feat: Make arrow primary interchange for online ODFV execution

sdk/python/feast/feature_store.py

@@ -2138,7 +2138,7 @@ def _augment_response_with_on_demand_transforms(
                 )
 
         initial_response = OnlineResponse(online_features_response)
-        initial_response_df: Optional[pd.DataFrame] = None
+        initial_response_arrow: Optional[pa.Table] = None
         initial_response_dict: Optional[Dict[str, List[Any]]] = None
 
         # Apply on demand transformations and augment the result rows
@@ -2148,18 +2148,14 @@ def _augment_response_with_on_demand_transforms(
             if odfv.mode == "python":
                 if initial_response_dict is None:
                     initial_response_dict = initial_response.to_dict()
-                transformed_features_dict: Dict[str, List[Any]] = (
-                    odfv.get_transformed_features(
-                        initial_response_dict,
-                        full_feature_names,
-                    )
+                transformed_features_dict: Dict[str, List[Any]] = odfv.transform_dict(
+                    initial_response_dict
                 )
             elif odfv.mode in {"pandas", "substrait"}:
-                if initial_response_df is None:
-                    initial_response_df = initial_response.to_df()
-                transformed_features_df: pd.DataFrame = odfv.get_transformed_features(
-                    initial_response_df,
-                    full_feature_names,
+                if initial_response_arrow is None:
+                    initial_response_arrow = initial_response.to_arrow()
+                transformed_features_arrow = odfv.transform_arrow(
+                    initial_response_arrow, full_feature_names
                 )
             else:
                 raise Exception(
@@ -2169,11 +2165,11 @@ def _augment_response_with_on_demand_transforms(
             transformed_features = (
                 transformed_features_dict
                 if odfv.mode == "python"
-                else transformed_features_df
+                else transformed_features_arrow
             )
             transformed_columns = (
-                transformed_features.columns
-                if isinstance(transformed_features, pd.DataFrame)
+                transformed_features.column_names
+                if isinstance(transformed_features, pa.Table)
                 else transformed_features
             )
             selected_subset = [f for f in transformed_columns if f in _feature_refs]
@@ -2183,6 +2179,10 @@ def _augment_response_with_on_demand_transforms(
                 feature_vector = transformed_features[selected_feature]
                 proto_values.append(
                     python_values_to_proto_values(feature_vector, ValueType.UNKNOWN)
+                    if odfv.mode == "python"
+                    else python_values_to_proto_values(
+                        feature_vector.to_numpy(), ValueType.UNKNOWN
+                    )
                 )
 
             odfv_result_names |= set(selected_subset)

sdk/python/feast/on_demand_feature_view.py

@@ -12,7 +12,6 @@
 from typeguard import typechecked
 
 from feast.base_feature_view import BaseFeatureView
-from feast.batch_feature_view import BatchFeatureView
 from feast.data_source import RequestSource
 from feast.errors import RegistryInferenceFailure, SpecifiedFeaturesNotPresentError
 from feast.feature_view import FeatureView
@@ -493,53 +492,7 @@ def transform_arrow(
             ]
         )
 
-    def get_transformed_features_df(
-        self,
-        df_with_features: pd.DataFrame,
-        full_feature_names: bool = False,
-    ) -> pd.DataFrame:
-        # Apply on demand transformations
-        if not isinstance(df_with_features, pd.DataFrame):
-            raise TypeError("get_transformed_features_df only accepts pd.DataFrame")
-        columns_to_cleanup = []
-        for source_fv_projection in self.source_feature_view_projections.values():
-            for feature in source_fv_projection.features:
-                full_feature_ref = f"{source_fv_projection.name}__{feature.name}"
-                if full_feature_ref in df_with_features.keys():
-                    # Make sure the partial feature name is always present
-                    df_with_features[feature.name] = df_with_features[full_feature_ref]
-                    columns_to_cleanup.append(feature.name)
-                elif feature.name in df_with_features.keys():
-                    # Make sure the full feature name is always present
-                    df_with_features[full_feature_ref] = df_with_features[feature.name]
-                    columns_to_cleanup.append(full_feature_ref)
-
-        # Compute transformed values and apply to each result row
-        df_with_transformed_features: pd.DataFrame = (
-            self.feature_transformation.transform(df_with_features)
-        )
-
-        # Work out whether the correct columns names are used.
-        rename_columns: Dict[str, str] = {}
-        for feature in self.features:
-            short_name = feature.name
-            long_name = self._get_projected_feature_name(feature.name)
-            if (
-                short_name in df_with_transformed_features.columns
-                and full_feature_names
-            ):
-                rename_columns[short_name] = long_name
-            elif not full_feature_names:
-                # Long name must be in dataframe.
-                rename_columns[long_name] = short_name
-
-        # Cleanup extra columns used for transformation
-        df_with_transformed_features = df_with_transformed_features[
-            [f.name for f in self.features]
-        ]
-        return df_with_transformed_features.rename(columns=rename_columns)
-
-    def get_transformed_features_dict(
+    def transform_dict(
         self,
         feature_dict: Dict[str, Any],  # type: ignore
     ) -> Dict[str, Any]:
@@ -566,29 +519,6 @@ def get_transformed_features_dict(
             del output_dict[feature_name]
         return output_dict
 
