feat: Create stream and batch feature view abstractions

CONTRIBUTING.md

@@ -176,6 +176,7 @@ The services with containerized replacements currently implemented are:
 - Datastore
 - DynamoDB
 - Redis
+- Trino
 
 You can run `make test-python-integration-container` to run tests against the containerized versions of dependencies.
 

sdk/python/feast/batch_feature_view.py

@@ -0,0 +1,58 @@
+from datetime import timedelta
+from typing import Dict, List, Optional, Union
+
+from feast.data_source import DataSource
+from feast.entity import Entity
+from feast.feature_view import FeatureView
+from feast.field import Field
+from feast.protos.feast.core.DataSource_pb2 import DataSource as DataSourceProto
+
+SUPPORTED_BATCH_SOURCES = {
+    "BigQuerySource",
+    "FileSource",
+    "RedshiftSource",
+    "SnowflakeSource",
+    "SparkSource",
+    "TrinoSource",
+}
+
+
+class BatchFeatureView(FeatureView):
+    def __init__(
+        self,
+        *,
+        name: Optional[str] = None,
+        entities: Optional[Union[List[Entity], List[str]]] = None,
+        ttl: Optional[timedelta] = None,
+        tags: Optional[Dict[str, str]] = None,
+        online: bool = True,
+        description: str = "",
+        owner: str = "",
+        schema: Optional[List[Field]] = None,
+        source: Optional[DataSource] = None,
+    ):
+
+        if source is None:
+            raise ValueError("Feature views need a source specified")
+        if (
+            type(source).__name__ not in SUPPORTED_BATCH_SOURCES
+            and source.to_proto().type != DataSourceProto.SourceType.CUSTOM_SOURCE
+        ):
+            raise ValueError(
+                f"Batch feature views need a batch source, expected one of {SUPPORTED_BATCH_SOURCES} "
+                f"or CUSTOM_SOURCE, got {type(source).__name__}: {source.name} instead "
+            )
+
+        super().__init__(
+            name=name,
+            entities=entities,
+            ttl=ttl,
+            batch_source=None,
+            stream_source=None,
+            tags=tags,
+            online=online,
+            description=description,
+            owner=owner,
+            schema=schema,
+            source=source,
+        )

sdk/python/feast/stream_feature_view.py

@@ -0,0 +1,54 @@
+from datetime import timedelta
+from typing import Dict, List, Optional, Union
+
+from feast.data_source import DataSource
+from feast.entity import Entity
+from feast.feature_view import FeatureView
+from feast.field import Field
+from feast.protos.feast.core.DataSource_pb2 import DataSource as DataSourceProto
+
+SUPPORTED_STREAM_SOURCES = {
+    "KafkaSource",
+    "KinesisSource",
+}
+
+
+class StreamFeatureView(FeatureView):
+    def __init__(
+        self,
+        *,
+        name: Optional[str] = None,
+        entities: Optional[Union[List[Entity], List[str]]] = None,
+        ttl: Optional[timedelta] = None,
+        tags: Optional[Dict[str, str]] = None,
+        online: bool = True,
+        description: str = "",
+        owner: str = "",
+        schema: Optional[List[Field]] = None,
+        source: Optional[DataSource] = None,
+    ):
+
+        if source is None:
+            raise ValueError("Feature views need a source specified")
+        if (
+            type(source).__name__ not in SUPPORTED_STREAM_SOURCES
+            and source.to_proto().type != DataSourceProto.SourceType.CUSTOM_SOURCE
+        ):
+            raise ValueError(
+                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 "
+            )
+
+        super().__init__(
+            name=name,
+            entities=entities,
+            ttl=ttl,
+            batch_source=None,
+            stream_source=None,
+            tags=tags,
+            online=online,
+            description=description,
+            owner=owner,
+            schema=schema,
+            source=source,
+        )

sdk/python/tests/unit/test_feature_views.py

@@ -0,0 +1,70 @@
+from datetime import timedelta
+
+import pytest
+
+from feast.batch_feature_view import BatchFeatureView
+from feast.data_format import AvroFormat
+from feast.data_source import KafkaSource
+from feast.infra.offline_stores.file_source import FileSource
+from feast.stream_feature_view import StreamFeatureView
+
+
+def test_create_batch_feature_view():
+    batch_source = FileSource(path="some path")
+    BatchFeatureView(
+        name="test batch feature view",
+        entities=[],
+        ttl=timedelta(days=30),
+        source=batch_source,
+    )
+
+    with pytest.raises(ValueError):
+        BatchFeatureView(
+            name="test batch feature view", entities=[], ttl=timedelta(days=30)
+        )
+
+    stream_source = KafkaSource(
+        name="kafka",
+        event_timestamp_column="",
+        bootstrap_servers="",
+        message_format=AvroFormat(""),
+        topic="topic",
+        batch_source=FileSource(path="some path"),
+    )
+    with pytest.raises(ValueError):
+        BatchFeatureView(
+            name="test batch feature view",
+            entities=[],
+            ttl=timedelta(days=30),
+            source=stream_source,
+        )
+
+
+def test_create_stream_feature_view():
+    stream_source = KafkaSource(
+        name="kafka",
+        event_timestamp_column="",
+        bootstrap_servers="",
+        message_format=AvroFormat(""),
+        topic="topic",
+        batch_source=FileSource(path="some path"),
+    )
+    StreamFeatureView(
+        name="test batch feature view",
+        entities=[],
+        ttl=timedelta(days=30),
+        source=stream_source,
+    )
+
+    with pytest.raises(ValueError):
+        StreamFeatureView(
+            name="test batch feature view", entities=[], ttl=timedelta(days=30)
+        )
+
+    with pytest.raises(ValueError):
+        StreamFeatureView(
+            name="test batch feature view",
+            entities=[],
+            ttl=timedelta(days=30),
+            source=FileSource(path="some path"),
+        )