perf: Make /push async (feast-dev#4650)

Author: robhowley

sdk/python/feast/feature_server.py

@@ -165,7 +165,7 @@ async def get_online_features(body=Depends(get_body)):
         )
 
     @app.post("/push", dependencies=[Depends(inject_user_details)])
-    def push(body=Depends(get_body)):
+    async def push(body=Depends(get_body)):
         request = PushFeaturesRequest(**json.loads(body))
         df = pd.DataFrame(request.df)
         actions = []
@@ -201,13 +201,22 @@ def push(body=Depends(get_body)):
         for feature_view in fvs_with_push_sources:
             assert_permissions(resource=feature_view, actions=actions)
 
-        store.push(
+        push_params = dict(
             push_source_name=request.push_source_name,
             df=df,
             allow_registry_cache=request.allow_registry_cache,
             to=to,
         )
 
+        should_push_async = (
+            store._get_provider().async_supported.online.write
+            and to in [PushMode.ONLINE, PushMode.ONLINE_AND_OFFLINE]
+        )
+        if should_push_async:
+            await store.push_async(**push_params)
+        else:
+            store.push(**push_params)
+
     @app.post("/write-to-online-store", dependencies=[Depends(inject_user_details)])
     def write_to_online_store(body=Depends(get_body)):
         request = WriteToFeatureStoreRequest(**json.loads(body))

sdk/python/feast/feature_store.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import asyncio
 import itertools
 import os
 import warnings
@@ -33,6 +34,7 @@
 import pandas as pd
 import pyarrow as pa
 from colorama import Fore, Style
+from fastapi.concurrency import run_in_threadpool
 from google.protobuf.timestamp_pb2 import Timestamp
 from tqdm import tqdm
 
@@ -1423,26 +1425,13 @@ def tqdm_builder(length):
                 end_date,
             )
 
-    def push(
-        self,
-        push_source_name: str,
-        df: pd.DataFrame,
-        allow_registry_cache: bool = True,
-        to: PushMode = PushMode.ONLINE,
-    ):
-        """
-        Push features to a push source. This updates all the feature views that have the push source as stream source.
-
-        Args:
-            push_source_name: The name of the push source we want to push data to.
-            df: The data being pushed.
-            allow_registry_cache: Whether to allow cached versions of the registry.
-            to: Whether to push to online or offline store. Defaults to online store only.
-        """
+    def _fvs_for_push_source_or_raise(
+        self, push_source_name: str, allow_cache: bool
+    ) -> set[FeatureView]:
         from feast.data_source import PushSource
 
-        all_fvs = self.list_feature_views(allow_cache=allow_registry_cache)
-        all_fvs += self.list_stream_feature_views(allow_cache=allow_registry_cache)
+        all_fvs = self.list_feature_views(allow_cache=allow_cache)
+        all_fvs += self.list_stream_feature_views(allow_cache=allow_cache)
 
         fvs_with_push_sources = {
             fv
@@ -1457,7 +1446,27 @@ def push(
         if not fvs_with_push_sources:
             raise PushSourceNotFoundException(push_source_name)
 
-        for fv in fvs_with_push_sources:
+        return fvs_with_push_sources
+
+    def push(
+        self,
+        push_source_name: str,
+        df: pd.DataFrame,
+        allow_registry_cache: bool = True,
+        to: PushMode = PushMode.ONLINE,
+    ):
+        """
+        Push features to a push source. This updates all the feature views that have the push source as stream source.
+
+        Args:
+            push_source_name: The name of the push source we want to push data to.
+            df: The data being pushed.
+            allow_registry_cache: Whether to allow cached versions of the registry.
+            to: Whether to push to online or offline store. Defaults to online store only.
+        """
+        for fv in self._fvs_for_push_source_or_raise(
+            push_source_name, allow_registry_cache
+        ):
             if to == PushMode.ONLINE or to == PushMode.ONLINE_AND_OFFLINE:
                 self.write_to_online_store(
                     fv.name, df, allow_registry_cache=allow_registry_cache
@@ -1467,22 +1476,42 @@ def push(
                     fv.name, df, allow_registry_cache=allow_registry_cache
                 )
 
