fix: Fix push API to respect feature view's already inferred entity types

Makefile

@@ -356,15 +356,11 @@ lint-go: compile-protos-go compile-go-lib
 
 # Docker
 
-build-docker: build-ci-docker build-feature-server-python-docker build-feature-server-python-aws-docker build-feature-transformation-server-docker build-feature-server-java-docker
+build-docker: build-feature-server-python-docker build-feature-server-python-aws-docker build-feature-transformation-server-docker build-feature-server-java-docker
 
 push-ci-docker:
 	docker push $(REGISTRY)/feast-ci:$(VERSION)
 
-# TODO(adchia): consider removing. This doesn't run successfully right now
-build-ci-docker:
-	docker buildx build -t $(REGISTRY)/feast-ci:$(VERSION) -f infra/docker/ci/Dockerfile --load .
-
 push-feature-server-python-docker:
 	docker push $(REGISTRY)/feature-server:$$VERSION
 

README.md

@@ -185,8 +185,8 @@ The list below contains the functionality that contributors are planning to deve
   * [ ] Batch transformation (In progress. See [RFC](https://docs.google.com/document/d/1964OkzuBljifDvkV-0fakp2uaijnVzdwWNGdz7Vz50A/edit))
 * **Streaming**
   * [x] [Custom streaming ingestion job support](https://docs.feast.dev/how-to-guides/creating-a-custom-provider)
-  * [x] [Push based streaming data ingestion to online store (Alpha)](https://docs.feast.dev/reference/data-sources/push)
-  * [x] [Push based streaming data ingestion to offline store (Alpha)](https://docs.feast.dev/reference/data-sources/push)
+  * [x] [Push based streaming data ingestion to online store](https://docs.feast.dev/reference/data-sources/push)
+  * [x] [Push based streaming data ingestion to offline store](https://docs.feast.dev/reference/data-sources/push)
 * **Deployments**
   * [x] AWS Lambda (Alpha release. See [RFC](https://docs.google.com/document/d/1eZWKWzfBif66LDN32IajpaG-j82LSHCCOzY6R7Ax7MI/edit))
   * [x] Kubernetes (See [guide](https://docs.feast.dev/how-to-guides/running-feast-in-production#4.3.-java-based-feature-server-deployed-on-kubernetes))
@@ -202,7 +202,7 @@ The list below contains the functionality that contributors are planning to deve
   * [x] Model-centric feature tracking (feature services)
   * [x] Amundsen integration (see [Feast extractor](https://github.com/amundsen-io/amundsen/blob/main/databuilder/databuilder/extractor/feast_extractor.py))
   * [x] DataHub integration (see [DataHub Feast docs](https://datahubproject.io/docs/generated/ingestion/sources/feast/))
-  * [x] Feast Web UI (Alpha release. See [docs](https://docs.feast.dev/reference/alpha-web-ui))
+  * [x] Feast Web UI (Beta release. See [docs](https://docs.feast.dev/reference/alpha-web-ui))
 
 
 ## 🎓 Important Resources

docs/reference/data-sources/push.md

@@ -1,7 +1,5 @@
 # Push source
 
-**Warning**: This is an _experimental_ feature. It's intended for early testing and feedback, and could change without warnings in future releases.
-
 ## Description
 
 Push sources allow feature values to be pushed to the online store and offline store in real time. This allows fresh feature values to be made available to applications. Push sources supercede the

