feast package

Subpackages

Submodules

feast.aggregation module

class feast.aggregation.Aggregation(column: str | None = '', function: str | None = '', time_window: timedelta | None = None, slide_interval: timedelta | None = None)[source]

Bases: object

NOTE: Feast-handled aggregations are not yet supported. This class provides a way to register user-defined aggregations.

column

str # Column name of the feature we are aggregating.

Type:: str

function

str # Provided built in aggregations sum, max, min, count mean

Type:: str

time_window

timedelta # The time window for this aggregation.

Type:: datetime.timedelta | None

slide_interval

timedelta # The sliding window for these aggregations

Type:: datetime.timedelta | None

column: str

classmethod from_proto(agg_proto: Aggregation)[source]

function: str

slide_interval: timedelta | None

time_window: timedelta | None

to_proto() → Aggregation[source]

feast.base_feature_view module

class feast.base_feature_view.BaseFeatureView(*, name: str, features: List[Field] | None = None, description: str = '', tags: Dict[str, str] | None = None, owner: str = '')[source]

Bases: ABC

A BaseFeatureView defines a logical group of features.

name

The unique name of the base feature view.

Type:: str

features

The list of features defined as part of this base feature view.

Type:: List[feast.field.Field]

description

A human-readable description.

Type:: str

tags

A dictionary of key-value pairs to store arbitrary metadata.

Type:: Dict[str, str]

owner

The owner of the base feature view, typically the email of the primary maintainer.

Type:: str

projection

The feature view projection storing modifications to be applied to this base feature view at retrieval time.

Type:: feast.feature_view_projection.FeatureViewProjection

created_timestamp

The time when the base feature view was created.

Type:: datetime.datetime | None

last_updated_timestamp

The time when the base feature view was last updated.

Type:: datetime.datetime | None

created_timestamp: datetime | None

description: str

ensure_valid()[source]

Validates the state of this feature view locally.

Raises:: ValueError – The feature view is invalid.

features: List[Field]

abstract classmethod from_proto(feature_view_proto)[source]

last_updated_timestamp: datetime | None

name: str

owner: str

projection: FeatureViewProjection

abstract property proto_class: Type[Message]

set_projection(feature_view_projection: FeatureViewProjection) → None[source]

Sets the feature view projection of this base feature view to the given projection.

Parameters:: feature_view_projection – The feature view projection to be set.
Raises:: ValueError – The name or features of the projection do not match.

tags: Dict[str, str]

abstract to_proto() → Message[source]

with_name(name: str)[source]

Returns a renamed copy of this base feature view. This renamed copy should only be used for query operations and will not modify the underlying base feature view.

Parameters:: name – The name to assign to the copy.

with_projection(feature_view_projection: FeatureViewProjection)[source]

Returns a copy of this base feature view with the feature view projection set to the given projection.

Parameters:: feature_view_projection – The feature view projection to assign to the copy.
Raises:: ValueError – The name or features of the projection do not match.

feast.batch_feature_view module

class feast.batch_feature_view.BatchFeatureView(*, name: str, source: DataSource, entities: List[Entity] | List[str] | None = None, ttl: timedelta | None = None, tags: Dict[str, str] | None = None, online: bool = True, description: str = '', owner: str = '', schema: List[Field] | None = None)[source]

Bases: FeatureView

A batch feature view defines a logical group of features that has only a batch data source.

name

The unique name of the batch feature view.

Type:: str

entities

List of entities or entity join keys.

Type:: List[str]

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.

Type:: datetime.timedelta | None

schema

The schema of the feature view, including feature, timestamp, and entity columns. If not specified, can be inferred from the underlying data source.

Type:: List[feast.field.Field]

source

The batch source of data where this group of features is stored.

Type:: feast.data_source.DataSource

online

A boolean indicating whether online retrieval is enabled for this feature view.

Type:: bool

description

A human-readable description.

Type:: str

tags

A dictionary of key-value pairs to store arbitrary metadata.

Type:: Dict[str, str]

owner

The owner of the batch feature view, typically the email of the primary maintainer.

Type:: str

description: str

entities: List[str]

entity_columns: List[Field]

features: List[Field]

materialization_intervals: List[Tuple[datetime, datetime]]

name: str

online: bool

owner: str

source: DataSource

tags: Dict[str, str]

timestamp_field: str

ttl: timedelta | None

feast.cli module

class feast.cli.NoOptionDefaultFormat(name: str | None, context_settings: MutableMapping[str, Any] | None = None, callback: Callable[[...], Any] | None = None, params: List[Parameter] | None = None, help: str | None = None, epilog: str | None = None, short_help: str | None = None, options_metavar: str | None = '[OPTIONS]', add_help_option: bool = True, no_args_is_help: bool = False, hidden: bool = False, deprecated: bool = False)[source]

Bases: Command

format_options(ctx: Context, formatter: HelpFormatter)[source]: Writes all the options into the formatter if they exist.

feast.constants module

feast.data_format module

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

Bases: 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

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.JsonFormat(schema_json: str)[source]

Bases: StreamFormat

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

to_proto()[source]: Convert this StreamFormat into its protobuf representation.

class feast.data_format.ParquetFormat[source]

Bases: 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: StreamFormat

Defines the Protobuf data format

to_proto()[source]: Convert this StreamFormat into its protobuf representation.

class feast.data_format.StreamFormat[source]

Bases: 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

Bases: ABC

DataSource that can be used to source features.

Parameters:

name – Name of data source, which should be unique within a project
timestamp_field (optional) – Event timestamp field 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.
description (optional) –
tags (optional) – A dictionary of key-value pairs to store arbitrary metadata.
owner (optional) – The owner of the data source, typically the email of the primary maintainer.
date_partition_column (optional) – Timestamp column used for partitioning. Not supported by all offline stores.

created_timestamp_column: str

date_partition_column: str

description: str

field_mapping: Dict[str, str]

abstract static from_proto(data_source: DataSource) → Any[source]

Converts data source config in protobuf 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: 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.

name: str

owner: str

abstract static source_datatype_to_feast_value_type() → Callable[[str], ValueType][source]: Returns the callable method that returns Feast type given the raw column type.

tags: Dict[str, str]

timestamp_field: str

abstract to_proto() → DataSource[source]: Converts a DataSourceProto object to its protobuf representation.

validate(config: RepoConfig)[source]

Validates the underlying data source.

Parameters:: config – Configuration object used to configure a feature store.

class feast.data_source.KafkaOptions(kafka_bootstrap_servers: str, message_format: StreamFormat, topic: str, watermark_delay_threshold: timedelta | None = None)[source]

Bases: object

DataSource Kafka options used to source features from Kafka messages

classmethod from_proto(kafka_options_proto: 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 KafkaOptions object based on the kafka_options protobuf

to_proto() → KafkaOptions[source]

Converts an KafkaOptionsProto object to its protobuf representation.