-    def write_to_online_store(
+    async def push_async(
+        self,
+        push_source_name: str,
+        df: pd.DataFrame,
+        allow_registry_cache: bool = True,
+        to: PushMode = PushMode.ONLINE,
+    ):
+        fvs = self._fvs_for_push_source_or_raise(push_source_name, allow_registry_cache)
+
+        if to == PushMode.ONLINE or to == PushMode.ONLINE_AND_OFFLINE:
+            _ = await asyncio.gather(
+                *[
+                    self.write_to_online_store_async(
+                        fv.name, df, allow_registry_cache=allow_registry_cache
+                    )
+                    for fv in fvs
+                ]
+            )
+
+        if to == PushMode.OFFLINE or to == PushMode.ONLINE_AND_OFFLINE:
+
+            def _offline_write():
+                for fv in fvs:
+                    self.write_to_offline_store(
+                        fv.name, df, allow_registry_cache=allow_registry_cache
+                    )
+
+            await run_in_threadpool(_offline_write)
+
+    def _get_feature_view_and_df_for_online_write(
         self,
         feature_view_name: str,
         df: Optional[pd.DataFrame] = None,
         inputs: Optional[Union[Dict[str, List[Any]], pd.DataFrame]] = None,
         allow_registry_cache: bool = True,
     ):
-        """
-        Persists a dataframe to the online store.
-
-        Args:
-            feature_view_name: The feature view to which the dataframe corresponds.
-            df: The dataframe to be persisted.
-            inputs: Optional the dictionary object to be written
-            allow_registry_cache (optional): Whether to allow retrieving feature views from a cached registry.
-        """
         feature_view_dict = {
             fv_proto.name: fv_proto
             for fv_proto in self.list_all_feature_views(allow_registry_cache)
@@ -1509,10 +1538,60 @@ def write_to_online_store(
                     df = pd.DataFrame(df)
                 except Exception as _:
                     raise DataFrameSerializationError(df)
+        return feature_view, df
+
+    def write_to_online_store(
+        self,
+        feature_view_name: str,
+        df: Optional[pd.DataFrame] = None,
+        inputs: Optional[Union[Dict[str, List[Any]], pd.DataFrame]] = None,
+        allow_registry_cache: bool = True,
+    ):
+        """
+        Persists a dataframe to the online store.
 
+        Args:
+            feature_view_name: The feature view to which the dataframe corresponds.
+            df: The dataframe to be persisted.
+            inputs: Optional the dictionary object to be written
+            allow_registry_cache (optional): Whether to allow retrieving feature views from a cached registry.
+        """
+
+        feature_view, df = self._get_feature_view_and_df_for_online_write(
+            feature_view_name=feature_view_name,
+            df=df,
+            inputs=inputs,
+            allow_registry_cache=allow_registry_cache,
+        )
         provider = self._get_provider()
         provider.ingest_df(feature_view, df)
 
+    async def write_to_online_store_async(
+        self,
+        feature_view_name: str,
+        df: Optional[pd.DataFrame] = None,
+        inputs: Optional[Union[Dict[str, List[Any]], pd.DataFrame]] = None,
+        allow_registry_cache: bool = True,
+    ):
+        """
+        Persists a dataframe to the online store asynchronously.
+
+        Args:
+            feature_view_name: The feature view to which the dataframe corresponds.
+            df: The dataframe to be persisted.
+            inputs: Optional the dictionary object to be written
+            allow_registry_cache (optional): Whether to allow retrieving feature views from a cached registry.
+        """
+
+        feature_view, df = self._get_feature_view_and_df_for_online_write(
+            feature_view_name=feature_view_name,
+            df=df,
+            inputs=inputs,
+            allow_registry_cache=allow_registry_cache,
+        )
+        provider = self._get_provider()
+        await provider.ingest_df_async(feature_view, df)
+
     def write_to_offline_store(
         self,
         feature_view_name: str,

sdk/python/feast/infra/online_stores/online_store.py

@@ -67,6 +67,33 @@ def online_write_batch(
         """
         pass
 
+    async def online_write_batch_async(
+        self,
+        config: RepoConfig,
+        table: FeatureView,
+        data: List[
+            Tuple[EntityKeyProto, Dict[str, ValueProto], datetime, Optional[datetime]]
+        ],
+        progress: Optional[Callable[[int], Any]],
+    ) -> None:
+        """
+        Writes a batch of feature rows to the online store asynchronously.
+
+        If a tz-naive timestamp is passed to this method, it is assumed to be UTC.
+
+        Args:
+            config: The config for the current feature store.
+            table: Feature view to which these feature rows correspond.
+            data: A list of quadruplets containing feature data. Each quadruplet contains an entity
+                key, a dict containing feature values, an event timestamp for the row, and the created
+                timestamp for the row if it exists.
+            progress: Function to be called once a batch of rows is written to the online store, used
+                to show progress.
+        """
+        raise NotImplementedError(
+            f"Online store {self.__class__.__name__} does not support online write batch async"
+        )
+
     @abstractmethod
     def online_read(
         self,

sdk/python/feast/infra/passthrough_provider.py

@@ -188,6 +188,20 @@ def online_write_batch(
         if self.online_store:
             self.online_store.online_write_batch(config, table, data, progress)
 