-    def get_transformed_features(
-        self,
-        features: Union[Dict[str, Any], pd.DataFrame],
-        full_feature_names: bool = False,
-    ) -> Union[Dict[str, Any], pd.DataFrame]:
-        # TODO: classic inheritance pattern....maybe fix this
-        if self.mode == "python" and isinstance(features, Dict):
-            # note full_feature_names is not needed for the dictionary
-            return self.get_transformed_features_dict(
-                feature_dict=features,
-            )
-        elif self.mode in {"pandas", "substrait"} and isinstance(
-            features, pd.DataFrame
-        ):
-            return self.get_transformed_features_df(
-                df_with_features=features,
-                full_feature_names=full_feature_names,
-            )
-        else:
-            raise Exception(
-                f'Invalid OnDemandFeatureMode: {self.mode}. Expected one of "pandas" or "python".'
-            )
-
     def infer_features(self) -> None:
         inferred_features = self.feature_transformation.infer_features(
             self._construct_random_input()
@@ -745,23 +675,6 @@ def decorator(user_function):
     return decorator
 
 
-def feature_view_to_batch_feature_view(fv: FeatureView) -> BatchFeatureView:
-    bfv = BatchFeatureView(
-        name=fv.name,
-        entities=fv.entities,
-        ttl=fv.ttl,
-        tags=fv.tags,
-        online=fv.online,
-        owner=fv.owner,
-        schema=fv.schema,
-        source=fv.batch_source,
-    )
-
-    bfv.features = copy.copy(fv.features)
-    bfv.entities = copy.copy(fv.entities)
-    return bfv
-
-
 def _empty_odfv_udf_fn(x: Any) -> Any:
     # just an identity mapping, otherwise we risk tripping some downstream tests
     return x

sdk/python/feast/online_response.py

@@ -15,6 +15,7 @@
 from typing import Any, Dict, List
 
 import pandas as pd
+import pyarrow as pa
 
 from feast.feature_view import DUMMY_ENTITY_ID
 from feast.protos.feast.serving.ServingService_pb2 import GetOnlineFeaturesResponse
@@ -77,3 +78,13 @@ def to_df(self, include_event_timestamps: bool = False) -> pd.DataFrame:
         """
 
         return pd.DataFrame(self.to_dict(include_event_timestamps))
+
+    def to_arrow(self, include_event_timestamps: bool = False) -> pa.Table:
+        """
+        Converts GetOnlineFeaturesResponse features into pyarrow Table.
+
+        Args:
+        is_with_event_timestamps: bool Optionally include feature timestamps in the table
+        """
+
+        return pa.Table.from_pydict(self.to_dict(include_event_timestamps))

sdk/python/feast/transformation/python_transformation.py

@@ -64,9 +64,6 @@ def __eq__(self, other):
                 "Comparisons should only involve PythonTransformation class objects."
             )
 
-        if not super().__eq__(other):
-            return False
-
         if (
             self.udf_string != other.udf_string
             or self.udf.__code__.co_code != other.udf.__code__.co_code

sdk/python/feast/transformation/substrait_transformation.py

@@ -77,9 +77,6 @@ def __eq__(self, other):
                 "Comparisons should only involve SubstraitTransformation class objects."
             )
 
-        if not super().__eq__(other):
-            return False
-
         return (
             self.substrait_plan == other.substrait_plan
             and self.ibis_function.__code__.co_code

sdk/python/tests/unit/test_on_demand_feature_view.py

@@ -204,7 +204,7 @@ def test_python_native_transformation_mode():
             }
         )
 
-    assert on_demand_feature_view_python_native.get_transformed_features(
+    assert on_demand_feature_view_python_native.transform_dict(
         {
             "feature1": 0,
             "feature2": 1,