Source code for feast.feature_view

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import warnings
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Tuple, Type

from google.protobuf.duration_pb2 import Duration
from typeguard import typechecked

from feast import utils
from feast.base_feature_view import BaseFeatureView
from feast.data_source import DataSource, KafkaSource, KinesisSource, PushSource
from feast.entity import Entity
from feast.feature_view_projection import FeatureViewProjection
from feast.field import Field
from feast.protos.feast.core.FeatureView_pb2 import FeatureView as FeatureViewProto
from feast.protos.feast.core.FeatureView_pb2 import (
    FeatureViewMeta as FeatureViewMetaProto,
from feast.protos.feast.core.FeatureView_pb2 import (
    FeatureViewSpec as FeatureViewSpecProto,
from feast.protos.feast.core.FeatureView_pb2 import (
    MaterializationInterval as MaterializationIntervalProto,
from feast.types import from_value_type
from feast.usage import log_exceptions
from feast.value_type import ValueType

warnings.simplefilter("once", DeprecationWarning)

# DUMMY_ENTITY is a placeholder entity used in entityless FeatureViews
DUMMY_ENTITY_ID = "__dummy_id"

[docs]@typechecked class FeatureView(BaseFeatureView): """ A FeatureView defines a logical group of features. Attributes: name: The unique name of the feature view. entities: The list of names of entities that this feature view is associated with. ttl: The amount of time this group of features lives. A ttl of 0 indicates that this group of features lives forever. Note that large ttl's or a ttl of 0 can result in extremely computationally intensive queries. batch_source: The batch source of data where this group of features is stored. This is optional ONLY if a push source is specified as the stream_source, since push sources contain their own batch sources. stream_source: The stream source of data where this group of features is stored. schema: The schema of the feature view, including feature, timestamp, and entity columns. If not specified, can be inferred from the underlying data source. entity_columns: The list of entity columns contained in the schema. If not specified, can be inferred from the underlying data source. features: The list of feature columns contained in the schema. If not specified, can be inferred from the underlying data source. online: A boolean indicating whether online retrieval is enabled for this feature view. description: A human-readable description. tags: A dictionary of key-value pairs to store arbitrary metadata. owner: The owner of the feature view, typically the email of the primary maintainer. """ name: str entities: List[str] ttl: Optional[timedelta] batch_source: DataSource stream_source: Optional[DataSource] schema: List[Field] entity_columns: List[Field] features: List[Field] online: bool description: str tags: Dict[str, str] owner: str materialization_intervals: List[Tuple[datetime, datetime]] @log_exceptions def __init__( self, *, name: str, source: DataSource, schema: Optional[List[Field]] = None, entities: List[Entity] = None, ttl: Optional[timedelta] = timedelta(days=0), online: bool = True, description: str = "", tags: Optional[Dict[str, str]] = None, owner: str = "", ): """ Creates a FeatureView object. Args: name: The unique name of the feature view. source: The source of data for this group of features. May be a stream source, or a batch source. If a stream source, the source should contain a batch_source for backfills & batch materialization. schema (optional): The schema of the feature view, including feature, timestamp, and entity columns. # TODO: clarify that schema is only useful here... entities (optional): The list of entities with which this group of features is associated. ttl (optional): The amount of time this group of features lives. A ttl of 0 indicates that this group of features lives forever. Note that large ttl's or a ttl of 0 can result in extremely computationally intensive queries. online (optional): A boolean indicating whether online retrieval is enabled for this feature view. description (optional): A human-readable description. tags (optional): A dictionary of key-value pairs to store arbitrary metadata. owner (optional): The owner of the feature view, typically the email of the primary maintainer. Raises: ValueError: A field mapping conflicts with an Entity or a