Returns:: KafkaOptionsProto protobuf

class feast.data_source.KafkaSource(*, name: str, timestamp_field: str, message_format: StreamFormat, bootstrap_servers: str | None = None, kafka_bootstrap_servers: str | None = None, topic: str | None = None, created_timestamp_column: str | None = '', field_mapping: Dict[str, str] | None = None, description: str | None = '', tags: Dict[str, str] | None = None, owner: str | None = '', batch_source: DataSource | None = None, watermark_delay_threshold: timedelta | None = None)[source]

Bases: DataSource

created_timestamp_column: str

date_partition_column: str

description: str

field_mapping: Dict[str, str]

static from_proto(data_source: DataSource)[source]

Converts data source config in protobuf 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: 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.

name: str

owner: str

static source_datatype_to_feast_value_type() → Callable[[str], ValueType][source]: Returns the callable method that returns Feast type given the raw column type.

tags: Dict[str, str]

timestamp_field: str

to_proto() → DataSource[source]: Converts a DataSourceProto object to its protobuf representation.

validate(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: 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: 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

to_proto() → KinesisOptions[source]

Converts an KinesisOptionsProto object to its protobuf representation.

Returns:: KinesisOptionsProto protobuf

class feast.data_source.KinesisSource(*, name: str, record_format: StreamFormat, region: str, stream_name: str, timestamp_field: str | None = '', created_timestamp_column: str | None = '', field_mapping: Dict[str, str] | None = None, description: str | None = '', tags: Dict[str, str] | None = None, owner: str | None = '', batch_source: DataSource | None = None)[source]

Bases: DataSource

created_timestamp_column: str

date_partition_column: str

description: str

field_mapping: Dict[str, str]

static from_proto(data_source: DataSource)[source]

Converts data source config in protobuf 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: 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.

name: str

owner: str

static source_datatype_to_feast_value_type() → Callable[[str], ValueType][source]: Returns the callable method that returns Feast type given the raw column type.

tags: Dict[str, str]

timestamp_field: str

to_proto() → DataSource[source]: Converts a DataSourceProto object to its protobuf representation.

validate(config: RepoConfig)[source]

Validates the underlying data source.

Parameters:: config – Configuration object used to configure a feature store.

class feast.data_source.PushMode(value)[source]

Bases: Enum

An enumeration.

OFFLINE = 2

ONLINE = 1

ONLINE_AND_OFFLINE = 3

class feast.data_source.PushSource(*, name: str, batch_source: DataSource, description: str | None = '', tags: Dict[str, str] | None = None, owner: str | None = '')[source]

Bases: DataSource

A source that can be used to ingest features on request

batch_source: DataSource

static from_proto(data_source: DataSource)[source]

Converts data source config in protobuf 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: 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.

static source_datatype_to_feast_value_type() → Callable[[str], ValueType][source]: Returns the callable method that returns Feast type given the raw column type.

to_proto() → DataSource[source]: Converts a DataSourceProto object to its protobuf representation.

validate(config: RepoConfig)[source]

Validates the underlying data source.

Parameters:: config – Configuration object used to configure a feature store.

class feast.data_source.RequestSource(*, name: str, schema: List[Field], description: str | None = '', tags: Dict[str, str] | None = None, owner: str | None = '')[source]

Bases: DataSource

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

name

Name of the request data source

Type:: str

schema

Schema mapping from the input feature name to a ValueType

Type:: List[feast.field.Field]

description

A human-readable description.

Type:: str

tags

A dictionary of key-value pairs to store arbitrary metadata.

Type:: Dict[str, str]

owner

The owner of the request data source, typically the email of the primary maintainer.

Type:: str

description: str

static from_proto(data_source: DataSource)[source]

Converts data source config in protobuf 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: 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.

name: str

owner: str

schema: List[Field]

static source_datatype_to_feast_value_type() → Callable[[str], ValueType][source]: Returns the callable method that returns Feast type given the raw column type.

tags: Dict[str, str]

to_proto() → DataSource[source]: Converts a DataSourceProto object to its protobuf representation.

validate(config: RepoConfig)[source]

Validates the underlying data source.

Parameters:: config – Configuration object used to configure a feature store.

feast.driver_test_data module

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

Bases: 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) → DataFrame[source]

Example df generated by this function:

|------------------+-------------+-----------------+---------------------+---------------------+------------------| | 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) → DataFrame[source]

Example df generated by this function:

|------------------+-----------+-----------+----------+-----------------+------------------| | 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_field_mapping_df(start_date, end_date) → DataFrame[source]: Example df generated by this function: | event_timestamp | column_name | created | |------------------+-------------+------------------| | 2021-03-17 19:00 | 99 | 2021-03-24 19:38 | | 2021-03-17 19:00 | 22 | 2021-03-24 19:38 | | 2021-03-17 19:00 | 7 | 2021-03-24 19:38 | | 2021-03-17 19:00 | 45 | 2021-03-24 19:38 |

feast.driver_test_data.create_global_daily_stats_df(start_date, end_date) → 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) → 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) → 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, join_keys: List[str] | None = None, value_type: ValueType | None = None, description: str = '', tags: Dict[str, str] | None = None, owner: str = '')[source]

Bases: object

An entity defines a collection of entities for which features can be defined. An entity can also contain associated metadata.

name

The unique name of the entity.

Type:: str

value_type

The type of the entity, such as string or float.

Type:: feast.value_type.ValueType

join_key

A property that uniquely identifies different entities within the collection. The join_key property is typically used for joining entities with their associated features. If not specified, defaults to the name.

Type:: str

description

A human-readable description.

Type:: str

tags

A dictionary of key-value pairs to store arbitrary metadata.

Type:: Dict[str, str]

owner

The owner of the entity, typically the email of the primary maintainer.

Type:: str

created_timestamp

The time when the entity was created.

Type:: datetime.datetime | None

last_updated_timestamp

The time when the entity was last updated.

Type:: datetime.datetime | None

created_timestamp: datetime | None

description: str

classmethod from_proto(entity_proto: Entity)[source]

Creates an entity from a protobuf representation of an entity.

Parameters:: entity_proto – A protobuf representation of an entity.
Returns:: An Entity object based on the entity protobuf.

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.

join_key: str

last_updated_timestamp: datetime | None

name: str

owner: str

tags: Dict[str, str]

to_proto() → Entity[source]

Converts an entity object to its protobuf representation.

