Source code for feast.feature_table

# Copyright 2020 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.
from typing import Dict, List, MutableMapping, Optional, Union

import yaml
from google.protobuf import json_format
from google.protobuf.duration_pb2 import Duration
from google.protobuf.json_format import MessageToDict, MessageToJson
from google.protobuf.timestamp_pb2 import Timestamp

from feast.data_source import DataSource, KafkaSource, KinesisSource
from feast.feature import Feature
from feast.loaders import yaml as feast_yaml
from feast.protos.feast.core.FeatureTable_pb2 import FeatureTable as FeatureTableProto
from feast.protos.feast.core.FeatureTable_pb2 import (
    FeatureTableMeta as FeatureTableMetaProto,
from feast.protos.feast.core.FeatureTable_pb2 import (
    FeatureTableSpec as FeatureTableSpecProto,
from feast.usage import log_exceptions
from feast.value_type import ValueType

[docs]class FeatureTable: """ Represents a collection of features and associated metadata. """ @log_exceptions def __init__( self, name: str, entities: List[str], features: List[Feature], batch_source: DataSource = None, stream_source: Optional[Union[KafkaSource, KinesisSource]] = None, max_age: Optional[Duration] = None, labels: Optional[MutableMapping[str, str]] = None, ): self._name = name self._entities = entities self._features = features self._batch_source = batch_source self._stream_source = stream_source self._labels: MutableMapping[str, str] if labels is None: self._labels = dict() else: self._labels = labels self._max_age = max_age self._created_timestamp: Optional[Timestamp] = None self._last_updated_timestamp: Optional[Timestamp] = None def __str__(self): return str(MessageToJson(self.to_proto())) def __hash__(self) -> int: return hash((id(self), def __eq__(self, other): if not isinstance(other, FeatureTable): raise TypeError( "Comparisons should only involve FeatureTable class objects." ) if ( self.labels != other.labels or != or self.max_age != other.max_age ): 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 @property def name(self): """ Returns the name of this feature table """ return self._name @name.setter def name(self, name: str): """ Sets the name of this feature table """ self._name = name @property def entities(self) -> List[str]: """ Returns the entities of this feature table """ return self._entities @entities.setter def entities(self, entities: List[str]): """ Sets the entities of this feature table """ self._entities = entities @property def features(self): """ Returns the features of this feature table """ return self._features @features.setter def features(self, features: List[Feature]): """ Sets the features of this feature table """ self._features = features @property def batch_source(self): """ Returns the batch source of this feature table """ return self._batch_source @batch_source.setter def batch_source(self, batch_source: DataSource): """ Sets the batch source of this feature table """ self._batch_source = batch_source @property def stream_source(self): """ Returns the stream source of this feature table """ return self._stream_source @stream_source.setter def stream_source(self, stream_source: Union[KafkaSource, KinesisSource]): """ Sets the stream source of this feature table """ self._stream_source = stream_source @property def max_age(self): """ Returns the maximum age of this feature table. This is the total maximum amount of staleness that will be allowed during feature retrieval for each specific feature that is looked up. """ return self._max_age @max_age.setter def max_age(self, max_age: Duration): """ Set the maximum age for this feature table """ self._max_age = max_age @property def labels(self): """ Returns the labels of this feature table. This is the user defined metadata defined as a dictionary. """ return self._labels @labels.setter def labels(self, labels: MutableMapping[str, str]): """ Set the labels for this feature table """ self._labels = labels @property def created_timestamp(self): """ Returns the created_timestamp of this feature table """ return self._created_timestamp @property def last_updated_timestamp(self): """ Returns the last_updated_timestamp of this feature table """ return self._last_updated_timestamp
[docs] def add_feature(self, feature: Feature): """ Adds a new feature to the feature table. """ self.features.append(feature)
