Feast Python API Documentation

Feature Store

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, List[Union[feast.feature_view.FeatureView, feast.on_demand_feature_view.OnDemandFeatureView, feast.request_feature_view.RequestFeatureView, feast.entity.Entity, feast.feature_service.FeatureService]]], commit: 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 registry has been updated, the apply method will update related infrastructure (e.g., create tables in an online store) in order to reflect these new definitions. 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.

  • commit – whether to commit changes to the registry

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(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.

Config

class feast.repo_config.FeastConfigBaseModel[source]

Feast Pydantic Configuration Class

exception feast.repo_config.FeastConfigError(error_message, config_path)[source]
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]

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]

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

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

Data Source

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]

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

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]

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

Entity

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]

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.

Feature View

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]

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.

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.

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.

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

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

Parameters

config – Configuration object used to configure the feature store.

Raises

RegistryInferenceFailure – The set of features could not be inferred.

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.

to_proto() feast.core.FeatureView_pb2.FeatureView[source]

Converts a feature view object to its protobuf representation.

Returns

A FeatureViewProto protobuf.

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.

On Demand Feature View

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

[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.

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.

to_proto() feast.core.OnDemandFeatureView_pb2.OnDemandFeatureView[source]

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

Returns

A OnDemandFeatureViewProto protobuf.

feast.on_demand_feature_view.on_demand_feature_view(features: List[feast.feature.Feature], inputs: Dict[str, feast.feature_view.FeatureView])[source]

Declare an on-demand feature view

Parameters
  • features – Output schema with feature names

  • inputs – The inputs passed into the transform.

Returns

An On Demand Feature View.

Feature

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

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]

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.

Feature Service

class feast.feature_service.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]

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.

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.

to_proto() feast.core.FeatureService_pb2.FeatureService[source]

Converts a FeatureService to its protobuf representation.

Returns

A FeatureServiceProto protobuf.

Registry

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

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

commit()[source]

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

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) feast.feature_view.FeatureView[source]

Retrieves a feature view.

Parameters
  • name – Name of feature view

  • project – Feast project that this feature view belongs to

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.

Provider

class feast.infra.provider.Provider(config: feast.repo_config.RepoConfig)[source]
get_feature_server_endpoint() Optional[str][source]

Returns endpoint for the feature server, if it exists.

abstract online_read(config: feast.repo_config.RepoConfig, table: Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView], entity_keys: List[feast.types.EntityKey_pb2.EntityKey], requested_features: Optional[List[str]] = None) List[Tuple[Optional[datetime.datetime], Optional[Dict[str, feast.types.Value_pb2.Value]]]][source]

Read feature values given an Entity Key. This is a low level interface, not expected to be used by the users directly.

Returns

Data is returned as a list, one item per entity key. Each item in the list is a tuple of event_ts for the row, and the feature data as a dict from feature names to values. Values are returned as Value proto message.

abstract online_write_batch(config: feast.repo_config.RepoConfig, table: Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView], data: List[Tuple[feast.types.EntityKey_pb2.EntityKey, Dict[str, feast.types.Value_pb2.Value], datetime.datetime, Optional[datetime.datetime]]], progress: Optional[Callable[[int], Any]]) None[source]

Write a batch of feature rows to the online store. This is a low level interface, not expected to be used by the users directly.

If a tz-naive timestamp is passed to this method, it is assumed to be UTC.

Parameters
  • config – The RepoConfig for the current FeatureStore.

  • table – Feast FeatureTable

  • data – a list of quadruplets containing Feature data. Each quadruplet contains an Entity Key, a dict containing feature values, an event timestamp for the row, and the created timestamp for the row if it exists.

  • progress – Optional function to be called once every mini-batch of rows is written to the online store. Can be used to display progress.

abstract teardown_infra(project: str, tables: Sequence[Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView]], entities: Sequence[feast.entity.Entity])[source]

Tear down all cloud resources for a repo.

Parameters
  • project – Feast project to which tables belong

  • tables – Tables that are declared in the feature repo.

  • entities – Entities that are declared in the feature repo.

abstract update_infra(project: str, tables_to_delete: Sequence[Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView]], tables_to_keep: Sequence[Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView]], entities_to_delete: Sequence[feast.entity.Entity], entities_to_keep: Sequence[feast.entity.Entity], partial: bool)[source]

Reconcile cloud resources with the objects declared in the feature repo.