Returns:: An EntityProto protobuf.

value_type: ValueType

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.DataSourceNoNameException[source]: Bases: Exception

exception feast.errors.DataSourceNotFoundException(path)[source]: Bases: Exception

exception feast.errors.DataSourceObjectNotFoundException(name, project=None)[source]: Bases: FeastObjectNotFoundException

exception feast.errors.DataSourceRepeatNamesException(ds_name: str)[source]: Bases: Exception

exception feast.errors.DockerDaemonNotRunning[source]: Bases: Exception

exception feast.errors.EntityDFNotDateTime[source]: Bases: Exception

exception feast.errors.EntityNotFoundException(name, project=None)[source]: Bases: FeastObjectNotFoundException

exception feast.errors.EntitySQLEmptyResults(entity_sql: str)[source]: Bases: Exception

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

exception feast.errors.FeastClassImportError(module_name: str, class_name: 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.FeastInvalidBaseClass(class_name: str, class_type: str)[source]: Bases: Exception

exception feast.errors.FeastInvalidInfraObjectType[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, class_name: str)[source]: Bases: Exception

exception feast.errors.FeastObjectNotFoundException[source]: Bases: Exception

exception feast.errors.FeastOfflineStoreInvalidName(offline_store_class_name: str)[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.FeastRegistryNotSetError[source]: Bases: Exception

exception feast.errors.FeastRegistryTypeInvalidError(registry_type: str)[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: FeastObjectNotFoundException

exception feast.errors.FeatureViewMissingDuringFeatureServiceInference(feature_view_name: str, feature_service_name: str)[source]: Bases: Exception

exception feast.errors.FeatureViewNotFoundException(name, project=None)[source]: Bases: 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.InvalidFeaturesParameterType(features: Any)[source]: Bases: Exception

exception feast.errors.OnDemandFeatureViewNotFoundException(name, project=None)[source]: Bases: FeastObjectNotFoundException

exception feast.errors.PushSourceNotFoundException(push_source_name: str)[source]: Bases: Exception

exception feast.errors.RedshiftCredentialsError[source]: Bases: Exception

exception feast.errors.RedshiftQueryError(details)[source]: Bases: Exception

exception feast.errors.RedshiftTableNameTooLong(table_name: str)[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: FeastObjectNotFoundException

exception feast.errors.RequestDataNotFoundInEntityRowsException(feature_names)[source]: Bases: FeastObjectNotFoundException

exception feast.errors.S3RegistryBucketForbiddenAccess(bucket)[source]: Bases: FeastObjectNotFoundException

exception feast.errors.S3RegistryBucketNotExist(bucket)[source]: Bases: FeastObjectNotFoundException

exception feast.errors.SavedDatasetLocationAlreadyExists(location: str)[source]: Bases: Exception

exception feast.errors.SavedDatasetNotFound(name: str, project: str)[source]: Bases: FeastObjectNotFoundException

exception feast.errors.SnowflakeCredentialsError[source]: Bases: Exception

exception feast.errors.SnowflakeIncompleteConfig(e: KeyError)[source]: Bases: Exception

exception feast.errors.SnowflakeQueryError(details)[source]: Bases: Exception

exception feast.errors.SnowflakeQueryUnknownError(query: str)[source]: Bases: Exception

exception feast.errors.SpecifiedFeaturesNotPresentError(specified_features: List[Field], inferred_features: List[Field], feature_view_name: str)[source]: Bases: Exception

exception feast.errors.ValidationReferenceNotFound(name: str, project: str)[source]: Bases: FeastObjectNotFoundException

feast.feast_object module

feast.feature module

class feast.feature.Feature(name: str, dtype: ValueType, description: str = '', labels: Dict[str, str] | None = 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 description: str: Gets the description of the feature

property dtype: ValueType: Gets the data type of this feature.

classmethod from_proto(feature_proto: 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() → FeatureSpecV2[source]

Converts Feature object to its Protocol Buffer representation.

Returns:: A FeatureSpecProto protobuf.

feast.feature_logging module

class feast.feature_logging.FeatureServiceLoggingSource(feature_service: FeatureService, project: str)[source]

Bases: LoggingSource

get_log_timestamp_column() → str[source]: Return timestamp column that must exist in generated schema.

get_schema(registry: BaseRegistry) → Schema[source]: Generate schema for logs destination.

class feast.feature_logging.LoggingConfig(destination: LoggingDestination, sample_rate: float = 1.0)[source]

Bases: object

destination: LoggingDestination

classmethod from_proto(config_proto: LoggingConfig) → LoggingConfig | None[source]

sample_rate: float

to_proto() → LoggingConfig[source]

class feast.feature_logging.LoggingDestination[source]

Bases: object

Logging destination contains details about where exactly logs should be written inside an offline store. It is implementation specific - each offline store must implement LoggingDestination subclass.

Kind of logging destination will be determined by matching attribute name in LoggingConfig protobuf message and “_proto_kind” property of each subclass.

abstract classmethod from_proto(config_proto: LoggingConfig) → LoggingDestination[source]

abstract to_data_source() → DataSource[source]: Convert this object into a data source to read logs from an offline store.

abstract to_proto() → LoggingConfig[source]

class feast.feature_logging.LoggingSource[source]

Bases: object

Logging source describes object that produces logs (eg, feature service produces logs of served features). It should be able to provide schema of produced logs table and additional metadata that describes logs data.

abstract get_log_timestamp_column() → str[source]: Return timestamp column that must exist in generated schema.

abstract get_schema(registry: BaseRegistry) → Schema[source]: Generate schema for logs destination.

feast.feature_server module

class feast.feature_server.FeastServeApplication(store: FeatureStore, **options)[source]

Bases: BaseApplication

load()[source]

load_config()[source]: This method is used to load the configuration from one or several input(s). Custom Command line, configuration file. You have to override this method in your class.

class feast.feature_server.MaterializeIncrementalRequest(*, end_ts: str, feature_views: List[str] | None = None)[source]

Bases: BaseModel

end_ts: str

feature_views: List[str] | None

class feast.feature_server.MaterializeRequest(*, start_ts: str, end_ts: str, feature_views: List[str] | None = None)[source]

Bases: BaseModel

end_ts: str

feature_views: List[str] | None

start_ts: str

class feast.feature_server.PushFeaturesRequest(*, push_source_name: str, df: dict, allow_registry_cache: bool = True, to: str = 'online')[source]

Bases: BaseModel

allow_registry_cache: bool

df: dict

push_source_name: str

to: str

class feast.feature_server.WriteToFeatureStoreRequest(*, feature_view_name: str, df: dict, allow_registry_cache: bool = True)[source]

Bases: BaseModel

allow_registry_cache: bool

df: dict

feature_view_name: str

feast.feature_server.get_app(store: FeatureStore)[source]

feast.feature_server.start_server(store: FeatureStore, host: str, port: int, no_access_log: bool, workers: int, keep_alive_timeout: int)[source]

feast.feature_service module

class feast.feature_service.FeatureService(*, name: str, features: List[FeatureView | OnDemandFeatureView], tags: Dict[str, str] = None, description: str = '', owner: str = '', logging_config: LoggingConfig | None = None)[source]

Bases: object

A feature service defines a logical group of features from one or more feature views. This group of features can be retrieved together during training or serving.

name

The unique name of the feature service.

Type:: str

feature_view_projections

A list containing feature views and feature view projections, representing the features in the feature service.

Type:: List[feast.feature_view_projection.FeatureViewProjection]

description

A human-readable description.

Type:: str

tags

A dictionary of key-value pairs to store arbitrary metadata.

Type:: Dict[str, str]

owner

The owner of the feature service, typically the email of the primary maintainer.

