damccorm commented on code in PR #30388: URL: https://github.com/apache/beam/pull/30388#discussion_r1506341395
########## sdks/python/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.py: ########## @@ -0,0 +1,289 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# 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 logging +from typing import List + +import proto +from google.api_core.exceptions import NotFound +from google.cloud import aiplatform + +import apache_beam as beam +from apache_beam.transforms.enrichment import EnrichmentSourceHandler +from apache_beam.transforms.enrichment_handlers.utils import ExceptionLevel + +__all__ = [ + 'VertexAIFeatureStoreEnrichmentHandler', + 'VertexAIFeatureStoreLegacyEnrichmentHandler' +] + +_LOGGER = logging.getLogger(__name__) + + +def _not_found_err_message( + feature_store_name: str, feature_view_name: str, entity_id: str) -> str: + """returns a string formatted with given parameters""" + return ( + "make sure the Feature Store: %s with Feature View " + "%s has entity_id: %s" % + (feature_store_name, feature_view_name, entity_id)) + + +class VertexAIFeatureStoreEnrichmentHandler(EnrichmentSourceHandler[beam.Row, + beam.Row]): + """Enrichment handler to interact with Vertex AI Feature Store. + + Use this handler with :class:`apache_beam.transforms.enrichment.Enrichment` + transform when the Vertex AI Feature Store is set up for + Bigtable Online serving. + + With the Bigtable Online serving approach, the client fetches all the + available features for an entity-id. To filter the features to enrich, use + the `join_fn` param in :class:`apache_beam.transforms.enrichment.Enrichment`. + + **NOTE:** The default severity to report exceptions is logging a warning. For + this handler, Vertex AI client returns the same exception + `Requested entity was not found` even though the feature store doesn't + exist. So make sure the feature store instance exists or set + `exception_level` as `ExceptionLevel.RAISE`. Review Comment: Rather than having this here, could we check this as part of `__enter__` and raise the exception no matter what there? ########## sdks/python/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.py: ########## @@ -0,0 +1,289 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# 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 logging +from typing import List + +import proto +from google.api_core.exceptions import NotFound +from google.cloud import aiplatform + +import apache_beam as beam +from apache_beam.transforms.enrichment import EnrichmentSourceHandler +from apache_beam.transforms.enrichment_handlers.utils import ExceptionLevel + +__all__ = [ + 'VertexAIFeatureStoreEnrichmentHandler', + 'VertexAIFeatureStoreLegacyEnrichmentHandler' +] + +_LOGGER = logging.getLogger(__name__) + + +def _not_found_err_message( + feature_store_name: str, feature_view_name: str, entity_id: str) -> str: + """returns a string formatted with given parameters""" + return ( + "make sure the Feature Store: %s with Feature View " + "%s has entity_id: %s" % + (feature_store_name, feature_view_name, entity_id)) + + +class VertexAIFeatureStoreEnrichmentHandler(EnrichmentSourceHandler[beam.Row, + beam.Row]): + """Enrichment handler to interact with Vertex AI Feature Store. + + Use this handler with :class:`apache_beam.transforms.enrichment.Enrichment` + transform when the Vertex AI Feature Store is set up for + Bigtable Online serving. + + With the Bigtable Online serving approach, the client fetches all the + available features for an entity-id. To filter the features to enrich, use + the `join_fn` param in :class:`apache_beam.transforms.enrichment.Enrichment`. + + **NOTE:** The default severity to report exceptions is logging a warning. For + this handler, Vertex AI client returns the same exception + `Requested entity was not found` even though the feature store doesn't + exist. So make sure the feature store instance exists or set + `exception_level` as `ExceptionLevel.RAISE`. + """ + def __init__( + self, + project: str, + location: str, + api_endpoint: str, + feature_store_name: str, + feature_view_name: str, + row_key: str, + *, + exception_level: ExceptionLevel = ExceptionLevel.WARN, + **kwargs, + ): + """Initializes an instance of `VertexAIFeatureStoreEnrichmentHandler`. + + Args: + project (str): The GCP project-id for the Vertex AI Feature Store. + location (str): The region for the Vertex AI Feature Store. + api_endpoint (str): The API endpoint for the Vertex AI Feature Store. + feature_store_name (str): The name of the Vertex AI Feature Store. + feature_view_name (str): The name of the feature view within the + Feature Store. + row_key (str): The row key field name containing the unique id + for the feature values. + exception_level: a `enum.Enum` value from + `apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel` + to set the level when an empty row is returned from the BigTable query. + Defaults to `ExceptionLevel.WARN`. + kwargs: Optional keyword arguments to configure the + `aiplatform.gapic.FeatureOnlineStoreServiceClient`. When using + `kwargs`, the `api_endpoint` param will be overridden with this config + if multiple values are found for `client_options`. Review Comment: Instead of this, can we ad api_endpoint to the `client_options` (if its non-empty) and throw if it already exists/is different? ########## sdks/python/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.py: ########## @@ -0,0 +1,289 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# 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 logging +from typing import List + +import proto +from google.api_core.exceptions import NotFound +from google.cloud import aiplatform + +import apache_beam as beam +from apache_beam.transforms.enrichment import EnrichmentSourceHandler +from apache_beam.transforms.enrichment_handlers.utils import ExceptionLevel + +__all__ = [ + 'VertexAIFeatureStoreEnrichmentHandler', + 'VertexAIFeatureStoreLegacyEnrichmentHandler' +] + +_LOGGER = logging.getLogger(__name__) + + +def _not_found_err_message( + feature_store_name: str, feature_view_name: str, entity_id: str) -> str: + """returns a string formatted with given parameters""" + return ( + "make sure the Feature Store: %s with Feature View " + "%s has entity_id: %s" % + (feature_store_name, feature_view_name, entity_id)) + + +class VertexAIFeatureStoreEnrichmentHandler(EnrichmentSourceHandler[beam.Row, + beam.Row]): + """Enrichment handler to interact with Vertex AI Feature Store. + + Use this handler with :class:`apache_beam.transforms.enrichment.Enrichment` + transform