fix: Update on demand feature view api

docs/tutorials/validating-historical-features.md

@@ -107,7 +107,7 @@ pyarrow.parquet.write_table(entities_2019_table, "entities.parquet")
 import pyarrow.parquet
 import pandas as pd
 
-from feast import FeatureView, Entity, FeatureStore, Field
+from feast import FeatureView, Entity, FeatureStore, Field, BatchFeatureView
 from feast.types import Float64, Int64
 from feast.value_type import ValueType
 from feast.data_format import ParquetFormat
@@ -134,7 +134,7 @@ taxi_entity = Entity(name='taxi', join_keys=['taxi_id'])
 
 
 ```python
-trips_stats_fv = FeatureView(
+trips_stats_fv = BatchFeatureView(
     name='trip_stats',
     entities=['taxi'],
     features=[
@@ -160,9 +160,9 @@ trips_stats_fv = FeatureView(
         Field("avg_trip_seconds", Float64),
         Field("earned_per_hour", Float64),
     ],
-    sources={
-        "stats": trips_stats_fv
-    }
+    sources=[
+      trips_stats_fv,
+    ]
 )
 def on_demand_stats(inp):
     out = pd.DataFrame()

examples/java-demo/feature_repo/driver_repo.py

@@ -7,15 +7,15 @@
 from google.protobuf.duration_pb2 import Duration
 from feast.field import Field
 
-from feast import Entity, Feature, FeatureView, FileSource, ValueType
+from feast import Entity, Feature, BatchFeatureView, FileSource, ValueType
 
 driver_hourly_stats = FileSource(
     path="data/driver_stats_with_string.parquet",
     timestamp_field="event_timestamp",
     created_timestamp_column="created",
 )
 driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id",)
-driver_hourly_stats_view = FeatureView(
+driver_hourly_stats_view = BatchFeatureView(
     name="driver_hourly_stats",
     entities=["driver_id"],
     ttl=Duration(seconds=86400000),
@@ -43,10 +43,10 @@
 # Define an on demand feature view which can generate new features based on
 # existing feature views and RequestSource features
 @on_demand_feature_view(
-    inputs={
-        "driver_hourly_stats": driver_hourly_stats_view,
-        "vals_to_add": input_request,
-    },
+    inputs=[
+        driver_hourly_stats_view,
+        input_request,
+    ],
     schema=[
         Field(name="conv_rate_plus_val1", dtype=Float64),
         Field(name="conv_rate_plus_val2", dtype=Float64),

sdk/python/feast/__init__.py

@@ -7,6 +7,7 @@
 from feast.infra.offline_stores.redshift_source import RedshiftSource
 from feast.infra.offline_stores.snowflake_source import SnowflakeSource
 
+from .batch_feature_view import BatchFeatureView
 from .data_source import (
     KafkaSource,
     KinesisSource,
@@ -23,6 +24,7 @@
 from .on_demand_feature_view import OnDemandFeatureView
 from .repo_config import RepoConfig
 from .request_feature_view import RequestFeatureView
+from .stream_feature_view import StreamFeatureView
 from .value_type import ValueType
 
 logging.basicConfig(
@@ -38,6 +40,7 @@
     pass
 
 __all__ = [
+    "BatchFeatureView",
     "Entity",
     "KafkaSource",
     "KinesisSource",
@@ -49,6 +52,7 @@
     "OnDemandFeatureView",
     "RepoConfig",
     "SourceType",
+    "StreamFeatureView",
     "ValueType",
     "BigQuerySource",
     "FileSource",

sdk/python/feast/feature_view.py

@@ -439,8 +439,9 @@ def from_proto(cls, feature_view_proto: FeatureViewProto):
                 else feature_view_proto.spec.ttl.ToTimedelta()
             ),
             source=batch_source,
-            stream_source=stream_source,
         )
+        if stream_source:
+            feature_view.stream_source = stream_source
 
         # FeatureViewProjections are not saved in the FeatureView proto.
         # Create the default projection.

sdk/python/feast/on_demand_feature_view.py

@@ -8,6 +8,7 @@
 import pandas as pd
 
 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 import Feature
@@ -25,6 +26,7 @@
 from feast.protos.feast.core.OnDemandFeatureView_pb2 import (
     UserDefinedFunction as UserDefinedFunctionProto,
 )
+from feast.stream_feature_view import StreamFeatureView
 from feast.type_map import (
     feast_value_type_to_pandas_type,
     python_type_to_feast_value_type,
@@ -67,13 +69,20 @@ class OnDemandFeatureView(BaseFeatureView):
     owner: str
 
     @log_exceptions
-    def __init__(
+    def __init__(  # noqa: C901
         self,
         *args,
         name: Optional[str] = None,
         features: Optional[List[Feature]] = None,
         sources: Optional[
-            Dict[str, Union[FeatureView, FeatureViewProjection, RequestSource]]
+            List[
+                Union[
+                    BatchFeatureView,
+                    StreamFeatureView,
+                    RequestSource,
+                    FeatureViewProjection,
+                ]
+            ]
         ] = None,
         udf: Optional[MethodType] = None,
         inputs: Optional[
@@ -92,11 +101,11 @@ def __init__(
             features (deprecated): The list of features in the output of the on demand
                 feature view, after the transformation has been applied.
