feat: Updating FeatureViewProjection and OnDemandFeatureView to add b…

…atch_source and entities (#4530) * feat: Updating protos for Projections to include more info Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> * adding unit test Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> * adding type checking where batch source is already serialized into protobuf Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> * almost got everything working and type validation behaving Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> * cleaned up and have tests behaving Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> * removed comment Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> * updated FeatureViewProjection batch_source serialization Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> * trying to debug a test Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> * handling snowflake issue, cant confirm why it is happening so just going to put a workaround Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> * linter Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> * trying to handle it correctly Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> * handling the else case for from_feature_view_definition Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> * adding print Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> * adding test of issue Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> * think i got everything working now Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> * removing print Signed-off-by: Francisco Javier Arceo <farceo@redhat.com> --------- Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
feast-dev · Sep 23, 2024 · 0795496 · 0795496
1 parent d5ef57e
commit 0795496
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Showing 12 changed files with 363 additions and 34 deletions.
diff --git a/protos/feast/core/FeatureViewProjection.proto b/protos/feast/core/FeatureViewProjection.proto
@@ -6,6 +6,7 @@ option java_outer_classname = "FeatureReferenceProto";
 option java_package = "feast.proto.core";
 
 import "feast/core/Feature.proto";
+import "feast/core/DataSource.proto";
 
 
 // A projection to be applied on top of a FeatureView. 
@@ -22,4 +23,13 @@ message FeatureViewProjection {
 
   // Map for entity join_key overrides of feature data entity join_key to entity data join_key
   map<string,string> join_key_map = 4;
+
+  string timestamp_field = 5;
+  string date_partition_column = 6;
+  string created_timestamp_column = 7;
+  // Batch/Offline DataSource where this view can retrieve offline feature data.
+  DataSource batch_source = 8;
+  // Streaming DataSource from where this view can consume "online" feature data.
+  DataSource stream_source = 9;
+
 }
diff --git a/protos/feast/core/OnDemandFeatureView.proto b/protos/feast/core/OnDemandFeatureView.proto
@@ -63,6 +63,12 @@ message OnDemandFeatureViewSpec {
     // Owner of the on demand feature view.
     string owner = 8;
     string mode = 11;
+    bool write_to_online_store = 12;
+
+    // List of names of entities associated with this feature view.
+    repeated string entities = 13;
+    // List of specifications for each entity defined as part of this feature view.
+    repeated FeatureSpecV2 entity_columns = 14;
 }
 
 message OnDemandFeatureViewMeta {

diff --git a/sdk/python/feast/base_feature_view.py b/sdk/python/feast/base_feature_view.py
@@ -18,6 +18,7 @@
 from google.protobuf.json_format import MessageToJson
 from google.protobuf.message import Message
 
+from feast.data_source import DataSource
 from feast.feature_view_projection import FeatureViewProjection
 from feast.field import Field
 from feast.protos.feast.core.FeatureView_pb2 import FeatureView as FeatureViewProto
@@ -65,6 +66,7 @@ def __init__(
         description: str = "",
         tags: Optional[Dict[str, str]] = None,
         owner: str = "",
+        source: Optional[DataSource] = None,
     ):
         """
         Creates a BaseFeatureView object.
@@ -76,7 +78,8 @@ def __init__(
             tags (optional): A dictionary of key-value pairs to store arbitrary metadata.
             owner (optional): The owner of the base feature view, typically the email of the
                 primary maintainer.
-
+            source (optional): The source of data for this group of features. May be a stream source, or a batch source.
+                If a stream source, the source should contain a batch_source for backfills & batch materialization.
         Raises:
             ValueError: A field mapping conflicts with an Entity or a Feature.
         """
@@ -90,6 +93,9 @@ def __init__(
         self.created_timestamp = None
         self.last_updated_timestamp = None
 
+        if source:
+            self.source = source
+
     @property
     @abstractmethod
     def proto_class(self) -> Type[Message]:
@@ -156,6 +162,10 @@ def __eq__(self, other):
             or self.tags != other.tags
             or self.owner != other.owner
         ):
+            # This is meant to ignore the File Source change to Push Source
+            if isinstance(type(self.source), type(other.source)):
+                if self.source != other.source:
+                    return False
             return False
 
         return True

diff --git a/sdk/python/feast/feature_view.py b/sdk/python/feast/feature_view.py
@@ -206,6 +206,7 @@ def __init__(
             description=description,
             tags=tags,
             owner=owner,
+            source=source,
         )
         self.online = online
         self.materialization_intervals = []
@@ -429,7 +430,9 @@ def from_proto(cls, feature_view_proto: FeatureViewProto):
 
         # FeatureViewProjections are not saved in the FeatureView proto.
         # Create the default projection.
-        feature_view.projection = FeatureViewProjection.from_definition(feature_view)
+        feature_view.projection = FeatureViewProjection.from_feature_view_definition(
+            feature_view
+        )
 
         if feature_view_proto.meta.HasField("created_timestamp"):
             feature_view.created_timestamp = (

diff --git a/sdk/python/feast/feature_view_projection.py b/sdk/python/feast/feature_view_projection.py
@@ -2,13 +2,15 @@
 
 from attr import dataclass
 
+from feast.data_source import DataSource
 from feast.field import Field
 from feast.protos.feast.core.FeatureViewProjection_pb2 import (
     FeatureViewProjection as FeatureViewProjectionProto,
 )
 
 if TYPE_CHECKING:
     from feast.base_feature_view import BaseFeatureView
+    from feast.feature_view import FeatureView
 