Parameters
  • project – Project to which tables belong

  • tables_to_delete – Tables that were deleted from the feature repo, so provider needs to clean up the corresponding cloud resources.

  • tables_to_keep – Tables that are still in the feature repo. Depending on implementation, provider may or may not need to update the corresponding resources.

  • entities_to_delete – Entities that were deleted from the feature repo, so provider needs to clean up the corresponding cloud resources.

  • entities_to_keep – Entities that are still in the feature repo. Depending on implementation, provider may or may not need to update the corresponding resources.

  • partial – if true, then tables_to_delete and tables_to_keep are not exhaustive lists. There may be other tables that are not touched by this update.

Passthrough Provider

class feast.infra.passthrough_provider.PassthroughProvider(config: feast.repo_config.RepoConfig)[source]

The Passthrough provider delegates all operations to the underlying online and offline stores.

online_read(config: feast.repo_config.RepoConfig, table: Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView], entity_keys: List[feast.types.EntityKey_pb2.EntityKey], requested_features: Optional[List[str]] = None) List[Tuple[Optional[datetime.datetime], Optional[Dict[str, feast.types.Value_pb2.Value]]]][source]

Read feature values given an Entity Key. This is a low level interface, not expected to be used by the users directly.

Returns

Data is returned as a list, one item per entity key. Each item in the list is a tuple of event_ts for the row, and the feature data as a dict from feature names to values. Values are returned as Value proto message.

online_write_batch(config: feast.repo_config.RepoConfig, table: Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView], data: List[Tuple[feast.types.EntityKey_pb2.EntityKey, Dict[str, feast.types.Value_pb2.Value], datetime.datetime, Optional[datetime.datetime]]], progress: Optional[Callable[[int], Any]]) None[source]

Write a batch of feature rows to the online store. This is a low level interface, not expected to be used by the users directly.

If a tz-naive timestamp is passed to this method, it is assumed to be UTC.

Parameters
  • config – The RepoConfig for the current FeatureStore.

  • table – Feast FeatureTable

  • data – a list of quadruplets containing Feature data. Each quadruplet contains an Entity Key, a dict containing feature values, an event timestamp for the row, and the created timestamp for the row if it exists.

  • progress – Optional function to be called once every mini-batch of rows is written to the online store. Can be used to display progress.

teardown_infra(project: str, tables: Sequence[Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView]], entities: Sequence[feast.entity.Entity]) None[source]

Tear down all cloud resources for a repo.

Parameters
  • project – Feast project to which tables belong

  • tables – Tables that are declared in the feature repo.

  • entities – Entities that are declared in the feature repo.

update_infra(project: str, tables_to_delete: Sequence[Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView]], tables_to_keep: Sequence[Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView]], entities_to_delete: Sequence[feast.entity.Entity], entities_to_keep: Sequence[feast.entity.Entity], partial: bool)[source]

Reconcile cloud resources with the objects declared in the feature repo.

Parameters
  • project – Project to which tables belong

  • tables_to_delete – Tables that were deleted from the feature repo, so provider needs to clean up the corresponding cloud resources.

  • tables_to_keep – Tables that are still in the feature repo. Depending on implementation, provider may or may not need to update the corresponding resources.

  • entities_to_delete – Entities that were deleted from the feature repo, so provider needs to clean up the corresponding cloud resources.

  • entities_to_keep – Entities that are still in the feature repo. Depending on implementation, provider may or may not need to update the corresponding resources.

  • partial – if true, then tables_to_delete and tables_to_keep are not exhaustive lists. There may be other tables that are not touched by this update.

Local Provider

class feast.infra.local.LocalProvider(config: feast.repo_config.RepoConfig)[source]

This class only exists for backwards compatibility.

GCP Provider

class feast.infra.gcp.GcpProvider(config: feast.repo_config.RepoConfig)[source]

This class only exists for backwards compatibility.

AWS Provider

class feast.infra.aws.AwsProvider(config: feast.repo_config.RepoConfig)[source]
get_feature_server_endpoint() Optional[str][source]

Returns endpoint for the feature server, if it exists.

teardown_infra(project: str, tables: Sequence[Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView]], entities: Sequence[feast.entity.Entity]) None[source]

Tear down all cloud resources for a repo.

Parameters
  • project – Feast project to which tables belong

  • tables – Tables that are declared in the feature repo.