             sources (optional): A map from input source names to the actual input sources,
-                which may be feature views, feature view projections, or request data sources.
+                which may be feature views, or request data sources.
                 These sources serve as inputs to the udf, which will refer to them by name.
             udf (optional): The user defined transformation function, which must take pandas
                 dataframes as inputs.
-            inputs (optional): A map from input source names to the actual input sources,
+            inputs (optional): (Deprecated) A map from input source names to the actual input sources,
                 which may be feature views, feature view projections, or request data sources.
                 These sources serve as inputs to the udf, which will refer to them by name.
             schema (optional): The list of features in the output of the on demand feature
@@ -123,8 +132,7 @@ def __init__(
                 ),
                 DeprecationWarning,
             )
-
-        _sources = sources or inputs
+        _sources = sources or []
         if inputs and sources:
             raise ValueError("At most one of `sources` or `inputs` can be specified.")
         elif inputs:
@@ -135,7 +143,17 @@ def __init__(
                 ),
                 DeprecationWarning,
             )
-
+            for _, source in inputs.items():
+                if isinstance(source, FeatureView):
+                    _sources.append(feature_view_to_batch_feature_view(source))
+                elif isinstance(source, RequestSource) or isinstance(
+                    source, FeatureViewProjection
+                ):
+                    _sources.append(source)
+                else:
+                    raise ValueError(
+                        "input can only accept FeatureView, FeatureViewProjection, or RequestSource"
+                    )
         _udf = udf
 
         if args:
@@ -169,7 +187,18 @@ def __init__(
                     DeprecationWarning,
                 )
             if len(args) >= 3:
-                _sources = args[2]
+                _inputs = args[2]
+                for _, source in _inputs.items():
+                    if isinstance(source, FeatureView):
+                        _sources.append(feature_view_to_batch_feature_view(source))
+                    elif isinstance(source, RequestSource) or isinstance(
+                        source, FeatureViewProjection
+                    ):
+                        _sources.append(source)
+                    else:
+                        raise ValueError(
+                            "input can only accept FeatureView, FeatureViewProjection, or RequestSource"
+                        )
                 warnings.warn(
                     (
                         "The `inputs` parameter is being deprecated. Please use `sources` instead. "
@@ -195,18 +224,17 @@ def __init__(
             tags=tags,
             owner=owner,
         )
-
         assert _sources is not None
         self.source_feature_view_projections: Dict[str, FeatureViewProjection] = {}
         self.source_request_sources: Dict[str, RequestSource] = {}
-        for source_name, odfv_source in _sources.items():
+        for odfv_source in _sources:
             if isinstance(odfv_source, RequestSource):
-                self.source_request_sources[source_name] = odfv_source
+                self.source_request_sources[odfv_source.name] = odfv_source
             elif isinstance(odfv_source, FeatureViewProjection):
-                self.source_feature_view_projections[source_name] = odfv_source
+                self.source_feature_view_projections[odfv_source.name] = odfv_source
             else:
                 self.source_feature_view_projections[
-                    source_name
+                    odfv_source.name
                 ] = odfv_source.projection
 
         if _udf is None:
@@ -219,12 +247,12 @@ def proto_class(self) -> Type[OnDemandFeatureViewProto]:
         return OnDemandFeatureViewProto
 
     def __copy__(self):
+
         fv = OnDemandFeatureView(
             name=self.name,
             schema=self.features,
-            sources=dict(
-                **self.source_feature_view_projections, **self.source_request_sources,
-            ),
+            sources=list(self.source_feature_view_projections.values())
+            + list(self.source_request_sources.values()),
             udf=self.udf,
             description=self.description,
             tags=self.tags,
@@ -302,22 +330,21 @@ def from_proto(cls, on_demand_feature_view_proto: OnDemandFeatureViewProto):
         Returns:
             A OnDemandFeatureView object based on the on-demand feature view protobuf.