Type:: str

created_timestamp

The time when the feature service was created.

Type:: datetime.datetime | None

last_updated_timestamp

The time when the feature service was last updated.

Type:: datetime.datetime | None

created_timestamp: datetime | None = None

description: str

feature_view_projections: List[FeatureViewProjection]

classmethod from_proto(feature_service_proto: FeatureService)[source]

Converts a FeatureServiceProto to a FeatureService object.

Parameters:: feature_service_proto – A protobuf representation of a FeatureService.

infer_features(fvs_to_update: Dict[str, FeatureView])[source]

Infers the features for the projections of this feature service, and updates this feature service in place.

This method is necessary since feature services may rely on feature views which require feature inference.

Parameters:: fvs_to_update – A mapping of feature view names to corresponding feature views that contains all the feature views necessary to run inference.

last_updated_timestamp: datetime | None = None

logging_config: LoggingConfig | None = None

name: str

owner: str

tags: Dict[str, str]

to_proto() → FeatureService[source]

Converts a feature service to its protobuf representation.

Returns:: A FeatureServiceProto protobuf.

validate()[source]

feast.feature_store module

class feast.feature_store.FeatureStore(repo_path: str | None = None, config: RepoConfig | None = None, fs_yaml_file: Path | None = None)[source]

Bases: object

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

config

The config for the feature store.

Type:: feast.repo_config.RepoConfig

repo_path

The path to the feature repo.

Type:: pathlib.Path

_registry

The registry for the feature store.

Type:: feast.infra.registry.base_registry.BaseRegistry

_provider

The provider for the feature store.

Type:: feast.infra.provider.Provider

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, FileSource, RepoConfig
>>> from datetime import timedelta
>>> fs = FeatureStore(repo_path="project/feature_repo")
>>> driver = Entity(name="driver_id", description="driver id")
>>> driver_hourly_stats = FileSource(
...     path="project/feature_repo/data/driver_stats.parquet",
...     timestamp_field="event_timestamp",
...     created_timestamp_column="created",
... )
>>> driver_hourly_stats_view = FeatureView(
...     name="driver_hourly_stats",
...     entities=[driver],
...     ttl=timedelta(seconds=86400 * 1),
...     source=driver_hourly_stats,
... )
>>> fs.apply([driver_hourly_stats_view, driver]) # register entity and feature view

config: RepoConfig

create_saved_dataset(from_: RetrievalJob, name: str, storage: SavedDatasetStorage, tags: Dict[str, str] | None = None, feature_service: FeatureService | None = None, allow_overwrite: bool = False) → SavedDataset[source]

Execute provided retrieval job and persist its outcome in given storage. Storage type (eg, BigQuery or Redshift) must be the same as globally configured offline store. After data successfully persisted saved dataset object with dataset metadata is committed to the registry. Name for the saved dataset should be unique within project, since it’s possible to overwrite previously stored dataset with the same name.

Parameters:

from – The retrieval job whose result should be persisted.
name – The name of the saved dataset.
storage – The saved dataset storage object indicating where the result should be persisted.
tags (optional) – A dictionary of key-value pairs to store arbitrary metadata.
feature_service (optional) – The feature service that should be associated with this saved dataset.
allow_overwrite (optional) – If True, the persisted result can overwrite an existing table or file.

Returns:

SavedDataset object with attached RetrievalJob

Raises:

ValueError if given retrieval job doesn't have metadata –

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.

static 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_data_source(name: str) → DataSource[source]

Retrieves the list of data sources from the registry.

Parameters:: name – Name of the data source.
Returns:: The specified data source.
Raises:: DataSourceObjectNotFoundException – The data source could not be found.

get_entity(name: str, allow_registry_cache: bool = False) → Entity[source]

Retrieves an entity.

Parameters:

name – Name of entity.
allow_registry_cache – (Optional) Whether to allow returning this entity from a cached registry

Returns:

The specified entity.

Raises:

EntityNotFoundException – The entity could not be found.

get_feature_server_endpoint() → str | None[source]: Returns endpoint for the feature server, if it exists.

get_feature_service(name: str, allow_cache: bool = False) → FeatureService[source]

Retrieves a feature service.

Parameters:

name – Name of feature service.
allow_cache – Whether to allow returning feature services from a cached registry.

Returns:

The specified feature service.

Raises:

FeatureServiceNotFoundException – The feature service could not be found.

get_feature_view(name: str, allow_registry_cache: bool = False) → FeatureView[source]

Retrieves a feature view.

Parameters:

name – Name of feature view.
allow_registry_cache – (Optional) Whether to allow returning this entity from a cached registry

Returns:

The specified feature view.

Raises:

FeatureViewNotFoundException – The feature view could not be found.

get_historical_features(entity_df: DataFrame | str, features: List[str] | FeatureService, full_feature_names: bool = False) → 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 – The list of features that should be retrieved from the offline store. These features can be specified either as a list of string feature references or as a feature service. String feature references must have format “feature_view:feature”, e.g. “customer_fv:daily_transactions”.
full_feature_names – If True, feature names will be prefixed with the corresponding feature view name, changing them from the format “feature” to “feature_view__feature” (e.g. “daily_transactions” changes to “customer_fv__daily_transactions”).

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="project/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()

static get_needed_request_data(grouped_odfv_refs: List[Tuple[OnDemandFeatureView, List[str]]], grouped_request_fv_refs: List[Tuple[RequestFeatureView, List[str]]]) → Tuple[Set[str], Set[str]][source]

get_on_demand_feature_view(name: str) → 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: List[str] | FeatureService, entity_rows: List[Dict[str, Any]], full_feature_names: bool = False) → 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 – The list of features that should be retrieved from the online store. These features can be specified either as a list of string feature references or as a feature service. String feature references must have format “feature_view:feature”, e.g. “customer_fv:daily_transactions”.
entity_rows – A list of dictionaries where each key-value is an entity-name, entity-value pair.
full_feature_names – If True, feature names will be prefixed with the corresponding feature view name, changing them from the format “feature” to “feature_view__feature” (e.g. “daily_transactions” changes to “customer_fv__daily_transactions”).

Returns:

OnlineResponse containing the feature data in records.

Raises:

Exception – No entity with the specified name exists.

Examples

Retrieve online features from an online store.

>>> from feast import FeatureStore, RepoConfig
>>> fs = FeatureStore(repo_path="project/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()

get_saved_dataset(name: str) → SavedDataset[source]

Find a saved dataset in the registry by provided name and create a retrieval job to pull whole dataset from storage (offline store).

If dataset couldn’t be found by provided name SavedDatasetNotFound exception will be raised.

Data will be retrieved from globally configured offline store.

Returns:: SavedDataset with RetrievalJob attached
Raises:: SavedDatasetNotFound –

get_stream_feature_view(name: str, allow_registry_cache: bool = False) → StreamFeatureView[source]

Retrieves a stream feature view.

