Source code for feast.feature_store

# Copyright 2019 The Feast Authors
#
# Licensed 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
#
#     https://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 copy
import itertools
import os
import warnings
from collections import Counter, defaultdict
from datetime import datetime
from pathlib import Path
from typing import (
    Any,
    Dict,
    Iterable,
    List,
    Mapping,
    NamedTuple,
    Optional,
    Sequence,
    Set,
    Tuple,
    Union,
    cast,
)

import pandas as pd
from colorama import Fore, Style
from google.protobuf.timestamp_pb2 import Timestamp
from tqdm import tqdm

from feast import feature_server, flags, flags_helper, utils
from feast.base_feature_view import BaseFeatureView
from feast.diff.FcoDiff import RegistryDiff
from feast.diff.infra_diff import InfraDiff, diff_infra_protos
from feast.entity import Entity
from feast.errors import (
    EntityNotFoundException,
    ExperimentalFeatureNotEnabled,
    FeatureNameCollisionError,
    FeatureViewNotFoundException,
    RequestDataNotFoundInEntityDfException,
    RequestDataNotFoundInEntityRowsException,
)
from feast.feature_service import FeatureService
from feast.feature_view import (
    DUMMY_ENTITY,
    DUMMY_ENTITY_ID,
    DUMMY_ENTITY_NAME,
    DUMMY_ENTITY_VAL,
    FeatureView,
)
from feast.inference import (
    update_data_sources_with_inferred_event_timestamp_col,
    update_entities_with_inferred_types_from_feature_views,
    update_feature_views_with_inferred_features,
)
from feast.infra.provider import Provider, RetrievalJob, get_provider
from feast.on_demand_feature_view import OnDemandFeatureView
from feast.online_response import OnlineResponse
from feast.protos.feast.core.InfraObject_pb2 import Infra as InfraProto
from feast.protos.feast.core.Registry_pb2 import Registry as RegistryProto
from feast.protos.feast.serving.ServingService_pb2 import (
    FieldStatus,
    GetOnlineFeaturesResponse,
)
from feast.protos.feast.types.EntityKey_pb2 import EntityKey as EntityKeyProto
from feast.protos.feast.types.Value_pb2 import RepeatedValue, Value
from feast.registry import Registry
from feast.repo_config import RepoConfig, load_repo_config
from feast.request_feature_view import RequestFeatureView
from feast.type_map import python_values_to_proto_values
from feast.usage import log_exceptions, log_exceptions_and_usage, set_usage_attribute
from feast.value_type import ValueType
from feast.version import get_version

warnings.simplefilter("once", DeprecationWarning)


[docs]class RepoContents(NamedTuple): feature_views: Set[FeatureView] on_demand_feature_views: Set[OnDemandFeatureView] request_feature_views: Set[RequestFeatureView] entities: Set[Entity] feature_services: Set[FeatureService]
[docs] def to_registry_proto(self) -> RegistryProto: registry_proto = RegistryProto() registry_proto.entities.extend([e.to_proto() for e in self.entities]) registry_proto.feature_views.extend( [fv.to_proto() for fv in self.feature_views] ) registry_proto.on_demand_feature_views.extend( [fv.to_proto() for fv in self.on_demand_feature_views] ) registry_proto.request_feature_views.extend( [fv.to_proto() for fv in self.request_feature_views] ) registry_proto.feature_services.extend( [fs.to_proto() for fs in self.feature_services] ) return registry_proto
[docs]class FeatureStore: """ A FeatureStore object is used to define, create, and retrieve features. Args: repo_path (optional): Path to a `feature_store.yaml` used to configure the feature store. config (optional): Configuration object used to configure the feature store. """ config: RepoConfig repo_path: Path _registry: Registry _provider: Provider @log_exceptions def __init__( self, repo_path: Optional[str] = None, config: Optional[RepoConfig] = None, ): """ Creates a FeatureStore object. Raises: ValueError: If both or neither of repo_path and config are specified. """ if repo_path is not None and config is not None: raise ValueError("You cannot specify both repo_path and config.") if config is not None: self.repo_path = Path(os.getcwd()) self.config = config elif repo_path is not None: self.repo_path = Path(repo_path) self.config = load_repo_config(Path(repo_path)) else: raise ValueError("Please specify one of repo_path or config.") registry_config = self.config.get_registry_config() self._registry = Registry(registry_config, repo_path=self.repo_path) self._provider = get_provider(self.config, self.repo_path)
[docs] @log_exceptions def version(self) -> str: """Returns the version of the current Feast SDK/CLI.""" return get_version()
@property def registry(self) -> Registry: """Gets the registry of this feature store.""" return self._registry @property def project(self) -> str: """Gets the project of this feature store.""" return self.config.project def _get_provider(self) -> Provider: # TODO: Bake self.repo_path into self.config so that we dont only have one interface to paths return self._provider
[docs] @log_exceptions_and_usage def refresh_registry(self): """Fetches and caches a copy of the feature registry in memory. Explicitly calling this method allows for direct control of the state of the registry cache. Every time this method is called the complete registry state will be retrieved from the remote registry store backend (e.g., GCS, S3), and the cache timer will be reset. If refresh_registry() is run before get_online_features() is called, then get_online_feature() will use the cached registry instead of retrieving (and caching) the registry itself. Additionally, the TTL for the registry cache can be set to infinity (by setting it to 0), which means that refresh_registry() will become the only way to update the cached registry. If the TTL is set to a value greater than 0, then once the cache becomes stale (more time than the TTL has passed), a new cache will be downloaded synchronously, which may increase latencies if the triggering method is get_online_features() """ registry_config = self.config.get_registry_config() registry = Registry(registry_config, repo_path=self.repo_path) registry.refresh() self._registry = registry
[docs] @log_exceptions_and_usage def list_entities(self, allow_cache: bool = False) -> List[Entity]: """ Retrieves the list of entities from the registry. Args: allow_cache: Whether to allow returning entities from a cached registry. Returns: A list of entities. """ return self._list_entities(allow_cache)
def _list_entities( self, allow_cache: bool = False, hide_dummy_entity: bool = True ) -> List[Entity]: all_entities = self._registry.list_entities( self.project, allow_cache=allow_cache ) return [ entity for entity in all_entities if entity.name != DUMMY_ENTITY_NAME or not hide_dummy_entity ]
[docs] @log_exceptions_and_usage def list_feature_services(self) -> List[FeatureService]: """ Retrieves the list of feature services from the registry. Returns: A list of feature services. """ return self._registry.list_feature_services(self.project)