         """
-        sources = {}
-        for (
-            source_name,
-            on_demand_source,
-        ) in on_demand_feature_view_proto.spec.sources.items():
+        sources = []
+        for (_, on_demand_source,) in on_demand_feature_view_proto.spec.sources.items():
             if on_demand_source.WhichOneof("source") == "feature_view":
-                sources[source_name] = FeatureView.from_proto(
-                    on_demand_source.feature_view
-                ).projection
+                sources.append(
+                    FeatureView.from_proto(on_demand_source.feature_view).projection
+                )
             elif on_demand_source.WhichOneof("source") == "feature_view_projection":
-                sources[source_name] = FeatureViewProjection.from_proto(
-                    on_demand_source.feature_view_projection
+                sources.append(
+                    FeatureViewProjection.from_proto(
+                        on_demand_source.feature_view_projection
+                    )
                 )
             else:
-                sources[source_name] = RequestSource.from_proto(
-                    on_demand_source.request_data_source
+                sources.append(
+                    RequestSource.from_proto(on_demand_source.request_data_source)
                 )
         on_demand_feature_view_obj = cls(
             name=on_demand_feature_view_proto.spec.name,
@@ -476,7 +503,16 @@ def get_requested_odfvs(feature_refs, project, registry):
 def on_demand_feature_view(
     *args,
     features: Optional[List[Feature]] = None,
-    sources: Optional[Dict[str, Union[FeatureView, RequestSource]]] = None,
+    sources: Optional[
+        List[
+            Union[
+                BatchFeatureView,
+                StreamFeatureView,
+                RequestSource,
+                FeatureViewProjection,
+            ]
+        ]
+    ] = None,
     inputs: Optional[Dict[str, Union[FeatureView, RequestSource]]] = None,
     schema: Optional[List[Field]] = None,
     description: str = "",
@@ -490,7 +526,7 @@ def on_demand_feature_view(
         features (deprecated): The list of features in the output of the on demand
             feature view, after the transformation has been applied.
         sources (optional): A map from input source names to the actual input sources,
-            which may be feature views, feature view projections, or request data sources.
+            which may be feature views, or request data sources.
             These sources serve as inputs to the udf, which will refer to them by name.
         inputs (optional): A map from input source names to the actual input sources,
             which may be feature views, feature view projections, or request data sources.
@@ -517,8 +553,7 @@ def on_demand_feature_view(
             ),
             DeprecationWarning,
         )
-
-    _sources = sources or inputs
+    _sources = sources or []
     if inputs and sources:
         raise ValueError("At most one of `sources` or `inputs` can be specified.")
     elif inputs:
@@ -529,6 +564,17 @@ def on_demand_feature_view(
             ),
             DeprecationWarning,
         )
+        for _, source in inputs.items():
+            if isinstance(source, FeatureView):
+                _sources.append(feature_view_to_batch_feature_view(source))
+            elif isinstance(source, RequestSource) or isinstance(
+                source, FeatureViewProjection
+            ):
+                _sources.append(source)
+            else:
+                raise ValueError(
+                    "input can only accept FeatureView, FeatureViewProjection, or RequestSource"
+                )
 
     if args:
         warnings.warn(
@@ -559,14 +605,25 @@ def on_demand_feature_view(
                 DeprecationWarning,
             )
         if len(args) >= 2:
-            _sources = args[1]
-            warnings.warn(
-                (
-                    "The `inputs` parameter is being deprecated. Please use `sources` instead. "
-                    "Feast 0.21 and onwards will not support the `inputs` parameter."
-                ),
-                DeprecationWarning,
-            )
+            _inputs = args[1]
+            for _, source in _inputs.items():
+                if isinstance(source, FeatureView):
+                    _sources.append(feature_view_to_batch_feature_view(source))
+                elif isinstance(source, RequestSource) or isinstance(
+                    source, FeatureViewProjection
+                ):
+                    _sources.append(source)
+                else:
+                    raise ValueError(
+                        "input can only accept FeatureView, FeatureViewProjection, or RequestSource"
+                    )
+                warnings.warn(
+                    (
+                        "The `inputs` parameter is being deprecated. Please use `sources` instead. "
+                        "Feast 0.21 and onwards will not support the `inputs` parameter."
+                    ),
+                    DeprecationWarning,
+                )
 
     if not _sources:
         raise ValueError("The `sources` parameter must be specified.")
@@ -587,3 +644,16 @@ def decorator(user_function):
         return on_demand_feature_view_obj
 
     return decorator
+
+
+def feature_view_to_batch_feature_view(fv: FeatureView) -> BatchFeatureView:
+    return BatchFeatureView(
+        name=fv.name,
+        entities=fv.entities,
+        ttl=fv.ttl,
+        tags=fv.tags,
+        online=fv.online,
+        owner=fv.owner,
+        schema=fv.schema,
+        source=fv.source,
+    )