fix: Update udf tests and add base functions to streaming fcos and fix some nonetype errors

sdk/python/feast/data_source.py

@@ -503,7 +503,7 @@ def __hash__(self):
     @staticmethod
     def from_proto(data_source: DataSourceProto):
         watermark = None
-        if data_source.kafka_options.HasField("watermark"):
+        if data_source.kafka_options.watermark:
             watermark = (
                 timedelta(days=0)
                 if data_source.kafka_options.watermark.ToNanoseconds() == 0

sdk/python/feast/stream_feature_view.py

@@ -1,3 +1,4 @@
+import copy
 import functools
 import warnings
 from datetime import timedelta
@@ -9,7 +10,7 @@
 
 from feast import utils
 from feast.aggregation import Aggregation
-from feast.data_source import DataSource, KafkaSource
+from feast.data_source import DataSource, KafkaSource, PushSource
 from feast.entity import Entity
 from feast.feature_view import FeatureView
 from feast.field import Field
@@ -39,6 +40,26 @@ class StreamFeatureView(FeatureView):
     """
     NOTE: Stream Feature Views are not yet fully implemented and exist to allow users to register their stream sources and
     schemas with Feast.
+
+    Attributes:
+        name: str. The unique name of the stream feature view.
+        entities: Union[List[Entity], List[str]]. List of entities or entity join keys.
+        ttl: timedelta. The amount of time this group of features lives. A ttl of 0 indicates that
+            this group of features lives forever. Note that large ttl's or a ttl of 0
+            can result in extremely computationally intensive queries.
+        tags: Dict[str, str]. A dictionary of key-value pairs to store arbitrary metadata.
+        online: bool. Defines whether this stream feature view is used in online feature retrieval.
+        description: str. A human-readable description.
+        owner: The owner of the on demand feature view, typically the email of the primary
+            maintainer.
+        schema: List[Field] The schema of the feature view, including feature, timestamp, and entity
+            columns. If not specified, can be inferred from the underlying data source.
+        source: DataSource. The stream source of data where this group of features
+            is stored.
+        aggregations (optional): List[Aggregation]. List of aggregations registered with the stream feature view.
+        mode(optional): str. The mode of execution.
+        timestamp_field (optional): Must be specified if aggregations are specified. Defines the timestamp column on which to aggregate windows.
+        udf (optional): MethodType The user defined transformation function. This transformation function should have all of the corresponding imports imported within the function.
     """
 
     def __init__(
@@ -54,18 +75,19 @@ def __init__(
         schema: Optional[List[Field]] = None,
         source: Optional[DataSource] = None,
         aggregations: Optional[List[Aggregation]] = None,
-        mode: Optional[str] = "spark",  # Mode of ingestion/transformation
-        timestamp_field: Optional[str] = "",  # Timestamp for aggregation
+        mode: Optional[str] = "spark",
+        timestamp_field: Optional[str] = "",
         udf: Optional[MethodType] = None,
     ):
         warnings.warn(
             "Stream Feature Views are experimental features in alpha development. "
             "Some functionality may still be unstable so functionality can change in the future.",
             RuntimeWarning,
         )
+
         if source is None:
-            raise ValueError("Stream Feature views need a source specified")
-        # source uses the batch_source of the kafkasource in feature_view
+            raise ValueError("Stream Feature views need a source to be specified")
+
         if (
             type(source).__name__ not in SUPPORTED_STREAM_SOURCES
             and source.to_proto().type != DataSourceProto.SourceType.CUSTOM_SOURCE
@@ -74,18 +96,26 @@ def __init__(
                 f"Stream feature views need a stream source, expected one of {SUPPORTED_STREAM_SOURCES} "
                 f"or CUSTOM_SOURCE, got {type(source).__name__}: {source.name} instead "
             )
+
+        if aggregations and not timestamp_field:
+            raise ValueError(
+                "aggregations must have a timestamp field associated with them to perform the aggregations"
+            )
+
         self.aggregations = aggregations or []
-        self.mode = mode
-        self.timestamp_field = timestamp_field
+        self.mode = mode or ""
+        self.timestamp_field = timestamp_field or ""
         self.udf = udf
         _batch_source = None
-        if isinstance(source, KafkaSource):
+        if isinstance(source, KafkaSource) or isinstance(source, PushSource):
             _batch_source = source.batch_source if source.batch_source else None
-
+        _ttl = ttl
+        if not _ttl:
+            _ttl = timedelta(days=0)
         super().__init__(
             name=name,
             entities=entities,
-            ttl=ttl,
+            ttl=_ttl,
             batch_source=_batch_source,
             stream_source=source,
             tags=tags,
@@ -102,7 +132,10 @@ def __eq__(self, other):
 