[docs] @log_exceptions_and_usage def list_feature_views(self, allow_cache: bool = False) -> List[FeatureView]: """ Retrieves the list of feature views from the registry. Args: allow_cache: Whether to allow returning entities from a cached registry. Returns: A list of feature views. """ return self._list_feature_views(allow_cache)
[docs] @log_exceptions_and_usage def list_request_feature_views( self, allow_cache: bool = False ) -> List[RequestFeatureView]: """ Retrieves the list of feature views from the registry. Args: allow_cache: Whether to allow returning entities from a cached registry. Returns: A list of feature views. """ return self._registry.list_request_feature_views( self.project, allow_cache=allow_cache )
def _list_feature_views( self, allow_cache: bool = False, hide_dummy_entity: bool = True, ) -> List[FeatureView]: feature_views = [] for fv in self._registry.list_feature_views( self.project, allow_cache=allow_cache ): if hide_dummy_entity and fv.entities[0] == DUMMY_ENTITY_NAME: fv.entities = [] feature_views.append(fv) return feature_views
[docs] @log_exceptions_and_usage def list_on_demand_feature_views( self, allow_cache: bool = False ) -> List[OnDemandFeatureView]: """ Retrieves the list of on demand feature views from the registry. Returns: A list of on demand feature views. """ return self._registry.list_on_demand_feature_views( self.project, allow_cache=allow_cache )
[docs] @log_exceptions_and_usage def get_entity(self, name: str) -> Entity: """ Retrieves an entity. Args: name: Name of entity. Returns: The specified entity. Raises: EntityNotFoundException: The entity could not be found. """ return self._registry.get_entity(name, self.project)
[docs] @log_exceptions_and_usage def get_feature_service( self, name: str, allow_cache: bool = False ) -> FeatureService: """ Retrieves a feature service. Args: name: Name of feature service. Returns: The specified feature service. Raises: FeatureServiceNotFoundException: The feature service could not be found. """ return self._registry.get_feature_service(name, self.project, allow_cache)
[docs] @log_exceptions_and_usage def get_feature_view(self, name: str) -> FeatureView: """ Retrieves a feature view. Args: name: Name of feature view. Returns: The specified feature view. Raises: FeatureViewNotFoundException: The feature view could not be found. """ return self._get_feature_view(name)
def _get_feature_view( self, name: str, hide_dummy_entity: bool = True ) -> FeatureView: feature_view = self._registry.get_feature_view(name, self.project) if hide_dummy_entity and feature_view.entities[0] == DUMMY_ENTITY_NAME: feature_view.entities = [] return feature_view
[docs] @log_exceptions_and_usage def get_on_demand_feature_view(self, name: str) -> OnDemandFeatureView: """ Retrieves a feature view. Args: name: Name of feature view. Returns: The specified feature view. Raises: FeatureViewNotFoundException: The feature view could not be found. """ return self._registry.get_on_demand_feature_view(name, self.project)
[docs] @log_exceptions_and_usage def delete_feature_view(self, name: str): """ Deletes a feature view. Args: name: Name of feature view. Raises: FeatureViewNotFoundException: The feature view could not be found. """ return self._registry.delete_feature_view(name, self.project)
[docs] @log_exceptions_and_usage def delete_feature_service(self, name: str): """ Deletes a feature service. Args: name: Name of feature service. Raises: FeatureServiceNotFoundException: The feature view could not be found. """ return self._registry.delete_feature_service(name, self.project)
def _get_features( self, features: Union[List[str], FeatureService], allow_cache: bool = False, ) -> List[str]: _features = features if not _features: raise ValueError("No features specified for retrieval") _feature_refs = [] if isinstance(_features, FeatureService): feature_service_from_registry = self.get_feature_service( _features.name, allow_cache ) if feature_service_from_registry != _features: warnings.warn( "The FeatureService object that has been passed in as an argument is" "inconsistent with the version from Registry. Potentially a newer version" "of the FeatureService has been applied to the registry." ) for projection in feature_service_from_registry.feature_view_projections: _feature_refs.extend( [ f"{projection.name_to_use()}:{f.name}" for f in projection.features ] ) else: assert isinstance(_features, list) _feature_refs = _features return _feature_refs
[docs] @log_exceptions_and_usage def plan( self, desired_repo_objects: RepoContents ) -> Tuple[RegistryDiff, InfraDiff]: """Dry-run registering objects to metadata store. The plan method dry-runs registering one or more definitions (e.g., Entity, FeatureView), and produces a list of all the changes the that would be introduced in the feature repo. The changes computed by the plan command are for informational purpose, and are not actually applied to the registry. Args: objects: A single object, or a list of objects that are intended to be registered with the Feature Store. objects_to_delete: A list of objects to be deleted from the registry. partial: If True, apply will only handle the specified objects; if False, apply will also delete all the objects in objects_to_delete. Raises: ValueError: The 'objects' parameter could not be parsed properly. Examples: Generate a plan adding an Entity and a FeatureView. >>> from feast import FeatureStore, Entity, FeatureView, Feature, ValueType, FileSource, RepoConfig >>> from feast.feature_store import RepoContents >>> from datetime import timedelta >>> fs = FeatureStore(repo_path="feature_repo") >>> driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id") >>> driver_hourly_stats = FileSource( ... path="feature_repo/data/driver_stats.parquet", ... event_timestamp_column="event_timestamp", ... created_timestamp_column="created", ... ) >>> driver_hourly_stats_view = FeatureView( ... name="driver_hourly_stats", ... entities=["driver_id"], ... ttl=timedelta(seconds=86400 * 1), ... batch_source=driver_hourly_stats, ... ) >>> registry_diff, infra_diff = fs.plan(RepoContents({driver_hourly_stats_view}, set(), set(), {driver}, set())) # register entity and feature view """ current_registry_proto = ( self._registry.cached_registry_proto.__deepcopy__() if self._registry.cached_registry_proto else RegistryProto() ) desired_registry_proto = desired_repo_objects.to_registry_proto() registry_diff = Registry.diff_between( current_registry_proto, desired_registry_proto ) current_infra_proto = ( self._registry.cached_registry_proto.infra.__deepcopy__() if self._registry.cached_registry_proto else InfraProto() ) new_infra_proto = self._provider.plan_infra( self.config, desired_registry_proto ).to_proto() infra_diff = diff_infra_protos(current_infra_proto, new_infra_proto) return (registry_diff, infra_diff)
