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…and constrained numpy support Add cross-language compatible type normalization to CacheKeyGenerator._normalize(): - Path/PurePath: .as_posix() for cross-platform consistency - UUID: str(uuid) standard format - Decimal: str(decimal) exact representation - Enum: .value (recursive normalization) - datetime: .isoformat() (UTC only, naive rejected) - numpy.ndarray: 1D, ≤100KB, i32/i64/f32/f64 dtypes Array constraints (per security review): - 256-bit Blake2b hash (collision resistance) - Little-endian byte order (cross-platform determinism) - 100KB per-array limit, 5MB aggregate limit (DoS prevention) - Version prefix __array_v1__ for protocol evolution Add custom key= parameter escape hatch for complex types: - 2D arrays, DataFrames, large data, custom objects - Integrated in both sync and async wrapper paths Rejected by design: - set/frozenset (mixed-type sorting crashes) - pandas.DataFrame (Parquet non-deterministic)
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- Add TestKeyNormalization: long key shortening, special char replacement - Add integration tests for custom key= parameter with L1-only mode - Test custom key caching behavior, numpy array support, error handling - Test async custom key function - key_generator.py coverage: 93% → 96% - wrapper.py coverage: 5% → 33%
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Summary
CacheKeyGenerator._normalize()with cross-language compatible type supportkey=parameter escape hatch for complex typesSupported Types
Path.as_posix()UUIDstr(uuid)Decimalstr(decimal)Enum.value(recursive)datetime.isoformat()(UTC only)numpy.ndarrayArray Constraints (per security review)
__array_v1__- protocol evolutionRejected by Design
set/frozenset- mixed-type sorting crashespandas.DataFrame- Parquet serialization is non-deterministicEscape Hatch
Test plan
make quick-checkpasses