feat: Override the udf name when provided as input to an on demand transformation (#5094)

blaketastic2 · web-flow · commit 8a714bb57093 · 2025-02-25T16:53:38.000-05:00
* allow setting name w/ udf

Signed-off-by: Blake &lt;blaketastic2@gmail.com&gt;

* refactor based on PR review

Signed-off-by: Blake &lt;blaketastic2@gmail.com&gt;

* removed comments

Signed-off-by: Blake &lt;blaketastic2@gmail.com&gt;

* pr comments

Signed-off-by: Blake &lt;blaketastic2@gmail.com&gt;

---------

Signed-off-by: Blake &lt;blaketastic2@gmail.com&gt;
diff --git a/README.md b/README.md
@@ -202,7 +202,7 @@ The list below contains the functionality that contributors are planning to deve
   * [x] Python Client
   * [x] [Python feature server](https://docs.feast.dev/reference/feature-servers/python-feature-server)
   * [x] [Java feature server (alpha)](https://github.com/feast-dev/feast/blob/master/infra/charts/feast/README.md)
-  * [x] [Go feature server (alpha)](https://github.com/feast-dev/feast/blob/master/go/README.md)
+  * [x] [Go feature server (alpha)](https://docs.feast.dev/reference/feature-servers/go-feature-server)
 * **Data Quality Management (See [RFC](https://docs.google.com/document/d/110F72d4NTv80p35wDSONxhhPBqWRwbZXG4f9mNEMd98/edit))**
   * [x] Data profiling and validation (Great Expectations)
 * **Feature Discovery and Governance**
@@ -249,4 +249,4 @@ Thanks goes to these incredible people:
 
 <a href="https://github.com/feast-dev/feast/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=feast-dev/feast" />
-</a>
+</a>
diff --git a/sdk/python/feast/on_demand_feature_view.py b/sdk/python/feast/on_demand_feature_view.py
@@ -722,6 +722,7 @@ def get_requested_odfvs(
 
 def on_demand_feature_view(
     *,
+    name: Optional[str] = None,
     entities: Optional[List[Entity]] = None,
     schema: list[Field],
     sources: list[
@@ -742,6 +743,7 @@ def on_demand_feature_view(
     Creates an OnDemandFeatureView object with the given user function as udf.
 
     Args:
+        name (optional): The name of the on demand feature view. If not provided, the name will be the name of the user function.
         entities (Optional): The list of names of entities that this feature view is associated with.
         schema: The list of features in the output of the on demand feature view, after
             the transformation has been applied.
@@ -791,7 +793,7 @@ def decorator(user_function):
             transformation = SubstraitTransformation.from_ibis(user_function, sources)
 
         on_demand_feature_view_obj = OnDemandFeatureView(
-            name=user_function.__name__,
+            name=name if name is not None else user_function.__name__,
             sources=sources,
             schema=schema,
             feature_transformation=transformation,
diff --git a/sdk/python/tests/unit/test_on_demand_feature_view.py b/sdk/python/tests/unit/test_on_demand_feature_view.py
@@ -24,6 +24,7 @@
     OnDemandFeatureView,
     PandasTransformation,
     PythonTransformation,
+    on_demand_feature_view,
 )
 from feast.types import Float32
 
@@ -356,3 +357,65 @@ def test_on_demand_feature_view_stored_writes():
     assert transformed_output["output3"] is not None and isinstance(
         transformed_output["output3"], datetime.datetime
     )
+
+
+def test_function_call_syntax():
+    CUSTOM_FUNCTION_NAME = "custom-function-name"
+    file_source = FileSource(name="my-file-source", path="test.parquet")
+    feature_view = FeatureView(
+        name="my-feature-view",
+        entities=[],
+        schema=[
+            Field(name="feature1", dtype=Float32),
+            Field(name="feature2", dtype=Float32),
+        ],
+        source=file_source,
+    )
+    sources = [feature_view]
+
+    def transform_features(features_df: pd.DataFrame) -> pd.DataFrame:
+        df = pd.DataFrame()
+        df["output1"] = features_df["feature1"]
+        df["output2"] = features_df["feature2"]
+        return df
+
+    odfv = on_demand_feature_view(
+        sources=sources,
+        schema=[
+            Field(name="output1", dtype=Float32),
+            Field(name="output2", dtype=Float32),
+        ],
+    )(transform_features)
+
+    assert odfv.name == transform_features.__name__
+    assert isinstance(odfv, OnDemandFeatureView)
+
+    proto = odfv.to_proto()
+    assert proto.spec.name == transform_features.__name__
+
+    deserialized = OnDemandFeatureView.from_proto(proto)
+    assert deserialized.name == transform_features.__name__
+
+    def another_transform(features_df: pd.DataFrame) -> pd.DataFrame:
+        df = pd.DataFrame()
+        df["output1"] = features_df["feature1"]
+        df["output2"] = features_df["feature2"]
+        return df
+
+    odfv_custom = on_demand_feature_view(
+        name=CUSTOM_FUNCTION_NAME,
+        sources=sources,
+        schema=[
+            Field(name="output1", dtype=Float32),
+            Field(name="output2", dtype=Float32),
+        ],
+    )(another_transform)
+
+    assert odfv_custom.name == CUSTOM_FUNCTION_NAME
+    assert isinstance(odfv_custom, OnDemandFeatureView)
+
+    proto = odfv_custom.to_proto()
+    assert proto.spec.name == CUSTOM_FUNCTION_NAME
+
+    deserialized = OnDemandFeatureView.from_proto(proto)
+    assert deserialized.name == CUSTOM_FUNCTION_NAME
