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fix(arrow): reject dict values that pyarrow stores as the wrong column (LAB-7490) - #459
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…n (LAB-7490) pa.table iterates every dict value as a column, so a str value became a column of its characters, bytes or bytearray a column of byte values, and a dict a column of its keys with its values dropped, all without an error. Through @cache the next call then returned that wrong DataFrame as a hit. _to_table now raises TypeError for str, bytes, bytearray and Mapping values before calling pa.table. Both serialize() and serialize_to_sink() go through _to_table, so both paths reject them, and the streaming path writes nothing to the sink. Dicts of lists, NumPy arrays, pandas Series and pyarrow arrays are unaffected. The test that asserted a nested dict serializes is replaced by tests for each rejected and accepted shape on both paths, plus a decorator test on a File backend. docs/serializers/arrow.md lists these values as unsupported and its type-checking example quotes the message the serializer raises.
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WalkthroughThe Arrow serializer now validates dictionary column values before converting them to a table. Documentation and tests describe and verify accepted values, rejected values, streaming behaviour, and cache handling after serialization fails. ChangesArrow dictionary-column validation
Priority: ⬇️ Low Estimated code review effort: 2 (Simple) | ~10 minutes Change: Bug fix Merge Risk: 🔵 Low · up to The serializer change is mergeable with a documentation correction: the current wording incorrectly rules out a supported column input. Security Architecture ReviewSecurity architecture risk: 🔵 Low · up to The change rejects inputs that previously produced incorrectly shaped cached data. Reviewed write paths contain rejection without publishing partial entries, and no new access or privilege boundary was identified. Existing incorrect cache entries are not repaired by this validation, and deployment-specific invalidation behavior remains unverified. Retained concerns Security review detailsSecurity Blast Radius
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…(LAB-7490) pa.table converts a value through __arrow_array__ or __arrow_c_array__ before it tries to iterate it, so a Mapping that implements either one is stored as the column it describes, not as its keys. The new value guard rejected it anyway, which broke a shape that round-tripped before. Skip the str/bytes/bytearray/Mapping rejection when the value exposes an Arrow array protocol. A test round-trips a Mapping adapter for each protocol on both the buffered and streaming paths; its keys differ from its column, so a wrong conversion would fail the test.
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Review comments at @docs/serializers/arrow.md:
- Line 104: Update the accepted column types description in the Arrow serializer
documentation to describe values as Arrow-convertible columns, including
supported mappings with an Arrow array protocol and typed memoryviews. Identify
the types that are rejected, while preserving the guidance about flattening
nested dictionaries or using AutoSerializer.
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…-7490) The sentence said every dict value must be a list or an array, which excluded inputs the serializer accepts and round-trips: tuples, typed memoryviews, and Mapping adapters that implement the Arrow array protocol.
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What
ArrowSerializer.serialize()andserialize_to_sink()now raiseTypeErrorfor a dict with astr,bytes,bytearrayor dict value, instead of storing a wrong table without an error. Dicts of lists, NumPy arrays, pandas Series and pyarrow arrays work as before.Why
_to_tablepassed any dict topa.table, which iterates every value as a column:Through
@cache(serializer="arrow", backend=FileBackend(...)), the second call was a hit that returned that DataFrame instead of running the function. A dict with a number, bool orNonevalue already raisedTypeError, so the validation was incomplete rather than absent.How
_to_table: beforepa.table(obj), anisinstance(value, (str, bytes, bytearray, Mapping))check over the dict's values raises inside the existingtry, so the existingTypeErrorwrapper produces the message. The message names the key and its type, for exampleGot a dict that is not convertible to an Arrow table: value for 'name' is str, not a list or array.Both write paths call_to_tablefirst, so the streaming path rejects the value before it writes a byte to the sink.__arrow_array__or__arrow_c_array__) stays accepted even if it is astr,bytesorMapping: pyarrow converts it through the protocol instead of iterating it, so it is stored as the column it describes.memoryviewvalues (a memoryview over a typed buffer round-trips correctly today, and a byte memoryview cannot be told apart from auint8buffer).docs/serializers/arrow.md: the "Not supported" list adds dicts with a string, bytes or dict value, the sentence that said such values are accepted but stored wrong is gone, and the type-checking example's comment quotes text the raisedTypeErrorcontains (it quoted a message the serializer never produced).Tests
test_non_columnar_dict_successfully_serialized, which asserted that{"key": {"nested": "value"}}serializes, is replaced bytest_non_column_dict_value_raises_type_error:str,bytes,bytearray, dict, and one bad column next to a good one, each on both paths.test_dict_of_columns_round_trips_on_both_paths: list, NumPy, Series and pyarrow columns (ints and strings) round-trip, buffered and streamed.test_mapping_with_arrow_array_protocol_round_trips: aMappingadapter for each protocol round-trips on both paths. Its keys differ from its column, so iterating it instead would fail the test.test_rejected_dict_writes_nothing_to_sink, andtest_decorator_reruns_instead_of_returning_a_wrong_hit: through@cacheon aFileBackendintmp_path, the second call runs the function and returns the original dict.mainand pass here. The 4 adapter tests fail without the protocol exemption.uv run pytest tests/unit/test_arrow_serializer.py: 82 passed, 2 skipped.uv run pytest tests/docs/: 70 passed.uv run pytest --markdown-docs README.md docs/: 133 passed.uv run pytest tests/unit tests/critical -m "not slow": 4103 passed, 25 skipped. Serializer, encryption-composability and File backend integration tests: 38 passed.ruff check,ruff format --checkand basedpyright are clean.