 
 @dataclass
@@ -27,50 +29,120 @@ class FeatureViewProjection:
             is not ready to be projected, i.e. still needs to go through feature inference.
         join_key_map: A map to modify join key columns during retrieval of this feature
             view projection.
+        timestamp_field: The timestamp field of the feature view projection.
+        date_partition_column: The date partition column of the feature view projection.
+        created_timestamp_column: The created timestamp column of the feature view projection.
+        batch_source: The batch source of data where this group of features
+            is stored. This is optional ONLY if a push source is specified as the
+            stream_source, since push sources contain their own batch sources.
+
     """
 
     name: str
     name_alias: Optional[str]
     desired_features: List[str]
     features: List[Field]
     join_key_map: Dict[str, str] = {}
+    timestamp_field: Optional[str] = None
+    date_partition_column: Optional[str] = None
+    created_timestamp_column: Optional[str] = None
+    batch_source: Optional[DataSource] = None
 
     def name_to_use(self):
         return self.name_alias or self.name
 
     def to_proto(self) -> FeatureViewProjectionProto:
+        batch_source = None
+        if getattr(self, "batch_source", None):
+            if isinstance(self.batch_source, DataSource):
+                batch_source = self.batch_source.to_proto()
+            else:
+                batch_source = self.batch_source
         feature_reference_proto = FeatureViewProjectionProto(
             feature_view_name=self.name,
             feature_view_name_alias=self.name_alias or "",
             join_key_map=self.join_key_map,
+            timestamp_field=self.timestamp_field or "",
+            date_partition_column=self.date_partition_column or "",
+            created_timestamp_column=self.created_timestamp_column or "",
+            batch_source=batch_source,
         )
         for feature in self.features:
             feature_reference_proto.feature_columns.append(feature.to_proto())
 
         return feature_reference_proto
 
     @staticmethod
-    def from_proto(proto: FeatureViewProjectionProto):
+    def from_proto(proto: FeatureViewProjectionProto) -> "FeatureViewProjection":
+        batch_source = (
+            DataSource.from_proto(proto.batch_source)
+            if str(getattr(proto, "batch_source"))
+            else None
+        )
         feature_view_projection = FeatureViewProjection(
             name=proto.feature_view_name,
             name_alias=proto.feature_view_name_alias or None,
             features=[],
             join_key_map=dict(proto.join_key_map),
             desired_features=[],
+            timestamp_field=proto.timestamp_field or None,
+            date_partition_column=proto.date_partition_column or None,
+            created_timestamp_column=proto.created_timestamp_column or None,
+            batch_source=batch_source,
         )
         for feature_column in proto.feature_columns:
             feature_view_projection.features.append(Field.from_proto(feature_column))
 
         return feature_view_projection
 
+    @staticmethod
+    def from_feature_view_definition(feature_view: "FeatureView"):
+        # TODO need to implement this for StreamFeatureViews
+        if getattr(feature_view, "batch_source", None):
+            return FeatureViewProjection(
+                name=feature_view.name,
+                name_alias=None,
+                features=feature_view.features,
+                desired_features=[],
+                timestamp_field=feature_view.batch_source.created_timestamp_column
+                or None,
+                created_timestamp_column=feature_view.batch_source.created_timestamp_column
+                or None,
+                date_partition_column=feature_view.batch_source.date_partition_column
+                or None,
+                batch_source=feature_view.batch_source or None,
+            )
+        else:
+            return FeatureViewProjection(
+                name=feature_view.name,
+                name_alias=None,
+                features=feature_view.features,
+                desired_features=[],
+            )
+
     @staticmethod
     def from_definition(base_feature_view: "BaseFeatureView"):
-        return FeatureViewProjection(
-            name=base_feature_view.name,
-            name_alias=None,
-            features=base_feature_view.features,
-            desired_features=[],
-        )
+        if getattr(base_feature_view, "batch_source", None):
+            return FeatureViewProjection(
+                name=base_feature_view.name,
+                name_alias=None,
+                features=base_feature_view.features,
+                desired_features=[],
+                timestamp_field=base_feature_view.batch_source.created_timestamp_column  # type:ignore[attr-defined]
+                or None,
+                created_timestamp_column=base_feature_view.batch_source.created_timestamp_column  # type:ignore[attr-defined]
+                or None,
+                date_partition_column=base_feature_view.batch_source.date_partition_column  # type:ignore[attr-defined]
+                or None,
+                batch_source=base_feature_view.batch_source or None,  # type:ignore[attr-defined]
+            )
+        else:
+            return FeatureViewProjection(
+                name=base_feature_view.name,
+                name_alias=None,
+                features=base_feature_view.features,
+                desired_features=[],
+            )
 
     def get_feature(self, feature_name: str) -> Field:
         try: