feast package

Subpackages

Submodules

feast.cli module

class feast.cli.NoOptionDefaultFormat(name, context_settings=None, callback=None, params=None, help=None, epilog=None, short_help=None, options_metavar='[OPTIONS]', add_help_option=True, no_args_is_help=False, hidden=False, deprecated=False)[source]

Bases: click.core.Command

format_options(ctx: click.core.Context, formatter: click.formatting.HelpFormatter)[source]

Writes all the options into the formatter if they exist.

feast.client module

feast.config module

feast.constants module

feast.data_format module

class feast.data_format.AvroFormat(schema_json: str)[source]

Bases: feast.data_format.StreamFormat

Defines the Avro streaming data format that encodes data in Avro format

to_proto()[source]

Convert this StreamFormat into its protobuf representation.

class feast.data_format.FileFormat[source]

Bases: abc.ABC

Defines an abtract file forma used to encode feature data in files

classmethod from_proto(proto)[source]

Construct this FileFormat from its protobuf representation. Raises NotImplementedError if FileFormat specified in given proto is not supported.

abstract to_proto()[source]

Convert this FileFormat into its protobuf representation.

class feast.data_format.ParquetFormat[source]

Bases: feast.data_format.FileFormat

Defines the Parquet data format

to_proto()[source]

Convert this FileFormat into its protobuf representation.

class feast.data_format.ProtoFormat(class_path: str)[source]

Bases: feast.data_format.StreamFormat

Defines the Protobuf data format

to_proto()[source]

Convert this StreamFormat into its protobuf representation.

class feast.data_format.StreamFormat[source]

Bases: abc.ABC

Defines an abtracts streaming data format used to encode feature data in streams

classmethod from_proto(proto)[source]

Construct this StreamFormat from its protobuf representation.

abstract to_proto()[source]

Convert this StreamFormat into its protobuf representation.

feast.data_source module

class feast.data_source.DataSource(event_timestamp_column: Optional[str] = None, created_timestamp_column: Optional[str] = None, field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = None)[source]

Bases: abc.ABC

DataSource that can be used to source features.

Parameters
  • event_timestamp_column (optional) – Event timestamp column used for point in time joins of feature values.

  • created_timestamp_column (optional) – Timestamp column indicating when the row was created, used for deduplicating rows.

  • field_mapping (optional) – A dictionary mapping of column names in this data source to feature names in a feature table or view. Only used for feature columns, not entity or timestamp columns.

  • date_partition_column (optional) – Timestamp column used for partitioning.

property created_timestamp_column: str

Returns the created timestamp column of this data source.

property date_partition_column: str

Returns the date partition column of this data source.

property event_timestamp_column: str

Returns the event timestamp column of this data source.

property field_mapping: Dict[str, str]

Returns the field mapping of this data source.

abstract static from_proto(data_source: feast.core.DataSource_pb2.DataSource) Any[source]

Converts data source config in FeatureTable spec to a DataSource class object.

Parameters

data_source – A protobuf representation of a DataSource.

Returns

A DataSource class object.

Raises

ValueError – The type of DataSource could not be identified.

get_table_column_names_and_types(config: feast.repo_config.RepoConfig) Iterable[Tuple[str, str]][source]

Returns the list of column names and raw column types.

Parameters

config – Configuration object used to configure a feature store.

get_table_query_string() str[source]

Returns a string that can directly be used to reference this table in SQL.

abstract static source_datatype_to_feast_value_type() Callable[[str], feast.value_type.ValueType][source]

Returns the callable method that returns Feast type given the raw column type.

abstract to_proto() feast.core.DataSource_pb2.DataSource[source]

Converts an DataSourceProto object to its protobuf representation.

validate(config: feast.repo_config.RepoConfig)[source]

Validates the underlying data source.

Parameters

config – Configuration object used to configure a feature store.

class feast.data_source.KafkaOptions(bootstrap_servers: str, message_format: feast.data_format.StreamFormat, topic: str)[source]

Bases: object

DataSource Kafka options used to source features from Kafka messages

property bootstrap_servers

Returns a comma-separated list of Kafka bootstrap servers

classmethod from_proto(kafka_options_proto: feast.core.DataSource_pb2.KafkaOptions)[source]

Creates a KafkaOptions from a protobuf representation of a kafka option

Parameters

kafka_options_proto – A protobuf representation of a DataSource

Returns

Returns a BigQueryOptions object based on the kafka_options protobuf

property message_format

Returns the data format that is used to encode the feature data in Kafka messages

to_proto() feast.core.DataSource_pb2.KafkaOptions[source]

Converts an KafkaOptionsProto object to its protobuf representation.

Returns

KafkaOptionsProto protobuf

property topic

Returns the Kafka topic to collect feature data from

class feast.data_source.KafkaSource(event_timestamp_column: str, bootstrap_servers: str, message_format: feast.data_format.StreamFormat, topic: str, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = '')[source]

Bases: feast.data_source.DataSource

static from_proto(data_source: feast.core.DataSource_pb2.DataSource)[source]

Converts data source config in FeatureTable spec to a DataSource class object.

Parameters

data_source – A protobuf representation of a DataSource.

Returns

A DataSource class object.

Raises

ValueError – The type of DataSource could not be identified.

get_table_column_names_and_types(config: feast.repo_config.RepoConfig) Iterable[Tuple[str, str]][source]

Returns the list of column names and raw column types.

Parameters

config – Configuration object used to configure a feature store.

property kafka_options

Returns the kafka options of this data source

static source_datatype_to_feast_value_type() Callable[[str], feast.value_type.ValueType][source]

Returns the callable method that returns Feast type given the raw column type.

to_proto() feast.core.DataSource_pb2.DataSource[source]

Converts an DataSourceProto object to its protobuf representation.

validate(config: feast.repo_config.RepoConfig)[source]

Validates the underlying data source.

Parameters

config – Configuration object used to configure a feature store.

class feast.data_source.KinesisOptions(record_format: feast.data_format.StreamFormat, region: str, stream_name: str)[source]

Bases: object

DataSource Kinesis options used to source features from Kinesis records

classmethod from_proto(kinesis_options_proto: feast.core.DataSource_pb2.KinesisOptions)[source]

Creates a KinesisOptions from a protobuf representation of a kinesis option

Parameters

kinesis_options_proto – A protobuf representation of a DataSource

Returns

Returns a KinesisOptions object based on the kinesis_options protobuf

property record_format

Returns the data format used to encode the feature data in the Kinesis records.

property region

Returns the AWS region of Kinesis stream

property stream_name

Returns the Kinesis stream name to obtain feature data from

to_proto() feast.core.DataSource_pb2.KinesisOptions[source]

Converts an KinesisOptionsProto object to its protobuf representation.

Returns

KinesisOptionsProto protobuf

class feast.data_source.KinesisSource(event_timestamp_column: str, created_timestamp_column: str, record_format: feast.data_format.StreamFormat, region: str, stream_name: str, field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = '')[source]

Bases: feast.data_source.DataSource

static from_proto(data_source: feast.core.DataSource_pb2.DataSource)[source]

Converts data source config in FeatureTable spec to a DataSource class object.

Parameters

data_source – A protobuf representation of a DataSource.

Returns

A DataSource class object.

Raises

ValueError – The type of DataSource could not be identified.

get_table_column_names_and_types(config: feast.repo_config.RepoConfig) Iterable[Tuple[str, str]][source]

Returns the list of column names and raw column types.

Parameters

config – Configuration object used to configure a feature store.

property kinesis_options

Returns the kinesis options of this data source

static source_datatype_to_feast_value_type() Callable[[str], feast.value_type.ValueType][source]

Returns the callable method that returns Feast type given the raw column type.

to_proto() feast.core.DataSource_pb2.DataSource[source]

Converts an DataSourceProto object to its protobuf representation.

validate(config: feast.repo_config.RepoConfig)[source]

Validates the underlying data source.

Parameters

config – Configuration object used to configure a feature store.

class feast.data_source.RequestDataSource(name: str, schema: Dict[str, feast.value_type.ValueType])[source]

Bases: feast.data_source.DataSource

RequestDataSource that can be used to provide input features for on demand transforms

Parameters
  • name – Name of the request data source

  • schema – Schema mapping from the input feature name to a ValueType

static from_proto(data_source: feast.core.DataSource_pb2.DataSource)[source]

Converts data source config in FeatureTable spec to a DataSource class object.

Parameters

data_source – A protobuf representation of a DataSource.

Returns

A DataSource class object.

Raises

ValueError – The type of DataSource could not be identified.

get_table_column_names_and_types(config: feast.repo_config.RepoConfig) Iterable[Tuple[str, str]][source]

Returns the list of column names and raw column types.

Parameters

config – Configuration object used to configure a feature store.

property name: str

Returns the name of this data source

property schema: Dict[str, feast.value_type.ValueType]

Returns the schema for this request data source

static source_datatype_to_feast_value_type() Callable[[str], feast.value_type.ValueType][source]

Returns the callable method that returns Feast type given the raw column type.

to_proto() feast.core.DataSource_pb2.DataSource[source]

Converts an DataSourceProto object to its protobuf representation.

validate(config: feast.repo_config.RepoConfig)[source]

Validates the underlying data source.

Parameters

config – Configuration object used to configure a feature store.

class feast.data_source.SourceType(value)[source]

Bases: enum.Enum

DataSource value type. Used to define source types in DataSource.

BATCH_BIGQUERY = 2
BATCH_FILE = 1
STREAM_KAFKA = 3
STREAM_KINESIS = 4
UNKNOWN = 0

feast.driver_test_data module

class feast.driver_test_data.EventTimestampType(value)[source]

Bases: enum.Enum

An enumeration.

TZ_AWARE_FIXED_OFFSET = 2
TZ_AWARE_US_PACIFIC = 3
TZ_AWARE_UTC = 1
TZ_NAIVE = 0
feast.driver_test_data.create_customer_daily_profile_df(customers, start_date, end_date) pandas.core.frame.DataFrame[source]

Example df generated by this function:

event_timestamp | customer_id | current_balance | avg_passenger_count | lifetime_trip_count | created |

|------------------+-------------+-----------------+---------------------+---------------------+------------------| | 2021-03-17 19:31 | 1010 | 0.889188 | 0.049057 | 412 | 2021-03-24 19:38 | | 2021-03-18 19:31 | 1010 | 0.979273 | 0.212630 | 639 | 2021-03-24 19:38 | | 2021-03-19 19:31 | 1010 | 0.976549 | 0.176881 | 70 | 2021-03-24 19:38 | | 2021-03-20 19:31 | 1010 | 0.273697 | 0.325012 | 68 | 2021-03-24 19:38 | | 2021-03-21 19:31 | 1010 | 0.438262 | 0.313009 | 192 | 2021-03-24 19:38 | | | … | … | … | … | | | 2021-03-19 19:31 | 1001 | 0.738860 | 0.857422 | 344 | 2021-03-24 19:38 | | 2021-03-20 19:31 | 1001 | 0.848397 | 0.745989 | 106 | 2021-03-24 19:38 | | 2021-03-21 19:31 | 1001 | 0.301552 | 0.185873 | 812 | 2021-03-24 19:38 | | 2021-03-22 19:31 | 1001 | 0.943030 | 0.561219 | 322 | 2021-03-24 19:38 | | 2021-03-23 19:31 | 1001 | 0.354919 | 0.810093 | 273 | 2021-03-24 19:38 |

feast.driver_test_data.create_driver_hourly_stats_df(drivers, start_date, end_date) pandas.core.frame.DataFrame[source]

Example df generated by this function:

event_timestamp | driver_id | conv_rate | acc_rate | avg_daily_trips | created |

|------------------+-----------+-----------+----------+-----------------+------------------| | 2021-03-17 19:31 | 5010 | 0.229297 | 0.685843 | 861 | 2021-03-24 19:34 | | 2021-03-17 20:31 | 5010 | 0.781655 | 0.861280 | 769 | 2021-03-24 19:34 | | 2021-03-17 21:31 | 5010 | 0.150333 | 0.525581 | 778 | 2021-03-24 19:34 | | 2021-03-17 22:31 | 5010 | 0.951701 | 0.228883 | 570 | 2021-03-24 19:34 | | 2021-03-17 23:31 | 5010 | 0.819598 | 0.262503 | 473 | 2021-03-24 19:34 | | | … | … | … | … | | | 2021-03-24 16:31 | 5001 | 0.061585 | 0.658140 | 477 | 2021-03-24 19:34 | | 2021-03-24 17:31 | 5001 | 0.088949 | 0.303897 | 618 | 2021-03-24 19:34 | | 2021-03-24 18:31 | 5001 | 0.096652 | 0.747421 | 480 | 2021-03-24 19:34 | | 2021-03-17 19:31 | 5005 | 0.142936 | 0.707596 | 466 | 2021-03-24 19:34 | | 2021-03-17 19:31 | 5005 | 0.142936 | 0.707596 | 466 | 2021-03-24 19:34 |

feast.driver_test_data.create_global_daily_stats_df(start_date, end_date) pandas.core.frame.DataFrame[source]

Example df generated by this function:

event_timestamp | num_rides | avg_ride_length | created |

|------------------+-------------+-----------------+------------------| | 2021-03-17 19:00 | 99 | 0.889188 | 2021-03-24 19:38 | | 2021-03-18 19:00 | 52 | 0.979273 | 2021-03-24 19:38 | | 2021-03-19 19:00 | 66 | 0.976549 | 2021-03-24 19:38 | | 2021-03-20 19:00 | 84 | 0.273697 | 2021-03-24 19:38 | | 2021-03-21 19:00 | 89 | 0.438262 | 2021-03-24 19:38 | | | … | … | | | 2021-03-24 19:00 | 54 | 0.738860 | 2021-03-24 19:38 | | 2021-03-25 19:00 | 58 | 0.848397 | 2021-03-24 19:38 | | 2021-03-26 19:00 | 69 | 0.301552 | 2021-03-24 19:38 | | 2021-03-27 19:00 | 63 | 0.943030 | 2021-03-24 19:38 | | 2021-03-28 19:00 | 79 | 0.354919 | 2021-03-24 19:38 |

feast.driver_test_data.create_location_stats_df(locations, start_date, end_date) pandas.core.frame.DataFrame[source]

Example df generated by this function:

event_timestamp | location_id | temperature | created |
feast.driver_test_data.create_orders_df(customers, drivers, start_date, end_date, order_count, locations=None) pandas.core.frame.DataFrame[source]

Example df generated by this function (if locations):

order_id | driver_id | customer_id | origin_id | destination_id | order_is_success | event_timestamp |

feast.entity module

class feast.entity.Entity(name: str, value_type: feast.value_type.ValueType = ValueType.UNKNOWN, description: str = '', join_key: Optional[str] = None, labels: Optional[Dict[str, str]] = None)[source]

Bases: object

Represents a collection of entities and associated metadata.

Parameters
  • name – Name of the entity.

  • value_type (optional) – The type of the entity, such as string or float.

  • description (optional) – Additional information to describe the entity.

  • join_key (optional) – A property that uniquely identifies different entities within the collection. Used as a key for joining entities with their associated features. If not specified, defaults to the name of the entity.

  • labels (optional) – User-defined metadata in dictionary form.

property created_timestamp: Optional[datetime.datetime]

Gets the created_timestamp of this entity.

property description: str

Gets the description of this entity.

classmethod from_dict(entity_dict)[source]

Creates an entity from a dict.

Parameters

entity_dict – A dict representation of an entity.

Returns

An EntityV2 object based on the entity dict.

classmethod from_proto(entity_proto: feast.core.Entity_pb2.Entity)[source]

Creates an entity from a protobuf representation of an entity.

Parameters

entity_proto – A protobuf representation of an entity.

Returns

An EntityV2 object based on the entity protobuf.

classmethod from_yaml(yml: str)[source]

Creates an entity from a YAML string body or a file path.

Parameters

yml – Either a file path containing a yaml file or a YAML string.

Returns

An EntityV2 object based on the YAML file.

is_valid()[source]

Validates the state of this entity locally.

Raises

ValueError – The entity does not have a name or does not have a type.

property join_key: str

Gets the join key of this entity.

property labels: Dict[str, str]

Gets the labels of this entity.

property last_updated_timestamp: Optional[datetime.datetime]

Gets the last_updated_timestamp of this entity.

property name: str

Gets the name of this entity.

to_dict() Dict[source]

Converts entity to dict.

Returns

Dictionary object representation of entity.

to_proto() feast.core.Entity_pb2.Entity[source]

Converts an entity object to its protobuf representation.

Returns

An EntityV2Proto protobuf.

to_spec_proto() feast.core.Entity_pb2.EntitySpecV2[source]

Converts an EntityV2 object to its protobuf representation. Used when passing EntitySpecV2 object to Feast request.

Returns

An EntitySpecV2 protobuf.

to_yaml()[source]

Converts a entity to a YAML string.

Returns

An entity string returned in YAML format.

property value_type: feast.value_type.ValueType

Gets the type of this entity.

feast.errors module

exception feast.errors.AwsAPIGatewayDoesNotExist(resource_name: str)[source]

Bases: Exception

exception feast.errors.AwsLambdaDoesNotExist(resource_name: str)[source]

Bases: Exception

exception feast.errors.BigQueryJobCancelled(job_id)[source]

Bases: Exception

exception feast.errors.BigQueryJobStillRunning(job_id)[source]

Bases: Exception

exception feast.errors.ConflictingFeatureViewNames(feature_view_name: str)[source]

Bases: Exception

exception feast.errors.DataSourceNotFoundException(path)[source]

Bases: Exception

exception feast.errors.DockerDaemonNotRunning[source]

Bases: Exception

exception feast.errors.EntityNotFoundException(name, project=None)[source]

Bases: feast.errors.FeastObjectNotFoundException

exception feast.errors.EntityTimestampInferenceException(expected_column_name: str)[source]

Bases: Exception

exception feast.errors.ExperimentalFeatureNotEnabled(feature_flag_name: str)[source]

Bases: Exception

exception feast.errors.FeastClassImportError(module_name, class_name, class_type='provider')[source]

Bases: Exception

exception feast.errors.FeastClassInvalidName(class_name: str, class_type: str)[source]

Bases: Exception

exception feast.errors.FeastEntityDFMissingColumnsError(expected, missing)[source]

Bases: Exception

exception feast.errors.FeastExtrasDependencyImportError(extras_type: str, nested_error: str)[source]

Bases: Exception

exception feast.errors.FeastFeatureServerTypeInvalidError(feature_server_type: str)[source]

Bases: Exception

exception feast.errors.FeastFeatureServerTypeSetError(feature_server_type: str)[source]

Bases: Exception

exception feast.errors.FeastJoinKeysDuringMaterialization(source: str, join_key_columns: Set[str], source_columns: Set[str])[source]

Bases: Exception

exception feast.errors.FeastModuleImportError(module_name: str, module_type: str)[source]

Bases: Exception

exception feast.errors.FeastObjectNotFoundException[source]

Bases: Exception

exception feast.errors.FeastOfflineStoreUnsupportedDataSource(offline_store_name: str, data_source_name: str)[source]

Bases: Exception

exception feast.errors.FeastOnlineStoreInvalidName(online_store_class_name: str)[source]

Bases: Exception

exception feast.errors.FeastOnlineStoreUnsupportedDataSource(online_store_name: str, data_source_name: str)[source]

Bases: Exception

exception feast.errors.FeastProviderLoginError[source]

Bases: Exception

Error class that indicates a user has not authenticated with their provider.

exception feast.errors.FeastProviderNotImplementedError(provider_name)[source]

Bases: Exception

exception feast.errors.FeastProviderNotSetError[source]

Bases: Exception

exception feast.errors.FeatureNameCollisionError(feature_refs_collisions: List[str], full_feature_names: bool)[source]

Bases: Exception

exception feast.errors.FeatureServiceNotFoundException(name, project=None)[source]

Bases: feast.errors.FeastObjectNotFoundException

exception feast.errors.FeatureTableNotFoundException(name, project=None)[source]

Bases: feast.errors.FeastObjectNotFoundException

exception feast.errors.FeatureViewNotFoundException(name, project=None)[source]

Bases: feast.errors.FeastObjectNotFoundException

exception feast.errors.IncompatibleRegistryStoreClass(actual_class: str, expected_class: str)[source]

Bases: Exception

exception feast.errors.InvalidEntityType(entity_type: type)[source]

Bases: Exception

exception feast.errors.OnDemandFeatureViewNotFoundException(name, project=None)[source]

Bases: feast.errors.FeastObjectNotFoundException

exception feast.errors.RedshiftCredentialsError[source]

Bases: Exception

exception feast.errors.RedshiftQueryError(details)[source]

Bases: Exception

exception feast.errors.RegistryInferenceFailure(repo_obj_type: str, specific_issue: str)[source]

Bases: Exception

exception feast.errors.RepoConfigPathDoesNotExist[source]

Bases: Exception

exception feast.errors.RequestDataNotFoundInEntityDfException(feature_name, feature_view_name)[source]

Bases: feast.errors.FeastObjectNotFoundException

exception feast.errors.RequestDataNotFoundInEntityRowsException(feature_names)[source]

Bases: feast.errors.FeastObjectNotFoundException

exception feast.errors.S3RegistryBucketForbiddenAccess(bucket)[source]

Bases: feast.errors.FeastObjectNotFoundException

exception feast.errors.S3RegistryBucketNotExist(bucket)[source]

Bases: feast.errors.FeastObjectNotFoundException

exception feast.errors.SpecifiedFeaturesNotPresentError(specified_features: List[str], feature_view_name: str)[source]

Bases: Exception

feast.feature module

class feast.feature.Feature(name: str, dtype: feast.value_type.ValueType, labels: Optional[Dict[str, str]] = None)[source]

Bases: object

A Feature represents a class of serveable feature.

Parameters
  • name – Name of the feature.

  • dtype – The type of the feature, such as string or float.

  • labels (optional) – User-defined metadata in dictionary form.

property dtype: feast.value_type.ValueType

Gets the data type of this feature.

classmethod from_proto(feature_proto: feast.core.Feature_pb2.FeatureSpecV2)[source]
Parameters

feature_proto – FeatureSpecV2 protobuf object

Returns

Feature object

property labels: Dict[str, str]

Gets the labels of this feature.

property name

Gets the name of this feature.

to_proto() feast.core.Feature_pb2.FeatureSpecV2[source]

Converts Feature object to its Protocol Buffer representation.

Returns

A FeatureSpecProto protobuf.

class feast.feature.FeatureRef(name: str, feature_table: str)[source]

Bases: object

Feature Reference represents a reference to a specific feature.

classmethod from_proto(proto: feast.serving.ServingService_pb2.FeatureReferenceV2)[source]

Construct a feature reference from the given FeatureReference proto

Parameters

proto – Protobuf FeatureReference to construct from

Returns

FeatureRef that refers to the given feature

classmethod from_str(feature_ref_str: str)[source]

Parse the given string feature reference into FeatureRef model String feature reference should be in the format feature_table:feature. Where “feature_table” and “name” are the feature_table name and feature name respectively.

Parameters

feature_ref_str – String representation of the feature reference

Returns

FeatureRef that refers to the given feature

to_proto() feast.serving.ServingService_pb2.FeatureReferenceV2[source]

Convert and return this feature table reference to protobuf.

Returns

Protobuf respresentation of this feature table reference.

feast.feature_store module

class feast.feature_store.FeatureStore(repo_path: Optional[str] = None, config: Optional[feast.repo_config.RepoConfig] = None)[source]

Bases: object

A FeatureStore object is used to define, create, and retrieve features.

Parameters
  • 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.

apply(objects: Union[feast.entity.Entity, feast.feature_view.FeatureView, feast.on_demand_feature_view.OnDemandFeatureView, feast.request_feature_view.RequestFeatureView, feast.feature_service.FeatureService, feast.feature_table.FeatureTable, List[Union[feast.feature_view.FeatureView, feast.on_demand_feature_view.OnDemandFeatureView, feast.request_feature_view.RequestFeatureView, feast.entity.Entity, feast.feature_service.FeatureService, feast.feature_table.FeatureTable]]], objects_to_delete: List[Union[feast.feature_view.FeatureView, feast.on_demand_feature_view.OnDemandFeatureView, feast.request_feature_view.RequestFeatureView, feast.entity.Entity, feast.feature_service.FeatureService, feast.feature_table.FeatureTable]] = [], partial: bool = True)[source]

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.

Parameters
  • 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,
... )
>>> fs.apply([driver_hourly_stats_view, driver]) # register entity and feature view
config: feast.repo_config.RepoConfig
delete_feature_service(name: str)[source]

Deletes a feature service.

Parameters

name – Name of feature service.

Raises

FeatureServiceNotFoundException – The feature view could not be found.

delete_feature_view(name: str)[source]

Deletes a feature view.

Parameters

name – Name of feature view.

Raises

FeatureViewNotFoundException – The feature view could not be found.

ensure_request_data_values_exist(needed_request_data: Set[str], needed_request_fv_features: Set[str], request_data_features: Dict[str, List[Any]])[source]
get_entity(name: str) feast.entity.Entity[source]

Retrieves an entity.

Parameters

name – Name of entity.

Returns

The specified entity.

Raises

EntityNotFoundException – The entity could not be found.

get_feature_server_endpoint() Optional[str][source]

Returns endpoint for the feature server, if it exists.

get_feature_service(name: str) feast.feature_service.FeatureService[source]

Retrieves a feature service.

Parameters

name – Name of feature service.

Returns

The specified feature service.

Raises

FeatureServiceNotFoundException – The feature service could not be found.

get_feature_view(name: str) feast.feature_view.FeatureView[source]

Retrieves a feature view.

Parameters

name – Name of feature view.

Returns

The specified feature view.

Raises

FeatureViewNotFoundException – The feature view could not be found.

get_historical_features(entity_df: Union[pandas.core.frame.DataFrame, str], features: Optional[Union[List[str], feast.feature_service.FeatureService]] = None, feature_refs: Optional[List[str]] = None, full_feature_names: bool = False) feast.infra.offline_stores.offline_store.RetrievalJob[source]

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.

Parameters
  • 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()
get_needed_request_data(grouped_odfv_refs: List[Tuple[feast.on_demand_feature_view.OnDemandFeatureView, List[str]]], grouped_request_fv_refs: List[Tuple[feast.request_feature_view.RequestFeatureView, List[str]]]) Tuple[Set[str], Set[str]][source]
get_on_demand_feature_view(name: str) feast.on_demand_feature_view.OnDemandFeatureView[source]

Retrieves a feature view.

Parameters

name – Name of feature view.

Returns

The specified feature view.

Raises

FeatureViewNotFoundException – The feature view could not be found.

get_online_features(features: Union[List[str], feast.feature_service.FeatureService], entity_rows: List[Dict[str, Any]], feature_refs: Optional[List[str]] = None, full_feature_names: bool = False) feast.online_response.OnlineResponse[source]

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).

Parameters
  • features – List of feature references that will be returned for each entity. Each feature reference should have the following format: “feature_table:feature” where “feature_table” & “feature” refer to the feature and feature table 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()
list_entities(allow_cache: bool = False) List[feast.entity.Entity][source]

Retrieves the list of entities from the registry.

Parameters

allow_cache – Whether to allow returning entities from a cached registry.

Returns

A list of entities.

list_feature_services() List[feast.feature_service.FeatureService][source]

Retrieves the list of feature services from the registry.

Returns

A list of feature services.

list_feature_views(allow_cache: bool = False) List[feast.feature_view.FeatureView][source]

Retrieves the list of feature views from the registry.

Parameters

allow_cache – Whether to allow returning entities from a cached registry.

Returns

A list of feature views.

list_on_demand_feature_views() List[feast.on_demand_feature_view.OnDemandFeatureView][source]

Retrieves the list of on demand feature views from the registry.

Returns

A list of on demand feature views.

list_request_feature_views(allow_cache: bool = False) List[feast.request_feature_view.RequestFeatureView][source]

Retrieves the list of feature views from the registry.

Parameters

allow_cache – Whether to allow returning entities from a cached registry.

Returns

A list of feature views.

materialize(start_date: datetime.datetime, end_date: datetime.datetime, feature_views: Optional[List[str]] = None) None[source]

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.