docs/roadmap.md

@@ -43,15 +43,16 @@ The list below contains the functionality that contributors are planning to deve
   * [ ] Batch transformation (In progress. See [RFC](https://docs.google.com/document/d/1964OkzuBljifDvkV-0fakp2uaijnVzdwWNGdz7Vz50A/edit))
 * **Streaming**
   * [x] [Custom streaming ingestion job support](https://docs.feast.dev/how-to-guides/creating-a-custom-provider)
-  * [x] [Push based streaming data ingestion to online store (Alpha)](https://docs.feast.dev/reference/data-sources/push)
-  * [x] [Push based streaming data ingestion to offline store (Alpha)](https://docs.feast.dev/reference/data-sources/push)
+  * [x] [Push based streaming data ingestion to online store](https://docs.feast.dev/reference/data-sources/push)
+  * [x] [Push based streaming data ingestion to offline store](https://docs.feast.dev/reference/data-sources/push)
 * **Deployments**
   * [x] AWS Lambda (Alpha release. See [RFC](https://docs.google.com/document/d/1eZWKWzfBif66LDN32IajpaG-j82LSHCCOzY6R7Ax7MI/edit))
   * [x] Kubernetes (See [guide](https://docs.feast.dev/how-to-guides/running-feast-in-production#4.3.-java-based-feature-server-deployed-on-kubernetes))
 * **Feature Serving**
   * [x] Python Client
   * [x] [Python feature server](https://docs.feast.dev/reference/feature-servers/python-feature-server)
-  * [x] [Go feature server](https://docs.feast.dev/reference/feature-servers/go-feature-server)
+  * [x] [Java feature server (alpha)](https://github.com/feast-dev/feast/blob/master/infra/charts/feast/README.md)
+  * [x] [Go feature server (alpha)](https://docs.feast.dev/reference/feature-servers/go-feature-server)
 * **Data Quality Management (See [RFC](https://docs.google.com/document/d/110F72d4NTv80p35wDSONxhhPBqWRwbZXG4f9mNEMd98/edit))**
   * [x] Data profiling and validation (Great Expectations)
 * **Feature Discovery and Governance**
@@ -60,4 +61,4 @@ The list below contains the functionality that contributors are planning to deve
   * [x] Model-centric feature tracking (feature services)
   * [x] Amundsen integration (see [Feast extractor](https://github.com/amundsen-io/amundsen/blob/main/databuilder/databuilder/extractor/feast_extractor.py))
   * [x] DataHub integration (see [DataHub Feast docs](https://datahubproject.io/docs/generated/ingestion/sources/feast/))
-  * [x] Feast Web UI (Alpha release. See [docs](https://docs.feast.dev/reference/alpha-web-ui))
+  * [x] Feast Web UI (Beta release. See [docs](https://docs.feast.dev/reference/alpha-web-ui))

infra/charts/feast/README.md

@@ -1,6 +1,6 @@
-# Feast Helm Charts
+# Feast Java Helm Charts (alpha)
 
-This repo contains Helm charts for Feast components that are being installed on Kubernetes:
+This repo contains Helm charts for Feast Java components that are being installed on Kubernetes:
 * Feast (root chart): The complete Helm chart containing all Feast components and dependencies. Most users will use this chart, but can selectively enable/disable subcharts using the values.yaml file.
     * [Feature Server](charts/feature-server): High performant JVM-based implementation of feature server.
     * [Transformation Service](charts/transformation-service): Transformation server for calculating on-demand features

infra/charts/feast/README.md.gotmpl

@@ -1,6 +1,6 @@
-# Feast Helm Charts
+# Feast Java Helm Charts (alpha)
 
-This repo contains Helm charts for Feast components that are being installed on Kubernetes:
+This repo contains Helm charts for Feast Java components that are being installed on Kubernetes:
 * Feast (root chart): The complete Helm chart containing all Feast components and dependencies. Most users will use this chart, but can selectively enable/disable subcharts using the values.yaml file.
     * [Feature Server](charts/feature-server): High performant JVM-based implementation of feature server. 
     * [Transformation Service](charts/transformation-service): Transformation server for calculating on-demand features

sdk/python/feast/feature_store.py

@@ -1478,13 +1478,8 @@ def write_to_online_store(
             feature_view = self.get_feature_view(
                 feature_view_name, allow_registry_cache=allow_registry_cache
             )
-        entities = []
-        for entity_name in feature_view.entities:
-            entities.append(
-                self.get_entity(entity_name, allow_registry_cache=allow_registry_cache)
-            )
         provider = self._get_provider()
-        provider.ingest_df(feature_view, entities, df)
+        provider.ingest_df(feature_view, df)
 
     @log_exceptions_and_usage
     def write_to_offline_store(

sdk/python/feast/infra/passthrough_provider.py

@@ -193,7 +193,6 @@ def online_read(
     def ingest_df(
         self,
         feature_view: FeatureView,
-        entities: List[Entity],
         df: pd.DataFrame,
     ):
         set_usage_attribute("provider", self.__class__.__name__)
@@ -204,7 +203,10 @@ def ingest_df(
                 table, feature_view.batch_source.field_mapping
             )
 
-        join_keys = {entity.join_key: entity.value_type for entity in entities}
+        join_keys = {
+            entity.name: entity.dtype.to_value_type()
+            for entity in feature_view.entity_columns
+        }
         rows_to_write = _convert_arrow_to_proto(table, feature_view, join_keys)
 
         self.online_write_batch(

sdk/python/feast/infra/provider.py

@@ -123,15 +123,13 @@ def online_write_batch(
     def ingest_df(
         self,
         feature_view: FeatureView,
-        entities: List[Entity],
         df: pd.DataFrame,
     ):
         """
         Persists a dataframe to the online store.
 
         Args:
             feature_view: The feature view to which the dataframe corresponds.
-            entities: The entities that are referenced by the dataframe.
             df: The dataframe to be persisted.
         """
         pass