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 re
import warnings
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Tuple, Union

from google.protobuf.duration_pb2 import Duration
from google.protobuf.json_format import MessageToJson

from feast import utils
from feast.data_source import DataSource
from feast.errors import RegistryInferenceFailure
from feast.feature import Feature
from feast.feature_view_projection import FeatureViewProjection
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.repo_config import RepoConfig
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]class FeatureView: """ A FeatureView defines a logical grouping of serveable features. Args: name: Name of the group of features. entities: The entities to which this group of features is associated. 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. input: The source of data where this group of features is stored. batch_source (optional): The batch source of data where this group of features is stored. stream_source (optional): The stream source of data where this group of features is stored. features (optional): The set of features defined as part of this FeatureView. tags (optional): A dictionary of key-value pairs used for organizing FeatureViews. """ name: str entities: List[str] features: List[Feature] tags: Optional[Dict[str, str]] ttl: timedelta online: bool input: DataSource batch_source: DataSource stream_source: Optional[DataSource] = None created_timestamp: Optional[datetime] = None last_updated_timestamp: Optional[datetime] = None materialization_intervals: List[Tuple[datetime, datetime]] projection: FeatureViewProjection @log_exceptions def __init__( self, name: str, entities: List[str], ttl: Union[Duration, timedelta], input: Optional[DataSource] = None, batch_source: Optional[DataSource] = None, stream_source: Optional[DataSource] = None, features: Optional[List[Feature]] = None, tags: Optional[Dict[str, str]] = None, online: bool = True, ): """ Creates a FeatureView object. Raises: ValueError: A field mapping conflicts with an Entity or a Feature. """ if input is not None: warnings.warn( ( "The argument 'input' is being deprecated. Please use 'batch_source' " "instead. Feast 0.13 and onwards will not support the argument 'input'." ), DeprecationWarning, ) _input = input or batch_source assert _input is not None _features = features or [] cols = [entity for entity in entities] + [ for feat in _features] for col in cols: if _input.field_mapping is not None and col in _input.field_mapping.keys(): raise ValueError( f"The field {col} is mapped to {_input.field_mapping[col]} for this data source. " f"Please either remove this field mapping or use {_input.field_mapping[col]} as the " f"Entity or Feature name." ) = name self.entities = entities if entities else [DUMMY_ENTITY_NAME] self.features = _features self.tags = tags if tags is not None else {} if isinstance(ttl, Duration): self.ttl = timedelta(seconds=int(ttl.seconds)) else: self.ttl = ttl = online self.input = _input self.batch_source = _input self.stream_source = stream_source self.materialization_intervals = [] self.created_timestamp: Optional[datetime] = None self.last_updated_timestamp: Optional[datetime] = None self.projection = FeatureViewProjection.from_definition(self) def __repr__(self): items = (f"{k} = {v}" for k, v in self.__dict__.items()) return f"<{self.__class__.__name__}({', '.join(items)})>" def __str__(self): return str(MessageToJson(self.to_proto())) def __hash__(self): return hash((id(self), def __copy__(self): fv = FeatureView(, entities=self.entities, ttl=self.ttl, input=self.input, batch_source=self.batch_source, stream_source=self.stream_source, features=self.features, tags=self.tags,, ) fv.projection = copy.copy(self.projection) return fv def __getitem__(self, item): assert isinstance(item, list) referenced_features = [] for feature in self.features: if in item: referenced_features.append(feature) cp = self.__copy__() cp.projection.features = referenced_features return self def __eq__(self, other): if not isinstance(other, FeatureView): raise TypeError( "Comparisons should only involve FeatureView class objects." ) if ( self.tags != other.tags or != or self.ttl != other.ttl or != ): return False if sorted(self.entities) != sorted(other.entities): return False if sorted(self.features) != sorted(other.features): return False if self.batch_source != other.batch_source: return False if self.stream_source != other.stream_source: return False return True
[docs] def is_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. """ if not raise ValueError("Feature view needs a name.") if not self.entities: raise ValueError("Feature view has no entities.")
[docs] def with_name(self, name: str): """ Renames this feature view by returning a copy of this feature view with an alias set for the feature view name. This rename operation is only used as part of query operations and will not modify the underlying FeatureView. Args: name: Name to assign to the FeatureView copy. Returns: A copy of this FeatureView with the name replaced with the 'name' input. """ cp = self.__copy__() cp.projection.name_alias = name return cp
[docs] def with_projection(self, feature_view_projection: FeatureViewProjection): """ Sets the feature view projection by returning a copy of this feature view with its projection set to the given projection. A projection is an object that stores the modifications to a feature view that is used during query operations. Args: feature_view_projection: The FeatureViewProjection object to link to this OnDemandFeatureView. Returns: A copy of this FeatureView with its projection replaced with the 'feature_view_projection' argument. """ if != raise ValueError( f"The projection for the {} FeatureView cannot be applied because it differs in name. " f"The projection is named {} and the name indicates which " "FeatureView the projection is for." ) for feature in feature_view_projection.features: if feature not in self.features: raise ValueError( f"The projection for {} cannot be applied because it contains {} which the " "FeatureView doesn't have." ) cp = self.__copy__() cp.projection = feature_view_projection return cp
[docs] def to_proto(self) -> FeatureViewProto: """ Converts a feature view object to its protobuf representation. Returns: A FeatureViewProto protobuf. """ 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) ttl_duration = None if self.ttl is not None: ttl_duration = Duration() ttl_duration.FromTimedelta(self.ttl) 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, features=[feature.to_proto() for feature in self.features], tags=self.tags, 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] @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(, entities=[entity for entity in feature_view_proto.spec.entities], features=[ Feature(, dtype=ValueType(feature.value_type), labels=dict(feature.labels), ) for feature in feature_view_proto.spec.features ], tags=dict(feature_view_proto.spec.tags),, ttl=( None if feature_view_proto.spec.ttl.seconds == 0 and feature_view_proto.spec.ttl.nanos == 0 else feature_view_proto.spec.ttl ), batch_source=batch_source, stream_source=stream_source, ) # 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])
[docs] def infer_features_from_batch_source(self, config: RepoConfig): """ Infers the set of features associated to this feature view from the input source. Args: config: Configuration object used to configure the feature store. Raises: RegistryInferenceFailure: The set of features could not be inferred. """ if not self.features: columns_to_exclude = { self.batch_source.event_timestamp_column, self.batch_source.created_timestamp_column, } | set(self.entities) if ( self.batch_source.event_timestamp_column in self.batch_source.field_mapping ): columns_to_exclude.add( self.batch_source.field_mapping[ self.batch_source.event_timestamp_column ] ) if ( self.batch_source.created_timestamp_column in self.batch_source.field_mapping ): columns_to_exclude.add( self.batch_source.field_mapping[ self.batch_source.created_timestamp_column ] ) for e in self.entities: if e in self.batch_source.field_mapping: columns_to_exclude.add(self.batch_source.field_mapping[e]) for ( col_name, col_datatype, ) in self.batch_source.get_table_column_names_and_types(config): if col_name not in columns_to_exclude and not re.match( "^__|__$", col_name, # double underscores often signal an internal-use column ): feature_name = ( self.batch_source.field_mapping[col_name] if col_name in self.batch_source.field_mapping else col_name ) self.features.append( Feature( feature_name, self.batch_source.source_datatype_to_feast_value_type()( col_datatype ), ) ) if not self.features: raise RegistryInferenceFailure( "FeatureView", f"Could not infer Features for the FeatureView named {}.", )