Parameters
  • 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...

...
materialize_incremental(end_date: datetime.datetime, feature_views: Optional[List[str]] = None) None[source]

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.

Parameters
  • 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...

...
property project: str

Gets the project of this feature store.

refresh_registry()[source]

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()

property registry: feast.registry.Registry

Gets the registry of this feature store.

repo_path: pathlib.Path
serve(host: str, port: int) None[source]

Start the feature consumption server locally on a given port.

serve_transformations(port: int) None[source]

Start the feature transformation server locally on a given port.

teardown()[source]

Tears down all local and cloud resources for the feature store.

version() str[source]

Returns the version of the current Feast SDK/CLI.

write_to_online_store(feature_view_name: str, df: pandas.core.frame.DataFrame, allow_registry_cache: bool = True)[source]

ingests data directly into the Online store

feast.feature_table module

class feast.feature_table.FeatureTable(name: str, entities: List[str], features: List[feast.feature.Feature], batch_source: feast.data_source.DataSource = None, stream_source: Optional[Union[feast.data_source.KafkaSource, feast.data_source.KinesisSource]] = None, max_age: Optional[google.protobuf.duration_pb2.Duration] = None, labels: Optional[MutableMapping[str, str]] = None)[source]

Bases: object

Represents a collection of features and associated metadata.

add_feature(feature: feast.feature.Feature)[source]

Adds a new feature to the feature table.

property batch_source

Returns the batch source of this feature table

property created_timestamp

Returns the created_timestamp of this feature table

property entities: List[str]

Returns the entities of this feature table

property features

Returns the features of this feature table

classmethod from_dict(ft_dict)[source]

Creates a feature table from a dict

Parameters

ft_dict – A dict representation of a feature table

Returns

Returns a FeatureTable object based on the feature table dict

classmethod from_proto(feature_table_proto: feast.core.FeatureTable_pb2.FeatureTable)[source]

Creates a feature table from a protobuf representation of a feature table

Parameters

feature_table_proto – A protobuf representation of a feature table

Returns

Returns a FeatureTableProto object based on the feature table protobuf

classmethod from_yaml(yml: str)[source]

Creates a feature table from a YAML string body or a file path

Parameters

yml – Either a file path containing a yaml file or a YAML string

Returns

Returns a FeatureTable object based on the YAML file

is_valid()[source]

Validates the state of a feature table locally. Raises an exception if feature table is invalid.

property labels

Returns the labels of this feature table. This is the user defined metadata defined as a dictionary.

property last_updated_timestamp

Returns the last_updated_timestamp of this feature table

property max_age

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.

property name

Returns the name of this feature table

property stream_source

Returns the stream source of this feature table

to_dict() Dict[source]

Converts feature table to dict

Returns

Dictionary object representation of feature table

to_proto() feast.core.FeatureTable_pb2.FeatureTable[source]

Converts an feature table object to its protobuf representation

Returns

FeatureTableProto protobuf

to_spec_proto() feast.core.FeatureTable_pb2.FeatureTableSpec[source]

Converts an FeatureTableProto object to its protobuf representation. Used when passing FeatureTableSpecProto object to Feast request.

Returns

FeatureTableSpecProto protobuf

to_yaml()[source]

Converts a feature table to a YAML string.

Returns

Feature table string returned in YAML format

feast.feature_view module

class feast.feature_view.FeatureView(name: str, entities: List[str], ttl: Union[google.protobuf.duration_pb2.Duration, datetime.timedelta], input: Optional[feast.data_source.DataSource] = None, batch_source: Optional[feast.data_source.DataSource] = None, stream_source: Optional[feast.data_source.DataSource] = None, features: Optional[List[feast.feature.Feature]] = None, tags: Optional[Dict[str, str]] = None, online: bool = True)[source]

Bases: feast.base_feature_view.BaseFeatureView

A FeatureView defines a logical grouping of serveable features.

Parameters
  • 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.

batch_source: feast.data_source.DataSource
created_timestamp: Optional[datetime.datetime] = None
ensure_valid()[source]

Validates the state of this feature view locally.

Raises

ValueError – The feature view does not have a name or does not have entities.

entities: List[str]
classmethod from_proto(feature_view_proto: feast.core.FeatureView_pb2.FeatureView)[source]

Creates a feature view from a protobuf representation of a feature view.

Parameters

feature_view_proto – A protobuf representation of a feature view.

Returns

A FeatureViewProto object based on the feature view protobuf.

input: feast.data_source.DataSource
last_updated_timestamp: Optional[datetime.datetime] = None
materialization_intervals: List[Tuple[datetime.datetime, datetime.datetime]]
property most_recent_end_time: Optional[datetime.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.

online: bool
property proto_class: Type[feast.core.FeatureView_pb2.FeatureView]
stream_source: Optional[feast.data_source.DataSource] = None
tags: Optional[Dict[str, str]]
to_proto() feast.core.FeatureView_pb2.FeatureView[source]

Converts a feature view object to its protobuf representation.

Returns

A FeatureViewProto protobuf.

ttl: datetime.timedelta
with_join_key_map(join_key_map: Dict[str, str])[source]

Sets the join_key_map by returning a copy of this feature view with that field set. This join_key mapping operation is only used as part of query operations and will not modify the underlying FeatureView.

Parameters

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.

Returns

A copy of this FeatureView with the join_key_map replaced with the ‘join_key_map’ input.

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”}

),

],

)

with_name(name: str)[source]

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.

Parameters

name – Name to assign to the FeatureView copy.

Returns

A copy of this FeatureView with the name replaced with the ‘name’ input.

with_projection(feature_view_projection: feast.feature_view_projection.FeatureViewProjection)[source]

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.

Parameters

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.

feast.names module

feast.online_response module

class feast.online_response.OnlineResponse(online_response_proto: feast.serving.ServingService_pb2.GetOnlineFeaturesResponse)[source]

Bases: object

Defines a online response in feast.

property field_values

Getter for GetOnlineResponse’s field_values.

to_df() pandas.core.frame.DataFrame[source]

Converts GetOnlineFeaturesResponse features into Panda dataframe form.

to_dict() Dict[str, Any][source]

Converts GetOnlineFeaturesResponse features into a dictionary form.

feast.registry module

class feast.registry.Registry(registry_config: feast.repo_config.RegistryConfig, repo_path: pathlib.Path)[source]

Bases: object

Registry: A registry allows for the management and persistence of feature definitions and related metadata.

apply_entity(entity: feast.entity.Entity, project: str, commit: bool = True)[source]

Registers a single entity with Feast

Parameters
  • entity – Entity that will be registered

  • project – Feast project that this entity belongs to

  • commit – Whether the change should be persisted immediately

apply_feature_service(feature_service: feast.feature_service.FeatureService, project: str, commit: bool = True)[source]

Registers a single feature service with Feast

Parameters
  • feature_service – A feature service that will be registered

  • project – Feast project that this entity belongs to

apply_feature_table(feature_table: feast.feature_table.FeatureTable, project: str, commit: bool = True)[source]

Registers a single feature table with Feast

Parameters
  • feature_table – Feature table that will be registered

  • project – Feast project that this feature table belongs to

  • commit – Whether the change should be persisted immediately

apply_feature_view(feature_view: feast.base_feature_view.BaseFeatureView, project: str, commit: bool = True)[source]

Registers a single feature view with Feast

Parameters
  • feature_view – Feature view that will be registered

  • project – Feast project that this feature view belongs to

  • commit – Whether the change should be persisted immediately

apply_materialization(feature_view: feast.feature_view.FeatureView, project: str, start_date: datetime.datetime, end_date: datetime.datetime, commit: bool = True)[source]

Updates materialization intervals tracked for a single feature view in Feast

Parameters
  • feature_view – Feature view that will be updated with an additional materialization interval tracked

  • project – Feast project that this feature view belongs to

  • start_date (datetime) – Start date of the materialization interval to track

  • end_date (datetime) – End date of the materialization interval to track

  • commit – Whether the change should be persisted immediately

cache_being_updated: bool = False
cached_registry_proto: Optional[feast.core.Registry_pb2.Registry] = None
cached_registry_proto_created: Optional[datetime.datetime] = None
cached_registry_proto_ttl: datetime.timedelta
commit()[source]

Commits the state of the registry cache to the remote registry store.

delete_entity(name: str, project: str, commit: bool = True)[source]

Deletes an entity or raises an exception if not found.

Parameters
  • name – Name of entity

  • project – Feast project that this entity belongs to

  • commit – Whether the change should be persisted immediately

delete_feature_service(name: str, project: str, commit: bool = True)[source]

Deletes a feature service or raises an exception if not found.

Parameters
  • name – Name of feature service

  • project – Feast project that this feature service belongs to

  • commit – Whether the change should be persisted immediately

delete_feature_table(name: str, project: str, commit: bool = True)[source]

Deletes a feature table or raises an exception if not found.

Parameters
  • name – Name of feature table

  • project – Feast project that this feature table belongs to

  • commit – Whether the change should be persisted immediately

delete_feature_view(name: str, project: str, commit: bool = True)[source]

Deletes a feature view or raises an exception if not found.

Parameters
  • name – Name of feature view

  • project – Feast project that this feature view belongs to

  • commit – Whether the change should be persisted immediately

get_entity(name: str, project: str, allow_cache: bool = False) feast.entity.Entity[source]

Retrieves an entity.

Parameters
  • name – Name of entity

  • project – Feast project that this entity belongs to

Returns

Returns either the specified entity, or raises an exception if none is found

get_feature_service(name: str, project: str, allow_cache: bool = False) feast.feature_service.FeatureService[source]

Retrieves a feature service.