when the Vertex AI Feature Store is set up for + Bigtable Online serving. + + With the Bigtable Online serving approach, the client fetches all the + available features for an entity-id. To filter the features to enrich, use + the `join_fn` param in :class:`apache_beam.transforms.enrichment.Enrichment`. + + **NOTE:** The default severity to report exceptions is logging a warning. For + this handler, Vertex AI client returns the same exception + `Requested entity was not found` even though the feature store doesn't + exist. So make sure the feature store instance exists or set + `exception_level` as `ExceptionLevel.RAISE`. + """ + def __init__( + self, + project: str, + location: str, + api_endpoint: str, + feature_store_name: str, + feature_view_name: str, + row_key: str, + *, + exception_level: ExceptionLevel = ExceptionLevel.WARN, + **kwargs, + ): + """Initializes an instance of `VertexAIFeatureStoreEnrichmentHandler`. + + Args: + project (str): The GCP project-id for the Vertex AI Feature Store. + location (str): The region for the Vertex AI Feature Store. + api_endpoint (str): The API endpoint for the Vertex AI Feature Store. + feature_store_name (str): The name of the Vertex AI Feature Store. + feature_view_name (str): The name of the feature view within the + Feature Store. + row_key (str): The row key field name containing the unique id + for the feature values. + exception_level: a `enum.Enum` value from + `apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel` + to set the level when an empty row is returned from the BigTable query. + Defaults to `ExceptionLevel.WARN`. + kwargs: Optional keyword arguments to configure the + `aiplatform.gapic.FeatureOnlineStoreServiceClient`. When using + `kwargs`, the `api_endpoint` param will be overridden with this config + if multiple values are found for `client_options`. + """ + self.project = project + self.location = location + self.api_endpoint = api_endpoint + self.feature_store_name = feature_store_name + self.feature_view_name = feature_view_name + self.row_key = row_key + self.exception_level = exception_level + self.kwargs = kwargs if kwargs else {} + + def __enter__(self): + """Connect with the Vertex AI Feature Store.""" + if 'client_options' in self.kwargs: + self.client = aiplatform.gapic.FeatureOnlineStoreServiceClient( + **self.kwargs) + else: + self.client = aiplatform.gapic.FeatureOnlineStoreServiceClient( + client_options={"api_endpoint": self.api_endpoint}, **self.kwargs) + self.feature_view_path = self.client.feature_view_path( + self.project, + self.location, + self.feature_store_name, + self.feature_view_name) + + def __call__(self, request: beam.Row, *args, **kwargs): + """Fetches feature value for an entity-id from Vertex AI Feature Store. + + Args: + request: the input `beam.Row` to enrich. + """ + try: + entity_id = request._asdict()[self.row_key] + except KeyError: + raise ValueError( + "no entry found for row_key %s in input row" % self.row_key) Review Comment: Could we add a little detail to this error (e.g. `All Enrichment requests must have a row key to join on. This will be used as the FeatureViewDataKey`). ########## sdks/python/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.py: ########## @@ -0,0 +1,289 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# 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 logging +from typing import List + +import proto +from google.api_core.exceptions import NotFound +from google.cloud import aiplatform + +import apache_beam as beam +from apache_beam.transforms.enrichment import EnrichmentSourceHandler +from apache_beam.transforms.enrichment_handlers.utils import ExceptionLevel + +__all__ = [ + 'VertexAIFeatureStoreEnrichmentHandler', + 'VertexAIFeatureStoreLegacyEnrichmentHandler' +] + +_LOGGER = logging.getLogger(__name__) + + +def _not_found_err_message( + feature_store_name: str, feature_view_name: str, entity_id: str) -> str: + """returns a string formatted with given parameters""" + return ( + "make sure the Feature Store: %s with Feature View " + "%s has entity_id: %s" % + (feature_store_name, feature_view_name, entity_id)) + + +class VertexAIFeatureStoreEnrichmentHandler(EnrichmentSourceHandler[beam.Row, + beam.Row]): + """Enrichment handler to interact with Vertex AI Feature Store. + + Use this handler with :class:`apache_beam.transforms.enrichment.Enrichment` + transform when the Vertex AI Feature Store is set up for + Bigtable Online serving. + + With the Bigtable Online serving approach, the client fetches all the + available features for an entity-id. To filter the features to enrich, use + the `join_fn` param in :class:`apache_beam.transforms.enrichment.Enrichment`. + + **NOTE:** The default severity to report exceptions is logging a warning. For + this handler, Vertex AI client returns the same exception + `Requested entity was not found` even though the feature store doesn't + exist. So make sure the feature store instance exists or set + `exception_level` as `ExceptionLevel.RAISE`. + """ + def __init__( + self, + project: str, + location: str, + api_endpoint: str, + feature_store_name: str, + feature_view_name: str, + row_key: str, + *, + exception_level: ExceptionLevel = ExceptionLevel.WARN, + **kwargs, + ): + """Initializes an instance of `VertexAIFeatureStoreEnrichmentHandler`. + + Args: + project (str): The GCP project-id for the Vertex AI Feature Store. + location (str): The region for the Vertex AI Feature Store. + api_endpoint (str): The API endpoint for the Vertex AI Feature Store. + feature_store_name (str): The name of the Vertex AI Feature Store. + feature_view_name (str): The name of the feature view within the + Feature Store. + row_key (str): The row key field name containing the unique id + for the feature values. + exception_level: a `enum.Enum` value from + `apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel` + to set the level when an empty row is returned from the BigTable query. + Defaults to `ExceptionLevel.WARN`. + kwargs: Optional keyword arguments to configure the + `aiplatform.gapic.FeatureOnlineStoreServiceClient`. When using + `kwargs`, the `api_endpoint` param will be overridden with this config + if multiple values are found for `client_options`. + """ + self.project = project + self.location = location + self.api_endpoint = api_endpoint + self.feature_store_name = feature_store_name + self.feature_view_name = feature_view_name + self.row_key = row_key + self.exception_level = exception_level + self.kwargs = kwargs if kwargs else {} + + def __enter__(self): + """Connect with the Vertex AI Feature