         if not super().__eq__(other):
             return False
-
+        if not self.udf:
+            return not other.udf
+        if not other.udf:
+            return False
         if (
             self.mode != other.mode
             or self.timestamp_field != other.timestamp_field
@@ -113,13 +146,14 @@ def __eq__(self, other):
 
         return True
 
-    def __hash__(self):
+    def __hash__(self) -> int:
         return super().__hash__()
 
     def to_proto(self):
         meta = StreamFeatureViewMetaProto(materialization_intervals=[])
         if self.created_timestamp:
             meta.created_timestamp.FromDatetime(self.created_timestamp)
+
         if self.last_updated_timestamp:
             meta.last_updated_timestamp.FromDatetime(self.last_updated_timestamp)
 
@@ -134,6 +168,7 @@ def to_proto(self):
             ttl_duration = Duration()
             ttl_duration.FromTimedelta(self.ttl)
 
+        batch_source_proto = None
         if self.batch_source:
             batch_source_proto = self.batch_source.to_proto()
             batch_source_proto.data_source_class_type = f"{self.batch_source.__class__.__module__}.{self.batch_source.__class__.__name__}"
@@ -143,23 +178,24 @@ def to_proto(self):
             stream_source_proto = self.stream_source.to_proto()
             stream_source_proto.data_source_class_type = f"{self.stream_source.__class__.__module__}.{self.stream_source.__class__.__name__}"
 
+        udf_proto = None
+        if self.udf:
+            udf_proto = UserDefinedFunctionProto(
+                name=self.udf.__name__, body=dill.dumps(self.udf, recurse=True),
+            )
         spec = StreamFeatureViewSpecProto(
             name=self.name,
             entities=self.entities,
             entity_columns=[field.to_proto() for field in self.entity_columns],
             features=[field.to_proto() for field in self.schema],
-            user_defined_function=UserDefinedFunctionProto(
-                name=self.udf.__name__, body=dill.dumps(self.udf, recurse=True),
-            )
-            if self.udf
-            else None,
+            user_defined_function=udf_proto,
             description=self.description,
             tags=self.tags,
             owner=self.owner,
-            ttl=(ttl_duration if ttl_duration is not None else None),
+            ttl=ttl_duration,
             online=self.online,
             batch_source=batch_source_proto or None,
-            stream_source=stream_source_proto,
+            stream_source=stream_source_proto or None,
             timestamp_field=self.timestamp_field,
             aggregations=[agg.to_proto() for agg in self.aggregations],
             mode=self.mode,
@@ -239,6 +275,25 @@ def from_proto(cls, sfv_proto):
 
         return sfv_feature_view
 
+    def __copy__(self):
+        fv = StreamFeatureView(
+            name=self.name,
+            schema=self.schema,
+            entities=self.entities,
+            ttl=self.ttl,
+            tags=self.tags,
+            online=self.online,
+            description=self.description,
+            owner=self.owner,
+            aggregations=self.aggregations,
+            mode=self.mode,
+            timestamp_field=self.timestamp_field,
+            sources=self.sources,
+            udf=self.udf,
+        )
+        fv.projection = copy.copy(self.projection)
+        return fv
+
 
 def stream_feature_view(
     *,
@@ -251,11 +306,13 @@ def stream_feature_view(
     schema: Optional[List[Field]] = None,
     source: Optional[DataSource] = None,
     aggregations: Optional[List[Aggregation]] = None,
-    mode: Optional[str] = "spark",  # Mode of ingestion/transformation
-    timestamp_field: Optional[str] = "",  # Timestamp for aggregation
+    mode: Optional[str] = "spark",
+    timestamp_field: Optional[str] = "",
 ):
     """
     Creates an StreamFeatureView object with the given user function as udf.
+    Please make sure that the udf contains all non-built in imports within the function to ensure that the execution
+    of a deserialized function does not miss imports.
     """
 
     def mainify(obj):