Parameters
  • name – Name of feature service

  • project – Feast project that this feature service belongs to

Returns

Returns either the specified feature service, or raises an exception if none is found

get_feature_table(name: str, project: str) feast.feature_table.FeatureTable[source]

Retrieves a feature table.

Parameters
  • name – Name of feature table

  • project – Feast project that this feature table belongs to

Returns

Returns either the specified feature table, or raises an exception if none is found

get_feature_view(name: str, project: str, allow_cache: bool = False) feast.feature_view.FeatureView[source]

Retrieves a feature view.

Parameters
  • name – Name of feature view

  • project – Feast project that this feature view belongs to

  • allow_cache – Allow returning feature view from the cached registry

Returns

Returns either the specified feature view, or raises an exception if none is found

get_on_demand_feature_view(name: str, project: str, allow_cache: bool = False) feast.on_demand_feature_view.OnDemandFeatureView[source]

Retrieves an on demand feature view.

Parameters
  • name – Name of on demand feature view

  • project – Feast project that this on demand feature belongs to

Returns

Returns either the specified on demand feature view, or raises an exception if none is found

list_entities(project: str, allow_cache: bool = False) List[feast.entity.Entity][source]

Retrieve a list of entities from the registry

Parameters
  • allow_cache – Whether to allow returning entities from a cached registry

  • project – Filter entities based on project name

Returns

List of entities

list_feature_services(project: str, allow_cache: bool = False) List[feast.feature_service.FeatureService][source]

Retrieve a list of feature services from the registry

Parameters
  • allow_cache – Whether to allow returning entities from a cached registry

  • project – Filter entities based on project name

Returns

List of feature services

list_feature_tables(project: str) List[feast.feature_table.FeatureTable][source]

Retrieve a list of feature tables from the registry

Parameters

project – Filter feature tables based on project name

Returns

List of feature tables

list_feature_views(project: str, allow_cache: bool = False) List[feast.feature_view.FeatureView][source]

Retrieve a list of feature views from the registry

Parameters
  • allow_cache – Allow returning feature views from the cached registry

  • project – Filter feature views based on project name

Returns

List of feature views

list_on_demand_feature_views(project: str, allow_cache: bool = False) List[feast.on_demand_feature_view.OnDemandFeatureView][source]

Retrieve a list of on demand feature views from the registry

Parameters
  • allow_cache – Whether to allow returning on demand feature views from a cached registry

  • project – Filter on demand feature views based on project name

Returns

List of on demand feature views

list_request_feature_views(project: str, allow_cache: bool = False) List[feast.request_feature_view.RequestFeatureView][source]

Retrieve a list of request feature views from the registry

Parameters
  • allow_cache – Allow returning feature views from the cached registry

  • project – Filter feature views based on project name

Returns

List of feature views

refresh()[source]

Refreshes the state of the registry cache by fetching the registry state from the remote registry store.

teardown()[source]

Tears down (removes) the registry.

to_dict(project: str) Dict[str, List[Any]][source]

Returns a dictionary representation of the registry contents for the specified project.

For each list in the dictionary, the elements are sorted by name, so this method can be used to compare two registries.

Parameters

project – Feast project to convert to a dict

feast.registry.get_registry_store_class_from_scheme(registry_path: str)[source]
feast.registry.get_registry_store_class_from_type(registry_store_type: str)[source]

feast.repo_config module

class feast.repo_config.FeastBaseModel(**extra_data: Any)[source]

Bases: pydantic.main.BaseModel

Feast Pydantic Configuration Class

class Config[source]

Bases: object

arbitrary_types_allowed = True
extra = 'allow'
class feast.repo_config.FeastConfigBaseModel[source]

Bases: pydantic.main.BaseModel

Feast Pydantic Configuration Class

class Config[source]

Bases: object

arbitrary_types_allowed = True
extra = 'forbid'
exception feast.repo_config.FeastConfigError(error_message, config_path)[source]

Bases: Exception

class feast.repo_config.RegistryConfig(*, registry_store_type: pydantic.types.StrictStr = None, path: pydantic.types.StrictStr, cache_ttl_seconds: pydantic.types.StrictInt = 600, **extra_data: Any)[source]

Bases: feast.repo_config.FeastBaseModel

Metadata Store Configuration. Configuration that relates to reading from and writing to the Feast registry.

cache_ttl_seconds: pydantic.types.StrictInt

The cache TTL is the amount of time registry state will be cached in memory. If this TTL is exceeded then the registry will be refreshed when any feature store method asks for access to registry state. The TTL can be set to infinity by setting TTL to 0 seconds, which means the cache will only be loaded once and will never expire. Users can manually refresh the cache by calling feature_store.refresh_registry()

Type

int

path: pydantic.types.StrictStr

Path to metadata store. Can be a local path, or remote object storage path, e.g. a GCS URI

Type

str

registry_store_type: Optional[pydantic.types.StrictStr]

Provider name or a class name that implements RegistryStore.

Type

str

class feast.repo_config.RepoConfig(*, registry: Union[pydantic.types.StrictStr, feast.repo_config.RegistryConfig] = 'data/registry.db', project: pydantic.types.StrictStr, provider: pydantic.types.StrictStr, online_store: Any = None, offline_store: Any = None, feature_server: Any = None, flags: Any = None, repo_path: pathlib.Path = None, **data: Any)[source]

Bases: feast.repo_config.FeastBaseModel

Repo config. Typically loaded from feature_store.yaml

feature_server: Optional[Any]

Feature server configuration (optional depending on provider)

Type

FeatureServerConfig

flags: Any

Feature flags for experimental features (optional)

Type

Flags

get_registry_config()[source]
offline_store: Any

Offline store configuration (optional depending on provider)

Type

OfflineStoreConfig

online_store: Any

Online store configuration (optional depending on provider)

Type

OnlineStoreConfig

project: pydantic.types.StrictStr

Feast project id. This can be any alphanumeric string up to 16 characters. You can have multiple independent feature repositories deployed to the same cloud provider account, as long as they have different project ids.

Type

str

provider: pydantic.types.StrictStr

local or gcp or aws

Type

str

registry: Union[pydantic.types.StrictStr, feast.repo_config.RegistryConfig]

Path to metadata store. Can be a local path, or remote object storage path, e.g. a GCS URI

Type

str

repo_path: Optional[pathlib.Path]
write_to_path(repo_path: pathlib.Path)[source]
feast.repo_config.get_data_source_class_from_type(data_source_type: str)[source]
feast.repo_config.get_feature_server_config_from_type(feature_server_type: str)[source]
feast.repo_config.get_offline_config_from_type(offline_store_type: str)[source]
feast.repo_config.get_online_config_from_type(online_store_type: str)[source]
feast.repo_config.load_repo_config(repo_path: pathlib.Path) feast.repo_config.RepoConfig[source]

feast.repo_operations module

class feast.repo_operations.ParsedRepo(feature_tables, feature_views, on_demand_feature_views, request_feature_views, entities, feature_services)[source]

Bases: tuple

property entities

Alias for field number 4

property feature_services

Alias for field number 5

property feature_tables

Alias for field number 0

property feature_views

Alias for field number 1

property on_demand_feature_views

Alias for field number 2

property request_feature_views

Alias for field number 3

feast.repo_operations.apply_total(repo_config: feast.repo_config.RepoConfig, repo_path: pathlib.Path, skip_source_validation: bool)[source]
feast.repo_operations.cli_check_repo(repo_path: pathlib.Path)[source]
feast.repo_operations.generate_project_name() str[source]

Generates a unique project name

feast.repo_operations.get_ignore_files(repo_root: pathlib.Path, ignore_paths: List[str]) Set[pathlib.Path][source]

Get all ignore files that match any of the user-defined ignore paths

feast.repo_operations.get_repo_files(repo_root: pathlib.Path) List[pathlib.Path][source]

Get the list of all repo files, ignoring undesired files & directories specified in .feastignore

feast.repo_operations.init_repo(repo_name: str, template: str)[source]
feast.repo_operations.is_valid_name(name: str) bool[source]

A name should be alphanumeric values and underscores but not start with an underscore

feast.repo_operations.parse_repo(repo_root: pathlib.Path) feast.repo_operations.ParsedRepo[source]

Collect feature table definitions from feature repo

feast.repo_operations.py_path_to_module(path: pathlib.Path, repo_root: pathlib.Path) str[source]
feast.repo_operations.read_feastignore(repo_root: pathlib.Path) List[str][source]

Read .feastignore in the repo root directory (if exists) and return the list of user-defined ignore paths

feast.repo_operations.registry_dump(repo_config: feast.repo_config.RepoConfig, repo_path: pathlib.Path)[source]

For debugging only: output contents of the metadata registry

feast.repo_operations.replace_str_in_file(file_path, match_str, sub_str)[source]
feast.repo_operations.teardown(repo_config: feast.repo_config.RepoConfig, repo_path: pathlib.Path)[source]

feast.telemetry module

feast.type_map module

feast.type_map.bq_to_feast_value_type(bq_type_as_str: str) feast.value_type.ValueType[source]
feast.type_map.feast_value_type_to_pandas_type(value_type: feast.value_type.ValueType) Any[source]
feast.type_map.feast_value_type_to_python_type(field_value_proto: feast.types.Value_pb2.Value) Any[source]

Converts field value Proto to Dict and returns each field’s Feast Value Type value in their respective Python value.