Parameters:

name – Name of stream feature view.
allow_registry_cache – (Optional) Whether to allow returning this entity from a cached registry

Returns:

The specified stream feature view.

Raises:

FeatureViewNotFoundException – The feature view could not be found.

get_validation_reference(name: str, allow_cache: bool = False) → ValidationReference[source]

Retrieves a validation reference.

Raises:: ValidationReferenceNotFoundException – The validation reference could not be found.

list_data_sources(allow_cache: bool = False) → List[DataSource][source]

Retrieves the list of data sources from the registry.

Parameters:: allow_cache – Whether to allow returning data sources from a cached registry.
Returns:: A list of data sources.

list_entities(allow_cache: bool = False) → List[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[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[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(allow_cache: bool = False) → List[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[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.

list_stream_feature_views(allow_cache: bool = False) → List[StreamFeatureView][source]

Retrieves the list of stream feature views from the registry.

Returns:: A list of stream feature views.

materialize(start_date: datetime, end_date: datetime, feature_views: List[str] | None = 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=”project/feature_repo”) >>> fs.materialize( … start_date=datetime.utcnow() - timedelta(hours=3), end_date=datetime.utcnow() - timedelta(minutes=10) … ) Materializing… <BLANKLINE> …

materialize_incremental(end_date: datetime, feature_views: List[str] | None = 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="project/feature_repo")
>>> fs.materialize_incremental(end_date=datetime.utcnow() - timedelta(minutes=5))
Materializing...

...

plan(desired_repo_contents: RepoContents) → Tuple[RegistryDiff, InfraDiff, Infra][source]

Dry-run registering objects to metadata store.

The plan method dry-runs registering one or more definitions (e.g., Entity, FeatureView), and produces a list of all the changes the that would be introduced in the feature repo. The changes computed by the plan command are for informational purposes, and are not actually applied to the registry.

Parameters:: desired_repo_contents – The desired repo state.
Raises:: ValueError – The ‘objects’ parameter could not be parsed properly.

Examples

Generate a plan adding an Entity and a FeatureView.

>>> from feast import FeatureStore, Entity, FeatureView, Feature, FileSource, RepoConfig
>>> from feast.feature_store import RepoContents
>>> from datetime import timedelta
>>> fs = FeatureStore(repo_path="project/feature_repo")
>>> driver = Entity(name="driver_id", description="driver id")
>>> driver_hourly_stats = FileSource(
...     path="project/feature_repo/data/driver_stats.parquet",
...     timestamp_field="event_timestamp",
...     created_timestamp_column="created",
... )
>>> driver_hourly_stats_view = FeatureView(
...     name="driver_hourly_stats",
...     entities=[driver],
...     ttl=timedelta(seconds=86400 * 1),
...     source=driver_hourly_stats,
... )
>>> registry_diff, infra_diff, new_infra = fs.plan(RepoContents(
...     data_sources=[driver_hourly_stats],
...     feature_views=[driver_hourly_stats_view],
...     on_demand_feature_views=list(),
...     stream_feature_views=list(),
...     request_feature_views=list(),
...     entities=[driver],
...     feature_services=list())) # register entity and feature view

property project: str: Gets the project of this feature store.

push(push_source_name: str, df: DataFrame, allow_registry_cache: bool = True, to: PushMode = PushMode.ONLINE)[source]

Push features to a push source. This updates all the feature views that have the push source as stream source.

Parameters:

push_source_name – The name of the push source we want to push data to.
df – The data being pushed.
allow_registry_cache – Whether to allow cached versions of the registry.
to – Whether to push to online or offline store. Defaults to online store only.

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_features() 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: BaseRegistry: Gets the registry of this feature store.

repo_path: Path

serve(host: str, port: int, type_: str, no_access_log: bool, no_feature_log: bool, workers: int, keep_alive_timeout: 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.

serve_ui(host: str, port: int, get_registry_dump: Callable, registry_ttl_sec: int, root_path: str = '') → None[source]: Start the UI server locally

teardown()[source]: Tears down all local and cloud resources for the feature store.

validate_logged_features(source: FeatureService, start: datetime, end: datetime, reference: ValidationReference, throw_exception: bool = True, cache_profile: bool = True) → ValidationFailed | None[source]

Load logged features from an offline store and validate them against provided validation reference.

Parameters:

source – Logs source object (currently only feature services are supported)
start – lower bound for loading logged features
end – upper bound for loading logged features
reference – validation reference
throw_exception – throw exception or return it as a result
cache_profile – store cached profile in Feast registry

Returns:

Throw or return (depends on parameter) ValidationFailed exception if validation was not successful or None if successful.

version() → str[source]: Returns the version of the current Feast SDK/CLI.

write_logged_features(logs: Table | Path, source: FeatureService)[source]

Write logs produced by a source (currently only feature service is supported as a source) to an offline store.

Parameters:

logs – Arrow Table or path to parquet dataset directory on disk
source – Object that produces logs

write_to_offline_store(feature_view_name: str, df: DataFrame, allow_registry_cache: bool = True, reorder_columns: bool = True)[source]

Persists the dataframe directly into the batch data source for the given feature view.

Fails if the dataframe columns do not match the columns of the batch data source. Optionally reorders the columns of the dataframe to match.

write_to_online_store(feature_view_name: str, df: DataFrame, allow_registry_cache: bool = True)[source]

Persists a dataframe to the online store.

Parameters:

feature_view_name – The feature view to which the dataframe corresponds.
df – The dataframe to be persisted.
allow_registry_cache (optional) – Whether to allow retrieving feature views from a cached registry.

feast.feature_store.apply_list_mapping(lst: Iterable[Any], mapping_indexes: Iterable[List[int]]) → Iterable[Any][source]

feast.feature_view module

class feast.feature_view.FeatureView(*, name: str, source: DataSource, schema: List[Field] | None = None, entities: List[Entity] = None, ttl: timedelta | None = datetime.timedelta(0), online: bool = True, description: str = '', tags: Dict[str, str] | None = None, owner: str = '')[source]

Bases: BaseFeatureView

A FeatureView defines a logical group of features.

name

The unique name of the feature view.

Type:: str

entities

The list of names of entities that this feature view is associated with.

Type:: List[str]

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.

Type:: datetime.timedelta | None

batch_source

The batch source of data where this group of features is stored. This is optional ONLY if a push source is specified as the stream_source, since push sources contain their own batch sources.

Type:: feast.data_source.DataSource

stream_source

The stream source of data where this group of features is stored.

Type:: feast.data_source.DataSource | None

schema: The schema of the feature view, including feature, timestamp, and entity columns. If not specified, can be inferred from the underlying data source.

entity_columns

The list of entity columns contained in the schema. If not specified, can be inferred from the underlying data source.

Type:: List[feast.field.Field]

features

The list of feature columns contained in the schema. If not specified, can be inferred from the underlying data source.

Type:: List[feast.field.Field]

online

A boolean indicating whether online retrieval is enabled for this feature view.

Type:: bool

description

A human-readable description.

Type:: str

tags

A dictionary of key-value pairs to store arbitrary metadata.