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

@@ -70,3 +70,71 @@ def simple_sfv(df):
     assert features["test_key"] == [1001]
     assert "dummy_field" in features
     assert features["dummy_field"] == [None]
+
+
+@pytest.mark.integration
+def test_stream_feature_view_udf(environment) -> None:
+    """
+    Test apply of StreamFeatureView udfs are serialized correctly and usable.
+    """
+    fs = environment.feature_store
+
+    # Create Feature Views
+    entity = Entity(name="driver_entity", join_keys=["test_key"])
+
+    stream_source = KafkaSource(
+        name="kafka",
+        timestamp_field="event_timestamp",
+        bootstrap_servers="",
+        message_format=AvroFormat(""),
+        topic="topic",
+        batch_source=FileSource(path="test_path", timestamp_field="event_timestamp"),
+        watermark=timedelta(days=1),
+    )
+
+    @stream_feature_view(
+        entities=[entity],
+        ttl=timedelta(days=30),
+        owner="[email protected]",
+        online=True,
+        schema=[Field(name="dummy_field", dtype=Float32)],
+        description="desc",
+        aggregations=[
+            Aggregation(
+                column="dummy_field", function="max", time_window=timedelta(days=1),
+            ),
+            Aggregation(
+                column="dummy_field2", function="count", time_window=timedelta(days=24),
+            ),
+        ],
+        timestamp_field="event_timestamp",
+        mode="spark",
+        source=stream_source,
+        tags={},
+    )
+    def pandas_view(pandas_df):
+        import pandas as pd
+
+        assert type(pandas_df) == pd.DataFrame
+        df = pandas_df.transform(lambda x: x + 10, axis=1)
+        df.insert(2, "C", [20.2, 230.0, 34.0], True)
+        return df
+
+    import pandas as pd
+
+    df = pd.DataFrame({"A": [1, 2, 3], "B": [10, 20, 30]})
+
+    fs.apply([entity, pandas_view])
+    stream_feature_views = fs.list_stream_feature_views()
+    assert len(stream_feature_views) == 1
+    assert stream_feature_views[0].name == "pandas_view"
+    assert stream_feature_views[0] == pandas_view
+
+    sfv = stream_feature_views[0]
+
+    new_df = sfv.udf(df)
+
+    expected_df = pd.DataFrame(
+        {"A": [11, 12, 13], "B": [20, 30, 40], "C": [20.2, 230.0, 34.0]}
+    )
+    assert new_df.equals(expected_df)

sdk/python/tests/unit/test_feature_views.py

@@ -9,7 +9,7 @@
 from feast.entity import Entity
 from feast.field import Field
 from feast.infra.offline_stores.file_source import FileSource
-from feast.stream_feature_view import StreamFeatureView
+from feast.stream_feature_view import StreamFeatureView, stream_feature_view
 from feast.types import Float32
 
 
@@ -129,3 +129,75 @@ def test_stream_feature_view_serialization():
 
     new_sfv = StreamFeatureView.from_proto(sfv_proto=sfv_proto)
     assert new_sfv == sfv
+
+
+def test_stream_feature_view_udfs():
+    entity = Entity(name="driver_entity", join_keys=["test_key"])
+    stream_source = KafkaSource(
+        name="kafka",
+        timestamp_field="event_timestamp",
+        bootstrap_servers="",
+        message_format=AvroFormat(""),
+        topic="topic",
+        batch_source=FileSource(path="some path"),
+    )
+
+    @stream_feature_view(
+        entities=[entity],
+        ttl=timedelta(days=30),
+        owner="[email protected]",
+        online=True,
+        schema=[Field(name="dummy_field", dtype=Float32)],
+        description="desc",
+        aggregations=[
+            Aggregation(
+                column="dummy_field", function="max", time_window=timedelta(days=1),
+            )
+        ],
+        timestamp_field="event_timestamp",
+        source=stream_source,
+    )
+    def pandas_udf(pandas_df):
+        import pandas as pd
+
+        assert type(pandas_df) == pd.DataFrame
+        df = pandas_df.transform(lambda x: x + 10, axis=1)
+        return df
+
+    import pandas as pd
+
+    df = pd.DataFrame({"A": [1, 2, 3], "B": [10, 20, 30]})
+    sfv = pandas_udf
+    sfv_proto = sfv.to_proto()
+    new_sfv = StreamFeatureView.from_proto(sfv_proto)
+    new_df = new_sfv.udf(df)
+
+    expected_df = pd.DataFrame({"A": [11, 12, 13], "B": [20, 30, 40]})
+
+    assert new_df.equals(expected_df)
+
+
+def test_stream_feature_view_initialization_with_optional_fields_omitted():
+    entity = Entity(name="driver_entity", join_keys=["test_key"])
+    stream_source = KafkaSource(
+        name="kafka",
+        timestamp_field="event_timestamp",
+        bootstrap_servers="",
+        message_format=AvroFormat(""),
+        topic="topic",
+        batch_source=FileSource(path="some path"),
+    )
+
+    sfv = StreamFeatureView(
+        name="test kafka stream feature view",
+        entities=[entity],
+        schema=[],
+        description="desc",
+        timestamp_field="event_timestamp",
+        source=stream_source,
+        tags={},
+    )
+    sfv_proto = sfv.to_proto()
+
+    new_sfv = StreamFeatureView.from_proto(sfv_proto=sfv_proto)
+    assert new_sfv == sfv