Parameters

field_value_proto – Field value Proto

Returns

Python native type representation/version of the given field_value_proto

feast.type_map.pa_to_feast_value_type(pa_type_as_str: str) feast.value_type.ValueType[source]
feast.type_map.pa_to_redshift_value_type(pa_type: pyarrow.lib.DataType) str[source]
feast.type_map.python_type_to_feast_value_type(name: str, value: Optional[Any] = None, recurse: bool = True, type_name: Optional[str] = None) feast.value_type.ValueType[source]

Finds the equivalent Feast Value Type for a Python value. Both native and Pandas types are supported. This function will recursively look for nested types when arrays are detected. All types must be homogenous.

Parameters
  • name – Name of the value or field

  • value – Value that will be inspected

  • recurse – Whether to recursively look for nested types in arrays

Returns

Feast Value Type

feast.type_map.python_value_to_proto_value(value: Any, feature_type: feast.value_type.ValueType = ValueType.UNKNOWN) feast.types.Value_pb2.Value[source]
feast.type_map.python_values_to_feast_value_type(name: str, values: Any, recurse: bool = True) feast.value_type.ValueType[source]
feast.type_map.redshift_to_feast_value_type(redshift_type_as_str: str) feast.value_type.ValueType[source]

feast.utils module

feast.utils.make_tzaware(t: datetime.datetime) datetime.datetime[source]

We assume tz-naive datetimes are UTC

feast.value_type module

class feast.value_type.ValueType(value)[source]

Bases: enum.Enum

Feature value type. Used to define data types in Feature Tables.

BOOL = 7
BOOL_LIST = 17
BYTES = 1
BYTES_LIST = 11
DOUBLE = 5
DOUBLE_LIST = 15
FLOAT = 6
FLOAT_LIST = 16
INT32 = 3
INT32_LIST = 13
INT64 = 4
INT64_LIST = 14
NULL = 19
STRING = 2
STRING_LIST = 12
UNIX_TIMESTAMP = 8
UNIX_TIMESTAMP_LIST = 18
UNKNOWN = 0
to_tfx_schema_feature_type()[source]

feast.version module

feast.version.get_version()[source]

Returns version information of the Feast Python Package.

feast.wait module

feast.wait.wait_retry_backoff(retry_fn: Callable[[], Tuple[Any, bool]], timeout_secs: int = 0, timeout_msg: Optional[str] = 'Timeout while waiting for retry_fn() to return True', max_interval_secs: int = 60) Any[source]

Repeatedly try calling given retry_fn until it returns a True boolean success flag. Waits with a exponential backoff between retries until timeout when it throws TimeoutError. :param retry_fn: Callable that returns a result and a boolean success flag. :param timeout_secs: timeout in seconds to give up retrying and throw TimeoutError,

or 0 to retry perpetually.

Parameters
  • timeout_msg – Message to use when throwing TimeoutError.

  • max_interval_secs – max wait in seconds to wait between retries.

Returns

Returned Result from retry_fn() if success flag is True.

Module contents

class feast.BigQuerySource(event_timestamp_column: Optional[str] = '', table_ref: Optional[str] = None, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = '', query: Optional[str] = None)[source]

Bases: feast.data_source.DataSource

property bigquery_options

Returns the bigquery options of this data source

static from_proto(data_source: feast.core.DataSource_pb2.DataSource)[source]

Converts data source config in FeatureTable spec to a DataSource class object.

Parameters

data_source – A protobuf representation of a DataSource.

Returns

A DataSource class object.

Raises

ValueError – The type of DataSource could not be identified.

get_table_column_names_and_types(config: feast.repo_config.RepoConfig) Iterable[Tuple[str, str]][source]

Returns the list of column names and raw column types.

Parameters

config – Configuration object used to configure a feature store.

get_table_query_string() str[source]

Returns a string that can directly be used to reference this table in SQL

property query
static source_datatype_to_feast_value_type() Callable[[str], feast.value_type.ValueType][source]

Returns the callable method that returns Feast type given the raw column type.

property table_ref
to_proto() feast.core.DataSource_pb2.DataSource[source]

Converts an DataSourceProto object to its protobuf representation.

validate(config: feast.repo_config.RepoConfig)[source]

Validates the underlying data source.

Parameters

config – Configuration object used to configure a feature store.

class feast.Entity(name: str, value_type: feast.value_type.ValueType = ValueType.UNKNOWN, description: str = '', join_key: Optional[str] = None, labels: Optional[Dict[str, str]] = None)[source]

Bases: object

Represents a collection of entities and associated metadata.

Parameters
  • name – Name of the entity.

  • value_type (optional) – The type of the entity, such as string or float.

  • description (optional) – Additional information to describe the entity.

  • join_key (optional) – A property that uniquely identifies different entities within the collection. Used as a key for joining entities with their associated features. If not specified, defaults to the name of the entity.

  • labels (optional) – User-defined metadata in dictionary form.

property created_timestamp: Optional[datetime.datetime]

Gets the created_timestamp of this entity.

property description: str

Gets the description of this entity.

classmethod from_dict(entity_dict)[source]

Creates an entity from a dict.

Parameters

entity_dict – A dict representation of an entity.

Returns

An EntityV2 object based on the entity dict.

classmethod from_proto(entity_proto: feast.core.Entity_pb2.Entity)[source]

Creates an entity from a protobuf representation of an entity.

Parameters

entity_proto – A protobuf representation of an entity.

Returns

An EntityV2 object based on the entity protobuf.

classmethod from_yaml(yml: str)[source]

Creates an entity from a YAML string body or a file path.

Parameters

yml – Either a file path containing a yaml file or a YAML string.

Returns

An EntityV2 object based on the YAML file.

is_valid()[source]

Validates the state of this entity locally.

Raises

ValueError – The entity does not have a name or does not have a type.

property join_key: str

Gets the join key of this entity.

property labels: Dict[str, str]

Gets the labels of this entity.

property last_updated_timestamp: Optional[datetime.datetime]

Gets the last_updated_timestamp of this entity.

property name: str

Gets the name of this entity.

to_dict() Dict[source]

Converts entity to dict.

Returns

Dictionary object representation of entity.

to_proto() feast.core.Entity_pb2.Entity[source]

Converts an entity object to its protobuf representation.

Returns

An EntityV2Proto protobuf.

to_spec_proto() feast.core.Entity_pb2.EntitySpecV2[source]

Converts an EntityV2 object to its protobuf representation. Used when passing EntitySpecV2 object to Feast request.

Returns

An EntitySpecV2 protobuf.

to_yaml()[source]

Converts a entity to a YAML string.

Returns

An entity string returned in YAML format.

property value_type: feast.value_type.ValueType

Gets the type of this entity.

class feast.Feature(name: str, dtype: feast.value_type.ValueType, labels: Optional[Dict[str, str]] = None)[source]

Bases: object

A Feature represents a class of serveable feature.

Parameters
  • name – Name of the feature.

  • dtype – The type of the feature, such as string or float.

  • labels (optional) – User-defined metadata in dictionary form.

property dtype: feast.value_type.ValueType

Gets the data type of this feature.

classmethod from_proto(feature_proto: feast.core.Feature_pb2.FeatureSpecV2)[source]
Parameters

feature_proto – FeatureSpecV2 protobuf object

Returns

Feature object

property labels: Dict[str, str]

Gets the labels of this feature.

property name

Gets the name of this feature.

to_proto() feast.core.Feature_pb2.FeatureSpecV2[source]

Converts Feature object to its Protocol Buffer representation.

Returns

A FeatureSpecProto protobuf.

class feast.FeatureService(name: str, features: List[Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView, feast.on_demand_feature_view.OnDemandFeatureView]], tags: Optional[Dict[str, str]] = None, description: Optional[str] = None)[source]

Bases: object

A feature service is a logical grouping of features for retrieval (training or serving). The features grouped by a feature service may come from any number of feature views.

Parameters
  • name – Unique name of the feature service.

  • features – A list of Features that are grouped as part of this FeatureService. The list may contain Feature Views, Feature Tables, or a subset of either.

  • tags (optional) – A dictionary of key-value pairs used for organizing Feature Services.

created_timestamp: Optional[datetime.datetime] = None
description: Optional[str] = None
feature_view_projections: List[feast.feature_view_projection.FeatureViewProjection]
static from_proto(feature_service_proto: feast.core.FeatureService_pb2.FeatureService)[source]

Converts a FeatureServiceProto to a FeatureService object.

Parameters

feature_service_proto – A protobuf representation of a FeatureService.

last_updated_timestamp: Optional[datetime.datetime] = None
name: str
tags: Dict[str, str]
to_proto() feast.core.FeatureService_pb2.FeatureService[source]

Converts a FeatureService to its protobuf representation.

Returns

A FeatureServiceProto protobuf.

validate()[source]
class feast.FeatureStore(repo_path: Optional[str] = None, config: Optional[feast.repo_config.RepoConfig] = None)[source]

Bases: object

A FeatureStore object is used to define, create, and retrieve features.

Parameters
  • 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.

apply(objects: Union[feast.entity.Entity, feast.feature_view.FeatureView, feast.on_demand_feature_view.OnDemandFeatureView, feast.request_feature_view.RequestFeatureView, feast.feature_service.FeatureService, feast.feature_table.FeatureTable, List[Union[feast.feature_view.FeatureView, feast.on_demand_feature_view.OnDemandFeatureView, feast.request_feature_view.RequestFeatureView, feast.entity.Entity, feast.feature_service.FeatureService, feast.feature_table.FeatureTable]]], objects_to_delete: List[Union[feast.feature_view.FeatureView, feast.on_demand_feature_view.OnDemandFeatureView, feast.request_feature_view.RequestFeatureView, feast.entity.Entity, feast.feature_service.FeatureService, feast.feature_table.FeatureTable]] = [], partial: bool = True)[source]

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.

