[FSTORE-743] Add online support for External Feature Groups

python/hsfs/core/external_feature_group_engine.py

@@ -47,6 +47,54 @@ def save(self, feature_group):
 
         self._feature_group_api.save(feature_group)
 
+    def insert(
+        self,
+        feature_group,
+        feature_dataframe,
+        write_options: dict,
+        validation_options: dict = {},
+    ):
+        if not feature_group.online_enabled:
+            raise FeatureStoreException(
+                "Online storage is not enabled for this feature group. External feature groups can only store data in"
+                + " online storage. To create an offline only external feature group, use the `save` method."
+            )
+
+        schema = engine.get_instance().parse_schema_feature_group(feature_dataframe)
+
+        if not feature_group._id:
+            # only save metadata if feature group does not exist
+            feature_group.features = schema
+            self.save(feature_group)
+        else:
+            # else, just verify that feature group schema matches user-provided dataframe
+            self._verify_schema_compatibility(feature_group.features, schema)
+
+        # ge validation on python and non stream feature groups on spark
+        ge_report = feature_group._great_expectation_engine.validate(
+            feature_group=feature_group,
+            dataframe=feature_dataframe,
+            validation_options=validation_options,
+            ingestion_result="INGESTED",
+            ge_type=False,
+        )
+
+        if ge_report is not None and ge_report.ingestion_result == "REJECTED":
+            return None, ge_report
+
+        return (
+            engine.get_instance().save_dataframe(
+                feature_group=feature_group,
+                dataframe=feature_dataframe,
+                operation=None,
+                online_enabled=feature_group.online_enabled,
+                storage="online",
+                offline_write_options=write_options,
+                online_write_options=write_options,
+            ),
+            ge_report,
+        )
+
     def _update_features_metadata(self, feature_group, features):
         # perform changes on copy in case the update fails, so we don't leave
         # the user object in corrupted state

python/hsfs/core/feature_group_base_engine.py

@@ -15,6 +15,7 @@
 #
 
 from hsfs.core import feature_group_api, storage_connector_api, tags_api, kafka_api
+from hsfs.client.exceptions import FeatureStoreException
 
 
 class FeatureGroupBaseEngine:
@@ -107,3 +108,49 @@ def new_feature_list(self, feature_group, updated_features):
             ):
                 new_features.append(feature)
         return new_features + updated_features
+
+    def _verify_schema_compatibility(self, feature_group_features, dataframe_features):
+        err = []
+        feature_df_dict = {feat.name: feat.type for feat in dataframe_features}
+        for feature_fg in feature_group_features:
+            fg_type = feature_fg.type.lower().replace(" ", "")
+            # check if feature exists dataframe
+            if feature_fg.name in feature_df_dict:
+                df_type = feature_df_dict[feature_fg.name].lower().replace(" ", "")
+                # remove match from lookup table
+                del feature_df_dict[feature_fg.name]
+
+                # check if types match
+                if fg_type != df_type:
+                    # don't check structs for exact match
+                    if fg_type.startswith("struct") and df_type.startswith("struct"):
+                        continue
+
+                    err += [
+                        f"{feature_fg.name} ("
+                        f"expected type: '{fg_type}', "
+                        f"derived from input: '{df_type}') has the wrong type."
+                    ]
+
+            else:
+                err += [
+                    f"{feature_fg.name} (type: '{feature_fg.type}') is missing from "
+                    f"input dataframe."
+                ]
+
+        # any features that are left in lookup table are superfluous
+        for feature_df_name, feature_df_type in feature_df_dict.items():
+            err += [
+                f"{feature_df_name} (type: '{feature_df_type}') does not exist "
+                f"in feature group."
+            ]
+
+        # raise exception if any errors were found.
+        if len(err) > 0:
+            raise FeatureStoreException(
+                "Features are not compatible with Feature Group schema: "
+                + "".join(["\n - " + e for e in err])
+            )
+
+    def get_subject(self, feature_group):
+        return self._kafka_api.get_topic_subject(feature_group._online_topic_name)

python/hsfs/core/feature_group_engine.py

@@ -17,7 +17,6 @@
 from hsfs import engine, client, util
 from hsfs import feature_group as fg
 from hsfs.client import exceptions
-from hsfs.client.exceptions import FeatureStoreException
 from hsfs.core import feature_group_base_engine, hudi_engine
 from hsfs.core.deltastreamer_jobconf import DeltaStreamerJobConf
 
