Skip to content

fix: Keep description, tags and nested types of ODFV schema fields #7091

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
shuchu merged 2 commits into from
Nov 4, 2026
Merged

fix: Keep description, tags and nested types of ODFV schema fields #7091

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
e9a61b0
fix: Keep description, tags and nested types of ODFV schema fields
Revinand Oct 29, 2026
d4c291b
Merge branch 'master' into fix/odfv-field-description-inference
shuchu Nov 4, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
19 changes: 7 additions & 12 deletions sdk/python/feast/on_demand_feature_view.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -858,16 +858,7 @@ def _handle_backward_compatible_udf(
@classmethod
def _parse_features_from_proto(cls, proto: OnDemandFeatureViewProto) -> List[Field]:
"""Parse features from the protobuf representation."""
return [
Field(
name=feature.name,
dtype=from_value_type(ValueType(feature.value_type)),
vector_index=feature.vector_index,
vector_length=feature.vector_length,
vector_search_metric=feature.vector_search_metric,
)
for feature in proto.spec.features
]
return [Field.from_proto(feature) for feature in proto.spec.features]

@classmethod
def _parse_optional_fields_from_proto(cls, proto: OnDemandFeatureViewProto) -> dict:
Expand Down Expand Up @@ -1207,8 +1198,12 @@ def _feature_exists_in_inferred(
Returns:
True if the feature exists in the inferred features, False otherwise
"""
# Check for exact feature match first
if specified_feature in inferred_features:
# Inferred features carry only a name and a type, so ignore the
# description, tags and vector settings of the specified feature
if any(
f.name == specified_feature.name and f.dtype == specified_feature.dtype
for f in inferred_features
):
return True

# For array types, we need to check by name since array types
Expand Down
59 changes: 58 additions & 1 deletion sdk/python/tests/unit/test_on_demand_feature_view.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@
import pytest

from feast.aggregation import Aggregation
from feast.errors import SpecifiedFeaturesNotPresentError
from feast.feature_view import FeatureView
from feast.field import Field
from feast.infra.offline_stores.file_source import FileSource
Expand All @@ -27,7 +28,7 @@
PythonTransformation,
on_demand_feature_view,
)
from feast.types import Float32
from feast.types import Array, Float32, Float64, Int64, String, Struct


def udf1(features_df: pd.DataFrame) -> pd.DataFrame:
Expand Down Expand Up @@ -748,3 +749,59 @@ def test_on_demand_feature_view_org_field():
odfv_org_a = OnDemandFeatureView(name="odfv-eq", org="ads", **common)
odfv_org_b = OnDemandFeatureView(name="odfv-eq", org="search", **common)
assert odfv_org_a != odfv_org_b


def test_schema_field_metadata_survives_inference_and_proto_round_trip():
"""Schema fields keep their description, tags and nested types end to end."""
file_source = FileSource(name="my-file-source", path="test.parquet")
feature_view = FeatureView(
name="my-feature-view",
entities=[],
schema=[
Field(name="feature1", dtype=Float64),
Field(name="feature2", dtype=Float64),
],
source=file_source,
)
transformation = PandasTransformation(udf=udf1, udf_string="udf1 source code")
odfv = OnDemandFeatureView(
name="described-odfv",
sources=[feature_view],
schema=[
Field(name="output1", dtype=Float64, description="Copy of feature1"),
Field(name="output2", dtype=Float64, tags={"team": "risk"}),
],
feature_transformation=transformation,
)

odfv.infer_features()

assert odfv.features[0].description == "Copy of feature1"
assert odfv.features[1].tags == {"team": "risk"}
assert OnDemandFeatureView.from_proto(odfv.to_proto()).features == odfv.features

nested_schema = [
Field(name="output1", dtype=Struct({"a": Int64})),
Field(name="output2", dtype=Array(Array(Int64))),
]
nested_odfv = OnDemandFeatureView(
name="nested-odfv",
sources=[feature_view],
schema=nested_schema,
feature_transformation=transformation,
)
restored = OnDemandFeatureView.from_proto(nested_odfv.to_proto())
assert restored.features == nested_schema

wrong_type_odfv = OnDemandFeatureView(
name="wrong-type-odfv",
sources=[feature_view],
schema=[
Field(name="output1", dtype=String, description="Copy of feature1"),
Field(name="output2", dtype=Float64),
],
feature_transformation=transformation,
)

with pytest.raises(SpecifiedFeaturesNotPresentError):
wrong_type_odfv.infer_features()