  • entities – Entities that are declared in the feature repo.

update_infra(project: str, tables_to_delete: Sequence[Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView]], tables_to_keep: Sequence[Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView]], entities_to_delete: Sequence[feast.entity.Entity], entities_to_keep: Sequence[feast.entity.Entity], partial: bool)[source]

Reconcile cloud resources with the objects declared in the feature repo.

Parameters
  • project – Project to which tables belong

  • tables_to_delete – Tables that were deleted from the feature repo, so provider needs to clean up the corresponding cloud resources.

  • tables_to_keep – Tables that are still in the feature repo. Depending on implementation, provider may or may not need to update the corresponding resources.

  • entities_to_delete – Entities that were deleted from the feature repo, so provider needs to clean up the corresponding cloud resources.

  • entities_to_keep – Entities that are still in the feature repo. Depending on implementation, provider may or may not need to update the corresponding resources.

  • partial – if true, then tables_to_delete and tables_to_keep are not exhaustive lists. There may be other tables that are not touched by this update.

Offline Store

class feast.infra.offline_stores.offline_store.OfflineStore[source]

OfflineStore is an object used for all interaction between Feast and the service used for offline storage of features.

abstract static pull_latest_from_table_or_query(config: feast.repo_config.RepoConfig, data_source: feast.data_source.DataSource, join_key_columns: List[str], feature_name_columns: List[str], event_timestamp_column: str, created_timestamp_column: Optional[str], start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob[source]

Note that join_key_columns, feature_name_columns, event_timestamp_column, and created_timestamp_column have all already been mapped to column names of the source table and those column names are the values passed into this function.

class feast.infra.offline_stores.offline_store.RetrievalJob[source]

RetrievalJob is used to manage the execution of a historical feature retrieval

to_arrow() pyarrow.lib.Table[source]

Return dataset as pyarrow Table synchronously

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

Return dataset as Pandas DataFrame synchronously including on demand transforms

File Offline Store

class feast.infra.offline_stores.file.FileOfflineStore[source]
static pull_latest_from_table_or_query(config: feast.repo_config.RepoConfig, data_source: feast.data_source.DataSource, join_key_columns: List[str], feature_name_columns: List[str], event_timestamp_column: str, created_timestamp_column: Optional[str], start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob[source]

Note that join_key_columns, feature_name_columns, event_timestamp_column, and created_timestamp_column have all already been mapped to column names of the source table and those column names are the values passed into this function.

class feast.infra.offline_stores.file.FileOfflineStoreConfig(*, type: typing_extensions.Literal[file] = 'file')[source]

Offline store config for local (file-based) store

type: typing_extensions.Literal[file]

Offline store type selector

class feast.infra.offline_stores.file.FileRetrievalJob(evaluation_function: Callable, full_feature_names: bool, on_demand_feature_views: Optional[List[feast.on_demand_feature_view.OnDemandFeatureView]])[source]

BigQuery Offline Store

class feast.infra.offline_stores.bigquery.BigQueryOfflineStore[source]
static pull_latest_from_table_or_query(config: feast.repo_config.RepoConfig, data_source: feast.data_source.DataSource, join_key_columns: List[str], feature_name_columns: List[str], event_timestamp_column: str, created_timestamp_column: Optional[str], start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob[source]

Note that join_key_columns, feature_name_columns, event_timestamp_column, and created_timestamp_column have all already been mapped to column names of the source table and those column names are the values passed into this function.

class feast.infra.offline_stores.bigquery.BigQueryOfflineStoreConfig(*, type: typing_extensions.Literal[bigquery] = 'bigquery', dataset: pydantic.types.StrictStr = 'feast', project_id: pydantic.types.StrictStr = None, location: pydantic.types.StrictStr = None)[source]

Offline store config for GCP BigQuery

dataset: pydantic.types.StrictStr

(optional) BigQuery Dataset name for temporary tables

location: Optional[pydantic.types.StrictStr]

(optional) GCP location name used for the BigQuery offline store. Examples of location names include US, EU, us-central1, us-west4. If a location is not specified, the location defaults to the US multi-regional location. For more information on BigQuery data locations see: https://cloud.google.com/bigquery/docs/locations

project_id: Optional[pydantic.types.StrictStr]

(optional) GCP project name used for the BigQuery offline store

type: typing_extensions.Literal[bigquery]