Store.""" + if 'client_options' in self.kwargs: + self.client = aiplatform.gapic.FeatureOnlineStoreServiceClient( + **self.kwargs) + else: + self.client = aiplatform.gapic.FeatureOnlineStoreServiceClient( + client_options={"api_endpoint": self.api_endpoint}, **self.kwargs) + self.feature_view_path = self.client.feature_view_path( + self.project, + self.location, + self.feature_store_name, + self.feature_view_name) + + def __call__(self, request: beam.Row, *args, **kwargs): + """Fetches feature value for an entity-id from Vertex AI Feature Store. + + Args: + request: the input `beam.Row` to enrich. + """ + try: + entity_id = request._asdict()[self.row_key] + except KeyError: + raise ValueError( + "no entry found for row_key %s in input row" % self.row_key) + try: + response = self.client.fetch_feature_values( + request=aiplatform.gapic.FetchFeatureValuesRequest( + data_key=aiplatform.gapic.FeatureViewDataKey(key=entity_id), + feature_view=self.feature_view_path, + data_format=aiplatform.gapic.FeatureViewDataFormat.PROTO_STRUCT, + )) + except NotFound: + if self.exception_level == ExceptionLevel.WARN: + _LOGGER.warning( + _not_found_err_message( + self.feature_store_name, self.feature_view_name, entity_id)) + return request, beam.Row() + elif self.exception_level == ExceptionLevel.RAISE: + raise ValueError( + _not_found_err_message( + self.feature_store_name, self.feature_view_name, entity_id)) + response_dict = dict(response.proto_struct) + return request, beam.Row(**response_dict) + + def __exit__(self, exc_type, exc_val, exc_tb): + """Clean the instantiated Vertex AI client.""" + self.client = None + + def get_cache_key(self, request: beam.Row) -> str: + """Returns a string formatted with unique entity-id for the feature values. + """ + return 'entity_id: %s' Review Comment: Should this be pulling in the entity id from `request`? ########## sdks/python/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.py: ########## @@ -0,0 +1,289 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# 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 logging +from typing import List + +import proto +from google.api_core.exceptions import NotFound +from google.cloud import aiplatform + +import apache_beam as beam +from apache_beam.transforms.enrichment import EnrichmentSourceHandler +from apache_beam.transforms.enrichment_handlers.utils import ExceptionLevel + +__all__ = [ + 'VertexAIFeatureStoreEnrichmentHandler', + 'VertexAIFeatureStoreLegacyEnrichmentHandler' +] + +_LOGGER = logging.getLogger(__name__) + + +def _not_found_err_message( + feature_store_name: str, feature_view_name: str, entity_id: str) -> str: + """returns a string formatted with given parameters""" + return ( + "make sure the Feature Store: %s with Feature View " + "%s has entity_id: %s" % + (feature_store_name, feature_view_name, entity_id)) + + +class VertexAIFeatureStoreEnrichmentHandler(EnrichmentSourceHandler[beam.Row, + beam.Row]): + """Enrichment handler to interact with Vertex AI Feature Store. + + Use this handler with :class:`apache_beam.transforms.enrichment.Enrichment` + transform when the Vertex AI Feature Store is set up for + Bigtable Online serving. + + With the Bigtable Online serving approach, the client fetches all the + available features for an entity-id. To filter the features to enrich, use + the `join_fn` param in :class:`apache_beam.transforms.enrichment.Enrichment`. + + **NOTE:** The default severity to report exceptions is logging a warning. For + this handler, Vertex AI client returns the same exception + `Requested entity was not found` even though the feature store doesn't + exist. So make sure the feature store instance exists or set + `exception_level` as `ExceptionLevel.RAISE`. + """ + def __init__( + self, + project: str, + location: str, + api_endpoint: str, + feature_store_name: str, + feature_view_name: str, + row_key: str, + *, + exception_level: ExceptionLevel = ExceptionLevel.WARN, + **kwargs, + ): + """Initializes an instance of `VertexAIFeatureStoreEnrichmentHandler`. + + Args: + project (str): The GCP project-id for the Vertex AI Feature Store. + location (str): The region for the Vertex AI Feature Store. + api_endpoint (str): The API endpoint for the Vertex AI Feature Store. + feature_store_name (str): The name of the Vertex AI Feature Store. + feature_view_name (str): The name of the feature view within the + Feature Store. + row_key (str): The row key field name containing the unique id + for the feature values. + exception_level: a `enum.Enum` value from + `apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel` + to set the level when an empty row is returned from the BigTable query. + Defaults to `ExceptionLevel.WARN`. + kwargs: Optional keyword arguments to configure the + `aiplatform.gapic.FeatureOnlineStoreServiceClient`. When using + `kwargs`, the `api_endpoint` param will be overridden with this config + if multiple values are found for `client_options`. + """ + self.project = project + self.location = location + self.api_endpoint = api_endpoint + self.feature_store_name = feature_store_name + self.feature_view_name = feature_view_name + self.row_key = row_key + self.exception_level = exception_level + self.kwargs = kwargs if kwargs else {} + + def __enter__(self): + """Connect with the Vertex AI Feature Store.""" + if 'client_options' in self.kwargs: + self.client = aiplatform.gapic.FeatureOnlineStoreServiceClient( + **self.kwargs) + else: + self.client = aiplatform.gapic.FeatureOnlineStoreServiceClient( + client_options={"api_endpoint": self.api_endpoint}, **self.kwargs) + self.feature_view_path = self.client.feature_view_path( + self.project, + self.location, + self.feature_store_name, + self.feature_view_name) + + def __call__(self, request: beam.Row, *args, **kwargs): + """Fetches feature value for an entity-id from Vertex AI Feature Store. + + Args: + request: the input `beam.Row` to enrich. + """ + try: + entity_id = request._asdict()[self.row_key] + except KeyError: + raise ValueError( + "no entry found for row_key %s in input row" % self.row_key) + try: + response = self.client.fetch_feature_values( + request=aiplatform.gapic.FetchFeatureValuesRequest( + data_key=aiplatform.gapic.FeatureViewDataKey(key=entity_id), + feature_view=self.feature_view_path, + data_format=aiplatform.gapic.FeatureViewDataFormat.PROTO_STRUCT, + )) + except NotFound: + if self.exception_level == ExceptionLevel.WARN: + _LOGGER.warning( + _not_found_err_message( + self.feature_store_name, self.feature_view_name, entity_id)) + return request, beam.Row() + elif self.exception_level == ExceptionLevel.RAISE: + raise ValueError( + _not_found_err_message( + self.feature_store_name, self.feature_view_name, entity_id)) + response_dict = dict(response.proto_struct) + return request, beam.Row(**response_dict) + + def __exit__(self, exc_type, exc_val, exc_tb): + """Clean the instantiated Vertex AI client.""" + self.client = None + + def get_cache_key(self, request: beam.Row) -> str: + """Returns a string formatted with unique entity-id for the feature values. + """ + return 'entity_id: %s' + + +class VertexAIFeatureStoreLegacyEnrichmentHandler(EnrichmentSourceHandler): + """Enrichment handler to interact with Vertex AI Feature Store (Legacy). + + Use this handler with :class:`apache_beam.transforms.enrichment.Enrichment` + transform for the Vertex AI Feature Store (Legacy). + + By default, it fetches all the features values for an entity-id. You can + specify the features names using `feature_ids` to fetch specific features. + """ + def __init__( + self, + project: str, + location: str, + api_endpoint: str, + feature_store_id: str, + entity_type_id: str, + feature_ids: List[str], + row_key: str, + *, + exception_level: ExceptionLevel = ExceptionLevel.WARN, + **kwargs, + ): + """Initializes an instance of `VertexAIFeatureStoreLegacyEnrichmentHandler`. + + Args: + project (str): The GCP project for the Vertex AI Feature Store (Legacy). + location (str): The region for the Vertex AI Feature Store (Legacy). + api_endpoint (str): The API endpoint for the + Vertex AI Feature Store (Legacy). + feature_store_id (str): The id of the Vertex AI Feature Store (Legacy). + entity_type_id (str): The entity type of the feature store. + feature_ids (List[str]): A list of feature-ids to fetch + from the Feature Store. + row_key (str): The row key field name containing the entity id + for the feature values. + exception_level: a `enum.Enum` value from + `apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel` + to set the level when an empty row is returned from the BigTable query. + Defaults to `ExceptionLevel.WARN`. + kwargs: Optional keyword arguments to configure the + `aiplatform.gapic.FeaturestoreOnlineServingServiceClient`. When using + `kwargs`, the `api_endpoint` param will be overridden with this config + if multiple values are found for `client_options`. + """ + self.project = project + self.location = location + self.api_endpoint = api_endpoint + self.feature_store_id = feature_store_id + self.entity_type_id = entity_type_id + self.feature_ids = feature_ids + self.row_key = row_key + self.exception_level = exception_level + self.kwargs = kwargs if kwargs else {} + + def __enter__(self): + """Connect with the Vertex AI Feature Store (Legacy).""" + try: + # checks if feature store exists + _ = aiplatform.Featurestore( + featurestore_name=self.feature_store_id, + project=self.project, + location=self.location, + credentials=self.kwargs.get('credentials'), + ) + if 'client_options' in self.kwargs: + self.client = aiplatform.gapic.FeaturestoreOnlineServingServiceClient( + **self.kwargs) + else: + self.client = aiplatform.gapic.FeaturestoreOnlineServingServiceClient( + client_options={'api_endpoint': self.api_endpoint}, **self.kwargs) + self.entity_type_path = self.client.entity_type_path( + self.project, + self.location, + self.feature_store_id, + self.entity_type_id) + except NotFound: + raise ValueError( + 'Vertex AI Feature Store %s does not exist' % self.feature_store_id) + + def __call__(self, request: beam.Row, *args, **kwargs): + """Fetches feature value for an entity-id from + Vertex AI Feature Store (Legacy). + + Args: + request: the input `beam.Row` to enrich. + """ + try: + entity_id = request._asdict()[self.row_key] + except KeyError: + raise ValueError( + "no entry found for row_key %s in input row" % self.row_key) + + try: + selector = aiplatform.gapic.FeatureSelector( + id_matcher=aiplatform.gapic.IdMatcher(ids=self.feature_ids)) + response = self.client.read_feature_values( + request=aiplatform.gapic.ReadFeatureValuesRequest( + entity_type=self.entity_type_path, + entity_id=entity_id, + feature_selector=selector)) + except NotFound: + raise ValueError( + _not_found_err_message( + self.feature_store_id, self.entity_type_id, entity_id)) + + response_dict = {} + proto_to_dict = proto.Message.to_dict(response.entity_view) + for key, msg in zip(response.header.feature_descriptors, + proto_to_dict['data']): + if msg and 'value' in msg: + for _, value in msg['value'].items(): + response_dict[key.id] = value + break # skip fetching the metadata Review Comment: Can this just be `response_dict[key.id] = msg['value'].items()[0]`? Is this guaranteed to be the first element? ########## sdks/python/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.py: ########## @@ -0,0 +1,289 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# 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 logging +from typing import List + +import proto +from google.api_core.exceptions import NotFound +from google.cloud import aiplatform + +import apache_beam as beam +from apache_beam.transforms.enrichment import EnrichmentSourceHandler +from apache_beam.transforms.enrichment_handlers.utils import ExceptionLevel + +__all__ = [ + 'VertexAIFeatureStoreEnrichmentHandler', + 'VertexAIFeatureStoreLegacyEnrichmentHandler' +] + +_LOGGER = logging.getLogger(__name__) + + +def _not_found_err_message( + feature_store_name: str, feature_view_name: str, entity_id: str) -> str: + """returns a string formatted with given parameters""" + return ( + "make sure the Feature Store: %s with Feature View " + "%s has entity_id: %s" % + (feature_store_name, feature_view_name, entity_id)) + + +class VertexAIFeatureStoreEnrichmentHandler(EnrichmentSourceHandler[beam.Row, + beam.Row]): + """Enrichment handler to interact with Vertex AI Feature Store. + + Use this handler with :class:`apache_beam.transforms.enrichment.Enrichment` + transform when the Vertex AI Feature Store is set up for + Bigtable Online serving. + + With the Bigtable Online serving approach, the client fetches all the + available features for an entity-id. To filter the features to enrich, use + the `join_fn` param in :class:`apache_beam.transforms.enrichment.Enrichment`. Review Comment: We should add a little detail about how the entity-id is pulled from the beam.Row object (using row_key) ########## sdks/python/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.py: ########## @@ -0,0 +1,289 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# 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 logging +from typing import List + +import proto +from