Type:: Dict[str, str]

owner

The owner of the feature view, typically the email of the primary maintainer.

Type:: str

batch_source: DataSource

description: str

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]

entity_columns: List[Field]

features: List[Field]

classmethod from_proto(feature_view_proto: 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.

get_ttl_duration()[source]

property join_keys: List[str]: Returns a list of all the join keys.

materialization_intervals: List[Tuple[datetime, datetime]]

property most_recent_end_time: datetime | None

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.

name: str

online: bool

owner: str

property proto_class: Type[FeatureView]

property schema: List[Field]

stream_source: DataSource | None

tags: Dict[str, str]

to_proto() → FeatureView[source]

Converts a feature view object to its protobuf representation.

Returns:: A FeatureViewProto protobuf.

to_proto_meta()[source]

ttl: timedelta | None

with_join_key_map(join_key_map: Dict[str, str])[source]

Returns a copy of this feature view with the join key map set to the given map. This join_key mapping operation is only used as part of query operations and will not modify the underlying FeatureView.

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.

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”}

),

],

)

feast.feature_view_projection module

class feast.feature_view_projection.FeatureViewProjection(name: str, name_alias: str | None, desired_features: List[str], features: List[Field], join_key_map: Dict[str, str] = {})[source]

Bases: object

A feature view projection represents a selection of one or more features from a single feature view.

name

The unique name of the feature view from which this projection is created.

Type:: str

name_alias

An optional alias for the name.

Type:: str | None

features

The list of features represented by the feature view projection.

Type:: List[feast.field.Field]

desired_features

The list of features that this feature view projection intends to select. If empty, the projection intends to select all features. This attribute is only used for feature service inference. It should only be set if the underlying feature view is not ready to be projected, i.e. still needs to go through feature inference.

Type:: List[str]

join_key_map

A map to modify join key columns during retrieval of this feature view projection.

Type:: Dict[str, str]

desired_features: List[str]

features: List[Field]

static from_definition(base_feature_view: BaseFeatureView)[source]

static from_proto(proto: FeatureViewProjection)[source]

get_feature(feature_name: str) → Field[source]

join_key_map: Dict[str, str]

name: str

name_alias: str | None

name_to_use()[source]

to_proto() → FeatureViewProjection[source]

feast.field module

class feast.field.Field(*, name: str, dtype: ComplexFeastType | PrimitiveFeastType, description: str = '', tags: Dict[str, str] | None = None)[source]

Bases: object

A Field represents a set of values with the same structure.

name

The name of the field.

Type:: str

dtype

The type of the field, such as string or float.

Type:: feast.types.ComplexFeastType | feast.types.PrimitiveFeastType

description

A human-readable description.

Type:: str

tags

User-defined metadata in dictionary form.

Type:: Dict[str, str]

description: str

dtype: ComplexFeastType | PrimitiveFeastType

classmethod from_feature(feature: Feature)[source]

Creates a Field object from a Feature object.

Parameters:: feature – Feature object to convert.

classmethod from_proto(field_proto: FeatureSpecV2)[source]

Creates a Field object from a protobuf representation.

Parameters:: field_proto – FieldProto protobuf object

name: str

tags: Dict[str, str]

to_proto() → FeatureSpecV2[source]: Converts a Field object to its protobuf representation.

feast.file_utils module

feast.file_utils.remove_lines_from_file(file_path, match_str, partial=True)[source]

Edit an ascii file (in-place) by removing all lines that match a given string (partially or totally). Does not return anything, side-effect only. Inputs are:

file_path, a string with the path to the ascii file to edit match_str, the string to look for in the file lines partial, a boolean: if True, any line with match_str as substring

will be removed; if False, only lines matching it entirely.

NOTE: not suitable for very large files (it does all in-memory).

feast.file_utils.replace_str_in_file(file_path, match_str, sub_str)[source]

Replace a string, in-place, in a text file, throughout. Does not return anything, side-effect only. Inputs are:

file_path, a string with the path to the ascii file to edit match_str, the substring to be replaced (as many times as it’s found) sub_str, the string to insert in place of match_str

NOTE: not suitable for very large files (it does all in-memory).

feast.file_utils.write_setting_or_remove(file_path, setting_value, setting_name, setting_placeholder_value)[source]

Utility to adapt a settings-file template to some provided values. Assumes the file has lines such as

” username: c_username”

(quotes excluded) where the placeholder might be replaced with actual value or the line might not be needed altogether. Then, calling

write_settings_or_remove(file_path, new_username, ‘username’, ‘c_username’)

the file is edited in-place in one of two ways:

if new_username is None, the line disappears completely
if e.g. new_username == ‘jenny’, the line becomes
” username: jenny”

This utility is called repeatedly (a bit inefficiently, admittedly) to refine the template feature-store yaml config to suit the parameters supplied during a “feast init” feature store setup.

feast.flags_helper module

feast.flags_helper.is_test() → bool[source]

feast.importer module

feast.importer.import_class(module_name: str, class_name: str, class_type: str | None = None)[source]

Dynamically loads and returns a class from a module.

Parameters:

module_name – The name of the module.
class_name – The name of the class.
class_type – Optional name of a base class of the class.

Raises:

FeastInvalidBaseClass – If the class name does not end with the specified suffix.
FeastModuleImportError – If the module cannot be imported.
FeastClassImportError – If the class cannot be imported.

feast.inference module

feast.inference.update_data_sources_with_inferred_event_timestamp_col(data_sources: List[DataSource], config: RepoConfig) → None[source]

feast.inference.update_feature_views_with_inferred_features_and_entities(fvs: List[FeatureView] | List[StreamFeatureView], entities: List[Entity], config: RepoConfig) → None[source]

Infers the features and entities associated with each feature view and updates it in place.

Columns whose names match a join key of an entity are considered to be entity columns; all other columns except designated timestamp columns are considered to be feature columns. If the feature view already has features, feature inference is skipped.

Note that this inference logic currently does not take any transformations (either a UDF or aggregations) into account. For example, even if a stream feature view has a transformation, this method assumes that the batch source contains transformed data with the correct final schema.

Parameters:

fvs – The feature views to be updated.
entities – A list containing entities associated with the feature views.
config – The config for the current feature store.

feast.names module

feast.on_demand_feature_view module

class feast.on_demand_feature_view.OnDemandFeatureView(*, name: str, schema: List[Field], sources: List[FeatureView | RequestSource | FeatureViewProjection], udf: function, udf_string: str = '', description: str = '', tags: Dict[str, str] | None = None, owner: str = '')[source]

Bases: BaseFeatureView

[Experimental] An OnDemandFeatureView defines a logical group of features that are generated by applying a transformation on a set of input sources, such as feature views and request data sources.

name

The unique name of the on demand feature view.

Type:: str

features

The list of features in the output of the on demand feature view.

Type:: List[feast.field.Field]

source_feature_view_projections

A map from input source names to actual input sources with type FeatureViewProjection.

