feat: Support compute engine to use multi feature views as source

protos/feast/core/FeatureView.proto

@@ -79,6 +79,8 @@ message FeatureViewSpec {
 
     // Whether these features should be written to the offline store
     bool offline = 13;
+
+    repeated FeatureViewSpec source_views = 14;
 }
 
 message FeatureViewMeta {

sdk/python/feast/batch_feature_view.py

@@ -53,6 +53,7 @@ class BatchFeatureView(FeatureView):
     entities: List[str]
     ttl: Optional[timedelta]
     source: DataSource
+    sink_source: Optional[DataSource] = None
     schema: List[Field]
     entity_columns: List[Field]
     features: List[Field]
@@ -65,7 +66,7 @@ class BatchFeatureView(FeatureView):
     materialization_intervals: List[Tuple[datetime, datetime]]
     udf: Optional[Callable[[Any], Any]]
     udf_string: Optional[str]
-    feature_transformation: Transformation
+    feature_transformation: Optional[Transformation]
     batch_engine: Optional[Field]
     aggregations: Optional[List[Aggregation]]
 
@@ -74,7 +75,8 @@ def __init__(
         *,
         name: str,
         mode: Union[TransformationMode, str] = TransformationMode.PYTHON,
-        source: DataSource,
+        source: Union[DataSource, "BatchFeatureView", List["BatchFeatureView"]],
+        sink_source: Optional[DataSource] = None,
         entities: Optional[List[Entity]] = None,
         ttl: Optional[timedelta] = None,
         tags: Optional[Dict[str, str]] = None,
@@ -83,7 +85,7 @@ def __init__(
         description: str = "",
         owner: str = "",
         schema: Optional[List[Field]] = None,
-        udf: Optional[Callable[[Any], Any]],
+        udf: Optional[Callable[[Any], Any]] = None,
         udf_string: Optional[str] = "",
         feature_transformation: Optional[Transformation] = None,
         batch_engine: Optional[Field] = None,
@@ -96,7 +98,7 @@ def __init__(
                 RuntimeWarning,
             )
 
-        if (
+        if isinstance(source, DataSource) and (
             type(source).__name__ not in SUPPORTED_BATCH_SOURCES
             and source.to_proto().type != DataSourceProto.SourceType.CUSTOM_SOURCE
         ):
@@ -124,14 +126,13 @@ def __init__(
             description=description,
             owner=owner,
             schema=schema,
-            source=source,
+            source=source,  # type: ignore[arg-type]
+            sink_source=sink_source,
         )
 
-    def get_feature_transformation(self) -> Transformation:
+    def get_feature_transformation(self) -> Optional[Transformation]:
         if not self.udf:
-            raise ValueError(
-                "Either a UDF or a feature transformation must be provided for BatchFeatureView"
-            )
+            return None
         if self.mode in (
             TransformationMode.PANDAS,
             TransformationMode.PYTHON,

sdk/python/feast/feature_store.py

@@ -922,7 +922,7 @@ def apply(
         for fv in itertools.chain(
             views_to_update, sfvs_to_update, odfvs_with_writes_to_update
         ):
-            if isinstance(fv, FeatureView):
+            if isinstance(fv, FeatureView) and fv.batch_source:
                 data_sources_set_to_update.add(fv.batch_source)
             if hasattr(fv, "stream_source"):
                 if fv.stream_source:

sdk/python/feast/feature_view.py

@@ -14,7 +14,7 @@
 import copy
 import warnings
 from datetime import datetime, timedelta
-from typing import Dict, List, Optional, Tuple, Type
+from typing import Dict, List, Optional, Tuple, Type, Union
 
 from google.protobuf.duration_pb2 import Duration
 from google.protobuf.message import Message
@@ -90,6 +90,7 @@ class FeatureView(BaseFeatureView):
     ttl: Optional[timedelta]
     batch_source: DataSource
     stream_source: Optional[DataSource]
+    source_views: Optional[List["FeatureView"]]
     entity_columns: List[Field]
     features: List[Field]
     online: bool
@@ -103,7 +104,8 @@ def __init__(
         self,
         *,
         name: str,
-        source: DataSource,
+        source: Union[DataSource, "FeatureView", List["FeatureView"]],
+        sink_source: Optional[DataSource] = None,
         schema: Optional[List[Field]] = None,
         entities: Optional[List[Entity]] = None,
         ttl: Optional[timedelta] = timedelta(days=0),
@@ -144,22 +146,45 @@ def __init__(
         self.ttl = ttl
         schema = schema or []
 
