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.data_source.DataSource, feast.entity.Entity, feast.feature_view.FeatureView, feast.on_demand_feature_view.OnDemandFeatureView, feast.request_feature_view.RequestFeatureView, feast.stream_feature_view.StreamFeatureView, feast.feature_service.FeatureService, feast.saved_dataset.ValidationReference, List[Union[feast.feature_view.FeatureView, feast.on_demand_feature_view.OnDemandFeatureView, feast.request_feature_view.RequestFeatureView, feast.entity.Entity, feast.feature_service.FeatureService, feast.data_source.DataSource, feast.saved_dataset.ValidationReference]]], objects_to_delete: Optional[List[Union[feast.feature_view.FeatureView, feast.on_demand_feature_view.OnDemandFeatureView, feast.request_feature_view.RequestFeatureView, feast.entity.Entity, feast.feature_service.FeatureService, feast.data_source.DataSource, feast.saved_dataset.ValidationReference]]] = None, partial: bool = True)[source]

Register objects to metadata store and update related infrastructure.

The apply method registers one or more definitions (e.g., Entity, FeatureView) and registers or updates these objects in the Feast registry. Once the apply method has updated the infrastructure (e.g., create tables in an online store), it will commit the updated registry. All operations are idempotent, meaning they can safely be rerun.

Parameters
  • objects – A single object, or a list of objects that should be registered with the Feature Store.

  • objects_to_delete – A list of objects to be deleted from the registry and removed from the provider’s infrastructure. This deletion will only be performed if partial is set to False.

  • partial – If True, apply will only handle the specified objects; if False, apply will also delete all the objects in objects_to_delete, and tear down any associated cloud resources.

Raises

ValueError – The ‘objects’ parameter could not be parsed properly.

Examples

Register an Entity and a FeatureView.

>>> from feast import FeatureStore, Entity, FeatureView, Feature, FileSource, RepoConfig
>>> from datetime import timedelta
>>> fs = FeatureStore(repo_path="feature_repo")
>>> driver = Entity(name="driver_id", description="driver id")
>>> driver_hourly_stats = FileSource(
...     path="feature_repo/data/driver_stats.parquet",
...     timestamp_field="event_timestamp",
...     created_timestamp_column="created",
... )
>>> driver_hourly_stats_view = FeatureView(
...     name="driver_hourly_stats",
...     entities=[driver],
...     ttl=timedelta(seconds=86400 * 1),
...     batch_source=driver_hourly_stats,
... )
>>> fs.apply([driver_hourly_stats_view, driver]) # register entity and feature view
config: feast.repo_config.RepoConfig
create_saved_dataset(from_: feast.infra.offline_stores.offline_store.RetrievalJob, name: str, storage: feast.saved_dataset.SavedDatasetStorage, tags: Optional[Dict[str, str]] = None, feature_service: Optional[feast.feature_service.FeatureService] = None) feast.saved_dataset.SavedDataset[source]

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

Returns

SavedDataset object with attached RetrievalJob

Raises

ValueError if given retrieval job doesn't have metadata

delete_feature_service(name: str)[source]

Deletes a feature service.

Parameters

name – Name of feature service.

Raises

FeatureServiceNotFoundException – The feature view could not be found.

delete_feature_view(name: str)[source]

Deletes a feature view.

Parameters

name – Name of feature view.

Raises

FeatureViewNotFoundException – The feature view could not be found.

static ensure_request_data_values_exist(needed_request_data: Set[str], needed_request_fv_features: Set[str], request_data_features: Dict[str, List[Any]])[source]
get_data_source(name: str) feast.data_source.DataSource[source]

Retrieves the list of data sources from the registry.

Parameters

name – Name of the data source.

Returns

The specified data source.

Raises

DataSourceObjectNotFoundException – The data source could not be found.

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

Retrieves an entity.

Parameters
  • name – Name of entity.

  • allow_registry_cache – (Optional) Whether to allow returning this entity from a cached registry

Returns

The specified entity.

Raises

EntityNotFoundException – The entity could not be found.

get_feature_server_endpoint() Optional[str][source]

Returns endpoint for the feature server, if it exists.

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

Retrieves a feature service.

Parameters
  • name – Name of feature service.

  • allow_cache – Whether to allow returning feature services from a cached registry.

Returns

The specified feature service.

Raises

FeatureServiceNotFoundException – The feature service could not be found.

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

Retrieves a feature view.

Parameters
  • name – Name of feature view.

  • allow_registry_cache – (Optional) Whether to allow returning this entity from a cached registry

Returns

The specified feature view.

