow` tightening
in SPARK-56612.
### How was this patch tested?
Updated `test_pandas_map.py`: removed the "returning list of DataFrames"
positive case from `test_map_in_pandas`, and added a `list_not_iter` negative
case to `check_other_than_dataframe_iter` asserting a returned `list` is
rejected, mirroring `test_arrow_map.py::test_other_than_recordbatch_iter`. Ran
`pyspark.sql.tests.pandas.test_pandas_map` and the Connect parity suite.
### Was this patch authored or co-authored using generative AI tooling?
No
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