Parameters
  • 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,
... )
>>> fs.apply([driver_hourly_stats_view, driver]) # register entity and feature view
config: feast.repo_config.RepoConfig
delete_feature_service(name: str)[source]

Deletes a feature service.

Parameters

name – Name of feature service.

Raises

FeatureServiceNotFoundException – The feature view could not be found.

delete_feature_view(name: str)[source]

Deletes a feature view.

Parameters

name – Name of feature view.

Raises

FeatureViewNotFoundException – The feature view could not be found.

ensure_request_data_values_exist(needed_request_data: Set[str], needed_request_fv_features: Set[str], request_data_features: Dict[str, List[Any]])[source]
get_entity(name: str) feast.entity.Entity[source]

Retrieves an entity.

Parameters

name – Name of entity.

Returns

The specified entity.

Raises

EntityNotFoundException – The entity could not be found.

get_feature_server_endpoint() Optional[str][source]

Returns endpoint for the feature server, if it exists.

get_feature_service(name: str) feast.feature_service.FeatureService[source]

Retrieves a feature service.

Parameters

name – Name of feature service.

Returns

The specified feature service.

Raises

FeatureServiceNotFoundException – The feature service could not be found.

get_feature_view(name: str) feast.feature_view.FeatureView[source]

Retrieves a feature view.

Parameters

name – Name of feature view.

Returns

The specified feature view.

Raises

FeatureViewNotFoundException – The feature view could not be found.

get_historical_features(entity_df: Union[pandas.core.frame.DataFrame, str], features: Optional[Union[List[str], feast.feature_service.FeatureService]] = None, feature_refs: Optional[List[str]] = None, full_feature_names: bool = False) feast.infra.offline_stores.offline_store.RetrievalJob[source]

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.

Parameters
  • 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()
get_needed_request_data(grouped_odfv_refs: List[Tuple[feast.on_demand_feature_view.OnDemandFeatureView, List[str]]], grouped_request_fv_refs: List[Tuple[feast.request_feature_view.RequestFeatureView, List[str]]]) Tuple[Set[str], Set[str]][source]
get_on_demand_feature_view(name: str) feast.on_demand_feature_view.OnDemandFeatureView[source]

Retrieves a feature view.

Parameters

name – Name of feature view.

Returns

The specified feature view.

Raises

FeatureViewNotFoundException – The feature view could not be found.

get_online_features(features: Union[List[str], feast.feature_service.FeatureService], entity_rows: List[Dict[str, Any]], feature_refs: Optional[List[str]] = None, full_feature_names: bool = False) feast.online_response.OnlineResponse[source]

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).

Parameters
  • features – List of feature references that will be returned for each entity. Each feature reference should have the following format: “feature_table:feature” where “feature_table” & “feature” refer to the feature and feature table 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()
list_entities(allow_cache: bool = False) List[feast.entity.Entity][source]

Retrieves the list of entities from the registry.

Parameters

allow_cache – Whether to allow returning entities from a cached registry.

Returns

A list of entities.

list_feature_services() List[feast.feature_service.FeatureService][source]

Retrieves the list of feature services from the registry.

Returns

A list of feature services.

list_feature_views(allow_cache: bool = False) List[feast.feature_view.FeatureView][source]

Retrieves the list of feature views from the registry.

Parameters

allow_cache – Whether to allow returning entities from a cached registry.

Returns

A list of feature views.

list_on_demand_feature_views() List[feast.on_demand_feature_view.OnDemandFeatureView][source]

Retrieves the list of on demand feature views from the registry.

Returns

A list of on demand feature views.

list_request_feature_views(allow_cache: bool = False) List[feast.request_feature_view.RequestFeatureView][source]

Retrieves the list of feature views from the registry.

Parameters

allow_cache – Whether to allow returning entities from a cached registry.

Returns

A list of feature views.

materialize(start_date: datetime.datetime, end_date: datetime.datetime, feature_views: Optional[List[str]] = None) None[source]

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.

Parameters
  • 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...

...
materialize_incremental(end_date: datetime.datetime, feature_views: Optional[List[str]] = None) None[source]

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.

Parameters
  • 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...

...
property project: str

Gets the project of this feature store.

refresh_registry()[source]

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()

property registry: feast.registry.Registry

Gets the registry of this feature store.

repo_path: pathlib.Path
serve(host: str, port: int) None[source]

Start the feature consumption server locally on a given port.

serve_transformations(port: int) None[source]

Start the feature transformation server locally on a given port.

teardown()[source]

Tears down all local and cloud resources for the feature store.

version() str[source]

Returns the version of the current Feast SDK/CLI.

write_to_online_store(feature_view_name: str, df: pandas.core.frame.DataFrame, allow_registry_cache: bool = True)[source]

ingests data directly into the Online store

class feast.FeatureTable(name: str, entities: List[str], features: List[feast.feature.Feature], batch_source: feast.data_source.DataSource = None, stream_source: Optional[Union[feast.data_source.KafkaSource, feast.data_source.KinesisSource]] = None, max_age: Optional[google.protobuf.duration_pb2.Duration] = None, labels: Optional[MutableMapping[str, str]] = None)[source]

Bases: object

Represents a collection of features and associated metadata.

add_feature(feature: feast.feature.Feature)[source]

Adds a new feature to the feature table.

property batch_source

Returns the batch source of this feature table

property created_timestamp

Returns the created_timestamp of this feature table

property entities: List[str]

Returns the entities of this feature table

property features

Returns the features of this feature table

classmethod from_dict(ft_dict)[source]

Creates a feature table from a dict

Parameters

ft_dict – A dict representation of a feature table

Returns

Returns a FeatureTable object based on the feature table dict

classmethod from_proto(feature_table_proto: feast.core.FeatureTable_pb2.FeatureTable)[source]

Creates a feature table from a protobuf representation of a feature table

Parameters

feature_table_proto – A protobuf representation of a feature table

Returns

Returns a FeatureTableProto object based on the feature table protobuf

classmethod from_yaml(yml: str)[source]

Creates a feature table from a YAML string body or a file path

Parameters

yml – Either a file path containing a yaml file or a YAML string

Returns

Returns a FeatureTable object based on the YAML file

is_valid()[source]

Validates the state of a feature table locally. Raises an exception if feature table is invalid.

property labels

Returns the labels of this feature table. This is the user defined metadata defined as a dictionary.

property last_updated_timestamp

Returns the last_updated_timestamp of this feature table

property max_age

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.

property name

Returns the name of this feature table

property stream_source

Returns the stream source of this feature table

to_dict() Dict[source]

Converts feature table to dict

Returns

Dictionary object representation of feature table

to_proto() feast.core.FeatureTable_pb2.FeatureTable[source]

Converts an feature table object to its protobuf representation

Returns

FeatureTableProto protobuf

to_spec_proto() feast.core.FeatureTable_pb2.FeatureTableSpec[source]

Converts an FeatureTableProto object to its protobuf representation. Used when passing FeatureTableSpecProto object to Feast request.

Returns

FeatureTableSpecProto protobuf

to_yaml()[source]

Converts a feature table to a YAML string.

Returns

Feature table string returned in YAML format

class feast.FeatureView(name: str, entities: List[str], ttl: Union[google.protobuf.duration_pb2.Duration, datetime.timedelta], input: Optional[feast.data_source.DataSource] = None, batch_source: Optional[feast.data_source.DataSource] = None, stream_source: Optional[feast.data_source.DataSource] = None, features: Optional[List[feast.feature.Feature]] = None, tags: Optional[Dict[str, str]] = None, online: bool = True)[source]

Bases: feast.base_feature_view.BaseFeatureView

A FeatureView defines a logical grouping of serveable features.

Parameters
  • 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.

batch_source: feast.data_source.DataSource
created_timestamp: Optional[datetime.datetime] = None
ensure_valid()[source]

Validates the state of this feature view locally.

Raises

ValueError – The feature view does not have a name or does not have entities.

entities: List[str]
classmethod from_proto(feature_view_proto: feast.core.FeatureView_pb2.FeatureView)[source]

Creates a feature view from a protobuf representation of a feature view.

Parameters

feature_view_proto – A protobuf representation of a feature view.

Returns

A FeatureViewProto object based on the feature view protobuf.

input: feast.data_source.DataSource
last_updated_timestamp: Optional[datetime.datetime] = None
materialization_intervals: List[Tuple[datetime.datetime, datetime.datetime]]
property most_recent_end_time: Optional[datetime.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.

online: bool
property proto_class: Type[feast.core.FeatureView_pb2.FeatureView]
stream_source: Optional[feast.data_source.DataSource] = None
tags: Optional[Dict[str, str]]
to_proto() feast.core.FeatureView_pb2.FeatureView[source]

Converts a feature view object to its protobuf representation.

Returns

A FeatureViewProto protobuf.

ttl: datetime.timedelta
with_join_key_map(join_key_map: Dict[str, str])[source]

Sets the join_key_map by returning a copy of this feature view with that field set. This join_key mapping operation is only used as part of query operations and will not modify the underlying FeatureView.

Parameters

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.

Returns

A copy of this FeatureView with the join_key_map replaced with the ‘join_key_map’ input.

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”}

),

],

)

with_name(name: str)[source]

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.

Parameters

name – Name to assign to the FeatureView copy.

Returns

A copy of this FeatureView with the name replaced with the ‘name’ input.

with_projection(feature_view_projection: feast.feature_view_projection.FeatureViewProjection)[source]

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.

