fix: Fix for materializing entityless feature views in Snowflake

sdk/python/feast/infra/materialization/snowflake_engine.py

@@ -14,7 +14,7 @@
 import feast
 from feast.batch_feature_view import BatchFeatureView
 from feast.entity import Entity
-from feast.feature_view import FeatureView
+from feast.feature_view import DUMMY_ENTITY_ID, FeatureView
 from feast.infra.materialization.batch_materialization_engine import (
     BatchMaterializationEngine,
     MaterializationJob,
@@ -274,7 +274,11 @@ def _materialize_one(
 
             fv_latest_values_sql = offline_job.to_sql()
 
-            if feature_view.entity_columns:
+            if (
+                feature_view.entity_columns[0].name == DUMMY_ENTITY_ID
+            ):  # entityless Feature View's placeholder entity
+                entities_to_write = 1
+            else:
                 join_keys = [entity.name for entity in feature_view.entity_columns]
                 unique_entities = '"' + '", "'.join(join_keys) + '"'
 
@@ -287,10 +291,6 @@ def _materialize_one(
 
                 with GetSnowflakeConnection(self.repo_config.offline_store) as conn:
                     entities_to_write = conn.cursor().execute(query).fetchall()[0][0]
-            else:
-                entities_to_write = (
-                    1  # entityless feature view has a placeholder entity
-                )
 
             if feature_view.batch_source.field_mapping is not None:
                 fv_latest_mapped_values_sql = _run_snowflake_field_mapping(

sdk/python/tests/integration/materialization/test_snowflake.py

@@ -185,3 +185,65 @@ def test_snowflake_materialization_consistency_internal_with_lists(
     finally:
         fs.teardown()
         snowflake_environment.data_source_creator.teardown()
+
+
+@pytest.mark.integration
+def test_snowflake_materialization_entityless_fv():
+    snowflake_config = IntegrationTestRepoConfig(
+        online_store=SNOWFLAKE_ONLINE_CONFIG,
+        offline_store_creator=SnowflakeDataSourceCreator,
+        batch_engine=SNOWFLAKE_ENGINE_CONFIG,
+    )
+    snowflake_environment = construct_test_environment(snowflake_config, None)
+
+    df = create_basic_driver_dataset()
+    entityless_df = df.drop("driver_id", axis=1)
+    ds = snowflake_environment.data_source_creator.create_data_source(
+        entityless_df,
+        snowflake_environment.feature_store.project,
+        field_mapping={"ts_1": "ts"},
+    )
+
+    fs = snowflake_environment.feature_store
+
+    # We include the driver entity so we can provide an entity ID when fetching features
+    driver = Entity(
+        name="driver_id",
+        join_keys=["driver_id"],
+    )
+
+    overall_stats_fv = FeatureView(
+        name="overall_hourly_stats",
+        entities=[],
+        ttl=timedelta(weeks=52),
+        source=ds,
+    )
+
+    try:
+        fs.apply([overall_stats_fv, driver])
+
+        # materialization is run in two steps and
+        # we use timestamp from generated dataframe as a split point
+        split_dt = df["ts_1"][4].to_pydatetime() - timedelta(seconds=1)
+
+        print(f"Split datetime: {split_dt}")
+
+        now = datetime.utcnow()
+
+        start_date = (now - timedelta(hours=5)).replace(tzinfo=utc)
+        end_date = split_dt
+        fs.materialize(
+            feature_views=[overall_stats_fv.name],
+            start_date=start_date,
+            end_date=end_date,
+        )
+
+        response_dict = fs.get_online_features(
+            [f"{overall_stats_fv.name}:value"],
+            [{"driver_id": 1}],  # Included because we need an entity
+        ).to_dict()
+        assert response_dict["value"] == [0.3]
+
+    finally:
+        fs.teardown()
+        snowflake_environment.data_source_creator.teardown()