Feature. """ = name self.entities = [ for e in entities] if entities else [DUMMY_ENTITY_NAME] self.ttl = ttl self.schema = schema or [] # Initialize data sources. if ( isinstance(source, PushSource) or isinstance(source, KafkaSource) or isinstance(source, KinesisSource) ): self.stream_source = source if not source.batch_source: raise ValueError( f"A batch_source needs to be specified for stream source `{}`" ) else: self.batch_source = source.batch_source else: self.stream_source = None self.batch_source = source # Initialize features and entity columns. features: List[Field] = [] self.entity_columns = [] join_keys: List[str] = [] if entities: for entity in entities: join_keys.append(entity.join_key) # Ensure that entities have unique join keys. if len(set(join_keys)) < len(join_keys): raise ValueError( "A feature view should not have entities that share a join key." ) for field in self.schema: if in join_keys: self.entity_columns.append(field) # Confirm that the inferred type matches the specified entity type, if it exists. matching_entities = ( [e for e in entities if e.join_key ==] if entities else [] ) assert len(matching_entities) == 1 entity = matching_entities[0] if entity.value_type != ValueType.UNKNOWN: if from_value_type(entity.value_type) != field.dtype: raise ValueError( f"Entity {} has type {entity.value_type}, which does not match the inferred type {field.dtype}." ) else: features.append(field) # TODO(felixwang9817): Add more robust validation of features. cols = [ for field in self.schema] for col in cols: if ( self.batch_source.field_mapping is not None and col in self.batch_source.field_mapping.keys() ): raise ValueError( f"The field {col} is mapped to {self.batch_source.field_mapping[col]} for this data source. " f"Please either remove this field mapping or use {self.batch_source.field_mapping[col]} as the " f"Entity or Feature name." ) super().__init__( name=name, features=features, description=description, tags=tags, owner=owner, ) = online self.materialization_intervals = [] def __hash__(self): return super().__hash__() def __copy__(self): fv = FeatureView(, ttl=self.ttl, source=self.stream_source if self.stream_source else self.batch_source, schema=self.schema, tags=self.tags,, ) # This is deliberately set outside of the FV initialization as we do not have the Entity objects. fv.entities = self.entities fv.features = copy.copy(self.features) fv.entity_columns = copy.copy(self.entity_columns) fv.projection = copy.copy(self.projection) return fv def __eq__(self, other): if not isinstance(other, FeatureView): raise TypeError( "Comparisons should only involve FeatureView class objects." ) if not super().__eq__(other): return False if ( sorted(self.entities) != sorted(other.entities) or self.ttl != other.ttl or != or self.batch_source != other.batch_source or self.stream_source != other.stream_source or sorted(self.entity_columns) != sorted(other.entity_columns) ): return False return True @property def join_keys(self) -> List[str]: """Returns a list of all the join keys.""" return [ for entity in self.entity_columns]
[docs] def ensure_valid(self): """ Validates the state of this feature view locally. Raises: ValueError: The feature view does not have a name or does not have entities. """ super().ensure_valid() if not self.entities: raise ValueError("Feature view has no entities.")
@property def proto_class(self) -> Type[FeatureViewProto]: return FeatureViewProto
[docs] def with_join_key_map(self, join_key_map: Dict[str, str]): """ Returns a copy of this feature view with the join key map set to the given map. This join_key mapping operation is only used as part of query operations and will not modify the underlying FeatureView. Args: join_key_map: A map of join keys in which the left is the join_key that corresponds with the feature data and the right corresponds with the entity data. Examples: Join a location feature data table to both the origin column and destination column of the entity data. temperatures_feature_service = FeatureService( name="temperatures", features=[ location_stats_feature_view .with_name("origin_stats") .with_join_key_map( {"location_id": "origin_id"} ), location_stats_feature_view .with_name("destination_stats") .with_join_key_map( {"location_id": "destination_id"} ), ], ) """ cp = self.__copy__() cp.projection.join_key_map = join_key_map return cp
