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fix: Ambiguous truth value of array during materialization - #6259
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ntkathole merged 2 commits intoApr 14, 2026
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| return [ProtoValue(unix_timestamp_val=ts) for ts in int_timestamps] # type: ignore | ||
| out = [] | ||
| for value in values: | ||
| if isinstance(value, np.ndarray) or ( |
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let's extract this into small helper and use it everywhere to avoid duplication:
def _is_array_like(value: Any) -> bool:
return isinstance(value, np.ndarray) or (
hasattr(value, "__len__") and not isinstance(value, (str, bytes))
)
ntkathole
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| out.append(ProtoValue()) | ||
| else: | ||
| (ts,) = _python_datetime_to_int_timestamp([value]) | ||
| out.append(ProtoValue(unix_timestamp_val=ts)) # type: ignore |
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if else logic is going through all rows, better to pre filter first:
if feast_value_type == ValueType.UNIX_TIMESTAMP:
out = [None] * len(values)
clean_indices = []
clean_values = []
for i, value in enumerate(values):
if _is_array_like(value) or value is None:
out[i] = ProtoValue()
else:
clean_indices.append(i)
clean_values.append(value)
if clean_values:
timestamps = _python_datetime_to_int_timestamp(clean_values)
for i, ts in zip(clean_indices, timestamps):
out[i] = ProtoValue(unix_timestamp_val=ts)
return out
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Thanks, I implemented your solution
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@alan-gauthier-jt I think even better if we fix this at https://github.com/feast-dev/feast/blob/master/sdk/python/feast/type_map.py by adding logic for scalar columns, skip array-like values when picking a sample. |
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Thanks @alan-gauthier-jt looks good
Signed-off-by: Alan Gauthier <alan.gauthier@jobteaser.com>
Signed-off-by: Alan Gauthier <alan.gauthier@jobteaser.com>
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# [0.63.0](v0.62.0...v0.63.0) (2026-05-04) ### Bug Fixes * Add project filter to apply_data_source and delete_data_source (closes [#6206](#6206)) ([#6322](#6322)) ([96562c4](96562c4)) * Add project_id filter to SnowflakeRegistry UPDATE path ([#6243](#6243)) ([6658b71](6658b71)), closes [#6208](#6208) [#6208](#6208) * Add subprocess timeouts to prevent test_e2e_local hanging on Dask atexit handler ([3de6556](3de6556)) * Ambiguous truth value of array during materialization ([#6259](#6259)) ([d0c8984](d0c8984)) * Auto-detect GCS/S3 registry store when registry is passed as string ([#6260](#6260)) ([7ebcf03](7ebcf03)) * **bigquery:** Prefer query over table in get_table_query_string ([#6360](#6360)) ([77ed779](77ed779)), closes [#6200](#6200) * correct project_id scoping in get_user_metadata and delete_project ([0c469a7](0c469a7)) * disable Redis RDB persistence in test deployments ([44cd682](44cd682)) * Disable snowflake tests temporarily in CI ([#6356](#6356)) ([31d5a98](31d5a98)) * Filter empty SQL commands at execute_snowflake_statement call sites ([#6249](#6249)) ([92ffbb9](92ffbb9)) * Fix five bugs in milvus online store ([#6275](#6275)) ([212504b](212504b)) * Fix issue with apply feature view ([835cda8](835cda8)) * Fix streaming materialization for exotic sources with lazy UDF pipelines ([c07972d](c07972d)) * Handle missing features gracefully instead of panicking ([7d00b3a](7d00b3a)) * Harden informer cache with label selectors and memory optimizations ([#6242](#6242)) ([3f11356](3f11356)) * **helm:** Avoid nil pointer for metrics.enabled inside podAnnotations ([#6251](#6251)) ([c833f1a](c833f1a)) * Include git in feast server image ([fb03c46](fb03c46)) * Include StreamFeatureView in freshness metric ([#6269](#6269)) ([463f16c](463f16c)) * Pre-create S3A event log dir before SparkContext init ([#6317](#6317)) ([9feca77](9feca77)) * Remote