Add arrow support for on demand feature views

sdk/python/feast/infra/offline_stores/bigquery.py

@@ -185,7 +185,7 @@ def full_feature_names(self) -> bool:
     def on_demand_feature_views(self) -> Optional[List[OnDemandFeatureView]]:
         return self._on_demand_feature_views
 
-    def to_df_internal(self) -> pd.DataFrame:
+    def _to_df_internal(self) -> pd.DataFrame:
         # TODO: Ideally only start this job when the user runs "get_historical_features", not when they run to_df()
         df = self.client.query(self.query).to_dataframe(create_bqstorage_client=True)
         return df
@@ -234,7 +234,7 @@ def to_bigquery(
         print(f"Done writing to '{job_config.destination}'.")
         return str(job_config.destination)
 
-    def to_arrow(self) -> pyarrow.Table:
+    def _to_arrow_internal(self) -> pyarrow.Table:
         return self.client.query(self.query).to_arrow()
 
 

sdk/python/feast/infra/offline_stores/file.py

@@ -51,12 +51,12 @@ def full_feature_names(self) -> bool:
     def on_demand_feature_views(self) -> Optional[List[OnDemandFeatureView]]:
         return self._on_demand_feature_views
 
-    def to_df_internal(self) -> pd.DataFrame:
+    def _to_df_internal(self) -> pd.DataFrame:
         # Only execute the evaluation function to build the final historical retrieval dataframe at the last moment.
         df = self.evaluation_function()
         return df
 
-    def to_arrow(self):
+    def _to_arrow_internal(self):
         # Only execute the evaluation function to build the final historical retrieval dataframe at the last moment.
         df = self.evaluation_function()
         return pyarrow.Table.from_pandas(df)

sdk/python/feast/infra/offline_stores/offline_store.py

@@ -40,7 +40,7 @@ def on_demand_feature_views(self) -> Optional[List[OnDemandFeatureView]]:
 
     def to_df(self) -> pd.DataFrame:
         """Return dataset as Pandas DataFrame synchronously including on demand transforms"""
-        features_df = self.to_df_internal()
+        features_df = self._to_df_internal()
         if self.on_demand_feature_views is None:
             return features_df
 
@@ -51,16 +51,27 @@ def to_df(self) -> pd.DataFrame:
         return features_df
 
     @abstractmethod
-    def to_df_internal(self) -> pd.DataFrame:
+    def _to_df_internal(self) -> pd.DataFrame:
         """Return dataset as Pandas DataFrame synchronously"""
         pass
 
-    # TODO(adchia): implement ODFV for to_arrow method
     @abstractmethod
-    def to_arrow(self) -> pyarrow.Table:
+    def _to_arrow_internal(self) -> pyarrow.Table:
         """Return dataset as pyarrow Table synchronously"""
         pass
 
+    def to_arrow(self) -> pyarrow.Table:
+        """Return dataset as pyarrow Table synchronously"""
+        if self.on_demand_feature_views is None:
+            return self._to_arrow_internal()
+
+        features_df = self._to_df_internal()
+        for odfv in self.on_demand_feature_views:
+            features_df = features_df.join(
+                odfv.get_transformed_features_df(self.full_feature_names, features_df)
+            )
+        return pyarrow.Table.from_pandas(features_df)
+
 
 class OfflineStore(ABC):
     """

sdk/python/feast/infra/offline_stores/redshift.py

@@ -234,7 +234,7 @@ def full_feature_names(self) -> bool:
     def on_demand_feature_views(self) -> Optional[List[OnDemandFeatureView]]:
         return self._on_demand_feature_views
 
-    def to_df_internal(self) -> pd.DataFrame:
+    def _to_df_internal(self) -> pd.DataFrame:
         with self._query_generator() as query:
             return aws_utils.unload_redshift_query_to_df(
                 self._redshift_client,
@@ -248,7 +248,7 @@ def to_df_internal(self) -> pd.DataFrame:
                 self._drop_columns,
             )
 
-    def to_arrow(self) -> pa.Table:
+    def _to_arrow_internal(self) -> pa.Table:
         with self._query_generator() as query:
             return aws_utils.unload_redshift_query_to_pa(
                 self._redshift_client,

sdk/python/feast/on_demand_feature_view.py

@@ -72,10 +72,12 @@ def to_proto(self) -> OnDemandFeatureViewProto:
         inputs = {}
         for feature_ref, input in self.inputs.items():
             if type(input) == FeatureView:
-                inputs[feature_ref] = OnDemandInput(feature_view=input.to_proto())
+                fv = cast(FeatureView, input)
+                inputs[feature_ref] = OnDemandInput(feature_view=fv.to_proto())
             else:
+                request_data_source = cast(RequestDataSource, input)
                 inputs[feature_ref] = OnDemandInput(
-                    request_data_source=input.to_proto()
+                    request_data_source=request_data_source.to_proto()
                 )
 
         spec = OnDemandFeatureViewSpec(

sdk/python/tests/integration/offline_store/test_universal_historical_retrieval.py

@@ -346,25 +346,17 @@ def test_historical_features(environment, universal_data_sources, full_feature_n
         event_timestamp,
     )
 
-    # on demand features is only plumbed through to to_df for now.
     table_from_df_entities: pd.DataFrame = job_from_df.to_arrow().to_pandas()
-    actual_df_from_df_entities_for_table = actual_df_from_df_entities.drop(
-        columns=["conv_rate_plus_100", "conv_rate_plus_val_to_add"]
-    )
-    assert "conv_rate_plus_100" not in table_from_df_entities.columns
-    assert "conv_rate_plus_val_to_add" not in table_from_df_entities.columns
 
     columns_expected_in_table = expected_df.columns.tolist()
-    columns_expected_in_table.remove("conv_rate_plus_100")
-    columns_expected_in_table.remove("conv_rate_plus_val_to_add")
 
     table_from_df_entities = (
         table_from_df_entities[columns_expected_in_table]
         .sort_values(by=[event_timestamp, "order_id", "driver_id", "customer_id"])
         .drop_duplicates()
         .reset_index(drop=True)
     )
-    assert_frame_equal(actual_df_from_df_entities_for_table, table_from_df_entities)
+    assert_frame_equal(actual_df_from_df_entities, table_from_df_entities)
 
     # If request data is missing that's needed for on demand transform, throw an error
     with pytest.raises(RequestDataNotFoundInEntityDfException):

sdk/python/tests/integration/registration/test_universal_types.py

@@ -288,10 +288,12 @@ def assert_expected_arrow_types(
     ]
     if feature_is_list:
         if provider == "gcp":
-            assert (
-                str(historical_features_arrow.schema.field_by_name("value").type)
-                == f"struct<list: list<item: struct<item: {arrow_type}>> not null>"
-            )
+            assert str(
+                historical_features_arrow.schema.field_by_name("value").type
+            ) in [
+                f"struct<list: list<item: struct<item: {arrow_type}>> not null>",
+                f"struct<list: list<item: struct<item: {arrow_type}>>>",
+            ]
         else:
             assert (
                 str(historical_features_arrow.schema.field_by_name("value").type)