Parameters

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.

class feast.FileSource(event_timestamp_column: Optional[str] = '', file_url: Optional[str] = None, path: Optional[str] = None, file_format: Optional[feast.data_format.FileFormat] = None, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = '', s3_endpoint_override: Optional[str] = None)[source]

Bases: feast.data_source.DataSource

static create_filesystem_and_path(path: str, s3_endpoint_override: str) Tuple[Optional[pyarrow._fs.FileSystem], str][source]
property file_options

Returns the file options of this data source

static from_proto(data_source: feast.core.DataSource_pb2.DataSource)[source]

Converts data source config in FeatureTable spec to a DataSource class object.

Parameters

data_source – A protobuf representation of a DataSource.

Returns

A DataSource class object.

Raises

ValueError – The type of DataSource could not be identified.

get_table_column_names_and_types(config: feast.repo_config.RepoConfig) Iterable[Tuple[str, str]][source]

Returns the list of column names and raw column types.

Parameters

config – Configuration object used to configure a feature store.

property path

Returns the file path of this feature data source

static source_datatype_to_feast_value_type() Callable[[str], feast.value_type.ValueType][source]

Returns the callable method that returns Feast type given the raw column type.

to_proto() feast.core.DataSource_pb2.DataSource[source]

Converts an DataSourceProto object to its protobuf representation.

validate(config: feast.repo_config.RepoConfig)[source]

Validates the underlying data source.

Parameters

config – Configuration object used to configure a feature store.

class feast.KafkaSource(event_timestamp_column: str, bootstrap_servers: str, message_format: feast.data_format.StreamFormat, topic: str, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = '')[source]

Bases: feast.data_source.DataSource

static from_proto(data_source: feast.core.DataSource_pb2.DataSource)[source]

Converts data source config in FeatureTable spec to a DataSource class object.

Parameters

data_source – A protobuf representation of a DataSource.

Returns

A DataSource class object.

Raises

ValueError – The type of DataSource could not be identified.

get_table_column_names_and_types(config: feast.repo_config.RepoConfig) Iterable[Tuple[str, str]][source]

Returns the list of column names and raw column types.

Parameters

config – Configuration object used to configure a feature store.

property kafka_options

Returns the kafka options of this data source

static source_datatype_to_feast_value_type() Callable[[str], feast.value_type.ValueType][source]

Returns the callable method that returns Feast type given the raw column type.

to_proto() feast.core.DataSource_pb2.DataSource[source]

Converts an DataSourceProto object to its protobuf representation.

validate(config: feast.repo_config.RepoConfig)[source]

Validates the underlying data source.

Parameters

config – Configuration object used to configure a feature store.

class feast.KinesisSource(event_timestamp_column: str, created_timestamp_column: str, record_format: feast.data_format.StreamFormat, region: str, stream_name: str, field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = '')[source]

Bases: feast.data_source.DataSource

static from_proto(data_source: feast.core.DataSource_pb2.DataSource)[source]

Converts data source config in FeatureTable spec to a DataSource class object.

Parameters

data_source – A protobuf representation of a DataSource.

Returns

A DataSource class object.

Raises

ValueError – The type of DataSource could not be identified.

get_table_column_names_and_types(config: feast.repo_config.RepoConfig) Iterable[Tuple[str, str]][source]

Returns the list of column names and raw column types.

Parameters

config – Configuration object used to configure a feature store.

property kinesis_options

Returns the kinesis options of this data source

static source_datatype_to_feast_value_type() Callable[[str], feast.value_type.ValueType][source]

Returns the callable method that returns Feast type given the raw column type.

to_proto() feast.core.DataSource_pb2.DataSource[source]

Converts an DataSourceProto object to its protobuf representation.

validate(config: feast.repo_config.RepoConfig)[source]

Validates the underlying data source.

Parameters

config – Configuration object used to configure a feature store.

class feast.OnDemandFeatureView(name: str, features: List[feast.feature.Feature], inputs: Dict[str, Union[feast.feature_view.FeatureView, feast.data_source.RequestDataSource]], udf: method)[source]

Bases: feast.base_feature_view.BaseFeatureView

[Experimental] An OnDemandFeatureView defines on demand transformations on existing feature view values and request data.

Parameters
  • name – Name of the group of features.

  • features – Output schema of transformation with feature names

  • inputs – The input feature views passed into the transform.

  • udf – User defined transformation function that takes as input pandas dataframes

classmethod from_proto(on_demand_feature_view_proto: feast.core.OnDemandFeatureView_pb2.OnDemandFeatureView)[source]

Creates an on demand feature view from a protobuf representation.

Parameters

on_demand_feature_view_proto – A protobuf representation of an on-demand feature view.

Returns

A OnDemandFeatureView object based on the on-demand feature view protobuf.

get_request_data_schema() Dict[str, feast.value_type.ValueType][source]
static get_requested_odfvs(feature_refs, project, registry)[source]
get_transformed_features_df(df_with_features: pandas.core.frame.DataFrame) pandas.core.frame.DataFrame[source]
infer_features()[source]

Infers the set of features associated to this feature view from the input source.

Raises

RegistryInferenceFailure – The set of features could not be inferred.

input_feature_views: Dict[str, feast.feature_view.FeatureView]
input_request_data_sources: Dict[str, feast.data_source.RequestDataSource]
inputs: Dict[str, Union[feast.feature_view.FeatureView, feast.data_source.RequestDataSource]]
property proto_class: Type[feast.core.OnDemandFeatureView_pb2.OnDemandFeatureView]
to_proto() feast.core.OnDemandFeatureView_pb2.OnDemandFeatureView[source]

Converts an on demand feature view object to its protobuf representation.

Returns

A OnDemandFeatureViewProto protobuf.

udf: method
class feast.RedshiftSource(event_timestamp_column: Optional[str] = '', table: Optional[str] = None, schema: Optional[str] = None, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = '', query: Optional[str] = None)[source]

Bases: feast.data_source.DataSource

static from_proto(data_source: feast.core.DataSource_pb2.DataSource)[source]

Creates a RedshiftSource from a protobuf representation of a RedshiftSource.

Parameters

data_source – A protobuf representation of a RedshiftSource

Returns

A RedshiftSource object based on the data_source protobuf.

get_table_column_names_and_types(config: feast.repo_config.RepoConfig) Iterable[Tuple[str, str]][source]

Returns a mapping of column names to types for this Redshift source.

Parameters

config – A RepoConfig describing the feature repo

get_table_query_string() str[source]

Returns a string that can directly be used to reference this table in SQL.

property query

Returns the Redshift options of this Redshift source.

property redshift_options

Returns the Redshift options of this Redshift source.

property schema

Returns the schema of this Redshift source.

static source_datatype_to_feast_value_type() Callable[[str], feast.value_type.ValueType][source]

Returns the callable method that returns Feast type given the raw column type.

property table

Returns the table of this Redshift source.

to_proto() feast.core.DataSource_pb2.DataSource[source]

Converts a RedshiftSource object to its protobuf representation.

Returns

A DataSourceProto object.

validate(config: feast.repo_config.RepoConfig)[source]

Validates the underlying data source.

Parameters

config – Configuration object used to configure a feature store.

class feast.RepoConfig(*, registry: Union[pydantic.types.StrictStr, feast.repo_config.RegistryConfig] = 'data/registry.db', project: pydantic.types.StrictStr, provider: pydantic.types.StrictStr, online_store: Any = None, offline_store: Any = None, feature_server: Any = None, flags: Any = None, repo_path: pathlib.Path = None, **data: Any)[source]

Bases: feast.repo_config.FeastBaseModel

Repo config. Typically loaded from feature_store.yaml

feature_server: Optional[Any]

Feature server configuration (optional depending on provider)

Type

FeatureServerConfig

flags: Any

Feature flags for experimental features (optional)

Type

Flags

get_registry_config()[source]
offline_store: Any

Offline store configuration (optional depending on provider)

Type

OfflineStoreConfig

online_store: Any

Online store configuration (optional depending on provider)

Type

OnlineStoreConfig

project: pydantic.types.StrictStr

Feast project id. This can be any alphanumeric string up to 16 characters. You can have multiple independent feature repositories deployed to the same cloud provider account, as long as they have different project ids.

Type

str

provider: pydantic.types.StrictStr

local or gcp or aws

Type

str

registry: Union[pydantic.types.StrictStr, feast.repo_config.RegistryConfig]

Path to metadata store. Can be a local path, or remote object storage path, e.g. a GCS URI

Type

str

repo_path: Optional[pathlib.Path]
write_to_path(repo_path: pathlib.Path)[source]
class feast.SourceType(value)[source]

Bases: enum.Enum

DataSource value type. Used to define source types in DataSource.

BATCH_BIGQUERY = 2
BATCH_FILE = 1
STREAM_KAFKA = 3
STREAM_KINESIS = 4
UNKNOWN = 0
class feast.ValueType(value)[source]

Bases: enum.Enum

Feature value type. Used to define data types in Feature Tables.

BOOL = 7
BOOL_LIST = 17
BYTES = 1
BYTES_LIST = 11
DOUBLE = 5
DOUBLE_LIST = 15
FLOAT = 6
FLOAT_LIST = 16
INT32 = 3
INT32_LIST = 13
INT64 = 4
INT64_LIST = 14
NULL = 19
STRING = 2
STRING_LIST = 12
UNIX_TIMESTAMP = 8
UNIX_TIMESTAMP_LIST = 18
UNKNOWN = 0
to_tfx_schema_feature_type()[source]