[docs] @log_exceptions_and_usage def apply( self, objects: Union[ Entity, FeatureView, OnDemandFeatureView, RequestFeatureView, FeatureService, List[ Union[ FeatureView, OnDemandFeatureView, RequestFeatureView, Entity, FeatureService, ] ], ], objects_to_delete: Optional[ List[ Union[ FeatureView, OnDemandFeatureView, RequestFeatureView, Entity, FeatureService, ] ] ] = None, partial: bool = True, ) -> RegistryDiff: """Register objects to metadata store and update related infrastructure. The apply method registers one or more definitions (e.g., Entity, FeatureView) and registers or updates these objects in the Feast registry. Once the apply method has updated the infrastructure (e.g., create tables in an online store), it will commit the updated registry. All operations are idempotent, meaning they can safely be rerun. Args: objects: A single object, or a list of objects that should be registered with the Feature Store. objects_to_delete: A list of objects to be deleted from the registry and removed from the provider's infrastructure. This deletion will only be performed if partial is set to False. partial: If True, apply will only handle the specified objects; if False, apply will also delete all the objects in objects_to_delete, and tear down any associated cloud resources. Raises: ValueError: The 'objects' parameter could not be parsed properly. Examples: Register an Entity and a FeatureView. >>> from feast import FeatureStore, Entity, FeatureView, Feature, ValueType, FileSource, RepoConfig >>> from datetime import timedelta >>> fs = FeatureStore(repo_path="feature_repo") >>> driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id") >>> driver_hourly_stats = FileSource( ... path="feature_repo/data/driver_stats.parquet", ... event_timestamp_column="event_timestamp", ... created_timestamp_column="created", ... ) >>> driver_hourly_stats_view = FeatureView( ... name="driver_hourly_stats", ... entities=["driver_id"], ... ttl=timedelta(seconds=86400 * 1), ... batch_source=driver_hourly_stats, ... ) >>> diff = fs.apply([driver_hourly_stats_view, driver]) # register entity and feature view """ # TODO: Add locking if not isinstance(objects, Iterable): objects = [objects] assert isinstance(objects, list) if not objects_to_delete: objects_to_delete = [] current_registry_proto = ( self._registry.cached_registry_proto.__deepcopy__() if self._registry.cached_registry_proto else RegistryProto() ) # Separate all objects into entities, feature services, and different feature view types. entities_to_update = [ob for ob in objects if isinstance(ob, Entity)] views_to_update = [ob for ob in objects if isinstance(ob, FeatureView)] request_views_to_update = [ ob for ob in objects if isinstance(ob, RequestFeatureView) ] odfvs_to_update = [ob for ob in objects if isinstance(ob, OnDemandFeatureView)] services_to_update = [ob for ob in objects if isinstance(ob, FeatureService)] if len(entities_to_update) + len(views_to_update) + len( request_views_to_update ) + len(odfvs_to_update) + len(services_to_update) != len(objects): raise ValueError("Unknown object type provided as part of apply() call") # Validate all types of feature views. if ( not flags_helper.enable_on_demand_feature_views(self.config) and len(odfvs_to_update) > 0 ): raise ExperimentalFeatureNotEnabled(flags.FLAG_ON_DEMAND_TRANSFORM_NAME) set_usage_attribute("odfv", bool(odfvs_to_update)) _validate_feature_views( [*views_to_update, *odfvs_to_update, *request_views_to_update] ) # Make inferences update_entities_with_inferred_types_from_feature_views( entities_to_update, views_to_update, self.config ) update_data_sources_with_inferred_event_timestamp_col( [view.batch_source for view in views_to_update], self.config ) update_feature_views_with_inferred_features( views_to_update, entities_to_update, self.config ) for odfv in odfvs_to_update: odfv.infer_features() # Handle all entityless feature views by using DUMMY_ENTITY as a placeholder entity. entities_to_update.append(DUMMY_ENTITY) # Add all objects to the registry and update the provider's infrastructure. for view in itertools.chain( views_to_update, odfvs_to_update, request_views_to_update ): self._registry.apply_feature_view(view, project=self.project, commit=False) for ent in entities_to_update: self._registry.apply_entity(ent, project=self.project, commit=False) for feature_service in services_to_update: self._registry.apply_feature_service( feature_service, project=self.project, commit=False ) if not partial: # Delete all registry objects that should not exist. entities_to_delete = [ ob for ob in objects_to_delete if isinstance(ob, Entity) ] views_to_delete = [ ob for ob in objects_to_delete if isinstance(ob, FeatureView) ] request_views_to_delete = [ ob for ob in objects_to_delete if isinstance(ob, RequestFeatureView) ] odfvs_to_delete = [ ob for ob in objects_to_delete if isinstance(ob, OnDemandFeatureView) ] services_to_delete = [ ob for ob in objects_to_delete if isinstance(ob, FeatureService) ] for entity in entities_to_delete: self._registry.delete_entity( entity.name, project=self.project, commit=False ) for view in views_to_delete: self._registry.delete_feature_view( view.name, project=self.project, commit=False ) for request_view in request_views_to_delete: self._registry.delete_feature_view( request_view.name, project=self.project, commit=False ) for odfv in odfvs_to_delete: self._registry.delete_feature_view( odfv.name, project=self.project, commit=False ) for service in services_to_delete: self._registry.delete_feature_service( service.name, project=self.project, commit=False ) new_registry_proto = ( self._registry.cached_registry_proto if self._registry.cached_registry_proto else RegistryProto() ) diffs = Registry.diff_between(current_registry_proto, new_registry_proto) entities_to_update = [ob for ob in objects if isinstance(ob, Entity)] views_to_update = [ob for ob in objects if isinstance(ob, FeatureView)] entities_to_delete = [ob for ob in objects_to_delete if isinstance(ob, Entity)] views_to_delete = [ ob for ob in objects_to_delete if isinstance(ob, FeatureView) ] self._get_provider().update_infra( project=self.project, tables_to_delete=views_to_delete if not partial else [], tables_to_keep=views_to_update, entities_to_delete=entities_to_delete if not partial else [], entities_to_keep=entities_to_update, partial=partial, ) self._registry.commit() return diffs
[docs] @log_exceptions_and_usage def teardown(self): """Tears down all local and cloud resources for the feature store.""" tables: List[FeatureView] = [] feature_views = self.list_feature_views() tables.extend(feature_views) entities = self.list_entities() self._get_provider().teardown_infra(self.project, tables, entities) self._registry.teardown()
