`ValueError: The truth value of an empty array is ambiguous` during materialization

alan-gauthier-jt

Expected Behavior

feast materialize should complete successfully even when the source DataFrame
contains an empty numpy array (np.array([])) in a scalar feature column.
The empty array should be treated as a null / missing value and produce an empty
ProtoValue(), consistent with how None and np.nan are already handled.

Current Behavior

feast materialize crashes with:

ValueError: The truth value of an empty array is ambiguous.
Use `array.size > 0` to check that an array is not empty.

Full stack trace:

Traceback (most recent call last):
  File "/opt/app-root/bin/feast", line 10, in <module>
    sys.exit(cli())
             ^^^^^
  File "/opt/app-root/lib64/python3.11/site-packages/click/core.py", line 1485, in __call__
    return self.main(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/app-root/lib64/python3.11/site-packages/click/core.py", line 1406, in main
    rv = self.invoke(ctx)
         ^^^^^^^^^^^^^^^^
  File "/opt/app-root/lib64/python3.11/site-packages/click/core.py", line 1873, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/app-root/lib64/python3.11/site-packages/click/core.py", line 1269, in invoke
    return ctx.invoke(self.callback, **ctx.params)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/app-root/lib64/python3.11/site-packages/click/core.py", line 824, in invoke
    return callback(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/app-root/lib64/python3.11/site-packages/click/decorators.py", line 34, in new_func
    return f(get_current_context(), *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/app-root/lib64/python3.11/site-packages/feast/cli/cli.py", line 393, in materialize_command
    store.materialize(
  File "/opt/app-root/lib64/python3.11/site-packages/feast/feature_store.py", line 1816, in materialize
    provider.materialize_single_feature_view(
  File "/opt/app-root/lib64/python3.11/site-packages/feast/infra/passthrough_provider.py", line 456, in materialize_single_feature_view
    raise e
  File "/opt/app-root/lib64/python3.11/site-packages/feast/infra/compute_engines/local/compute.py", line 84, in _materialize_one
    plan.execute(context)
  File "/opt/app-root/lib64/python3.11/site-packages/feast/infra/compute_engines/dag/plan.py", line 51, in execute
    output = node.execute(context)
             ^^^^^^^^^^^^^^^^^^^^^
  File "/opt/app-root/lib64/python3.11/site-packages/feast/infra/compute_engines/local/nodes.py", line 274, in execute
    rows_to_write = _convert_arrow_to_proto(
                    ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/app-root/lib64/python3.11/site-packages/feast/utils.py", line 281, in _convert_arrow_to_proto
    return _convert_arrow_fv_to_proto(table, feature_view, join_keys)  # type: ignore[arg-type]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/app-root/lib64/python3.11/site-packages/feast/utils.py", line 298, in _convert_arrow_fv_to_proto
    proto_values_by_column = {
                             ^
  File "/opt/app-root/lib64/python3.11/site-packages/feast/utils.py", line 299, in <dictcomp>
    column: python_values_to_proto_values(
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/app-root/lib64/python3.11/site-packages/feast/type_map.py", line 840, in python_values_to_proto_values
    proto_values = _python_value_to_proto_value(value_type, values)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/app-root/lib64/python3.11/site-packages/feast/type_map.py", line 772, in _python_value_to_proto_value
    elif not pd.isnull(value):
ValueError: The truth value of an empty array is ambiguous. Use `array.size > 0` to check that an array is not empty.

The root cause is in sdk/python/feast/type_map.py,
function _convert_scalar_values_to_proto (around line 968):

# Generic scalar conversion
out = []
for value in values:
    if isinstance(value, ProtoValue):
        out.append(value)
    elif not pd.isnull(value):          # ← crashes here
        out.append(ProtoValue(**{field_name: func(value)}))
    else:
        out.append(ProtoValue())

pd.isnull() is vectorised: when value is a numpy array (including an empty
one), it returns a numpy array of booleans instead of a scalar boolean. Applying
Python's not to that array raises ValueError. The same pattern exists a few lines
above in the ValueType.BOOL path (if not pd.isnull(value)).

Steps to reproduce

import numpy as np
from feast.type_map import python_values_to_proto_values
from feast.value_type import ValueType

# A scalar column where one row contains an empty array
python_values_to_proto_values([np.array([]), 1.0, 2.0], ValueType.DOUBLE)
# → ValueError: The truth value of an empty array is ambiguous

Specifications

Version: v0.60.0 (also reproducible on main as of 2026-04-10)
Platform: Linux (Python 3.11), macOS (Python 3.11)
Subsystem: feast/type_map.py – _convert_scalar_values_to_proto

Possible Solution

The fix belongs in sdk/python/feast/type_map.py, specifically the generic scalar conversion loop at line 963–975 (elif not pd.isnull(value)).

Before calling not pd.isnull(value), check whether the value is array-like.
pd.isnull() is vectorised and returns an np.ndarray for array inputs, so
calling not on it raises ValueError. The fix must handle three sub-cases:

Value	Expected outcome
Empty array (`size == 0`)	null → `ProtoValue()`
Non-empty array containing any null	null → `ProtoValue()`
Non-empty array with all valid data	convert → `ProtoValue(**{field_name: func(value)})`
Plain scalar null	null → `ProtoValue()`
Plain scalar non-null	convert → `ProtoValue(**{field_name: func(value)})`

# Generic scalar conversion
out = []
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))
    ):
        # Array-like value in a scalar column
        if hasattr(value, "size") and value.size == 0:
            # Empty numpy array – treat as null
            out.append(ProtoValue())
        else:
            is_null = pd.isnull(value)
            if hasattr(is_null, "any"):
                # pd.isnull returned an array; null if any element is null
                out.append(ProtoValue() if is_null.any() else ProtoValue(**{field_name: func(value)}))
            elif not is_null:
                out.append(ProtoValue(**{field_name: func(value)}))
            else:
                out.append(ProtoValue())
    elif not pd.isnull(value):
        out.append(ProtoValue(**{field_name: func(value)}))
    else:
        out.append(ProtoValue())
return out