-        # Initialize data sources.
+        # Normalize source
+        self.stream_source = None
+        self.data_source: Optional[DataSource] = None
+        self.source_views: List[FeatureView] = []
+
+        if isinstance(source, DataSource):
+            self.data_source = source
+        elif isinstance(source, FeatureView):
+            self.source_views = [source]
+        elif isinstance(source, list) and all(
+            isinstance(sv, FeatureView) for sv in source
+        ):
+            self.source_views = source
+        else:
+            raise TypeError(
+                "source must be a DataSource, a FeatureView, or a list of FeatureView."
+            )
+
+        # Set up stream, batch and derived view sources
         if (
-            isinstance(source, PushSource)
-            or isinstance(source, KafkaSource)
-            or isinstance(source, KinesisSource)
+            isinstance(self.data_source, PushSource)
+            or isinstance(self.data_source, KafkaSource)
+            or isinstance(self.data_source, KinesisSource)
         ):
-            self.stream_source = source
-            if not source.batch_source:
+            # Stream source definition
+            self.stream_source = self.data_source
+            if not self.data_source.batch_source:
                 raise ValueError(
-                    f"A batch_source needs to be specified for stream source `{source.name}`"
+                    f"A batch_source needs to be specified for stream source `{self.data_source.name}`"
                 )
-            else:
-                self.batch_source = source.batch_source
+            self.batch_source = self.data_source.batch_source
+        elif self.data_source:
+            # Batch source definition
+            self.batch_source = self.data_source
         else:
-            self.stream_source = None
-            self.batch_source = source
+            # Derived view source definition
+            if not sink_source:
+                raise ValueError("Derived FeatureView must specify `sink_source`.")
+            self.batch_source = sink_source
 
         # Initialize features and entity columns.
         features: List[Field] = []
@@ -201,25 +226,26 @@ def __init__(
         )
 
         # TODO(felixwang9817): Add more robust validation of features.
-        cols = [field.name for field in schema]
-        for col in cols:
-            if (
-                self.batch_source.field_mapping is not None
-                and col in self.batch_source.field_mapping.keys()
-            ):
-                raise ValueError(
-                    f"The field {col} is mapped to {self.batch_source.field_mapping[col]} for this data source. "
-                    f"Please either remove this field mapping or use {self.batch_source.field_mapping[col]} as the "
-                    f"Entity or Feature name."
-                )
+        if self.batch_source is not None:
+            cols = [field.name for field in schema]
+            for col in cols:
+                if (
+                    self.batch_source.field_mapping is not None
+                    and col in self.batch_source.field_mapping.keys()
+                ):
+                    raise ValueError(
+                        f"The field {col} is mapped to {self.batch_source.field_mapping[col]} for this data source. "
+                        f"Please either remove this field mapping or use {self.batch_source.field_mapping[col]} as the "
+                        f"Entity or Feature name."
+                    )
 
         super().__init__(
             name=name,
             features=features,
             description=description,
             tags=tags,
             owner=owner,
-            source=source,
+            source=self.batch_source,
         )
         self.online = online
         self.offline = offline
@@ -348,13 +374,18 @@ def to_proto(self) -> FeatureViewProto:
         meta = self.to_proto_meta()
         ttl_duration = self.get_ttl_duration()
 
-        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__}"
+        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__}"
 
         stream_source_proto = None
         if self.stream_source:
             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__}"
+        source_view_protos = None
+        if self.source_views:
+            source_view_protos = [view.to_proto().spec for view in self.source_views]
         spec = FeatureViewSpecProto(
             name=self.name,
             entities=self.entities,
@@ -368,6 +399,7 @@ def to_proto(self) -> FeatureViewProto:
             offline=self.offline,
             batch_source=batch_source_proto,
             stream_source=stream_source_proto,
+            source_views=source_view_protos,
         )
 
         return FeatureViewProto(spec=spec, meta=meta)
@@ -403,12 +435,21 @@ def from_proto(cls, feature_view_proto: FeatureViewProto):
         Returns:
             A FeatureViewProto object based on the feature view protobuf.
         """
-        batch_source = DataSource.from_proto(feature_view_proto.spec.batch_source)
+        batch_source = (
+            DataSource.from_proto(feature_view_proto.spec.batch_source)
+            if feature_view_proto.spec.HasField("batch_source")
+            else None
+        )
         stream_source = (
             DataSource.from_proto(feature_view_proto.spec.stream_source)
             if feature_view_proto.spec.HasField("stream_source")
             else None
         )
+        source_views = [
+            FeatureView.from_proto(FeatureViewProto(spec=view_spec, meta=None))
+            for view_spec in feature_view_proto.spec.source_views
+        ]
+
         feature_view = cls(
             name=feature_view_proto.spec.name,
             description=feature_view_proto.spec.description,
@@ -421,7 +462,7 @@ def from_proto(cls, feature_view_proto: FeatureViewProto):
                 if feature_view_proto.spec.ttl.ToNanoseconds() == 0
                 else feature_view_proto.spec.ttl.ToTimedelta()
             ),
-            source=batch_source,
+            source=batch_source if batch_source else source_views,
         )
         if stream_source:
             feature_view.stream_source = stream_source