[docs] @log_exceptions_and_usage def get_historical_features( self, entity_df: Union[pd.DataFrame, str], features: Union[List[str], FeatureService], full_feature_names: bool = False, ) -> RetrievalJob: """Enrich an entity dataframe with historical feature values for either training or batch scoring. This method joins historical feature data from one or more feature views to an entity dataframe by using a time travel join. Each feature view is joined to the entity dataframe using all entities configured for the respective feature view. All configured entities must be available in the entity dataframe. Therefore, the entity dataframe must contain all entities found in all feature views, but the individual feature views can have different entities. Time travel is based on the configured TTL for each feature view. A shorter TTL will limit the amount of scanning that will be done in order to find feature data for a specific entity key. Setting a short TTL may result in null values being returned. Args: entity_df (Union[pd.DataFrame, str]): An entity dataframe is a collection of rows containing all entity columns (e.g., customer_id, driver_id) on which features need to be joined, as well as a event_timestamp column used to ensure point-in-time correctness. Either a Pandas DataFrame can be provided or a string SQL query. The query must be of a format supported by the configured offline store (e.g., BigQuery) features: A list of features, that should be retrieved from the offline store. Either a list of string feature references can be provided or a FeatureService object. Feature references are of the format "feature_view:feature", e.g., "customer_fv:daily_transactions". full_feature_names: A boolean that provides the option to add the feature view prefixes to the feature names, changing them from the format "feature" to "feature_view__feature" (e.g., "daily_transactions" changes to "customer_fv__daily_transactions"). By default, this value is set to False. Returns: RetrievalJob which can be used to materialize the results. Raises: ValueError: Both or neither of features and feature_refs are specified. Examples: Retrieve historical features from a local offline store. >>> from feast import FeatureStore, RepoConfig >>> import pandas as pd >>> fs = FeatureStore(repo_path="feature_repo") >>> entity_df = pd.DataFrame.from_dict( ... { ... "driver_id": [1001, 1002], ... "event_timestamp": [ ... datetime(2021, 4, 12, 10, 59, 42), ... datetime(2021, 4, 12, 8, 12, 10), ... ], ... } ... ) >>> retrieval_job = fs.get_historical_features( ... entity_df=entity_df, ... features=[ ... "driver_hourly_stats:conv_rate", ... "driver_hourly_stats:acc_rate", ... "driver_hourly_stats:avg_daily_trips", ... ], ... ) >>> feature_data = retrieval_job.to_df() """ _feature_refs = self._get_features(features) ( all_feature_views, all_request_feature_views, all_on_demand_feature_views, ) = self._get_feature_views_to_use(features) # TODO(achal): _group_feature_refs returns the on demand feature views, but it's no passed into the provider. # This is a weird interface quirk - we should revisit the `get_historical_features` to # pass in the on demand feature views as well. fvs, odfvs, request_fvs, request_fv_refs = _group_feature_refs( _feature_refs, all_feature_views, all_request_feature_views, all_on_demand_feature_views, ) feature_views = list(view for view, _ in fvs) on_demand_feature_views = list(view for view, _ in odfvs) request_feature_views = list(view for view, _ in request_fvs) set_usage_attribute("odfv", bool(on_demand_feature_views)) set_usage_attribute("request_fv", bool(request_feature_views)) # Check that the right request data is present in the entity_df if type(entity_df) == pd.DataFrame: entity_pd_df = cast(pd.DataFrame, entity_df) for fv in request_feature_views: for feature in fv.features: if feature.name not in entity_pd_df.columns: raise RequestDataNotFoundInEntityDfException( feature_name=feature.name, feature_view_name=fv.name ) for odfv in on_demand_feature_views: odfv_request_data_schema = odfv.get_request_data_schema() for feature_name in odfv_request_data_schema.keys(): if feature_name not in entity_pd_df.columns: raise RequestDataNotFoundInEntityDfException( feature_name=feature_name, feature_view_name=odfv.name, ) _validate_feature_refs(_feature_refs, full_feature_names) # Drop refs that refer to RequestFeatureViews since they don't need to be fetched and # already exist in the entity_df _feature_refs = [ref for ref in _feature_refs if ref not in request_fv_refs] provider = self._get_provider() job = provider.get_historical_features( self.config, feature_views, _feature_refs, entity_df, self._registry, self.project, full_feature_names, ) return job
[docs] @log_exceptions_and_usage def materialize_incremental( self, end_date: datetime, feature_views: Optional[List[str]] = None, ) -> None: """ Materialize incremental new data from the offline store into the online store. This method loads incremental new feature data up to the specified end time from either the specified feature views, or all feature views if none are specified, into the online store where it is available for online serving. The start time of the interval materialized is either the most recent end time of a prior materialization or (now - ttl) if no such prior materialization exists. Args: end_date (datetime): End date for time range of data to materialize into the online store feature_views (List[str]): Optional list of feature view names. If selected, will only run materialization for the specified feature views. Raises: Exception: A feature view being materialized does not have a TTL set. Examples: Materialize all features into the online store up to 5 minutes ago. >>> from feast import FeatureStore, RepoConfig >>> from datetime import datetime, timedelta >>> fs = FeatureStore(repo_path="feature_repo") >>> fs.materialize_incremental(end_date=datetime.utcnow() - timedelta(minutes=5)) Materializing... <BLANKLINE> ... """ feature_views_to_materialize: List[FeatureView] = [] if feature_views is None: feature_views_to_materialize = self._list_feature_views( hide_dummy_entity=False ) feature_views_to_materialize = [ fv for fv in feature_views_to_materialize if fv.online ] else: for name in feature_views: feature_view = self._get_feature_view(name, hide_dummy_entity=False) if not feature_view.online: raise ValueError( f"FeatureView {feature_view.name} is not configured to be served online." ) feature_views_to_materialize.append(feature_view) _print_materialization_log( None, end_date, len(feature_views_to_materialize), self.config.online_store.type, ) # TODO paging large loads for feature_view in feature_views_to_materialize: start_date = feature_view.most_recent_end_time if start_date is None: if feature_view.ttl is None: raise Exception( f"No start time found for feature view {feature_view.name}. materialize_incremental() requires" f" either a ttl to be set or for materialize() to have been run at least once." ) start_date = datetime.utcnow() - feature_view.ttl provider = self._get_provider() print( f"{Style.BRIGHT + Fore.GREEN}{feature_view.name}{Style.RESET_ALL}" f" from {Style.BRIGHT + Fore.GREEN}{start_date.replace(microsecond=0).astimezone()}{Style.RESET_ALL}" f" to {Style.BRIGHT + Fore.GREEN}{end_date.replace(microsecond=0).astimezone()}{Style.RESET_ALL}:" ) def tqdm_builder(length): return tqdm(total=length, ncols=100) start_date = utils.make_tzaware(start_date) end_date = utils.make_tzaware(end_date) provider.materialize_single_feature_view( config=self.config, feature_view=feature_view, start_date=start_date, end_date=end_date, registry=self._registry, project=self.project, tqdm_builder=tqdm_builder, ) self._registry.apply_materialization( feature_view, self.project, start_date, end_date )