Raises

FeatureViewNotFoundException – The feature view could not be found.

get_historical_features(entity_df: Union[pandas.core.frame.DataFrame, str], features: Union[List[str], feast.feature_service.FeatureService], 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 – The list of features that should be retrieved from the offline store. These features can be specified either as a list of string feature references or as a feature service. String feature references must have format “feature_view:feature”, e.g. “customer_fv:daily_transactions”.

  • full_feature_names – If True, feature names will be prefixed with the corresponding feature view name, changing them from the format “feature” to “feature_view__feature” (e.g. “daily_transactions” changes to “customer_fv__daily_transactions”).

Returns

RetrievalJob which can be used to materialize the results.

Raises

ValueError – Both or neither of features and feature_refs are specified.

Examples

Retrieve historical features from a local offline store.

>>> from feast import FeatureStore, RepoConfig
>>> import pandas as pd
>>> fs = FeatureStore(repo_path="feature_repo")
>>> entity_df = pd.DataFrame.from_dict(
...     {
...         "driver_id": [1001, 1002],
...         "event_timestamp": [
...             datetime(2021, 4, 12, 10, 59, 42),
...             datetime(2021, 4, 12, 8, 12, 10),
...         ],
...     }
... )
>>> retrieval_job = fs.get_historical_features(
...     entity_df=entity_df,
...     features=[
...         "driver_hourly_stats:conv_rate",
...         "driver_hourly_stats:acc_rate",
...         "driver_hourly_stats:avg_daily_trips",
...     ],
... )
>>> feature_data = retrieval_job.to_df()
static get_needed_request_data(grouped_odfv_refs: List[Tuple[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]], 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 – The list of features that should be retrieved from the online store. These features can be specified either as a list of string feature references or as a feature service. String feature references must have format “feature_view:feature”, e.g. “customer_fv:daily_transactions”.

  • entity_rows – A list of dictionaries where each key-value is an entity-name, entity-value pair.

  • full_feature_names – If True, feature names will be prefixed with the corresponding feature view name, changing them from the format “feature” to “feature_view__feature” (e.g. “daily_transactions” changes to “customer_fv__daily_transactions”).

Returns

OnlineResponse containing the feature data in records.

Raises

Exception – No entity with the specified name exists.

Examples

Retrieve online features from an online store.

>>> from feast import FeatureStore, RepoConfig
>>> fs = FeatureStore(repo_path="feature_repo")
>>> online_response = fs.get_online_features(
...     features=[
...         "driver_hourly_stats:conv_rate",
...         "driver_hourly_stats:acc_rate",
...         "driver_hourly_stats:avg_daily_trips",
...     ],
...     entity_rows=[{"driver_id": 1001}, {"driver_id": 1002}, {"driver_id": 1003}, {"driver_id": 1004}],
... )
>>> online_response_dict = online_response.to_dict()
get_saved_dataset(name: str) feast.saved_dataset.SavedDataset[source]

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

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

Data will be retrieved from globally configured offline store.

Returns

SavedDataset with RetrievalJob attached

Raises

SavedDatasetNotFound

get_stream_feature_view(name: str, allow_registry_cache: bool = False) feast.stream_feature_view.StreamFeatureView[source]

Retrieves a stream feature view.

Parameters
  • name – Name of stream feature view.

  • allow_registry_cache – (Optional) Whether to allow returning this entity from a cached registry

Returns

The specified stream feature view.

Raises

FeatureViewNotFoundException – The feature view could not be found.

get_validation_reference(name: str, allow_cache: bool = False) feast.saved_dataset.ValidationReference[source]

Retrieves a validation reference.

Raises

ValidationReferenceNotFoundException – The validation reference could not be found.

list_data_sources(allow_cache: bool = False) List[feast.data_source.DataSource][source]

Retrieves the list of data sources from the registry.

Parameters

allow_cache – Whether to allow returning data sources from a cached registry.

Returns

A list of data sources.

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

list_stream_feature_views(allow_cache: bool = False) List[feast.stream_feature_view.StreamFeatureView][source]

Retrieves the list of stream feature views from the registry.

Returns

A list of stream 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… <BLANKLINE> …

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.

push(push_source_name: str, df: pandas.core.frame.DataFrame, allow_registry_cache: bool = True, to: feast.data_source.PushMode = PushMode.ONLINE)[source]

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

Parameters
  • push_source_name – The name of the push source we want to push data to.

  • df – The data being pushed.

  • allow_registry_cache – Whether to allow cached versions of the registry.

  • to – Whether to push to online or offline store. Defaults to online store only.

refresh_registry()[source]

Fetches and caches a copy of the feature registry in memory.