Offline store type selector

class feast.infra.offline_stores.bigquery.BigQueryRetrievalJob(query: Union[str, Callable[[], AbstractContextManager[str]]], client: google.cloud.bigquery.client.Client, config: feast.repo_config.RepoConfig, full_feature_names: bool, on_demand_feature_views: Optional[List[feast.on_demand_feature_view.OnDemandFeatureView]])[source]
to_bigquery(job_config: Optional[google.cloud.bigquery.job.query.QueryJobConfig] = None, timeout: int = 1800, retry_cadence: int = 10) Optional[str][source]

Triggers the execution of a historical feature retrieval query and exports the results to a BigQuery table. Runs for a maximum amount of time specified by the timeout parameter (defaulting to 30 minutes).

Parameters
  • job_config – An optional bigquery.QueryJobConfig to specify options like destination table, dry run, etc.

  • timeout – An optional number of seconds for setting the time limit of the QueryJob.

  • retry_cadence – An optional number of seconds for setting how long the job should checked for completion.

Returns

Returns the destination table name or returns None if job_config.dry_run is True.

to_sql() str[source]

Returns the SQL query that will be executed in BigQuery to build the historical feature table.

feast.infra.offline_stores.bigquery.block_until_done(client: google.cloud.bigquery.client.Client, bq_job: Union[google.cloud.bigquery.job.query.QueryJob, google.cloud.bigquery.job.load.LoadJob], timeout: int = 1800, retry_cadence: float = 1)[source]

Waits for bq_job to finish running, up to a maximum amount of time specified by the timeout parameter (defaulting to 30 minutes).

Parameters
  • client – A bigquery.client.Client to monitor the bq_job.

  • bq_job – The bigquery.job.QueryJob that blocks until done runnning.

  • timeout – An optional number of seconds for setting the time limit of the job.

  • retry_cadence – An optional number of seconds for setting how long the job should checked for completion.

Raises
  • BigQueryJobStillRunning exception if the function has blocked longer than 30 minutes.

  • BigQueryJobCancelled exception to signify when that the job has been cancelled (i.e. from timeout or KeyboardInterrupt)

Redshift Offline Store

class feast.infra.offline_stores.redshift.RedshiftOfflineStore[source]
static pull_latest_from_table_or_query(config: feast.repo_config.RepoConfig, data_source: feast.data_source.DataSource, join_key_columns: List[str], feature_name_columns: List[str], event_timestamp_column: str, created_timestamp_column: Optional[str], start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob[source]

Note that join_key_columns, feature_name_columns, event_timestamp_column, and created_timestamp_column have all already been mapped to column names of the source table and those column names are the values passed into this function.

class feast.infra.offline_stores.redshift.RedshiftOfflineStoreConfig(*, type: typing_extensions.Literal[redshift] = 'redshift', cluster_id: pydantic.types.StrictStr, region: pydantic.types.StrictStr, user: pydantic.types.StrictStr, database: pydantic.types.StrictStr, s3_staging_location: pydantic.types.StrictStr, iam_role: pydantic.types.StrictStr)[source]

Offline store config for AWS Redshift

cluster_id: pydantic.types.StrictStr

Redshift cluster identifier

database: pydantic.types.StrictStr

Redshift database name

iam_role: pydantic.types.StrictStr

IAM Role for Redshift, granting it access to S3

region: pydantic.types.StrictStr

Redshift cluster’s AWS region

s3_staging_location: pydantic.types.StrictStr

S3 path for importing & exporting data to Redshift

type: typing_extensions.Literal[redshift]

Offline store type selector

user: pydantic.types.StrictStr

Redshift user name

class feast.infra.offline_stores.redshift.RedshiftRetrievalJob(query: Union[str, Callable[[], AbstractContextManager[str]]], redshift_client, s3_resource, config: feast.repo_config.RepoConfig, full_feature_names: bool, on_demand_feature_views: Optional[List[feast.on_demand_feature_view.OnDemandFeatureView]])[source]
to_redshift(table_name: str) None[source]

Save dataset as a new Redshift table

to_s3() str[source]

Export dataset to S3 in Parquet format and return path

Online Store

class feast.infra.online_stores.online_store.OnlineStore[source]

OnlineStore is an object used for all interaction between Feast and the service used for online storage of features.

abstract online_read(config: feast.repo_config.RepoConfig, table: Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView], entity_keys: List[feast.types.EntityKey_pb2.EntityKey], requested_features: Optional[List[str]] = None) List[Tuple[Optional[datetime.datetime], Optional[Dict[str, feast.types.Value_pb2.Value]]]][source]

Read feature values given an Entity Key. This is a low level interface, not expected to be used by the users directly.