sdk/python/feast/inference.py

@@ -26,6 +26,8 @@ def update_data_sources_with_inferred_event_timestamp_col(
 ) -> None:
     ERROR_MSG_PREFIX = "Unable to infer DataSource timestamp_field"
     for data_source in data_sources:
+        if data_source is None:
+            continue
         if isinstance(data_source, RequestSource):
             continue
         if isinstance(data_source, PushSource):

sdk/python/feast/infra/compute_engines/algorithms/topo.py

@@ -0,0 +1,43 @@
+from typing import List, Set
+
+from feast.infra.compute_engines.dag.node import DAGNode
+
+
+def topo_sort(root: DAGNode) -> List[DAGNode]:
+    """
+    Topologically sort a DAG starting from a single root node.
+
+    Args:
+        root: The root DAGNode.
+
+    Returns:
+        A list of DAGNodes in topological order (dependencies first).
+    """
+    return topo_sort_multiple([root])
+
+
+def topo_sort_multiple(roots: List[DAGNode]) -> List[DAGNode]:
+    """
+    Topologically sort a DAG with multiple roots.
+
+    Args:
+        roots: List of root DAGNodes.
+
+    Returns:
+        A list of all reachable DAGNodes in execution-safe order.
+    """
+    visited: Set[int] = set()
+    ordered: List[DAGNode] = []
+
+    def dfs(node: DAGNode):
+        if id(node) in visited:
+            return
+        visited.add(id(node))
+        for input_node in node.inputs:
+            dfs(input_node)
+        ordered.append(node)
+
+    for root in roots:
+        dfs(root)
+
+    return ordered

sdk/python/feast/infra/compute_engines/base.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import List, Optional, Sequence, Union
+from typing import List, Sequence, Union
 
 import pyarrow as pa
 
@@ -12,13 +12,12 @@
     MaterializationTask,
 )
 from feast.infra.common.retrieval_task import HistoricalRetrievalTask
-from feast.infra.compute_engines.dag.context import ColumnInfo, ExecutionContext
+from feast.infra.compute_engines.dag.context import ExecutionContext
 from feast.infra.offline_stores.offline_store import OfflineStore, RetrievalJob
 from feast.infra.online_stores.online_store import OnlineStore
 from feast.infra.registry.base_registry import BaseRegistry
 from feast.on_demand_feature_view import OnDemandFeatureView
 from feast.stream_feature_view import StreamFeatureView
-from feast.utils import _get_column_names
 
 
 class ComputeEngine(ABC):
@@ -124,52 +123,11 @@ def get_execution_context(
         if hasattr(task, "entity_df") and task.entity_df is not None:
             entity_df = task.entity_df
 
-        column_info = self.get_column_info(registry, task)
         return ExecutionContext(
             project=task.project,
             repo_config=self.repo_config,
             offline_store=self.offline_store,
             online_store=self.online_store,
             entity_defs=entity_defs,
-            column_info=column_info,
             entity_df=entity_df,
         )
-
-    def get_column_info(
-        self,
-        registry: BaseRegistry,
-        task: Union[MaterializationTask, HistoricalRetrievalTask],
-    ) -> ColumnInfo:
-        entities = []
-        for entity_name in task.feature_view.entities:
-            entities.append(registry.get_entity(entity_name, task.project))
-
-        join_keys, feature_cols, ts_col, created_ts_col = _get_column_names(
-            task.feature_view, entities
-        )
-        field_mapping = self.get_field_mapping(task.feature_view)
-
-        return ColumnInfo(
-            join_keys=join_keys,
-            feature_cols=feature_cols,
-            ts_col=ts_col,
-            created_ts_col=created_ts_col,
-            field_mapping=field_mapping,
-        )
-
-    def get_field_mapping(
-        self, feature_view: Union[BatchFeatureView, StreamFeatureView, FeatureView]
-    ) -> Optional[dict]:
-        """
-        Get the field mapping for a feature view.
-        Args:
-            feature_view: The feature view to get the field mapping for.
-
-        Returns:
-            A dictionary mapping field names to column names.
-        """
-        if feature_view.stream_source:
-            return feature_view.stream_source.field_mapping
-        if feature_view.batch_source:
-            return feature_view.batch_source.field_mapping
-        return None