[docs] @log_exceptions_and_usage def materialize( self, start_date: datetime, end_date: datetime, feature_views: Optional[List[str]] = None, ) -> None: """ Materialize data from the offline store into the online store. This method loads feature data in the specified interval from either the specified feature views, or all feature views if none are specified, into the online store where it is available for online serving. Args: start_date (datetime): Start date for time range of data to materialize into the online store end_date (datetime): End date for time range of data to materialize into the online store feature_views (List[str]): Optional list of feature view names. If selected, will only run materialization for the specified feature views. Examples: Materialize all features into the online store over the interval from 3 hours ago to 10 minutes ago. >>> from feast import FeatureStore, RepoConfig >>> from datetime import datetime, timedelta >>> fs = FeatureStore(repo_path="feature_repo") >>> fs.materialize( ... start_date=datetime.utcnow() - timedelta(hours=3), end_date=datetime.utcnow() - timedelta(minutes=10) ... ) Materializing... <BLANKLINE> ... """ if utils.make_tzaware(start_date) > utils.make_tzaware(end_date): raise ValueError( f"The given start_date {start_date} is greater than the given end_date {end_date}." ) feature_views_to_materialize: List[FeatureView] = [] if feature_views is None: feature_views_to_materialize = self._list_feature_views( hide_dummy_entity=False ) feature_views_to_materialize = [ fv for fv in feature_views_to_materialize if fv.online ] else: for name in feature_views: feature_view = self._get_feature_view(name, hide_dummy_entity=False) if not feature_view.online: raise ValueError( f"FeatureView {feature_view.name} is not configured to be served online." ) feature_views_to_materialize.append(feature_view) _print_materialization_log( start_date, end_date, len(feature_views_to_materialize), self.config.online_store.type, ) # TODO paging large loads for feature_view in feature_views_to_materialize: provider = self._get_provider() print(f"{Style.BRIGHT + Fore.GREEN}{feature_view.name}{Style.RESET_ALL}:") def tqdm_builder(length): return tqdm(total=length, ncols=100) start_date = utils.make_tzaware(start_date) end_date = utils.make_tzaware(end_date) provider.materialize_single_feature_view( config=self.config, feature_view=feature_view, start_date=start_date, end_date=end_date, registry=self._registry, project=self.project, tqdm_builder=tqdm_builder, ) self._registry.apply_materialization( feature_view, self.project, start_date, end_date )
[docs] @log_exceptions_and_usage def write_to_online_store( self, feature_view_name: str, df: pd.DataFrame, allow_registry_cache: bool = True, ): """ ingests data directly into the Online store """ if not flags_helper.enable_direct_ingestion_to_online_store(self.config): raise ExperimentalFeatureNotEnabled( flags.FLAG_DIRECT_INGEST_TO_ONLINE_STORE ) # TODO: restrict this to work with online StreamFeatureViews and validate the FeatureView type feature_view = self._registry.get_feature_view( feature_view_name, self.project, allow_cache=allow_registry_cache ) entities = [] for entity_name in feature_view.entities: entities.append(self._registry.get_entity(entity_name, self.project)) provider = self._get_provider() provider.ingest_df(feature_view, entities, df)
[docs] @log_exceptions_and_usage def get_online_features( self, features: Union[List[str], FeatureService], entity_rows: List[Dict[str, Any]], full_feature_names: bool = False, ) -> OnlineResponse: """ Retrieves the latest online feature data. Note: This method will download the full feature registry the first time it is run. If you are using a remote registry like GCS or S3 then that may take a few seconds. The registry remains cached up to a TTL duration (which can be set to infinity). If the cached registry is stale (more time than the TTL has passed), then a new registry will be downloaded synchronously by this method. This download may introduce latency to online feature retrieval. In order to avoid synchronous downloads, please call refresh_registry() prior to the TTL being reached. Remember it is possible to set the cache TTL to infinity (cache forever). Args: features: List of feature references that will be returned for each entity. Each feature reference should have the following format: "feature_view:feature" where "feature_view" & "feature" refer to the Feature and FeatureView names respectively. Only the feature name is required. entity_rows: A list of dictionaries where each key-value is an entity-name, entity-value pair. Returns: OnlineResponse containing the feature data in records. Raises: Exception: No entity with the specified name exists. Examples: Materialize all features into the online store over the interval from 3 hours ago to 10 minutes ago, and then retrieve these online features. >>> from feast import FeatureStore, RepoConfig >>> fs = FeatureStore(repo_path="feature_repo") >>> online_response = fs.get_online_features( ... features=[ ... "driver_hourly_stats:conv_rate", ... "driver_hourly_stats:acc_rate", ... "driver_hourly_stats:avg_daily_trips", ... ], ... entity_rows=[{"driver_id": 1001}, {"driver_id": 1002}, {"driver_id": 1003}, {"driver_id": 1004}], ... ) >>> online_response_dict = online_response.to_dict() """ columnar: Dict[str, List[Any]] = {k: [] for k in entity_rows[0].keys()} for entity_row in entity_rows: for key, value in entity_row.items(): try: columnar[key].append(value) except KeyError as e: raise ValueError("All entity_rows must have the same keys.") from e return self._get_online_features( features=features, entity_values=columnar, full_feature_names=full_feature_names, native_entity_values=True, )