[docs] def to_proto(self) -> FeatureViewProto: """ Converts a feature view object to its protobuf representation. Returns: A FeatureViewProto protobuf. """ meta = self.to_proto_meta() ttl_duration = self.get_ttl_duration() batch_source_proto = self.batch_source.to_proto() batch_source_proto.data_source_class_type = f"{self.batch_source.__class__.__module__}.{self.batch_source.__class__.__name__}" stream_source_proto = None if self.stream_source: stream_source_proto = self.stream_source.to_proto() stream_source_proto.data_source_class_type = f"{self.stream_source.__class__.__module__}.{self.stream_source.__class__.__name__}" spec = FeatureViewSpecProto(, entities=self.entities, entity_columns=[field.to_proto() for field in self.entity_columns], features=[field.to_proto() for field in self.features], description=self.description, tags=self.tags, owner=self.owner, ttl=(ttl_duration if ttl_duration is not None else None),, batch_source=batch_source_proto, stream_source=stream_source_proto, ) return FeatureViewProto(spec=spec, meta=meta)
[docs] def to_proto_meta(self): meta = FeatureViewMetaProto(materialization_intervals=[]) if self.created_timestamp: meta.created_timestamp.FromDatetime(self.created_timestamp) if self.last_updated_timestamp: meta.last_updated_timestamp.FromDatetime(self.last_updated_timestamp) for interval in self.materialization_intervals: interval_proto = MaterializationIntervalProto() interval_proto.start_time.FromDatetime(interval[0]) interval_proto.end_time.FromDatetime(interval[1]) meta.materialization_intervals.append(interval_proto) return meta
[docs] def get_ttl_duration(self): ttl_duration = None if self.ttl is not None: ttl_duration = Duration() ttl_duration.FromTimedelta(self.ttl) return ttl_duration
[docs] @classmethod def from_proto(cls, feature_view_proto: FeatureViewProto): """ Creates a feature view from a protobuf representation of a feature view. Args: feature_view_proto: A protobuf representation of a feature view. Returns: A FeatureViewProto object based on the feature view protobuf. """ batch_source = DataSource.from_proto(feature_view_proto.spec.batch_source) stream_source = ( DataSource.from_proto(feature_view_proto.spec.stream_source) if feature_view_proto.spec.HasField("stream_source") else None ) feature_view = cls(, description=feature_view_proto.spec.description, tags=dict(feature_view_proto.spec.tags), owner=feature_view_proto.spec.owner,, ttl=( timedelta(days=0) if feature_view_proto.spec.ttl.ToNanoseconds() == 0 else feature_view_proto.spec.ttl.ToTimedelta() ), source=batch_source, ) if stream_source: feature_view.stream_source = stream_source # This avoids the deprecation warning. feature_view.entities = feature_view_proto.spec.entities # Instead of passing in a schema, we set the features and entity columns. feature_view.features = [ Field.from_proto(field_proto) for field_proto in feature_view_proto.spec.features ] feature_view.entity_columns = [ Field.from_proto(field_proto) for field_proto in feature_view_proto.spec.entity_columns ] if len(feature_view.entities) != len(feature_view.entity_columns): warnings.warn( f"There are some mismatches in your feature view's registered entities. Please check if you have applied your entities correctly." f"Entities: {feature_view.entities} vs Entity Columns: {feature_view.entity_columns}" ) # FeatureViewProjections are not saved in the FeatureView proto. # Create the default projection. feature_view.projection = FeatureViewProjection.from_definition(feature_view) if feature_view_proto.meta.HasField("created_timestamp"): feature_view.created_timestamp = ( feature_view_proto.meta.created_timestamp.ToDatetime() ) if feature_view_proto.meta.HasField("last_updated_timestamp"): feature_view.last_updated_timestamp = ( feature_view_proto.meta.last_updated_timestamp.ToDatetime() ) for interval in feature_view_proto.meta.materialization_intervals: feature_view.materialization_intervals.append( ( utils.make_tzaware(interval.start_time.ToDatetime()), utils.make_tzaware(interval.end_time.ToDatetime()), ) ) return feature_view
@property def most_recent_end_time(self) -> Optional[datetime]: """ Retrieves the latest time up to which the feature view has been materialized. Returns: The latest time, or None if the feature view has not been materialized. """ if len(self.materialization_intervals) == 0: return None return max([interval[1] for interval in self.materialization_intervals])