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GeezerVison is showing github.com — fix: Ambiguous truth value of array during materialization by alan-gauthier-jt · Pull Request #6259 · feast-dev/feast

fix: Ambiguous truth value of array during materialization #6259

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ntkathole merged 2 commits into feast-dev:master from alan-gauthier-jt:fix-array-materialize

Apr 14, 2026

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fix: Ambiguous truth value of array during materialization #6259

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b18f71e

fix: ambiguous truth value of array during materialization

alan-gauthier-jt Apr 10, 2026

b91978e

chore: refacto from pr feedback

alan-gauthier-jt Apr 14, 2026

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77 changes: 63 additions & 14 deletions 77 sdk/python/feast/type_map.py

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Original file line number	Diff line number	Diff line change
Expand Up		@@ -909,6 +909,16 @@ def _convert_list_values_to_proto(
		]


		def _is_array_like(value: Any) -> bool:
		"""Return True if value is array-like (numpy array or any sized,
		non-string, non-bytes container). Array-like values in a scalar
		feature column cannot be mapped to a protobuf scalar field and are
		therefore always treated as null."""
		return isinstance(value, np.ndarray) or (
		hasattr(value, "__len__") and not isinstance(value, (str, bytes))
		)


		def _convert_scalar_values_to_proto(
		feast_value_type: ValueType,
		values: List[Any],
Expand All		@@ -929,16 +939,34 @@ def _convert_scalar_values_to_proto(
		return [ProtoValue()] * len(values)

		if feast_value_type == ValueType.UNIX_TIMESTAMP:
		int_timestamps = _python_datetime_to_int_timestamp(values)
		return [ProtoValue(unix_timestamp_val=ts) for ts in int_timestamps] # type: ignore
		out: List[Any] = [None] * len(values)
		clean_indices: List[int] = []
		clean_values: List[Any] = []
		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) # type: ignore
		return out

		field_name, func, valid_scalar_types = PYTHON_SCALAR_VALUE_TYPE_TO_PROTO_VALUE[
		feast_value_type
		]

		# Validate scalar types
		# Validate scalar types. The caller guarantees that sample is not
		# array-like (array-like values are filtered out when picking the sample
		# for scalar columns in python_values_to_proto_values).
		if valid_scalar_types:
		if (sample == 0 or sample == 0.0) and feast_value_type != ValueType.BOOL:
		try:
		is_zero = sample == 0 or sample == 0.0
		except (ValueError, TypeError):
		is_zero = False
		if is_zero and feast_value_type != ValueType.BOOL:
		# Numpy converts 0 to int, but column type may be float
		allowed_types = {np.int64, int, np.float64, float, decimal.Decimal}
		assert type(sample) in allowed_types, (
Expand All		@@ -951,20 +979,35 @@ def _convert_scalar_values_to_proto(

		# Handle BOOL specially due to np.bool_ conversion requirement
		if feast_value_type == ValueType.BOOL:
		return [
		ProtoValue(
		**{field_name: func(bool(value) if type(value) is np.bool_ else value)}
		) # type: ignore
		if not pd.isnull(value)
		else ProtoValue()
		for value in values
		]
		out = []
		for value in values:
		if _is_array_like(value):
		# Array-like value in a scalar BOOL column: treat as null.
		out.append(ProtoValue())
		elif not pd.isnull(value):
		out.append(
		ProtoValue(
		**{
		field_name: func(
		bool(value) if type(value) is np.bool_ else value
		)
		}
		) # type: ignore
		)
		else:
		out.append(ProtoValue())
		return out

		# Generic scalar conversion
		out = []
		for value in values:
		if isinstance(value, ProtoValue):
		out.append(value)
		elif _is_array_like(value):
		# Array-like value in a scalar column: always treat as null.
		# pd.isnull() is vectorised and would return an ndarray here,
		# making `not pd.isnull(value)` raise ValueError.
		out.append(ProtoValue())
		elif not pd.isnull(value):
		out.append(ProtoValue(**{field_name: func(value)}))
		else:
Expand Down Expand Up		@@ -1107,12 +1150,18 @@ def _python_value_to_proto_value(
		if "set" in type_name_lower:
		return _python_set_to_proto_values(feast_value_type, values)

		# Scalar types
		# Scalar types — pick a sample that is not array-like so that the type
		# validation in _convert_scalar_values_to_proto always receives a plain
		# scalar (array-like values in a scalar column are treated as null).
		if (
		feast_value_type in PYTHON_SCALAR_VALUE_TYPE_TO_PROTO_VALUE
		or feast_value_type == ValueType.UNIX_TIMESTAMP
		):
		return _convert_scalar_values_to_proto(feast_value_type, values, sample)
		scalar_sample = next(
		(v for v in values if _non_empty_value(v) and not _is_array_like(v)),
Comment thread ntkathole marked this conversation as resolved. Resolved Uh oh! There was an error while loading. Please reload this page.
		None,
		)
		return _convert_scalar_values_to_proto(feast_value_type, values, scalar_sample)

		raise Exception(f"Unsupported data type: {feast_value_type}")

