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fix: Fix STRING type handling in on-demand feature views #5669

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Merged
franciscojavierarceo merged 1 commit into from
Oct 15, 2025
Merged

fix: Fix STRING type handling in on-demand feature views #5669

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10 changes: 10 additions & 0 deletions sdk/python/feast/type_map.py
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Original file line number Diff line number Diff line change
Expand Up @@ -172,6 +172,16 @@ def python_type_to_feast_value_type(
if type_name in type_map:
return type_map[type_name]

# Handle pandas "object" dtype by inspecting the actual value
if type_name == "object" and value is not None:
# Check the actual type of the value
actual_type = type(value).__name__.lower()
if actual_type == "str":
return ValueType.STRING
# If it's a different type wrapped in object, try to infer from the value
elif actual_type in type_map:
return type_map[actual_type]

if isinstance(value, np.ndarray) and str(value.dtype) in type_map:
item_type = type_map[str(value.dtype)]
return ValueType[item_type.name + "_LIST"]
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25 changes: 11 additions & 14 deletions sdk/python/tests/unit/infra/test_inference_unit_tests.py
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Original file line number Diff line number Diff line change
Expand Up @@ -96,29 +96,27 @@ def python_native_test_view(input_dict: dict[str, Any]) -> dict[str, Any]:

python_native_test_view.infer_features()


def test_on_demand_features_invalid_type_inference():
# Create Feature Views
date_request = RequestSource(
name="date_request",
schema=[Field(name="some_date", dtype=UnixTimestamp)],
)

@on_demand_feature_view(
sources=[date_request],
schema=[
Field(name="output", dtype=UnixTimestamp),
Field(name="object_output", dtype=String),
],
)
def invalid_test_view(features_df: pd.DataFrame) -> pd.DataFrame:
def object_string_test_view(features_df: pd.DataFrame) -> pd.DataFrame:
data = pd.DataFrame()
data["output"] = features_df["some_date"]
data["object_output"] = features_df["some_date"].astype(str)
return data

with pytest.raises(ValueError, match="Value with native type object"):
invalid_test_view.infer_features()
object_string_test_view.infer_features()


def test_on_demand_features_invalid_type_inference():
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@franciscojavierarceo franciscojavierarceo Oct 15, 2025

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if this is invalid, shouldn't it raise?

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@ntkathole ntkathole Oct 15, 2025

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it's a negative test.

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@ntkathole ntkathole Oct 15, 2025

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There is with pytest.raises() sections which catch exceptions raised when mode is mismatched or feature is missing

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@franciscojavierarceo franciscojavierarceo Oct 15, 2025

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ooh nice i see on line 138, thank you.

date_request = RequestSource(
name="date_request",
schema=[Field(name="some_date", dtype=UnixTimestamp)],
)

@on_demand_feature_view(
schema=[
Expand Down Expand Up @@ -184,14 +182,13 @@ def test_view(features_df: pd.DataFrame) -> pd.DataFrame:
Field(name="object_output", dtype=String),
],
)
def invalid_test_view(features_df: pd.DataFrame) -> pd.DataFrame:
def object_string_view(features_df: pd.DataFrame) -> pd.DataFrame:
data = pd.DataFrame()
data["output"] = features_df["some_date"]
data["object_output"] = features_df["some_date"].astype(str)
return data

with pytest.raises(ValueError, match="Value with native type object"):
invalid_test_view.infer_features()
object_string_view.infer_features()

@on_demand_feature_view(
sources=[date_request],
Expand Down
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