def _get_online_features( self, features: Union[List[str], FeatureService], entity_values: Mapping[ str, Union[Sequence[Any], Sequence[Value], RepeatedValue] ], full_feature_names: bool = False, native_entity_values: bool = True, ): _feature_refs = self._get_features(features, allow_cache=True) ( requested_feature_views, requested_request_feature_views, requested_on_demand_feature_views, ) = self._get_feature_views_to_use( features=features, allow_cache=True, hide_dummy_entity=False ) entity_name_to_join_key_map, entity_type_map = self._get_entity_maps( requested_feature_views ) # Extract Sequence from RepeatedValue Protobuf. entity_value_lists: Dict[str, Union[List[Any], List[Value]]] = { k: list(v) if isinstance(v, Sequence) else list(v.val) for k, v in entity_values.items() } entity_proto_values: Dict[str, List[Value]] if native_entity_values: # Convert values to Protobuf once. entity_proto_values = { k: python_values_to_proto_values( v, entity_type_map.get(k, ValueType.UNKNOWN) ) for k, v in entity_value_lists.items() } else: entity_proto_values = entity_value_lists num_rows = _validate_entity_values(entity_proto_values) _validate_feature_refs(_feature_refs, full_feature_names) ( grouped_refs, grouped_odfv_refs, grouped_request_fv_refs, _, ) = _group_feature_refs( _feature_refs, requested_feature_views, requested_request_feature_views, requested_on_demand_feature_views, ) set_usage_attribute("odfv", bool(grouped_odfv_refs)) set_usage_attribute("request_fv", bool(grouped_request_fv_refs)) # All requested features should be present in the result. requested_result_row_names = { feat_ref.replace(":", "__") for feat_ref in _feature_refs } if not full_feature_names: requested_result_row_names = { name.rpartition("__")[-1] for name in requested_result_row_names } feature_views = list(view for view, _ in grouped_refs) needed_request_data, needed_request_fv_features = self.get_needed_request_data( grouped_odfv_refs, grouped_request_fv_refs ) join_key_values: Dict[str, List[Value]] = {} request_data_features: Dict[str, List[Value]] = {} # Entity rows may be either entities or request data. for entity_name, values in entity_proto_values.items(): # Found request data if ( entity_name in needed_request_data or entity_name in needed_request_fv_features ): if entity_name in needed_request_fv_features: # If the data was requested as a feature then # make sure it appears in the result. requested_result_row_names.add(entity_name) request_data_features[entity_name] = values else: try: join_key = entity_name_to_join_key_map[entity_name] except KeyError: raise EntityNotFoundException(entity_name, self.project) # All join keys should be returned in the result. requested_result_row_names.add(join_key) join_key_values[join_key] = values self.ensure_request_data_values_exist( needed_request_data, needed_request_fv_features, request_data_features ) # Populate online features response proto with join keys and request data features online_features_response = GetOnlineFeaturesResponse( results=[GetOnlineFeaturesResponse.FeatureVector() for _ in range(num_rows)] ) self._populate_result_rows_from_columnar( online_features_response=online_features_response, data=dict(**join_key_values, **request_data_features), ) # Add the Entityless case after populating result rows to avoid having to remove # it later. entityless_case = DUMMY_ENTITY_NAME in [ entity_name for feature_view in feature_views for entity_name in feature_view.entities ] if entityless_case: join_key_values[DUMMY_ENTITY_ID] = python_values_to_proto_values( [DUMMY_ENTITY_VAL] * num_rows, DUMMY_ENTITY.value_type ) # Initialize the set of EntityKeyProtos once and reuse them for each FeatureView # to avoid initialization overhead. entity_keys = [EntityKeyProto() for _ in range(num_rows)] provider = self._get_provider() for table, requested_features in grouped_refs: # Get the correct set of entity values with the correct join keys. table_entity_values = self._get_table_entity_values( table, entity_name_to_join_key_map, join_key_values, ) # Set the EntityKeyProtos inplace. self._set_table_entity_keys( table_entity_values, entity_keys, ) # Populate the result_rows with the Features from the OnlineStore inplace. self._populate_result_rows_from_feature_view( online_features_response, entity_keys, full_feature_names, provider, requested_features, table, ) if grouped_odfv_refs: self._augment_response_with_on_demand_transforms( online_features_response, _feature_refs, requested_on_demand_feature_views, full_feature_names, ) self._drop_unneeded_columns( online_features_response, requested_result_row_names ) return OnlineResponse(online_features_response) @staticmethod def _get_columnar_entity_values( rowise: Optional[List[Dict[str, Any]]], columnar: Optional[Dict[str, List[Any]]] ) -> Dict[str, List[Any]]: if (rowise is None and columnar is None) or ( rowise is not None and columnar is not None ): raise ValueError( "Exactly one of `columnar_entity_values` and `rowise_entity_values` must be set." ) if rowise is not None: # Convert entity_rows from rowise to columnar. res = defaultdict(list) for entity_row in rowise: for key, value in entity_row.items(): res[key].append(value) return res return cast(Dict[str, List[Any]], columnar) def _get_entity_maps(self, feature_views): entities = self._list_entities(allow_cache=True, hide_dummy_entity=False) entity_name_to_join_key_map: Dict[str, str] = {} entity_type_map: Dict[str, ValueType] = {} for entity in entities: entity_name_to_join_key_map[entity.name] = entity.join_key entity_type_map[entity.name] = entity.value_type for feature_view in feature_views: for entity_name in feature_view.entities: entity = self._registry.get_entity( entity_name, self.project, allow_cache=True ) # User directly uses join_key as the entity reference in the entity_rows for the # entity mapping case. entity_name = feature_view.projection.join_key_map.get( entity.join_key, entity.name ) join_key = feature_view.projection.join_key_map.get( entity.join_key, entity.join_key ) entity_name_to_join_key_map[entity_name] = join_key entity_type_map[join_key] = entity.value_type return entity_name_to_join_key_map, entity_type_map @staticmethod def _get_table_entity_values( table: FeatureView, entity_name_to_join_key_map: Dict[str, str], join_key_proto_values: Dict[str, List[Value]], ) -> Dict[str, List[Value]]: # The correct join_keys expected by the OnlineStore for this Feature View. table_join_keys = [ entity_name_to_join_key_map[entity_name] for entity_name in table.entities ] # If the FeatureView has a Projection then the join keys may be aliased. alias_to_join_key_map = {v: k for k, v in table.projection.join_key_map.items()} # Subset to columns which are relevant to this FeatureView and # give them the correct names. entity_values = { alias_to_join_key_map.get(k, k): v for k, v in join_key_proto_values.items() if alias_to_join_key_map.get(k, k) in table_join_keys } return entity_values @staticmethod def _set_table_entity_keys( entity_values: Dict[str, List[Value]], entity_keys: List[EntityKeyProto], ): """ This method sets the a list of EntityKeyProtos inplace. """ keys = entity_values.keys() # Columar to rowise (dict keys and values are guaranteed to have the same order). rowise_values = zip(*entity_values.values()) for entity_key in entity_keys: # Make sure entity_keys are empty before setting. entity_key.Clear() entity_key.join_keys.extend(keys) entity_key.entity_values.extend(next(rowise_values)) @staticmethod def _populate_result_rows_from_columnar( online_features_response: GetOnlineFeaturesResponse, data: Dict[str, List[Value]], ): timestamp = Timestamp() # Only initialize this timestamp once. # Add more values to the existing result rows for feature_name, feature_values in data.items(): online_features_response.metadata.feature_names.val.append(feature_name) for row_idx, proto_value in enumerate(feature_values): result_row = online_features_response.results[row_idx] result_row.values.append(proto_value) result_row.statuses.append(FieldStatus.PRESENT) result_row.event_timestamps.append(timestamp)