Type:: Dict[str, feast.feature_view_projection.FeatureViewProjection]

source_request_sources

A map from input source names to the actual input sources with type RequestSource.

Type:: Dict[str, feast.data_source.RequestSource]

udf

The user defined transformation function, which must take pandas dataframes as inputs.

Type:: function

description

A human-readable description.

Type:: str

tags

A dictionary of key-value pairs to store arbitrary metadata.

Type:: Dict[str, str]

owner

The owner of the on demand feature view, typically the email of the primary maintainer.

Type:: str

description: str

features: List[Field]

classmethod from_proto(on_demand_feature_view_proto: 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, ValueType][source]

static get_requested_odfvs(feature_refs, project, registry)[source]

get_transformed_features_df(df_with_features: DataFrame, full_feature_names: bool = False) → 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.

name: str

owner: str

property proto_class: Type[OnDemandFeatureView]

source_feature_view_projections: Dict[str, FeatureViewProjection]

source_request_sources: Dict[str, RequestSource]

tags: Dict[str, str]

to_proto() → OnDemandFeatureView[source]

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

Returns:: A OnDemandFeatureViewProto protobuf.

udf: function

udf_string: str

feast.on_demand_feature_view.feature_view_to_batch_feature_view(fv: FeatureView) → BatchFeatureView[source]

feast.on_demand_feature_view.on_demand_feature_view(*, schema: List[Field], sources: List[FeatureView | RequestSource | FeatureViewProjection], description: str = '', tags: Dict[str, str] | None = None, owner: str = '')[source]

Creates an OnDemandFeatureView object with the given user function as udf.

Parameters:

schema – The list of features in the output of the on demand feature view, after the transformation has been applied.
sources – A map from input source names to the actual input sources, which may be feature views, or request data sources. These sources serve as inputs to the udf, which will refer to them by name.
description (optional) – A human-readable description.
tags (optional) – A dictionary of key-value pairs to store arbitrary metadata.
owner (optional) – The owner of the on demand feature view, typically the email of the primary maintainer.

feast.online_response module

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

Bases: object

Defines an online response in feast.

to_df(include_event_timestamps: bool = False) → DataFrame[source]

Converts GetOnlineFeaturesResponse features into Panda dataframe form.

Args: is_with_event_timestamps: bool Optionally include feature timestamps in the dataframe

to_dict(include_event_timestamps: bool = False) → Dict[str, Any][source]

Converts GetOnlineFeaturesResponse features into a dictionary form.

Args: is_with_event_timestamps: bool Optionally include feature timestamps in the dictionary

feast.project_metadata module

class feast.project_metadata.ProjectMetadata(*args, project_name: str | None = None, project_uuid: str | None = None)[source]

Bases: object

Tracks project level metadata

project_name

The registry-scoped unique name of the project.

Type:: str

project_uuid

The UUID for this project

Type:: str

classmethod from_proto(project_metadata_proto: ProjectMetadata)[source]

Creates project metadata from a protobuf representation.

Parameters:: project_metadata_proto – A protobuf representation of project metadata.
Returns:: A ProjectMetadata object based on the protobuf.

project_name: str

project_uuid: str

to_proto() → ProjectMetadata[source]

Converts a project metadata object to its protobuf representation.

Returns:: An ProjectMetadataProto protobuf.

feast.proto_json module

feast.proto_json.patch()[source]: Patch Protobuf JSON Encoder / Decoder with all desired Feast types.

feast.repo_config module

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

Bases: BaseModel

Feast Pydantic Configuration Class

class Config[source]

Bases: object

arbitrary_types_allowed = True

extra = 'allow'

class feast.repo_config.FeastConfigBaseModel[source]

Bases: 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_type: StrictStr = 'file', registry_store_type: StrictStr | None = None, path: StrictStr = '', cache_ttl_seconds: StrictInt = 600, s3_additional_kwargs: Dict[str, str] | None = None, **extra_data: Any)[source]

Bases: FeastBaseModel

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

cache_ttl_seconds: 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: StrictStr

Path to metadata store. If registry_type is ‘file’, then an be a local path, or remote object storage path, e.g. a GCS URI If registry_type is ‘sql’, then this is a database URL as expected by SQLAlchemy

Type:: str

registry_store_type: StrictStr | None

Provider name or a class name that implements RegistryStore.

Type:: str

registry_type: StrictStr

Provider name or a class name that implements Registry.

Type:: str

s3_additional_kwargs: Dict[str, str] | None

Extra arguments to pass to boto3 when writing the registry file to S3.

Type:: Dict[str, str]

class feast.repo_config.RepoConfig(*, project: StrictStr, provider: StrictStr, feature_server: Any | None = None, flags: Any = None, repo_path: Path | None = None, entity_key_serialization_version: StrictInt = 1, coerce_tz_aware: bool | None = True, **data: Any)[source]

Bases: FeastBaseModel

Repo config. Typically loaded from feature_store.yaml

class Config[source]

Bases: object

allow_population_by_field_name = True

property batch_engine

coerce_tz_aware: bool | None: If True, coerces entity_df timestamp columns to be timezone aware (to UTC by default).

entity_key_serialization_version: StrictInt

This version is used to control what serialization scheme is used when writing data to the online store. A value <= 1 uses the serialization scheme used by feast up to Feast 0.22. A value of 2 uses a newer serialization scheme, supported as of Feast 0.23. The main difference between the two scheme is that the serialization scheme v1 stored long values as `int`s, which would result in errors trying to serialize a range of values. v2 fixes this error, but v1 is kept around to ensure backwards compatibility - specifically the ability to read feature values for entities that have already been written into the online store.

Type:: Entity key serialization version

feature_server: Any | None

Feature server configuration (optional depending on provider)

Type:: FeatureServerConfig

flags: Any

Feature flags for experimental features

Type:: Flags (deprecated field)

property offline_store

property online_store

project: 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: StrictStr

local or gcp or aws

Type:: str

property registry

repo_path: Path | None

write_to_path(repo_path: Path)[source]

feast.repo_config.get_batch_engine_config_from_type(batch_engine_type: str)[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.get_registry_config_from_type(registry_type: str)[source]

feast.repo_config.load_repo_config(repo_path: Path, fs_yaml_file: Path) → RepoConfig[source]

feast.repo_contents module

class feast.repo_contents.RepoContents(data_sources: List[DataSource], feature_views: List[FeatureView], on_demand_feature_views: List[OnDemandFeatureView], request_feature_views: List[RequestFeatureView], stream_feature_views: List[StreamFeatureView], entities: List[Entity], feature_services: List[FeatureService])[source]

Bases: tuple

Represents the objects in a Feast feature repo.

