[WIP] POC for serializer changes #54075
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Extract the struct flattening/wrapping logic from ArrowStreamUDFSerializer into reusable transformers in a new transformers.py module.
…use-flatten-struct' into SPARK-55176/refactor/extract-arrow-to-pandas-converter
Resolved conflicts by: - Adopting create_converter architecture while keeping arrow_to_pandas compatibility - Using _load_group_dataframes helper from upstream - Removing GroupArrowUDFSerializer (not in upstream) - Keeping input_type (singular) parameter name
…er/mapper layers
This refactoring separates concerns between serialization and data transformation:
- Serializers now only handle data serialization/deserialization
- Wrappers handle UDF wrapping and data format conversion
- Mappers handle UDF result aggregation using transformer utilities
Key changes:
1. Simplified serializer hierarchy by removing redundant classes:
- Removed ArrowStreamPandasUDFSerializer, ArrowStreamGroupUDFSerializer,
ArrowStreamArrowUDFSerializer, ArrowStreamUDTFSerializer
- Renamed ArrowStreamMapIterSerializer to ArrowStreamGroupSerializer
- Unified grouped/non-grouped UDF handling in ArrowStreamGroupSerializer
2. Introduced transformer utility classes:
- ArrowBatchTransformer: Arrow batch operations (wrap_struct, flatten_struct,
partial_batch, partial_table, concat_batches, merge_batches, reorder_columns)
- PandasBatchTransformer: Pandas/Arrow conversions (to_arrow, concat_series_batches)
3. Moved data transformations from serializers to wrappers/mappers:
- Moved to_arrow conversion from mappers to wrappers for Pandas agg UDFs
- Wrappers now return RecordBatch directly instead of (result, arrow_type) tuples
- Mappers simplified to use transformer methods for common operations
Benefits:
- Clearer separation of concerns
- Reduced code duplication through transformer utilities
- Easier to maintain and extend
- Consistent data format handling across UDF types
- Use ArrowBatchTransformer.zip_batches for type coercion instead of manual loop - Simplify error handling logic - Handle empty struct case properly - Unwrap wrapped batches from worker before type coercion, then wrap back for JVM - All 46/47 UDTF tests pass (1 known failure unrelated to this change)
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This commit fixes several issues with pandas UDF handling after the
serializer refactoring:
1. Fix parameter initialization order in read_udfs():
- Move pandas_udf_* parameter defaults BEFORE the if-elif chain
- Previously they were reset AFTER being set, causing df_for_struct
to always be False for scalar pandas UDFs
2. Add struct_in_pandas="dict" to scalar UDF wrappers:
- wrap_scalar_pandas_udf: enables DataFrame→struct array conversion
- wrap_pandas_batch_iter_udf: same fix for iter variant
3. Fix grouped map UDF column matching:
- Use assign_cols_by_name to match DataFrame columns by name when
available, otherwise by position
- Handle empty DataFrame (0 columns) by creating empty struct array
4. Fix Arrow batch handling:
- zip_batches: convert items to list for pa.RecordBatch.from_arrays
- mapper: special handling for SQL_ARROW_BATCHED_UDF to return raw
result instead of calling zip_batches
5. Fix error handling in create_array:
- Only catch ArrowInvalid for arrow_cast retry (not ArrowTypeError)
- Add ArrowTypeError to TypeError handler for proper error messages
- Update error message format to match expected test output
All 264+ pandas UDF tests pass including struct type tests.
Remove timezone, int_to_decimal_coercion_enabled, and assign_cols_by_name parameters that were stored but never used by the serializer or subclasses.
…to-pandas conversion - Extract common verification functions: verify_result_length, verify_result_type, verify_is_iterable, verify_element_type - Simplify wrapper functions using common verification utilities - Centralize Arrow-to-pandas conversion in read_udfs mapper/func - Remove unused pandas_udf_* variables from read_udfs - Fix is_scalar_pandas_iter to not convert Arrow iter UDFs to pandas
- Separate iter UDF branches by type for clarity (scalar pandas/arrow, map pandas, map arrow) - Wrapper functions now return (result, arrow_return_type) tuples, with output conversion centralized in mapper/func - Grouped map pandas UDFs now receive Iterator[DataFrame] directly instead of Iterator[List[Series]] - Inline concat_series_batches and series_batches_to_dataframe methods
…implify-serializers
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