Parameters
  • config – The RepoConfig for the current FeatureStore.

  • table – Feast FeatureTable or FeatureView

  • entity_keys – a list of entity keys that should be read from the FeatureStore.

  • requested_features – (Optional) A subset of the features that should be read from the FeatureStore.

Returns

Data is returned as a list, one item per entity key. Each item in the list is a tuple of event_ts for the row, and the feature data as a dict from feature names to values. Values are returned as Value proto message.

abstract online_write_batch(config: feast.repo_config.RepoConfig, table: Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView], data: List[Tuple[feast.types.EntityKey_pb2.EntityKey, Dict[str, feast.types.Value_pb2.Value], datetime.datetime, Optional[datetime.datetime]]], progress: Optional[Callable[[int], Any]]) None[source]

Write a batch of feature rows to the online store. This is a low level interface, not expected to be used by the users directly.

If a tz-naive timestamp is passed to this method, it should be assumed to be UTC by implementors.

Parameters
  • config – The RepoConfig for the current FeatureStore.

  • table – Feast FeatureTable or FeatureView

  • data – a list of quadruplets containing Feature data. Each quadruplet contains an Entity Key,

  • values (a dict containing feature) –

  • row (an event timestamp for the) –

  • and

  • exists. (the created timestamp for the row if it) –

  • progress – Optional function to be called once every mini-batch of rows is written to

  • progress. (the online store. Can be used to display) –

Sqlite Online Store

class feast.infra.online_stores.sqlite.SqliteOnlineStore[source]

OnlineStore is an object used for all interaction between Feast and the service used for offline storage of features.

online_read(config: feast.repo_config.RepoConfig, table: Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView], entity_keys: List[feast.types.EntityKey_pb2.EntityKey], requested_features: Optional[List[str]] = None) List[Tuple[Optional[datetime.datetime], Optional[Dict[str, feast.types.Value_pb2.Value]]]][source]

Read feature values given an Entity Key. This is a low level interface, not expected to be used by the users directly.

Parameters
  • config – The RepoConfig for the current FeatureStore.

  • table – Feast FeatureTable or FeatureView

  • entity_keys – a list of entity keys that should be read from the FeatureStore.

  • requested_features – (Optional) A subset of the features that should be read from the FeatureStore.

Returns

Data is returned as a list, one item per entity key. Each item in the list is a tuple of event_ts for the row, and the feature data as a dict from feature names to values. Values are returned as Value proto message.

online_write_batch(config: feast.repo_config.RepoConfig, table: Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView], data: List[Tuple[feast.types.EntityKey_pb2.EntityKey, Dict[str, feast.types.Value_pb2.Value], datetime.datetime, Optional[datetime.datetime]]], progress: Optional[Callable[[int], Any]]) None[source]

Write a batch of feature rows to the online store. This is a low level interface, not expected to be used by the users directly.

If a tz-naive timestamp is passed to this method, it should be assumed to be UTC by implementors.

Parameters
  • config – The RepoConfig for the current FeatureStore.

  • table – Feast FeatureTable or FeatureView

  • data – a list of quadruplets containing Feature data. Each quadruplet contains an Entity Key,

  • values (a dict containing feature) –

  • row (an event timestamp for the) –

  • and

  • exists. (the created timestamp for the row if it) –

  • progress – Optional function to be called once every mini-batch of rows is written to

  • progress. (the online store. Can be used to display) –

class feast.infra.online_stores.sqlite.SqliteOnlineStoreConfig(*, type: typing_extensions.Literal[sqlite, feast.infra.online_stores.sqlite.SqliteOnlineStore] = 'sqlite', path: pydantic.types.StrictStr = 'data/online.db')[source]

Online store config for local (SQLite-based) store

path: pydantic.types.StrictStr

(optional) Path to sqlite db

type: typing_extensions.Literal[sqlite, feast.infra.online_stores.sqlite.SqliteOnlineStore]

Online store type selector

Datastore Online Store

class feast.infra.online_stores.datastore.DatastoreOnlineStore[source]

OnlineStore is an object used for all interaction between Feast and the service used for offline storage of features.

online_read(config: feast.repo_config.RepoConfig, table: Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView], entity_keys: List[feast.types.EntityKey_pb2.EntityKey], requested_features: Optional[List[str]] = None) List[Tuple[Optional[datetime.datetime], Optional[Dict[str, feast.types.Value_pb2.Value]]]][source]

Read feature values given an Entity Key. This is a low level interface, not expected to be used by the users directly.