google.api_core.exceptions import NotFound +from google.cloud import aiplatform + +import apache_beam as beam +from apache_beam.transforms.enrichment import EnrichmentSourceHandler +from apache_beam.transforms.enrichment_handlers.utils import ExceptionLevel + +__all__ = [ + 'VertexAIFeatureStoreEnrichmentHandler', + 'VertexAIFeatureStoreLegacyEnrichmentHandler' +] + +_LOGGER = logging.getLogger(__name__) + + +def _not_found_err_message( + feature_store_name: str, feature_view_name: str, entity_id: str) -> str: + """returns a string formatted with given parameters""" + return ( + "make sure the Feature Store: %s with Feature View " + "%s has entity_id: %s" % + (feature_store_name, feature_view_name, entity_id)) + + +class VertexAIFeatureStoreEnrichmentHandler(EnrichmentSourceHandler[beam.Row, + beam.Row]): + """Enrichment handler to interact with Vertex AI Feature Store. + + Use this handler with :class:`apache_beam.transforms.enrichment.Enrichment` + transform when the Vertex AI Feature Store is set up for + Bigtable Online serving. + + With the Bigtable Online serving approach, the client fetches all the + available features for an entity-id. To filter the features to enrich, use + the `join_fn` param in :class:`apache_beam.transforms.enrichment.Enrichment`. + + **NOTE:** The default severity to report exceptions is logging a warning. For + this handler, Vertex AI client returns the same exception + `Requested entity was not found` even though the feature store doesn't + exist. So make sure the feature store instance exists or set + `exception_level` as `ExceptionLevel.RAISE`. + """ + def __init__( + self, + project: str, + location: str, + api_endpoint: str, + feature_store_name: str, + feature_view_name: str, + row_key: str, + *, + exception_level: ExceptionLevel = ExceptionLevel.WARN, + **kwargs, + ): + """Initializes an instance of `VertexAIFeatureStoreEnrichmentHandler`. + + Args: + project (str): The GCP project-id for the Vertex AI Feature Store. + location (str): The region for the Vertex AI Feature Store. + api_endpoint (str): The API endpoint for the Vertex AI Feature Store. + feature_store_name (str): The name of the Vertex AI Feature Store. + feature_view_name (str): The name of the feature view within the + Feature Store. + row_key (str): The row key field name containing the unique id + for the feature values. + exception_level: a `enum.Enum` value from + `apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel` + to set the level when an empty row is returned from the BigTable query. + Defaults to `ExceptionLevel.WARN`. + kwargs: Optional keyword arguments to configure the + `aiplatform.gapic.FeatureOnlineStoreServiceClient`. When using + `kwargs`, the `api_endpoint` param will be overridden with this config + if multiple values are found for `client_options`. + """ + self.project = project + self.location = location + self.api_endpoint = api_endpoint + self.feature_store_name = feature_store_name + self.feature_view_name = feature_view_name + self.row_key = row_key + self.exception_level = exception_level + self.kwargs = kwargs if kwargs else {} + + def __enter__(self): + """Connect with the Vertex AI Feature Store.""" + if 'client_options' in self.kwargs: + self.client = aiplatform.gapic.FeatureOnlineStoreServiceClient( + **self.kwargs) + else: + self.client = aiplatform.gapic.FeatureOnlineStoreServiceClient( + client_options={"api_endpoint": self.api_endpoint}, **self.kwargs) + self.feature_view_path = self.client.feature_view_path( + self.project, + self.location, + self.feature_store_name, + self.feature_view_name) + + def __call__(self, request: beam.Row, *args, **kwargs): + """Fetches feature value for an entity-id from Vertex AI Feature Store. + + Args: + request: the input `beam.Row` to enrich. + """ + try: + entity_id = request._asdict()[self.row_key] + except KeyError: + raise ValueError( + "no entry found for row_key %s in input row" % self.row_key) + try: + response = self.client.fetch_feature_values( + request=aiplatform.gapic.FetchFeatureValuesRequest( + data_key=aiplatform.gapic.FeatureViewDataKey(key=entity_id), + feature_view=self.feature_view_path, + data_format=aiplatform.gapic.FeatureViewDataFormat.PROTO_STRUCT, + )) + except NotFound: + if self.exception_level == ExceptionLevel.WARN: + _LOGGER.warning( + _not_found_err_message( + self.feature_store_name, self.feature_view_name, entity_id)) + return request, beam.Row() + elif self.exception_level == ExceptionLevel.RAISE: + raise ValueError( + _not_found_err_message( + self.feature_store_name, self.feature_view_name, entity_id)) + response_dict = dict(response.proto_struct) + return request, beam.Row(**response_dict) + + def __exit__(self, exc_type, exc_val, exc_tb): + """Clean the instantiated Vertex AI client.""" + self.client = None + + def get_cache_key(self, request: beam.Row) -> str: + """Returns a string formatted with unique entity-id for the feature values. + """ + return 'entity_id: %s' + + +class VertexAIFeatureStoreLegacyEnrichmentHandler(EnrichmentSourceHandler): + """Enrichment handler to interact with Vertex AI Feature Store (Legacy). + + Use this handler with :class:`apache_beam.transforms.enrichment.Enrichment` + transform for the Vertex AI Feature Store (Legacy). + + By default, it fetches all the features values for an entity-id. You can + specify the features names using `feature_ids` to fetch specific features. + """ + def __init__( + self, + project: str, + location: str, + api_endpoint: str, + feature_store_id: str, + entity_type_id: str, + feature_ids: List[str], + row_key: str, + *, + exception_level: ExceptionLevel = ExceptionLevel.WARN, + **kwargs, + ): + """Initializes an instance of `VertexAIFeatureStoreLegacyEnrichmentHandler`. + + Args: + project (str): The GCP project for the Vertex AI Feature Store (Legacy). + location (str): The region for the Vertex AI Feature Store (Legacy). + api_endpoint (str): The API endpoint for the + Vertex AI Feature Store (Legacy). + feature_store_id (str): The id of the Vertex AI Feature Store (Legacy). + entity_type_id (str): The entity type of the feature store. + feature_ids (List[str]): A list of feature-ids to fetch + from the Feature Store. + row_key (str): The row key field name containing the entity id + for the feature values. + exception_level: a `enum.Enum` value from + `apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel` + to set the level when an empty row is returned from the BigTable query. + Defaults to `ExceptionLevel.WARN`. + kwargs: Optional keyword arguments to configure the + `aiplatform.gapic.FeaturestoreOnlineServingServiceClient`. When using + `kwargs`, the `api_endpoint` param will be overridden with this config + if multiple values are found for `client_options`. + """ + self.project = project + self.location = location + self.api_endpoint = api_endpoint + self.feature_store_id = feature_store_id + self.entity_type_id = entity_type_id + self.feature_ids = feature_ids + self.row_key = row_key + self.exception_level = exception_level + self.kwargs = kwargs if kwargs else {} + + def __enter__(self): + """Connect with the Vertex AI Feature Store (Legacy).""" + try: + # checks if feature store exists + _ = aiplatform.Featurestore( + featurestore_name=self.feature_store_id, + project=self.project, + location=self.location, + credentials=self.kwargs.get('credentials'), + ) + if 'client_options' in self.kwargs: + self.client = aiplatform.gapic.FeaturestoreOnlineServingServiceClient( + **self.kwargs) + else: + self.client = aiplatform.gapic.FeaturestoreOnlineServingServiceClient( + client_options={'api_endpoint': self.api_endpoint}, **self.kwargs) + self.entity_type_path = self.client.entity_type_path( + self.project, + self.location, + self.feature_store_id, + self.entity_type_id) + except NotFound: + raise ValueError( + 'Vertex AI Feature Store %s does not exist' % self.feature_store_id) + + def __call__(self, request: beam.Row, *args, **kwargs): + """Fetches feature value for an entity-id from + Vertex AI Feature Store (Legacy). + + Args: + request: the input `beam.Row` to enrich. + """ + try: + entity_id = request._asdict()[self.row_key] + except KeyError: + raise ValueError( + "no entry found for row_key %s in input row" % self.row_key) Review Comment: Could we add more info here (like mentioned above) ########## sdks/python/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.py: ########## @@ -0,0 +1,289 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# 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 logging +from typing import List + +import proto +from google.api_core.exceptions import NotFound +from google.cloud import aiplatform + +import apache_beam as beam +from apache_beam.transforms.enrichment import EnrichmentSourceHandler +from apache_beam.transforms.enrichment_handlers.utils import ExceptionLevel + +__all__ = [ + 'VertexAIFeatureStoreEnrichmentHandler', + 'VertexAIFeatureStoreLegacyEnrichmentHandler' +] + +_LOGGER = logging.getLogger(__name__) + + +def _not_found_err_message( + feature_store_name: str, feature_view_name: str, entity_id: str) -> str: + """returns a string formatted with given parameters""" + return ( + "make sure the Feature Store: %s with Feature View " + "%s has entity_id: %s" % + (feature_store_name, feature_view_name, entity_id)) + + +class VertexAIFeatureStoreEnrichmentHandler(EnrichmentSourceHandler[beam.Row, + beam.Row]): + """Enrichment handler to interact with Vertex AI Feature Store. + + Use this handler with :class:`apache_beam.transforms.enrichment.Enrichment` + transform when the Vertex AI Feature Store is set up for + Bigtable Online serving. + + With the Bigtable Online serving approach, the client fetches all the + available features for an entity-id. To filter the features to enrich, use + the `join_fn` param in :class:`apache_beam.transforms.enrichment.Enrichment`. + + **NOTE:** The default severity to report exceptions is logging a warning. For + this handler, Vertex AI client returns the same exception + `Requested entity was not found` even though the feature store doesn't + exist. So make sure the feature store instance exists or set + `exception_level` as `ExceptionLevel.RAISE`. + """ + def __init__( + self, + project: str, + location: str, + api_endpoint: str, + feature_store_name: str, + feature_view_name: str, + row_key: str, + *, + exception_level: ExceptionLevel = ExceptionLevel.WARN, + **kwargs, + ): + """Initializes an instance of `VertexAIFeatureStoreEnrichmentHandler`. + + Args: + project (str): The GCP project-id for the Vertex AI Feature Store. + location (str): The region for the Vertex AI Feature Store. + api_endpoint (str): The API endpoint for the Vertex AI Feature Store. + feature_store_name (str): The name of the Vertex AI Feature Store. + feature_view_name (str): The name of the feature view within the + Feature Store. + row_key (str): The row key field name containing the unique id + for the feature values. + exception_level: a `enum.Enum` value from + `apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel` + to set the level when an empty row is returned from the BigTable query. + Defaults to `ExceptionLevel.WARN`. + kwargs: Optional keyword arguments to configure the + `aiplatform.gapic.FeatureOnlineStoreServiceClient`. When using + `kwargs`, the `api_endpoint` param will be overridden with this config + if multiple values are found for `client_options`. Review Comment: We can do this in `__init__` ########## sdks/python/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.py: ########## @@ -0,0 +1,289 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# 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 logging +from typing import List + +import proto +from google.api_core.exceptions import NotFound +from google.cloud import aiplatform + +import apache_beam as beam +from apache_beam.transforms.enrichment import EnrichmentSourceHandler +from apache_beam.transforms.enrichment_handlers.utils import ExceptionLevel + +__all__ = [ + 'VertexAIFeatureStoreEnrichmentHandler', + 'VertexAIFeatureStoreLegacyEnrichmentHandler' +] + +_LOGGER = logging.getLogger(__name__) + + +def _not_found_err_message( + feature_store_name: str, feature_view_name: str, entity_id: str) -> str: + """returns a string formatted with given parameters""" + return ( + "make sure the Feature Store: %s with Feature View " + "%s has entity_id: %s" % + (feature_store_name, feature_view_name, entity_id)) + + +class VertexAIFeatureStoreEnrichmentHandler(EnrichmentSourceHandler[beam.Row, + beam.Row]): + """Enrichment handler to interact with Vertex AI Feature Store. + + Use this handler with :class:`apache_beam.transforms.enrichment.Enrichment` + transform when the Vertex AI Feature Store is set up for + Bigtable Online serving. + + With the Bigtable Online serving approach, the client fetches all the + available features for an entity-id. To filter the features to enrich, use + the `join_fn` param in :class:`apache_beam.transforms.enrichment.Enrichment`. + + **NOTE:** The default severity to report exceptions is logging a warning. For + this handler, Vertex AI client returns the same exception + `Requested entity was not found` even