feat: Scaffold for unified push api (feast-dev#2796)

* Skaffolding for offline store push

Signed-off-by: Kevin Zhang <kzhang@tecton.ai>

* LInt

Signed-off-by: Kevin Zhang <kzhang@tecton.ai>

* Fix api

Signed-off-by: Kevin Zhang <kzhang@tecton.ai>

* Fix lint

Signed-off-by: Kevin Zhang <kzhang@tecton.ai>

* Fix

Signed-off-by: Kevin Zhang <kzhang@tecton.ai>

* Fixed

Signed-off-by: Kevin Zhang <kzhang@tecton.ai>

sdk/python/feast/feature_store.py

@@ -1370,6 +1370,28 @@ def write_to_online_store(
         provider = self._get_provider()
         provider.ingest_df(feature_view, entities, df)
 
+    @log_exceptions_and_usage
+    def write_to_offline_store(
+        self,
+        feature_view_name: str,
+        df: pd.DataFrame,
+        allow_registry_cache: bool = True,
+    ):
+        """
+        ingests data directly into the Online store
+        """
+        # TODO: restrict this to work with online StreamFeatureViews and validate the FeatureView type
+        try:
+            feature_view = self.get_stream_feature_view(
+                feature_view_name, allow_registry_cache=allow_registry_cache
+            )
+        except FeatureViewNotFoundException:
+            feature_view = self.get_feature_view(
+                feature_view_name, allow_registry_cache=allow_registry_cache
+            )
+        provider = self._get_provider()
+        provider.ingest_df_to_offline_store(feature_view, df)
+
     @log_exceptions_and_usage
     def get_online_features(
         self,

sdk/python/feast/infra/offline_stores/offline_store.py

@@ -15,7 +15,7 @@
 from abc import ABC, abstractmethod
 from datetime import datetime
 from pathlib import Path
-from typing import TYPE_CHECKING, List, Optional, Union
+from typing import TYPE_CHECKING, Any, Callable, List, Optional, Union
 
 import pandas as pd
 import pyarrow
@@ -270,3 +270,25 @@ def write_logged_features(
         This is an optional method that could be supported only be some stores.
         """
         raise NotImplementedError()
+
+    @staticmethod
+    def offline_write_batch(
+        config: RepoConfig,
+        table: FeatureView,
+        data: pd.DataFrame,
+        progress: Optional[Callable[[int], Any]],
+    ):
+        """
+        Write features to a specified destination in the offline store.
+        Data can be appended to an existing table (destination) or a new one will be created automatically
+         (if it doesn't exist).
+        Hence, this function can be called repeatedly with the same destination config to write features.
+
+        Args:
+            config: Repo configuration object
+            table: FeatureView to write the data to.
+            data: dataframe containing feature data and timestamp column for historical feature retrieval
+            progress: Optional function to be called once every mini-batch of rows is written to
+            the online store. Can be used to display progress.
+        """
+        raise NotImplementedError()

sdk/python/feast/infra/passthrough_provider.py

@@ -1,7 +1,7 @@
 from datetime import datetime, timedelta
 from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
 
-import pandas
+import pandas as pd
 import pyarrow
 import pyarrow as pa
 from tqdm import tqdm
@@ -100,6 +100,17 @@ def online_write_batch(
         if self.online_store:
             self.online_store.online_write_batch(config, table, data, progress)
 
+    def offline_write_batch(
+        self,
+        config: RepoConfig,
+        table: FeatureView,
+        data: pd.DataFrame,
+        progress: Optional[Callable[[int], Any]],
+    ) -> None:
+        set_usage_attribute("provider", self.__class__.__name__)
+        if self.offline_store:
+            self.offline_store.offline_write_batch(config, table, data, progress)
+
     @log_exceptions_and_usage(sampler=RatioSampler(ratio=0.001))
     def online_read(
         self,
@@ -117,7 +128,7 @@ def online_read(
         return result
 
     def ingest_df(
-        self, feature_view: FeatureView, entities: List[Entity], df: pandas.DataFrame,
+        self, feature_view: FeatureView, entities: List[Entity], df: pd.DataFrame,
     ):
         set_usage_attribute("provider", self.__class__.__name__)
         table = pa.Table.from_pandas(df)
@@ -193,7 +204,7 @@ def get_historical_features(
         config: RepoConfig,
         feature_views: List[FeatureView],
         feature_refs: List[str],
-        entity_df: Union[pandas.DataFrame, str],
+        entity_df: Union[pd.DataFrame, str],
         registry: BaseRegistry,
         project: str,
         full_feature_names: bool,

sdk/python/feast/infra/provider.py

@@ -5,7 +5,7 @@
 from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
 
 import dask.dataframe as dd
-import pandas
+import pandas as pd
 import pyarrow
 from tqdm import tqdm
 
@@ -119,13 +119,21 @@ def online_write_batch(
         ...
 
     def ingest_df(
-        self, feature_view: FeatureView, entities: List[Entity], df: pandas.DataFrame,
+        self, feature_view: FeatureView, entities: List[Entity], df: pd.DataFrame,
     ):
         """
         Ingests a DataFrame directly into the online store
         """
         pass
 
+    def ingest_df_to_offline_store(
+        self, feature_view: FeatureView, df: pd.DataFrame,
+    ):
+        """
+        Ingests a DataFrame directly into the offline store
+        """
+        pass
+
     @abc.abstractmethod
     def materialize_single_feature_view(
         self,
@@ -145,7 +153,7 @@ def get_historical_features(
         config: RepoConfig,
         feature_views: List[FeatureView],
         feature_refs: List[str],
-        entity_df: Union[pandas.DataFrame, str],
+        entity_df: Union[pd.DataFrame, str],
         registry: BaseRegistry,
         project: str,
         full_feature_names: bool,
@@ -367,14 +375,14 @@ def _run_dask_field_mapping(
 
 def _coerce_datetime(ts):
     """
-    Depending on underlying time resolution, arrow to_pydict() sometimes returns pandas
+    Depending on underlying time resolution, arrow to_pydict() sometimes returns pd
     timestamp type (for nanosecond resolution), and sometimes you get standard python datetime
     (for microsecond resolution).
-    While pandas timestamp class is a subclass of python datetime, it doesn't always behave the
+    While pd timestamp class is a subclass of python datetime, it doesn't always behave the
     same way. We convert it to normal datetime so that consumers downstream don't have to deal
     with these quirks.
     """
-    if isinstance(ts, pandas.Timestamp):
+    if isinstance(ts, pd.Timestamp):
         return ts.to_pydatetime()
     else:
         return ts
@@ -418,7 +426,7 @@ def _convert_arrow_to_proto(
     # Convert event_timestamps
     event_timestamps = [
         _coerce_datetime(val)
-        for val in pandas.to_datetime(
+        for val in pd.to_datetime(
             table.column(feature_view.batch_source.timestamp_field).to_numpy(
                 zero_copy_only=False
             )
@@ -429,7 +437,7 @@ def _convert_arrow_to_proto(
     if feature_view.batch_source.created_timestamp_column:
         created_timestamps = [
             _coerce_datetime(val)
-            for val in pandas.to_datetime(
+            for val in pd.to_datetime(
                 table.column(
                     feature_view.batch_source.created_timestamp_column
                 ).to_numpy(zero_copy_only=False)