@@ -234,9 +233,6 @@ def update_description(self, feature_group, description):
             feature_group, copy_feature_group, "updateMetadata"
         )
 
-    def get_subject(self, feature_group):
-        return self._kafka_api.get_topic_subject(feature_group._online_topic_name)
-
     def insert_stream(
         self,
         feature_group,
@@ -305,49 +301,6 @@ def insert_stream(
 
         return streaming_query
 
-    def _verify_schema_compatibility(self, feature_group_features, dataframe_features):
-        err = []
-        feature_df_dict = {feat.name: feat.type for feat in dataframe_features}
-        for feature_fg in feature_group_features:
-            fg_type = feature_fg.type.lower().replace(" ", "")
-            # check if feature exists dataframe
-            if feature_fg.name in feature_df_dict:
-                df_type = feature_df_dict[feature_fg.name].lower().replace(" ", "")
-                # remove match from lookup table
-                del feature_df_dict[feature_fg.name]
-
-                # check if types match
-                if fg_type != df_type:
-                    # don't check structs for exact match
-                    if fg_type.startswith("struct") and df_type.startswith("struct"):
-                        continue
-
-                    err += [
-                        f"{feature_fg.name} ("
-                        f"expected type: '{fg_type}', "
-                        f"derived from input: '{df_type}') has the wrong type."
-                    ]
-
-            else:
-                err += [
-                    f"{feature_fg.name} (type: '{feature_fg.type}') is missing from "
-                    f"input dataframe."
-                ]
-
-        # any features that are left in lookup table are superfluous
-        for feature_df_name, feature_df_type in feature_df_dict.items():
-            err += [
-                f"{feature_df_name} (type: '{feature_df_type}') does not exist "
-                f"in feature group."
-            ]
-
-        # raise exception if any errors were found.
-        if len(err) > 0:
-            raise FeatureStoreException(
-                "Features are not compatible with Feature Group schema: "
-                + "".join(["\n - " + e for e in err])
-            )
-
     def _save_feature_group_metadata(
         self, feature_group, dataframe_features, write_options
     ):

python/hsfs/engine/python.py

@@ -44,6 +44,7 @@
 from sqlalchemy import sql
 
 from hsfs import client, feature, util
+from hsfs.feature_group import ExternalFeatureGroup
 from hsfs.client.exceptions import FeatureStoreException
 from hsfs.core import (
     feature_group_api,
@@ -436,7 +437,10 @@ def save_dataframe(
         online_write_options: dict,
         validation_id: int = None,
     ):
-        if feature_group.stream:
+        if (
+            isinstance(feature_group, ExternalFeatureGroup)
+            and feature_group.online_enabled
+        ) or feature_group.stream:
             return self._write_dataframe_kafka(
                 feature_group, dataframe, offline_write_options
             )
@@ -896,13 +900,16 @@ def acked(err, msg):
             progress_bar.close()
 
         # start backfilling job
-        if offline_write_options is not None and offline_write_options.get(
-            "start_offline_backfill", True
+        if (
+            not isinstance(feature_group, ExternalFeatureGroup)
+            and offline_write_options is not None
+            and offline_write_options.get("start_offline_backfill", True)
         ):
             feature_group.backfill_job.run(
                 await_termination=offline_write_options.get("wait_for_job", True)
             )
-
+        if isinstance(feature_group, ExternalFeatureGroup):
+            return None
         return feature_group.backfill_job
 
     def _kafka_produce(

python/hsfs/engine/spark.py

@@ -66,6 +66,7 @@
     pass
 
 from hsfs import feature, training_dataset_feature, client, util
+from hsfs.feature_group import ExternalFeatureGroup
 from hsfs.storage_connector import StorageConnector
 from hsfs.client.exceptions import FeatureStoreException
 from hsfs.core import hudi_engine, transformation_function_engine, kafka_api
@@ -261,7 +262,10 @@ def save_dataframe(
         validation_id=None,
     ):
         try:
-            if feature_group.stream:
+            if (
+                isinstance(feature_group, ExternalFeatureGroup)
+                and feature_group.online_enabled
+            ) or feature_group.stream:
                 self._save_online_dataframe(
                     feature_group, dataframe, online_write_options
                 )