Online Store Type Inference Error with All-NULL Columns ([#6063](#6063)) ([de67bdd](de67bdd)) * Remove selector with kustomize overlay using a JSON 6902 patch ([9107a43](9107a43)) * Resolve multiple bugs in SnowflakeRegistry and Snowflake connection handling ([#6315](#6315)) ([7e66a2e](7e66a2e)) * **spark:** BatchFeatureView with TransformationMode.PYTHON now reads all source columns ([a310eaf](a310eaf)) * **spark:** Use SELECT * when feature_name_columns is empty in pull_all_from_table_or_query ([e1b1d2d](e1b1d2d)) * Support pandas mode in feature builder and fix dask column extraction ([863315e](863315e)) * support SQL string as entity_df in RemoteOfflineStore.get_historical_features ([c559889](c559889)) * Wrap LocalOutputNode return value in ArrowTableValue for consist… ([#6286](#6286)) ([a16cd55](a16cd55)) ### Features * Add agent skills and Cursor/Claude rules for Feast development ([312eea3](312eea3)) * Add feature view versioning support to FAISS online store ([b36acb7](b36acb7)) * Add feature view versioning support to Redis and DynamoDB online stores ([#6257](#6257)) ([edf25af](edf25af)), closes [#6164](#6164) [#6163](#6163) * Add optional 'org' in feature view ([#6288](#6288)) ([#6301](#6301)) ([608b105](608b105)) * Add RaySource, to_ray_dataset first-class method, docs, and tests ([1c98157](1c98157)) * Add TLS support for Go Feature Server ([#6229](#6229)) ([28a58d0](28a58d0)) * Add Vector Search support to MongoDBOnlineStore ([#6344](#6344)) ([c102738](c102738)) * Add versioning support to Milvus online store ([#6330](#6330)) ([3268ced](3268ced)) * Addresses performance issues in the Redis online store ([2e50da0](2e50da0)) * Allow to set gpu for ray ([5580ab4](5580ab4)) * Bump redis-py version cap from <5 to <8 ([#6339](#6339)) ([9538180](9538180)) * Expose feature_server, materialization, and openlineage configuration via FeatureStore CRD ([ec6ecfd](ec6ecfd)) * Make online_write_batch_size configurable in MaterializationConfig ([#6268](#6268)) ([d41becf](d41becf)) * Make udf optional if agg defined ([#5689](#5689)) ([#6328](#6328)) ([f630056](f630056)) * MongoDB offline store ([#6138](#6138)) ([8eebad7](8eebad7)) * Optional input_schema for ODFV ([#6308](#6308)) ([#6312](#6312)) ([f08b4e8](f08b4e8)) * Provision minimal TokenReview RBAC for OIDC auth and add SSL error logging in token parser ([#6240](#6240)) ([dca57e8](dca57e8)) * **spark:** Add compute-on-read support for BatchFeatureView in get_… ([#6357](#6357)) ([630d9f8](630d9f8))
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What this PR does / why we need it:
feast materializecrashes withValueError: The truth value of an empty array is ambiguouswhen a scalar feature column contains an empty numpy array (e.g.np.array([])). This is a real-world scenario when a DataFrame row has a missing value represented as an empty array rather thanNoneornp.nan.Root cause: In
_convert_scalar_values_to_proto(sdk/python/feast/type_map.py), the null check usesnot pd.isnull(value)for every value in the loop.pd.isnull()is vectorised — whenvalueis a numpy array, it returns a boolean array instead of a scalar. Applying Python'snotoperator to that array raisesValueError. The same issue exists in:BOOLscalar path (not pd.isnull(value)in a list comprehension)UNIX_TIMESTAMPearly-return path (_python_datetime_to_int_timestamp(values)called with the raw values list, including any array-like values)sample == 0)Fix: Before calling
pd.isnull(), guard both scalar conversion loops (generic and BOOL) and the sample type-validation with an explicitisinstance(value, np.ndarray)check. Any array-like value in a scalar feature column is unmappable to a protobuf scalar field anyway, so it is safely treated as null →ProtoValue().np.array([])(empty)ValueErrorcrashProtoValue()(null)np.array([np.nan, 1.0])ValueErrorcrashProtoValue()(null)np.array([1.0, 2.0])ValueErrorcrashProtoValue()(null)NoneProtoValue()(null)ProtoValue(field=value)Which issue(s) this PR fixes:
Fixes #6255
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