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fix: Ambiguous truth value of array during materialization by alan-gauthier-jt · Pull Request #6259 · feast-dev/feast · GitHub
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fix: Ambiguous truth value of array during materialization #6259

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ntkathole merged 2 commits into from
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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chore: refacto from pr feedback 
Signed-off-by: Alan Gauthier <alan.gauthier@jobteaser.com>
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@alan-gauthier-jt @ntkathole
alan-gauthier-jt authored and ntkathole committed Apr 14, 2026
commit b91978ea9503951782d38da1af2c706807d0d26d
61 changes: 36 additions & 25 deletions 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 @@ -929,30 +939,29 @@ def _convert_scalar_values_to_proto(
return [ProtoValue()] * len(values)

if feast_value_type == ValueType.UNIX_TIMESTAMP:
out = []
for value in values:
if isinstance(value, np.ndarray) or (
hasattr(value, "__len__") and not isinstance(value, (str, bytes))
):
# Array-like value in a scalar UNIX_TIMESTAMP column: treat as null.
out.append(ProtoValue())
elif value is None:
out.append(ProtoValue())
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:
(ts,) = _python_datetime_to_int_timestamp([value])
out.append(ProtoValue(unix_timestamp_val=ts)) # type: ignore
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 — skip for array-like samples (they will be treated
# as null or raw values in the conversion loop below).
if valid_scalar_types and not (
isinstance(sample, np.ndarray)
or (hasattr(sample, "__len__") and not isinstance(sample, (str, bytes)))
):
# 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:
try:
is_zero = sample == 0 or sample == 0.0
except (ValueError, TypeError):
Expand All @@ -972,9 +981,7 @@ def _convert_scalar_values_to_proto(
if feast_value_type == ValueType.BOOL:
out = []
for value in values:
if isinstance(value, np.ndarray) or (
hasattr(value, "__len__") and not isinstance(value, (str, bytes))
):
if _is_array_like(value):
# Array-like value in a scalar BOOL column: treat as null.
out.append(ProtoValue())
elif not pd.isnull(value):
Expand All @@ -996,9 +1003,7 @@ def _convert_scalar_values_to_proto(
for value in values:
if isinstance(value, ProtoValue):
out.append(value)
elif isinstance(value, np.ndarray) or (
hasattr(value, "__len__") and not isinstance(value, (str, bytes))
):
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.
Expand Down Expand Up @@ -1145,12 +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)),
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None,
)
return _convert_scalar_values_to_proto(feast_value_type, values, scalar_sample)

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

Expand Down
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