[docs] @staticmethod def get_needed_request_data( grouped_odfv_refs: List[Tuple[OnDemandFeatureView, List[str]]], grouped_request_fv_refs: List[Tuple[RequestFeatureView, List[str]]], ) -> Tuple[Set[str], Set[str]]: needed_request_data: Set[str] = set() needed_request_fv_features: Set[str] = set() for odfv, _ in grouped_odfv_refs: odfv_request_data_schema = odfv.get_request_data_schema() needed_request_data.update(odfv_request_data_schema.keys()) for request_fv, _ in grouped_request_fv_refs: for feature in request_fv.features: needed_request_fv_features.add(feature.name) return needed_request_data, needed_request_fv_features
[docs] @staticmethod def ensure_request_data_values_exist( needed_request_data: Set[str], needed_request_fv_features: Set[str], request_data_features: Dict[str, List[Any]], ): if len(needed_request_data) + len(needed_request_fv_features) != len( request_data_features.keys() ): missing_features = [ x for x in itertools.chain( needed_request_data, needed_request_fv_features ) if x not in request_data_features ] raise RequestDataNotFoundInEntityRowsException( feature_names=missing_features )
def _populate_result_rows_from_feature_view( self, online_features_response: GetOnlineFeaturesResponse, entity_keys: List[EntityKeyProto], full_feature_names: bool, provider: Provider, requested_features: List[str], table: FeatureView, ): read_rows = provider.online_read( config=self.config, table=table, entity_keys=entity_keys, requested_features=requested_features, ) requested_feature_refs = [ f"{table.projection.name_to_use()}__{feature_name}" if full_feature_names else feature_name for feature_name in requested_features ] online_features_response.metadata.feature_names.val.extend( requested_feature_refs ) # Each row is a set of features for a given entity key for row_idx, read_row in enumerate(read_rows): row_ts, feature_data = read_row result_row = online_features_response.results[row_idx] row_ts_proto = Timestamp() if row_ts is not None: row_ts_proto.FromDatetime(row_ts) result_row.event_timestamps.extend([row_ts_proto] * len(requested_features)) if feature_data is None: result_row.statuses.extend( [FieldStatus.NOT_FOUND] * len(requested_features) ) result_row.values.extend([Value()] * len(requested_features)) else: for feature_name in requested_features: if feature_name not in feature_data: result_row.statuses.append(FieldStatus.NOT_FOUND) result_row.values.append(Value()) else: result_row.statuses.append(FieldStatus.PRESENT) result_row.values.append(feature_data[feature_name]) @staticmethod def _augment_response_with_on_demand_transforms( online_features_response: GetOnlineFeaturesResponse, feature_refs: List[str], requested_on_demand_feature_views: List[OnDemandFeatureView], full_feature_names: bool, ): """Computes on demand feature values and adds them to the result rows. Assumes that 'online_features_response' already contains the necessary request data and input feature views for the on demand feature views. Unneeded feature values such as request data and unrequested input feature views will be removed from 'online_features_response'. Args: online_features_response: Protobuf object to populate feature_refs: List of all feature references to be returned. requested_on_demand_feature_views: List of all odfvs that have been requested. full_feature_names: A boolean that provides the option to add the feature view prefixes to the feature names, changing them from the format "feature" to "feature_view__feature" (e.g., "daily_transactions" changes to "customer_fv__daily_transactions"). result_rows: List of result rows to be augmented with on demand feature values. """ requested_odfv_map = { odfv.name: odfv for odfv in requested_on_demand_feature_views } requested_odfv_feature_names = requested_odfv_map.keys() odfv_feature_refs = defaultdict(list) for feature_ref in feature_refs: view_name, feature_name = feature_ref.split(":") if view_name in requested_odfv_feature_names: odfv_feature_refs[view_name].append( f"{requested_odfv_map[view_name].projection.name_to_use()}__{feature_name}" if full_feature_names else feature_name ) initial_response = OnlineResponse(online_features_response) initial_response_df = initial_response.to_df() # Apply on demand transformations and augment the result rows odfv_result_names = set() for odfv_name, _feature_refs in odfv_feature_refs.items(): odfv = requested_odfv_map[odfv_name] transformed_features_df = odfv.get_transformed_features_df( initial_response_df, full_feature_names, ) selected_subset = [ f for f in transformed_features_df.columns if f in _feature_refs ] proto_values = [ python_values_to_proto_values( transformed_features_df[feature].values, ValueType.UNKNOWN ) for feature in selected_subset ] odfv_result_names |= set(selected_subset) online_features_response.metadata.feature_names.val.extend(selected_subset) for row_idx in range(len(online_features_response.results)): result_row = online_features_response.results[row_idx] for feature_idx, transformed_feature in enumerate(selected_subset): result_row.values.append(proto_values[feature_idx][row_idx]) result_row.statuses.append(FieldStatus.PRESENT) result_row.event_timestamps.append(Timestamp()) @staticmethod def _drop_unneeded_columns( online_features_response: GetOnlineFeaturesResponse, requested_result_row_names: Set[str], ): """ Unneeded feature values such as request data and unrequested input feature views will be removed from 'online_features_response'. Args: online_features_response: Protobuf object to populate requested_result_row_names: Fields from 'result_rows' that have been requested, and therefore should not be dropped. """ # Drop values that aren't needed unneeded_feature_indices = [ idx for idx, val in enumerate( online_features_response.metadata.feature_names.val ) if val not in requested_result_row_names ] for idx in reversed(unneeded_feature_indices): del online_features_response.metadata.feature_names.val[idx] for row_idx in range(len(online_features_response.results)): result_row = online_features_response.results[row_idx] for idx in reversed(unneeded_feature_indices): del result_row.values[idx] del result_row.statuses[idx] del result_row.event_timestamps[idx] def _get_feature_views_to_use( self, features: Optional[Union[List[str], FeatureService]], allow_cache=False, hide_dummy_entity: bool = True, ) -> Tuple[List[FeatureView], List[RequestFeatureView], List[OnDemandFeatureView]]: fvs = { fv.name: fv for fv in self._list_feature_views(allow_cache, hide_dummy_entity) } request_fvs = { fv.name: fv for fv in self._registry.list_request_feature_views( project=self.project, allow_cache=allow_cache ) } od_fvs = { fv.name: fv for fv in self._registry.list_on_demand_feature_views( project=self.project, allow_cache=allow_cache ) } if isinstance(features, FeatureService): fvs_to_use, request_fvs_to_use, od_fvs_to_use = [], [], [] for fv_name, projection in [ (projection.name, projection) for projection in features.feature_view_projections ]: if fv_name in fvs: fvs_to_use.append( fvs[fv_name].with_projection(copy.copy(projection)) ) elif fv_name in request_fvs: request_fvs_to_use.append( request_fvs[fv_name].with_projection(copy.copy(projection)) ) elif fv_name in od_fvs: odfv = od_fvs[fv_name].with_projection(copy.copy(projection)) od_fvs_to_use.append(odfv) # Let's make sure to include an FVs which the ODFV requires Features from. for projection in odfv.input_feature_view_projections.values(): fv = fvs[projection.name].with_projection(copy.copy(projection)) if fv not in fvs_to_use: fvs_to_use.append(fv) else: raise ValueError( f"The provided feature service {features.name} contains a reference to a feature view" f"{fv_name} which doesn't exist. Please make sure that you have created the feature view" f'{fv_name} and that you have registered it by running "apply".' ) views_to_use = (fvs_to_use, request_fvs_to_use, od_fvs_to_use) else: views_to_use = ( [*fvs.values()], [*request_fvs.values()], [*od_fvs.values()], ) return views_to_use
[docs] @log_exceptions_and_usage def serve(self, host: str, port: int, no_access_log: bool) -> None: """Start the feature consumption server locally on a given port.""" if not flags_helper.enable_python_feature_server(self.config): raise ExperimentalFeatureNotEnabled(flags.FLAG_PYTHON_FEATURE_SERVER_NAME) feature_server.start_server(self, host, port, no_access_log)
[docs] @log_exceptions_and_usage def get_feature_server_endpoint(self) -> Optional[str]: """Returns endpoint for the feature server, if it exists.""" return self._provider.get_feature_server_endpoint()
[docs] @log_exceptions_and_usage def serve_transformations(self, port: int) -> None: """Start the feature transformation server locally on a given port.""" if not flags_helper.enable_python_feature_server(self.config): raise ExperimentalFeatureNotEnabled(flags.FLAG_PYTHON_FEATURE_SERVER_NAME) if not flags_helper.enable_on_demand_feature_views(self.config): raise ExperimentalFeatureNotEnabled(flags.FLAG_ON_DEMAND_TRANSFORM_NAME) from feast import transformation_server transformation_server.start_server(self, port)
def _validate_entity_values(join_key_values: Dict[str, List[Value]]): set_of_row_lengths = {len(v) for v in join_key_values.values()} if len(set_of_row_lengths) > 1: raise ValueError("All entity rows must have the same columns.") return set_of_row_lengths.pop() def _validate_feature_refs(feature_refs: List[str], full_feature_names: bool = False): collided_feature_refs = [] if full_feature_names: collided_feature_refs = [ ref for ref, occurrences in Counter(feature_refs).items() if occurrences > 1 ] else: feature_names = [ ref.split(":")[1] if ":" in ref else ref for ref in feature_refs ] collided_feature_names = [ ref for ref, occurrences in Counter(feature_names).items() if occurrences > 1 ] for feature_name in collided_feature_names: collided_feature_refs.extend( [ref for ref in feature_refs if ref.endswith(":" + feature_name)] ) if len(collided_feature_refs) > 0: raise FeatureNameCollisionError(collided_feature_refs, full_feature_names) def _group_feature_refs( features: List[str], all_feature_views: List[FeatureView], all_request_feature_views: List[RequestFeatureView], all_on_demand_feature_views: List[OnDemandFeatureView], ) -> Tuple[ List[Tuple[FeatureView, List[str]]], List[Tuple[OnDemandFeatureView, List[str]]], List[Tuple[RequestFeatureView, List[str]]], Set[str], ]: """ Get list of feature views and corresponding feature names based on feature references""" # view name to view proto view_index = {view.projection.name_to_use(): view for view in all_feature_views} # request view name to proto request_view_index = { view.projection.name_to_use(): view for view in all_request_feature_views } # on demand view to on demand view proto on_demand_view_index = { view.projection.name_to_use(): view for view in all_on_demand_feature_views } # view name to feature names views_features = defaultdict(set) request_views_features = defaultdict(set) request_view_refs = set() # on demand view name to feature names on_demand_view_features = defaultdict(set) for ref in features: view_name, feat_name = ref.split(":") if view_name in view_index: views_features[view_name].add(feat_name) elif view_name in on_demand_view_index: on_demand_view_features[view_name].add(feat_name) # Let's also add in any FV Feature dependencies here. for input_fv_projection in on_demand_view_index[ view_name ].input_feature_view_projections.values(): for input_feat in input_fv_projection.features: views_features[input_fv_projection.name].add(input_feat.name) elif view_name in request_view_index: request_views_features[view_name].add(feat_name) request_view_refs.add(ref) else: raise FeatureViewNotFoundException(view_name) fvs_result: List[Tuple[FeatureView, List[str]]] = [] odfvs_result: List[Tuple[OnDemandFeatureView, List[str]]] = [] request_fvs_result: List[Tuple[RequestFeatureView, List[str]]] = [] for view_name, feature_names in views_features.items(): fvs_result.append((view_index[view_name], list(feature_names))) for view_name, feature_names in request_views_features.items(): request_fvs_result.append((request_view_index[view_name], list(feature_names))) for view_name, feature_names in on_demand_view_features.items(): odfvs_result.append((on_demand_view_index[view_name], list(feature_names))) return fvs_result, odfvs_result, request_fvs_result, request_view_refs def _print_materialization_log( start_date, end_date, num_feature_views: int, online_store: str ): if start_date: print( f"Materializing {Style.BRIGHT + Fore.GREEN}{num_feature_views}{Style.RESET_ALL} feature views" f" from {Style.BRIGHT + Fore.GREEN}{start_date.replace(microsecond=0).astimezone()}{Style.RESET_ALL}" f" to {Style.BRIGHT + Fore.GREEN}{end_date.replace(microsecond=0).astimezone()}{Style.RESET_ALL}" f" into the {Style.BRIGHT + Fore.GREEN}{online_store}{Style.RESET_ALL} online store.\n" ) else: print( f"Materializing {Style.BRIGHT + Fore.GREEN}{num_feature_views}{Style.RESET_ALL} feature views" f" to {Style.BRIGHT + Fore.GREEN}{end_date.replace(microsecond=0).astimezone()}{Style.RESET_ALL}" f" into the {Style.BRIGHT + Fore.GREEN}{online_store}{Style.RESET_ALL} online store.\n" ) def _validate_feature_views(feature_views: List[BaseFeatureView]): """ Verify feature views have case-insensitively unique names""" fv_names = set() for fv in feature_views: case_insensitive_fv_name = fv.name.lower() if case_insensitive_fv_name in fv_names: raise ValueError( f"More than one feature view with name {case_insensitive_fv_name} found. " f"Please ensure that all feature view names are case-insensitively unique. " f"It may be necessary to ignore certain files in your feature repository by using a .feastignore file." ) else: fv_names.add(case_insensitive_fv_name)