Parameters
  • config – The RepoConfig for the current FeatureStore.

  • table – Feast FeatureTable or FeatureView

  • entity_keys – a list of entity keys that should be read from the FeatureStore.

  • requested_features – (Optional) A subset of the features that should be read from the FeatureStore.

Returns

Data is returned as a list, one item per entity key. Each item in the list is a tuple of event_ts for the row, and the feature data as a dict from feature names to values. Values are returned as Value proto message.

online_write_batch(config: feast.repo_config.RepoConfig, table: Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView], data: List[Tuple[feast.types.EntityKey_pb2.EntityKey, Dict[str, feast.types.Value_pb2.Value], datetime.datetime, Optional[datetime.datetime]]], progress: Optional[Callable[[int], Any]]) None[source]

Write a batch of feature rows to the online store. This is a low level interface, not expected to be used by the users directly.

If a tz-naive timestamp is passed to this method, it should be assumed to be UTC by implementors.

Parameters
  • config – The RepoConfig for the current FeatureStore.

  • table – Feast FeatureTable or FeatureView

  • data – a list of quadruplets containing Feature data. Each quadruplet contains an Entity Key,

  • values (a dict containing feature) –

  • row (an event timestamp for the) –

  • and

  • exists. (the created timestamp for the row if it) –

  • progress – Optional function to be called once every mini-batch of rows is written to

  • progress. (the online store. Can be used to display) –

teardown(config: feast.repo_config.RepoConfig, tables: Sequence[Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView]], entities: Sequence[feast.entity.Entity])[source]

There’s currently no teardown done for Datastore.

update(config: feast.repo_config.RepoConfig, tables_to_delete: Sequence[Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView]], tables_to_keep: Sequence[Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView]], entities_to_delete: Sequence[feast.entity.Entity], entities_to_keep: Sequence[feast.entity.Entity], partial: bool)[source]
class feast.infra.online_stores.datastore.DatastoreOnlineStoreConfig(*, type: typing_extensions.Literal[datastore] = 'datastore', project_id: pydantic.types.StrictStr = None, namespace: pydantic.types.StrictStr = None, write_concurrency: pydantic.types.PositiveInt = 40, write_batch_size: pydantic.types.PositiveInt = 50)[source]

Online store config for GCP Datastore

namespace: Optional[pydantic.types.StrictStr]

(optional) Datastore namespace

project_id: Optional[pydantic.types.StrictStr]

(optional) GCP Project Id

type: typing_extensions.Literal[datastore]

Online store type selector

write_batch_size: Optional[pydantic.types.PositiveInt]

(optional) Amount of feature rows per batch being written into Datastore

write_concurrency: Optional[pydantic.types.PositiveInt]

(optional) Amount of threads to use when writing batches of feature rows into Datastore

DynamoDB Online Store

class feast.infra.online_stores.dynamodb.DynamoDBOnlineStore[source]

Online feature store for AWS DynamoDB.

online_read(config: feast.repo_config.RepoConfig, table: Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView], entity_keys: List[feast.types.EntityKey_pb2.EntityKey], requested_features: Optional[List[str]] = None) List[Tuple[Optional[datetime.datetime], Optional[Dict[str, feast.types.Value_pb2.Value]]]][source]

Read feature values given an Entity Key. This is a low level interface, not expected to be used by the users directly.

Parameters
  • config – The RepoConfig for the current FeatureStore.

  • table – Feast FeatureTable or FeatureView

  • entity_keys – a list of entity keys that should be read from the FeatureStore.

  • requested_features – (Optional) A subset of the features that should be read from the FeatureStore.

Returns

Data is returned as a list, one item per entity key. Each item in the list is a tuple of event_ts for the row, and the feature data as a dict from feature names to values. Values are returned as Value proto message.

online_write_batch(config: feast.repo_config.RepoConfig, table: Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView], data: List[Tuple[feast.types.EntityKey_pb2.EntityKey, Dict[str, feast.types.Value_pb2.Value], datetime.datetime, Optional[datetime.datetime]]], progress: Optional[Callable[[int], Any]]) None[source]

Write a batch of feature rows to the online store. This is a low level interface, not expected to be used by the users directly.