+    async def online_write_batch_async(
+        self,
+        config: RepoConfig,
+        table: Union[FeatureView, BaseFeatureView, OnDemandFeatureView],
+        data: List[
+            Tuple[EntityKeyProto, Dict[str, ValueProto], datetime, Optional[datetime]]
+        ],
+        progress: Optional[Callable[[int], Any]],
+    ) -> None:
+        if self.online_store:
+            await self.online_store.online_write_batch_async(
+                config, table, data, progress
+            )
+
     def offline_write_batch(
         self,
         config: RepoConfig,
@@ -291,8 +305,8 @@ def retrieve_online_documents(
             )
         return result
 
-    def ingest_df(
-        self,
+    @staticmethod
+    def _prep_rows_to_write_for_ingestion(
         feature_view: Union[BaseFeatureView, FeatureView, OnDemandFeatureView],
         df: pd.DataFrame,
         field_mapping: Optional[Dict] = None,
@@ -307,10 +321,6 @@ def ingest_df(
                 for entity in feature_view.entity_columns
             }
             rows_to_write = _convert_arrow_to_proto(table, feature_view, join_keys)
-
-            self.online_write_batch(
-                self.repo_config, feature_view, rows_to_write, progress=None
-            )
         else:
             if hasattr(feature_view, "entity_columns"):
                 join_keys = {
@@ -336,9 +346,37 @@ def ingest_df(
                     join_keys[entity.name] = entity.dtype.to_value_type()
             rows_to_write = _convert_arrow_to_proto(table, feature_view, join_keys)
 
-            self.online_write_batch(
-                self.repo_config, feature_view, rows_to_write, progress=None
-            )
+        return rows_to_write
+
+    def ingest_df(
+        self,
+        feature_view: Union[BaseFeatureView, FeatureView, OnDemandFeatureView],
+        df: pd.DataFrame,
+        field_mapping: Optional[Dict] = None,
+    ):
+        rows_to_write = self._prep_rows_to_write_for_ingestion(
+            feature_view=feature_view,
+            df=df,
+            field_mapping=field_mapping,
+        )
+        self.online_write_batch(
+            self.repo_config, feature_view, rows_to_write, progress=None
+        )
+
+    async def ingest_df_async(
+        self,
+        feature_view: Union[BaseFeatureView, FeatureView, OnDemandFeatureView],
+        df: pd.DataFrame,
+        field_mapping: Optional[Dict] = None,
+    ):
+        rows_to_write = self._prep_rows_to_write_for_ingestion(
+            feature_view=feature_view,
+            df=df,
+            field_mapping=field_mapping,
+        )
+        await self.online_write_batch_async(
+            self.repo_config, feature_view, rows_to_write, progress=None
+        )
 
     def ingest_df_to_offline_store(self, feature_view: FeatureView, table: pa.Table):
         if feature_view.batch_source.field_mapping is not None:

sdk/python/feast/infra/provider.py

@@ -141,6 +141,32 @@ def online_write_batch(
         """
         pass
 
+    @abstractmethod
+    async def online_write_batch_async(
+        self,
+        config: RepoConfig,
+        table: FeatureView,
+        data: List[
+            Tuple[EntityKeyProto, Dict[str, ValueProto], datetime, Optional[datetime]]
+        ],
+        progress: Optional[Callable[[int], Any]],
+    ) -> None:
+        """
+        Writes a batch of feature rows to the online store asynchronously.
+
+        If a tz-naive timestamp is passed to this method, it is assumed to be UTC.
+
+        Args:
+            config: The config for the current feature store.
+            table: Feature view to which these feature rows correspond.
+            data: A list of quadruplets containing feature data. Each quadruplet contains an entity
+                key, a dict containing feature values, an event timestamp for the row, and the created
+                timestamp for the row if it exists.
+            progress: Function to be called once a batch of rows is written to the online store, used
+                to show progress.
+        """
+        pass
+
     def ingest_df(
         self,
         feature_view: Union[BaseFeatureView, FeatureView, OnDemandFeatureView],
@@ -157,6 +183,22 @@ def ingest_df(
         """
         pass
 
+    async def ingest_df_async(
+        self,
+        feature_view: Union[BaseFeatureView, FeatureView, OnDemandFeatureView],
+        df: pd.DataFrame,
+        field_mapping: Optional[Dict] = None,
+    ):
+        """
+        Persists a dataframe to the online store asynchronously.
+
+        Args:
+            feature_view: The feature view to which the dataframe corresponds.
+            df: The dataframe to be persisted.
+            field_mapping: A dictionary mapping dataframe column names to feature names.
+        """
+        pass
+
     def ingest_df_to_offline_store(
         self,
         feature_view: FeatureView,