Expand Down

108 changes: 108 additions & 0 deletions 108 sdk/python/tests/unit/test_type_map.py

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Original file line number	Diff line number	Diff line change
Expand Up		@@ -1845,3 +1845,111 @@ def test_pa_to_feast_value_type_nested(self):
		pa_to_feast_value_type("list<item: list<item: double>>")
		== ValueType.VALUE_LIST
		)


		class TestEmptyArrayAsNull:
		"""Regression tests for https://github.com/feast-dev/feast/issues/6255
		Ensure that an empty numpy array in a scalar feature column is treated as
		null rather than raising ``ValueError: The truth value of an empty array is
		ambiguous``.
		"""

		def test_empty_numpy_array_treated_as_null_double(self):
		from feast.protos.feast.types.Value_pb2 import Value as ProtoValue

		result = python_values_to_proto_values(
		[np.array([]), 1.0, None], ValueType.DOUBLE
		)
		assert result[0] == ProtoValue(), (
		"empty array should produce an empty ProtoValue"
		)
		assert result[1].double_val == 1.0
		assert result[2] == ProtoValue(), (
		"None should still produce an empty ProtoValue"
		)

		def test_empty_numpy_array_treated_as_null_int64(self):
		from feast.protos.feast.types.Value_pb2 import Value as ProtoValue

		result = python_values_to_proto_values(
		[np.array([]), 42, None], ValueType.INT64
		)
		assert result[0] == ProtoValue(), (
		"empty array should produce an empty ProtoValue"
		)
		assert result[1].int64_val == 42
		assert result[2] == ProtoValue()

		def test_empty_numpy_array_treated_as_null_bool(self):
		from feast.protos.feast.types.Value_pb2 import Value as ProtoValue

		result = python_values_to_proto_values(
		[np.array([]), True, None], ValueType.BOOL
		)
		assert result[0] == ProtoValue(), (
		"empty array should produce an empty ProtoValue"
		)
		assert result[1].bool_val is True
		assert result[2] == ProtoValue()

		def test_array_with_null_element_treated_as_null(self):
		"""A non-empty array containing any null element in a scalar column is treated as null."""
		from feast.protos.feast.types.Value_pb2 import Value as ProtoValue

		result = python_values_to_proto_values(
		[np.array([np.nan, 1.0]), 3.0], ValueType.DOUBLE
		)
		assert result[0] == ProtoValue(), (
		"array with null element should produce an empty ProtoValue"
		)
		assert result[1].double_val == 3.0

		def test_non_empty_array_without_nulls_is_treated_as_null(self):
		"""A non-empty numpy array in a scalar column is always treated as null.

		A scalar feature column cannot hold an ndarray value (protobuf would
		reject it), so any array-like value – empty or not – is mapped to an
		empty ProtoValue() rather than crashing with ValueError.
		"""
		from feast.protos.feast.types.Value_pb2 import Value as ProtoValue

		result = python_values_to_proto_values(
		[np.array([1.0, 2.0]), 3.0, None], ValueType.DOUBLE
		)
		# array-like value in a scalar column → null, not a crash
		assert result[0] == ProtoValue(), (
		"non-empty array in scalar column should be null"
		)
		assert result[1].double_val == 3.0
		assert result[2] == ProtoValue()

		def test_empty_numpy_array_treated_as_null_unix_timestamp(self):
		"""Array-like values in a scalar UNIX_TIMESTAMP column must not crash."""
		from datetime import datetime, timezone

		from feast.protos.feast.types.Value_pb2 import Value as ProtoValue

		ts = datetime(2024, 1, 1, tzinfo=timezone.utc)
		result = python_values_to_proto_values(
		[np.array([]), ts, None], ValueType.UNIX_TIMESTAMP
		)
		assert result[0] == ProtoValue(), (
		"empty array in UNIX_TIMESTAMP scalar column should produce null"
		)
		assert result[1].unix_timestamp_val == int(ts.timestamp())
		assert result[2] == ProtoValue()

		def test_non_empty_array_treated_as_null_unix_timestamp(self):
		"""Non-empty array in a UNIX_TIMESTAMP scalar column should produce null, not crash."""
		from datetime import datetime, timezone

		from feast.protos.feast.types.Value_pb2 import Value as ProtoValue

		ts = datetime(2024, 6, 15, tzinfo=timezone.utc)
		result = python_values_to_proto_values(
		[np.array([1, 2, 3]), ts], ValueType.UNIX_TIMESTAMP
		)
		assert result[0] == ProtoValue(), (
		"non-empty array in UNIX_TIMESTAMP scalar column should produce null"
		)
		assert result[1].unix_timestamp_val == int(ts.timestamp())

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