If a tz-naive timestamp is passed to this method, it should be assumed to be UTC by implementors.

Parameters
  • config – The RepoConfig for the current FeatureStore.

  • table – Feast FeatureTable or FeatureView

  • data – a list of quadruplets containing Feature data. Each quadruplet contains an Entity Key,

  • values (a dict containing feature) –

  • row (an event timestamp for the) –

  • and

  • exists. (the created timestamp for the row if it) –

  • progress – Optional function to be called once every mini-batch of rows is written to

  • progress. (the online store. Can be used to display) –

class feast.infra.online_stores.dynamodb.DynamoDBOnlineStoreConfig(*, type: typing_extensions.Literal[dynamodb] = 'dynamodb', region: pydantic.types.StrictStr)[source]

Online store config for DynamoDB store

region: pydantic.types.StrictStr

AWS Region Name

type: typing_extensions.Literal[dynamodb]

Online store type selector

Redis Online Store

class feast.infra.online_stores.redis.RedisOnlineStore[source]
online_read(config: feast.repo_config.RepoConfig, table: Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView], entity_keys: List[feast.types.EntityKey_pb2.EntityKey], requested_features: Optional[List[str]] = None) List[Tuple[Optional[datetime.datetime], Optional[Dict[str, feast.types.Value_pb2.Value]]]][source]

Read feature values given an Entity Key. This is a low level interface, not expected to be used by the users directly.

Parameters
  • config – The RepoConfig for the current FeatureStore.

  • table – Feast FeatureTable or FeatureView

  • entity_keys – a list of entity keys that should be read from the FeatureStore.

  • requested_features – (Optional) A subset of the features that should be read from the FeatureStore.

Returns

Data is returned as a list, one item per entity key. Each item in the list is a tuple of event_ts for the row, and the feature data as a dict from feature names to values. Values are returned as Value proto message.

online_write_batch(config: feast.repo_config.RepoConfig, table: Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView], data: List[Tuple[feast.types.EntityKey_pb2.EntityKey, Dict[str, feast.types.Value_pb2.Value], datetime.datetime, Optional[datetime.datetime]]], progress: Optional[Callable[[int], Any]]) None[source]

Write a batch of feature rows to the online store. This is a low level interface, not expected to be used by the users directly.

If a tz-naive timestamp is passed to this method, it should be assumed to be UTC by implementors.

Parameters
  • config – The RepoConfig for the current FeatureStore.

  • table – Feast FeatureTable or FeatureView

  • data – a list of quadruplets containing Feature data. Each quadruplet contains an Entity Key,

  • values (a dict containing feature) –

  • row (an event timestamp for the) –

  • and

  • exists. (the created timestamp for the row if it) –

  • progress – Optional function to be called once every mini-batch of rows is written to

  • progress. (the online store. Can be used to display) –

teardown(config: feast.repo_config.RepoConfig, tables: Sequence[Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView]], entities: Sequence[feast.entity.Entity])[source]

We delete the keys in redis for tables/views being removed.

update(config: feast.repo_config.RepoConfig, tables_to_delete: Sequence[Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView]], tables_to_keep: Sequence[Union[feast.feature_table.FeatureTable, feast.feature_view.FeatureView]], entities_to_delete: Sequence[feast.entity.Entity], entities_to_keep: Sequence[feast.entity.Entity], partial: bool)[source]

We delete the keys in redis for tables/views being removed.

class feast.infra.online_stores.redis.RedisOnlineStoreConfig(*, type: typing_extensions.Literal[redis] = 'redis', redis_type: feast.infra.online_stores.redis.RedisType = RedisType.redis, connection_string: pydantic.types.StrictStr = 'localhost:6379')[source]

Online store config for Redis store

connection_string: pydantic.types.StrictStr

Connection string containing the host, port, and configuration parameters for Redis format: host:port,parameter1,parameter2 eg. redis:6379,db=0

redis_type: feast.infra.online_stores.redis.RedisType

redis or redis_cluster

Type

Redis type

type: typing_extensions.Literal[redis]

Online store type selector

class feast.infra.online_stores.redis.RedisType(value)[source]

An enumeration.