though the feature store doesn't + exist. So make sure the feature store instance exists or set + `exception_level` as `ExceptionLevel.RAISE`. + """ + def __init__( + self, + project: str, + location: str, + api_endpoint: str, + feature_store_name: str, + feature_view_name: str, + row_key: str, + *, + exception_level: ExceptionLevel = ExceptionLevel.WARN, + **kwargs, + ): + """Initializes an instance of `VertexAIFeatureStoreEnrichmentHandler`. + + Args: + project (str): The GCP project-id for the Vertex AI Feature Store. + location (str): The region for the Vertex AI Feature Store. + api_endpoint (str): The API endpoint for the Vertex AI Feature Store. + feature_store_name (str): The name of the Vertex AI Feature Store. + feature_view_name (str): The name of the feature view within the + Feature Store. + row_key (str): The row key field name containing the unique id + for the feature values. + exception_level: a `enum.Enum` value from + `apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel` + to set the level when an empty row is returned from the BigTable query. + Defaults to `ExceptionLevel.WARN`. + kwargs: Optional keyword arguments to configure the + `aiplatform.gapic.FeatureOnlineStoreServiceClient`. When using + `kwargs`, the `api_endpoint` param will be overridden with this config + if multiple values are found for `client_options`. Review Comment: Same comment applies below ########## sdks/python/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.py: ########## @@ -0,0 +1,289 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# 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 logging +from typing import List + +import proto +from google.api_core.exceptions import NotFound +from google.cloud import aiplatform + +import apache_beam as beam +from apache_beam.transforms.enrichment import EnrichmentSourceHandler +from apache_beam.transforms.enrichment_handlers.utils import ExceptionLevel + +__all__ = [ + 'VertexAIFeatureStoreEnrichmentHandler', + 'VertexAIFeatureStoreLegacyEnrichmentHandler' +] + +_LOGGER = logging.getLogger(__name__) + + +def _not_found_err_message( + feature_store_name: str, feature_view_name: str, entity_id: str) -> str: + """returns a string formatted with given parameters""" + return ( + "make sure the Feature Store: %s with Feature View " + "%s has entity_id: %s" % + (feature_store_name, feature_view_name, entity_id)) + + +class VertexAIFeatureStoreEnrichmentHandler(EnrichmentSourceHandler[beam.Row, + beam.Row]): + """Enrichment handler to interact with Vertex AI Feature Store. + + Use this handler with :class:`apache_beam.transforms.enrichment.Enrichment` + transform when the Vertex AI Feature Store is set up for + Bigtable Online serving. + + With the Bigtable Online serving approach, the client fetches all the + available features for an entity-id. To filter the features to enrich, use + the `join_fn` param in :class:`apache_beam.transforms.enrichment.Enrichment`. Review Comment: Same comment applies in the other handler (and probably BigTable if we don't already describe this) ########## sdks/python/apache_beam/transforms/enrichment_handlers/vertex_ai_feature_store.py: ########## @@ -0,0 +1,289 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# 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 logging +from typing import List + +import proto +from google.api_core.exceptions import NotFound +from google.cloud import aiplatform + +import apache_beam as beam +from apache_beam.transforms.enrichment import EnrichmentSourceHandler +from apache_beam.transforms.enrichment_handlers.utils import ExceptionLevel + +__all__ = [ + 'VertexAIFeatureStoreEnrichmentHandler', + 'VertexAIFeatureStoreLegacyEnrichmentHandler' +] + +_LOGGER = logging.getLogger(__name__) + + +def _not_found_err_message( + feature_store_name: str, feature_view_name: str, entity_id: str) -> str: + """returns a string formatted with given parameters""" + return ( + "make sure the Feature Store: %s with Feature View " + "%s has entity_id: %s" % + (feature_store_name, feature_view_name, entity_id)) + + +class VertexAIFeatureStoreEnrichmentHandler(EnrichmentSourceHandler[beam.Row, + beam.Row]): + """Enrichment handler to interact with Vertex AI Feature Store. + + Use this handler with :class:`apache_beam.transforms.enrichment.Enrichment` + transform when the Vertex AI Feature Store is set up for + Bigtable Online serving. + + With the Bigtable Online serving approach, the client fetches all the + available features for an entity-id. To filter the features to enrich, use + the `join_fn` param in :class:`apache_beam.transforms.enrichment.Enrichment`. + + **NOTE:** The default severity to report exceptions is logging a warning. For + this handler, Vertex AI client returns the same exception + `Requested entity was not found` even though the feature store doesn't + exist. So make sure the feature store instance exists or set + `exception_level` as `ExceptionLevel.RAISE`. + """ + def __init__( + self, + project: str, + location: str, + api_endpoint: str, + feature_store_name: str, + feature_view_name: str, + row_key: str, + *, + exception_level: ExceptionLevel = ExceptionLevel.WARN, + **kwargs, + ): + """Initializes an instance of `VertexAIFeatureStoreEnrichmentHandler`. + + Args: + project (str): The GCP project-id for the Vertex AI Feature Store. + location (str): The region for the Vertex AI Feature Store. + api_endpoint (str): The API endpoint for the Vertex AI Feature Store. + feature_store_name (str): The name of the Vertex AI Feature Store. + feature_view_name (str): The name of the feature view within the + Feature Store. + row_key (str): The row key field name containing the unique id + for the feature values. + exception_level: a `enum.Enum` value from + `apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel` + to set the level when an empty row is returned from the BigTable query. + Defaults to `ExceptionLevel.WARN`. + kwargs: Optional keyword arguments to configure the + `aiplatform.gapic.FeatureOnlineStoreServiceClient`. When using + `kwargs`, the `api_endpoint` param will be overridden with this config + if multiple values are found for `client_options`. + """ + self.project = project + self.location = location + self.api_endpoint = api_endpoint + self.feature_store_name = feature_store_name + self.feature_view_name = feature_view_name + self.row_key = row_key + self.exception_level = exception_level + self.kwargs = kwargs if kwargs else {} + + def __enter__(self): + """Connect with the Vertex AI Feature Store.""" + if 'client_options' in self.kwargs: + self.client = aiplatform.gapic.FeatureOnlineStoreServiceClient( + **self.kwargs) + else: + self.client = aiplatform.gapic.FeatureOnlineStoreServiceClient( + client_options={"api_endpoint": self.api_endpoint}, **self.kwargs) + self.feature_view_path = self.client.feature_view_path( + self.project, + self.location, + self.feature_store_name, + self.feature_view_name) + + def __call__(self, request: beam.Row, *args, **kwargs): + """Fetches feature value for an entity-id from Vertex AI Feature Store. + + Args: + request: the input `beam.Row` to enrich. + """ + try: + entity_id = request._asdict()[self.row_key] + except KeyError: + raise ValueError( + "no entry found for row_key %s in input row" % self.row_key) + try: + response = self.client.fetch_feature_values( + request=aiplatform.gapic.FetchFeatureValuesRequest( + data_key=aiplatform.gapic.FeatureViewDataKey(key=entity_id), + feature_view=self.feature_view_path, + data_format=aiplatform.gapic.FeatureViewDataFormat.PROTO_STRUCT, + )) + except NotFound: + if self.exception_level == ExceptionLevel.WARN: + _LOGGER.warning( + _not_found_err_message( + self.feature_store_name, self.feature_view_name, entity_id)) + return request, beam.Row() + elif self.exception_level == ExceptionLevel.RAISE: + raise ValueError( + _not_found_err_message( + self.feature_store_name, self.feature_view_name, entity_id)) + response_dict = dict(response.proto_struct) + return request, beam.Row(**response_dict) + + def __exit__(self, exc_type, exc_val, exc_tb): + """Clean the instantiated Vertex AI client.""" + self.client = None + + def get_cache_key(self, request: beam.Row) -> str: + """Returns a string formatted with unique entity-id for the feature values. + """ + return 'entity_id: %s' + + +class VertexAIFeatureStoreLegacyEnrichmentHandler(EnrichmentSourceHandler): + """Enrichment handler to interact with Vertex AI Feature Store (Legacy). + + Use this handler with :class:`apache_beam.transforms.enrichment.Enrichment` + transform for the Vertex AI Feature Store (Legacy). + + By default, it fetches all the features values for an entity-id. You can + specify the features names using `feature_ids` to fetch specific features. + """ + def __init__( + self, + project: str, + location: str, + api_endpoint: str, + feature_store_id: str, + entity_type_id: str, + feature_ids: List[str], + row_key: str, + *, + exception_level: ExceptionLevel = ExceptionLevel.WARN, + **kwargs, + ): + """Initializes an instance of `VertexAIFeatureStoreLegacyEnrichmentHandler`. + + Args: + project (str): The GCP project for the Vertex AI Feature Store (Legacy). + location (str): The region for the Vertex AI Feature Store (Legacy). + api_endpoint (str): The API endpoint for the + Vertex AI Feature Store (Legacy). + feature_store_id (str): The id of the Vertex AI Feature Store (Legacy). + entity_type_id (str): The entity type of the feature store. + feature_ids (List[str]): A list of feature-ids to fetch + from the Feature Store. + row_key (str): The row key field name containing the entity id + for the feature values. + exception_level: a `enum.Enum` value from + `apache_beam.transforms.enrichment_handlers.utils.ExceptionLevel` + to set the level when an empty row is returned from the BigTable query. + Defaults to `ExceptionLevel.WARN`. + kwargs: Optional keyword arguments to configure the + `aiplatform.gapic.FeaturestoreOnlineServingServiceClient`. When using + `kwargs`, the `api_endpoint` param will be overridden with this config + if multiple values are found for `client_options`. + """ + self.project = project + self.location = location + self.api_endpoint = api_endpoint + self.feature_store_id = feature_store_id + self.entity_type_id = entity_type_id + self.feature_ids = feature_ids + self.row_key = row_key + self.exception_level = exception_level + self.kwargs = kwargs if kwargs else {} + + def __enter__(self): + """Connect with the Vertex AI Feature Store (Legacy).""" + try: + # checks if feature store exists + _ = aiplatform.Featurestore( + featurestore_name=self.feature_store_id, + project=self.project, + location=self.location, + credentials=self.kwargs.get('credentials'), + ) + if 'client_options' in self.kwargs: + self.client = aiplatform.gapic.FeaturestoreOnlineServingServiceClient( + **self.kwargs) + else: + self.client = aiplatform.gapic.FeaturestoreOnlineServingServiceClient( + client_options={'api_endpoint': self.api_endpoint}, **self.kwargs) + self.entity_type_path = self.client.entity_type_path( + self.project, + self.location, + self.feature_store_id, + self.entity_type_id) + except NotFound: + raise ValueError( + 'Vertex AI Feature Store %s does not exist' % self.feature_store_id) + + def __call__(self, request: beam.Row, *args, **kwargs): + """Fetches feature value for an entity-id from + Vertex AI Feature Store (Legacy). + + Args: + request: the input `beam.Row` to enrich. + """ + try: + entity_id = request._asdict()[self.row_key] + except KeyError: + raise ValueError( + "no entry found for row_key %s in input row" % self.row_key) + + try: + selector = aiplatform.gapic.FeatureSelector( + id_matcher=aiplatform.gapic.IdMatcher(ids=self.feature_ids)) + response = self.client.read_feature_values( + request=aiplatform.gapic.ReadFeatureValuesRequest( + entity_type=self.entity_type_path, + entity_id=entity_id, + feature_selector=selector)) + except NotFound: + raise ValueError( + _not_found_err_message( + self.feature_store_id, self.entity_type_id, entity_id)) + + response_dict = {} + proto_to_dict = proto.Message.to_dict(response.entity_view) + for key, msg in zip(response.header.feature_descriptors, + proto_to_dict['data']): + if msg and 'value' in msg: + for _, value in msg['value'].items(): + response_dict[key.id] = value + break # skip fetching the metadata + elif self.exception_level == ExceptionLevel.RAISE: + raise ValueError( + _not_found_err_message( + self.feature_store_id, self.entity_type_id, entity_id)) + elif self.exception_level == ExceptionLevel.WARN: + _LOGGER.warning( + _not_found_err_message( + self.feature_store_id, self.entity_type_id, entity_id)) + return request, beam.Row(**response_dict) + + def __exit__(self, exc_type, exc_val, exc_tb): + """Clean the instantiated Vertex AI client.""" + self.client = None + + def get_cache_key(self, request: beam.Row) -> str: + """Returns a string formatted with unique entity-id for the feature values. + """ + return 'entity_id: %s' Review Comment: Same as above, should we be grabbing entity_id from the request? -- This is an automated message from the Apache Git Service. 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