data_sources: List[DataSource]: Alias for field number 0

entities: List[Entity]: Alias for field number 5

feature_services: List[FeatureService]: Alias for field number 6

feature_views: List[FeatureView]: Alias for field number 1

on_demand_feature_views: List[OnDemandFeatureView]: Alias for field number 2

request_feature_views: List[RequestFeatureView]: Alias for field number 3

stream_feature_views: List[StreamFeatureView]: Alias for field number 4

to_registry_proto() → Registry[source]

feast.repo_operations module

feast.repo_operations.apply_total(repo_config: RepoConfig, repo_path: Path, skip_source_validation: bool)[source]

feast.repo_operations.apply_total_with_repo_instance(store: FeatureStore, project: str, registry: Registry, repo: RepoContents, skip_source_validation: bool)[source]

feast.repo_operations.cli_check_repo(repo_path: Path, fs_yaml_file: Path)[source]

feast.repo_operations.create_feature_store(ctx: Context) → FeatureStore[source]

feast.repo_operations.extract_objects_for_apply_delete(project, registry, repo)[source]

feast.repo_operations.generate_project_name() → str[source]: Generates a unique project name

feast.repo_operations.get_ignore_files(repo_root: Path, ignore_paths: List[str]) → Set[Path][source]: Get all ignore files that match any of the user-defined ignore paths

feast.repo_operations.get_repo_files(repo_root: Path) → List[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.log_infra_changes(views_to_keep: Set[FeatureView], views_to_delete: Set[FeatureView])[source]

feast.repo_operations.parse_repo(repo_root: Path) → RepoContents[source]

Collects unique Feast object definitions from the given feature repo.

Specifically, if an object foo has already been added, bar will still be added if (bar == foo), but not if (bar is foo). This ensures that import statements will not result in duplicates, but defining two equal objects will.

feast.repo_operations.plan(repo_config: RepoConfig, repo_path: Path, skip_source_validation: bool)[source]

feast.repo_operations.py_path_to_module(path: Path) → str[source]

feast.repo_operations.read_feastignore(repo_root: 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: RepoConfig, repo_path: Path) → str[source]: For debugging only: output contents of the metadata registry

feast.repo_operations.teardown(repo_config: RepoConfig, repo_path: Path)[source]

feast.repo_upgrade module

class feast.repo_upgrade.RepoUpgrader(repo_path: str, write: bool)[source]

Bases: object

static import_remover(class_name)[source]

static remove_argument_transform(node: Node, argument: str)[source]

Removes the specified argument. For example, if the argument is “join_key”, this method transforms driver = Entity(

name=”driver_id”, join_key=”driver_id”,

) into driver = Entity(

name=”driver_id”,

) This method assumes that node represents a class call that already has an arglist.

remove_date_partition_column()[source]

static rename_arguments_in_children(children: List[Node], renames: Dict[str, str]) → None[source]: Renames the arguments in the children list of a node by searching for the argument list or trailing list and renaming all keys in renames dict to corresponding value.

static rename_class_call(node: Node, new_class_name: str)[source]

Rename the class being instantiated. f = Feature(

name=”driver_id”, join_key=”driver_id”,

) into f = Field(

name=”driver_id”,

) This method assumes that node represents a class call that already has an arglist.

rename_features_to_schema()[source]

rename_inputs_to_sources()[source]

upgrade()[source]

feast.request_feature_view module

class feast.request_feature_view.RequestFeatureView(name: str, request_data_source: RequestSource, description: str = '', tags: Dict[str, str] | None = None, owner: str = '')[source]

Bases: BaseFeatureView

[Experimental] A RequestFeatureView defines a logical group of features that should be available as an input to an on demand feature view at request time.

name

The unique name of the request feature view.

Type:: str

request_source

The request source that specifies the schema and features of the request feature view.

Type:: feast.data_source.RequestSource

features

The list of features defined as part of this request feature view.

Type:: List[feast.field.Field]

description

A human-readable description.

Type:: str

tags

A dictionary of key-value pairs to store arbitrary metadata.

Type:: Dict[str, str]

owner

The owner of the request feature view, typically the email of the primary maintainer.

Type:: str

description: str

features: List[Field]

classmethod from_proto(request_feature_view_proto: RequestFeatureView)[source]

Creates a request feature view from a protobuf representation.

Parameters:: request_feature_view_proto – A protobuf representation of an request feature view.
Returns:: A RequestFeatureView object based on the request feature view protobuf.

name: str

owner: str

property proto_class: Type[RequestFeatureView]

request_source: RequestSource

tags: Dict[str, str]

to_proto() → RequestFeatureView[source]

Converts an request feature view object to its protobuf representation.

Returns:: A RequestFeatureViewProto protobuf.

feast.saved_dataset module

class feast.saved_dataset.SavedDataset(name: str, features: List[str], join_keys: List[str], storage: SavedDatasetStorage, full_feature_names: bool = False, tags: Dict[str, str] | None = None, feature_service_name: str | None = None)[source]

Bases: object

as_reference(name: str, profiler: Profiler) → ValidationReference[source]

created_timestamp: datetime | None = None

feature_service_name: str | None = None

features: List[str]

static from_proto(saved_dataset_proto: SavedDataset)[source]

Converts a SavedDatasetProto to a SavedDataset object.

Parameters:: saved_dataset_proto – A protobuf representation of a SavedDataset.

full_feature_names: bool

get_profile(profiler: Profiler) → Profile[source]

join_keys: List[str]

last_updated_timestamp: datetime | None = None

max_event_timestamp: datetime | None = None

min_event_timestamp: datetime | None = None

name: str

storage: SavedDatasetStorage

tags: Dict[str, str]

to_arrow() → Table[source]

to_df() → DataFrame[source]

to_proto() → SavedDataset[source]

Converts a SavedDataset to its protobuf representation.

Returns:: A SavedDatasetProto protobuf.

with_retrieval_job(retrieval_job: RetrievalJob) → SavedDataset[source]

class feast.saved_dataset.SavedDatasetStorage[source]

Bases: object

static from_data_source(data_source: DataSource) → SavedDatasetStorage[source]

static from_proto(storage_proto: SavedDatasetStorage) → SavedDatasetStorage[source]

abstract to_data_source() → DataSource[source]

abstract to_proto() → SavedDatasetStorage[source]

class feast.saved_dataset.ValidationReference(name: str, dataset_name: str, profiler: Profiler, description: str = '', tags: Dict[str, str] | None = None)[source]

Bases: object

dataset_name: str

description: str

classmethod from_proto(proto: ValidationReference) → ValidationReference[source]

classmethod from_saved_dataset(name: str, dataset: SavedDataset, profiler: Profiler)[source]: Internal constructor to create validation reference object with actual saved dataset object (regular constructor requires only its name).

name: str

property profile: Profile

profiler: Profiler

tags: Dict[str, str]

to_proto() → ValidationReference[source]

feast.saved_dataset.get_saved_dataset_storage_class_from_path(saved_dataset_storage_path: str)[source]

feast.stream_feature_view module

class feast.stream_feature_view.StreamFeatureView(*, name: str, source: DataSource, entities: List[Entity] | List[str] | None = None, ttl: timedelta = datetime.timedelta(0), tags: Dict[str, str] | None = None, online: bool | None = True, description: str | None = '', owner: str