[docs] def is_valid(self): """ Validates the state of a feature table locally. Raises an exception if feature table is invalid. """ if not raise ValueError("No name found in feature table.") if not self.entities: raise ValueError("No entities found in feature table {}.")
[docs] @classmethod def from_yaml(cls, yml: str): """ Creates a feature table from a YAML string body or a file path Args: yml: Either a file path containing a yaml file or a YAML string Returns: Returns a FeatureTable object based on the YAML file """ return cls.from_dict(feast_yaml.yaml_loader(yml, load_single=True))
[docs] @classmethod def from_dict(cls, ft_dict): """ Creates a feature table from a dict Args: ft_dict: A dict representation of a feature table Returns: Returns a FeatureTable object based on the feature table dict """ feature_table_proto = json_format.ParseDict( ft_dict, FeatureTableProto(), ignore_unknown_fields=True ) return cls.from_proto(feature_table_proto)
[docs] @classmethod def from_proto(cls, feature_table_proto: FeatureTableProto): """ Creates a feature table from a protobuf representation of a feature table Args: feature_table_proto: A protobuf representation of a feature table Returns: Returns a FeatureTableProto object based on the feature table protobuf """ feature_table = cls(, entities=[entity for entity in feature_table_proto.spec.entities], features=[ Feature(, dtype=ValueType(feature.value_type), labels=dict(feature.labels), ) for feature in feature_table_proto.spec.features ], labels=feature_table_proto.spec.labels, max_age=( None if feature_table_proto.spec.max_age.seconds == 0 and feature_table_proto.spec.max_age.nanos == 0 else feature_table_proto.spec.max_age ), batch_source=DataSource.from_proto(feature_table_proto.spec.batch_source), stream_source=( None if not feature_table_proto.spec.stream_source.ByteSize() else DataSource.from_proto(feature_table_proto.spec.stream_source) ), ) feature_table._created_timestamp = feature_table_proto.meta.created_timestamp return feature_table
[docs] def to_proto(self) -> FeatureTableProto: """ Converts an feature table object to its protobuf representation Returns: FeatureTableProto protobuf """ meta = FeatureTableMetaProto( created_timestamp=self.created_timestamp, last_updated_timestamp=self.last_updated_timestamp, ) 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 = FeatureTableSpecProto(, entities=self.entities, features=[ feature.to_proto() if type(feature) == Feature else feature for feature in self.features ], labels=self.labels, max_age=self.max_age, batch_source=batch_source_proto, stream_source=stream_source_proto, ) return FeatureTableProto(spec=spec, meta=meta)
[docs] def to_spec_proto(self) -> FeatureTableSpecProto: """ Converts an FeatureTableProto object to its protobuf representation. Used when passing FeatureTableSpecProto object to Feast request. Returns: FeatureTableSpecProto protobuf """ spec = FeatureTableSpecProto(, entities=self.entities, features=[ feature.to_proto() if type(feature) == Feature else feature for feature in self.features ], labels=self.labels, max_age=self.max_age, batch_source=( self.batch_source.to_proto() if issubclass(type(self.batch_source), DataSource) else self.batch_source ), stream_source=( self.stream_source.to_proto() if issubclass(type(self.stream_source), DataSource) else self.stream_source ), ) return spec
[docs] def to_dict(self) -> Dict: """ Converts feature table to dict :return: Dictionary object representation of feature table """ feature_table_dict = MessageToDict(self.to_proto()) # Remove meta when empty for more readable exports if feature_table_dict["meta"] == {}: del feature_table_dict["meta"] return feature_table_dict
[docs] def to_yaml(self): """ Converts a feature table to a YAML string. :return: Feature table string returned in YAML format """ feature_table_dict = self.to_dict() return yaml.dump(feature_table_dict, allow_unicode=True, sort_keys=False)
def _update_from_feature_table(self, feature_table): """ Deep replaces one feature table with another Args: feature_table: Feature table to use as a source of configuration """ = self.entities = feature_table.entities self.features = feature_table.features self.labels = feature_table.labels self.max_age = feature_table.max_age self.batch_source = feature_table.batch_source self.stream_source = feature_table.stream_source self._created_timestamp = feature_table.created_timestamp self._last_updated_timestamp = feature_table.last_updated_timestamp def __repr__(self): return f"FeatureTable <{}>"