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 – Path to a feature_store.yaml used to configure the feature store

  • config (RepoConfig) – Configuration object used to configure the feature store

apply(objects: Union[feast.entity.Entity, feast.feature_view.FeatureView, List[Union[feast.feature_view.FeatureView, feast.entity.Entity]]])[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 (List[Union[FeatureView, Entity]]) – A list of FeatureView or Entity objects that should be registered

Examples

Register a single Entity and FeatureView.

>>> from feast.feature_store import FeatureStore
>>> from feast import Entity, FeatureView, Feature, ValueType, FileSource
>>> from datetime import timedelta
>>>
>>> fs = FeatureStore()
>>> customer_entity = Entity(name="customer", value_type=ValueType.INT64, description="customer entity")
>>> customer_feature_view = FeatureView(
>>>     name="customer_fv",
>>>     entities=["customer"],
>>>     features=[Feature(name="age", dtype=ValueType.INT64)],
>>>     input=FileSource(path="file.parquet", event_timestamp_column="timestamp"),
>>>     ttl=timedelta(days=1)
>>> )
>>> fs.apply([customer_entity, customer_feature_view])
config: feast.repo_config.RepoConfig
delete_feature_view(name: str)[source]

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

Parameters

name – Name of feature view

get_entity(name: str) feast.entity.Entity[source]

Retrieves an entity.

Parameters

name – Name of entity

Returns

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

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

Retrieves a feature view.

Parameters

name – Name of feature view

Returns

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

get_historical_features(entity_df: Union[pandas.core.frame.DataFrame, str], feature_refs: List[str]) 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)

  • feature_refs – A list of features that should be retrieved from the offline store. Feature references are of the format “feature_view:feature”, e.g., “customer_fv:daily_transactions”.

Returns

RetrievalJob which can be used to materialize the results.

Examples

Retrieve historical features using a BigQuery SQL entity dataframe

>>> from feast.feature_store import FeatureStore
>>>
>>> fs = FeatureStore(config=RepoConfig(provider="gcp"))
>>> retrieval_job = fs.get_historical_features(
>>>     entity_df="SELECT event_timestamp, order_id, customer_id from gcp_project.my_ds.customer_orders",
>>>     feature_refs=["customer:age", "customer:avg_orders_1d", "customer:avg_orders_7d"]
>>> )
>>> feature_data = retrieval_job.to_df()
>>> model.fit(feature_data) # insert your modeling framework here.
get_online_features(feature_refs: List[str], entity_rows: List[Dict[str, Any]]) 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
  • feature_refs – 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.

Examples

>>> from feast import FeatureStore
>>>
>>> store = FeatureStore(repo_path="...")
>>> feature_refs = ["sales:daily_transactions"]
>>> entity_rows = [{"customer_id": 0},{"customer_id": 1}]
>>>
>>> online_response = store.get_online_features(
>>>     feature_refs, entity_rows)
>>> online_response_dict = online_response.to_dict()
>>> print(online_response_dict)
{'sales:daily_transactions': [1.1,1.2], 'sales:customer_id': [0,1]}
list_entities(allow_cache: bool = False) List[feast.entity.Entity][source]

Retrieve a list of entities from the registry

Parameters

allow_cache (bool) – Whether to allow returning entities from a cached registry

Returns

List of entities

list_feature_views() List[feast.feature_view.FeatureView][source]

Retrieve a list of feature views from the registry

Returns

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 datetime import datetime, timedelta
>>> from feast.feature_store import FeatureStore
>>>
>>> fs = FeatureStore(config=RepoConfig(provider="gcp"))
>>> fs.materialize(
>>>   start_date=datetime.utcnow() - timedelta(hours=3), end_date=datetime.utcnow() - timedelta(minutes=10)
>>> )
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.

Examples

Materialize all features into the online store up to 5 minutes ago.

>>> from datetime import datetime, timedelta
>>> from feast.feature_store import FeatureStore
>>>
>>> fs = FeatureStore(config=RepoConfig(provider="gcp", registry="gs://my-fs/", project="my_fs_proj"))
>>> fs.materialize_incremental(end_date=datetime.utcnow() - timedelta(minutes=5))
property project: str
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()

repo_path: pathlib.Path
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(*, 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

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, repo_path: pathlib.Path = None, **data: Any)[source]

Repo config. Typically loaded from feature_store.yaml

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

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.BigQueryOptions(table_ref: Optional[str], query: Optional[str])[source]

DataSource BigQuery options used to source features from BigQuery query

classmethod from_proto(bigquery_options_proto: feast.core.DataSource_pb2.BigQueryOptions)[source]

Creates a BigQueryOptions from a protobuf representation of a BigQuery option

Parameters

bigquery_options_proto – A protobuf representation of a DataSource

Returns

Returns a BigQueryOptions object based on the bigquery_options protobuf

property query

Returns the BigQuery SQL query referenced by this source

property table_ref

Returns the table ref of this BQ table

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

Converts an BigQueryOptionsProto object to its protobuf representation.

Returns

BigQueryOptionsProto protobuf

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

Returns the bigquery options of this data source

get_table_query_string() str[source]

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

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

Converts an DataSourceProto object to its protobuf representation.

validate()[source]

Validates the underlying data source.

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

DataSource that can be used source features

property created_timestamp_column

Returns the created timestamp column of this data source

property date_partition_column

Returns the date partition column of this data source

property event_timestamp_column

Returns the event timestamp column of this data source

property field_mapping

Returns the field mapping of this data source

static from_proto(data_source)[source]

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

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

Converts an DataSourceProto object to its protobuf representation.

validate()[source]

Validates the underlying data source.

class feast.data_source.FileOptions(file_format: Optional[feast.data_format.FileFormat], file_url: Optional[str])[source]

DataSource File options used to source features from a file

property file_format

Returns the file format of this file

property file_url

Returns the file url of this file

classmethod from_proto(file_options_proto: feast.core.DataSource_pb2.FileOptions)[source]

Creates a FileOptions from a protobuf representation of a file option

Parameters

file_options_proto – a protobuf representation of a datasource

Returns

Returns a FileOptions object based on the file_options protobuf

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

Converts an FileOptionsProto object to its protobuf representation.

Returns

FileOptionsProto protobuf

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

Returns the file options of this data source

property path

Returns the file path of this feature data source

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

Converts an DataSourceProto object to its protobuf representation.

validate()[source]

Validates the underlying data source.

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[MutableMapping[str, str]] = None)[source]

Represents a collection of entities and associated metadata.

property created_timestamp

Returns the created_timestamp of this entity

property description

Returns 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

Returns a 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

Returns a 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

Returns a EntityV2 object based on the YAML file

is_valid()[source]

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

property join_key

Returns the join key of this entity

property labels

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

property last_updated_timestamp

Returns the last_updated_timestamp of this entity

property name

Returns 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

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

EntitySpecV2 protobuf

to_yaml()[source]

Converts a entity to a YAML string.

Returns

Entity string returned in YAML format

property value_type: feast.value_type.ValueType

Returns the type of this entity

Feature View

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

A FeatureView defines a logical grouping of serveable features.

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

Returns a FeatureViewProto object based on the feature view protobuf

is_valid()[source]

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

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

Converts an feature view object to its protobuf representation.

Returns

FeatureViewProto protobuf

Feature

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

Feature field type

property dtype: feast.value_type.ValueType

Getter for data type of this field

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

feature_proto – FeatureSpecV2 protobuf object

Returns

Feature object

property labels: MutableMapping[str, str]

Getter for labels of this field

property name

Getter for name of this field

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

Converts Feature object to its Protocol Buffer representation

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.