Explicitly calling this method allows for direct control of the state of the registry cache. Every time this method is called the complete registry state will be retrieved from the remote registry store backend (e.g., GCS, S3), and the cache timer will be reset. If refresh_registry() is run before get_online_features() is called, then get_online_features() will use the cached registry instead of retrieving (and caching) the registry itself.

Additionally, the TTL for the registry cache can be set to infinity (by setting it to 0), which means that refresh_registry() will become the only way to update the cached registry. If the TTL is set to a value greater than 0, then once the cache becomes stale (more time than the TTL has passed), a new cache will be downloaded synchronously, which may increase latencies if the triggering method is get_online_features().

property registry: feast.registry.BaseRegistry

Gets the registry of this feature store.

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

Start the feature consumption server locally on a given port.

serve_transformations(port: int) None[source]

Start the feature transformation server locally on a given port.

serve_ui(host: str, port: int, get_registry_dump: Callable, registry_ttl_sec: int) None[source]

Start the UI server locally

teardown()[source]

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

validate_logged_features(source: feast.feature_service.FeatureService, start: datetime.datetime, end: datetime.datetime, reference: feast.saved_dataset.ValidationReference, throw_exception: bool = True, cache_profile: bool = True) Optional[feast.dqm.errors.ValidationFailed][source]

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

Parameters
  • source – Logs source object (currently only feature services are supported)

  • start – lower bound for loading logged features

  • end – upper bound for loading logged features

  • reference – validation reference

  • throw_exception – throw exception or return it as a result

  • cache_profile – store cached profile in Feast registry

Returns

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

version() str[source]

Returns the version of the current Feast SDK/CLI.

write_logged_features(logs: Union[pyarrow.lib.Table, pathlib.Path], source: feast.feature_service.FeatureService)[source]

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

Parameters
  • logs – Arrow Table or path to parquet dataset directory on disk

  • source – Object that produces logs

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

ingests data directly into the Online store

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

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_type: pydantic.types.StrictStr = 'file', 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

registry_type: pydantic.types.StrictStr

Provider name or a class name that implements RegistryStore. If specified, registry_store_type should be redundant.

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, feature_server: Any = None, flags: Any = None, repo_path: pathlib.Path = None, go_feature_retrieval: bool = False, **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

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, description: Optional[str] = '', tags: Optional[Dict[str, str]] = None, owner: Optional[str] = '', name: Optional[str] = None, timestamp_field: Optional[str] = None)[source]

DataSource that can be used to source features.

Parameters
  • name – Name of data source, which should be unique within a project

  • event_timestamp_column (optional) – (Deprecated in favor of timestamp_field) 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.

  • description (optional) –

  • tags (optional) – A dictionary of key-value pairs to store arbitrary metadata.

  • owner (optional) – The owner of the data source, typically the email of the primary maintainer.

  • timestamp_field (optional) – Event timestamp field used for point in time joins of feature values.

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

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

Parameters

data_source – A protobuf representation of a DataSource.

Returns

A DataSource class object.

Raises

ValueError – The type of DataSource could not be identified.

get_table_column_names_and_types(config: 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 a 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.PushMode(value)[source]

An enumeration.

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

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

Request Source

class feast.data_source.RequestSource(*args, name: Optional[str] = None, schema: Optional[Union[Dict[str, feast.value_type.ValueType], List[feast.field.Field]]] = None, description: Optional[str] = '', tags: Optional[Dict[str, str]] = None, owner: Optional[str] = '')[source]

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

name

Name of the request data source

Type

str

schema

Schema mapping from the input feature name to a ValueType

Type

List[feast.field.Field]

description

A human-readable description.

Type

str

tags

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

Type

Dict[str, str]

owner

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

Type

str

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

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

Parameters

data_source – A protobuf representation of a DataSource.

Returns

A DataSource class object.

Raises

ValueError – The type of DataSource could not be identified.

get_table_column_names_and_types(config: 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.

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 a 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.

Push Source

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

A source that can be used to ingest features on request

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

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

Parameters

data_source – A protobuf representation of a DataSource.

Returns

A DataSource class object.

Raises

ValueError – The type of DataSource could not be identified.

get_table_column_names_and_types(config: 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.

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 a 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.

BigQuery Source

class feast.infra.offline_stores.bigquery_source.BigQueryLoggingDestination(*, table_ref)[source]
to_data_source() feast.data_source.DataSource[source]

Convert this object into a data source to read logs from an offline store.

class feast.infra.offline_stores.bigquery_source.BigQuerySource(*, event_timestamp_column: Optional[str] = '', table: Optional[str] = None, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = None, query: Optional[str] = None, name: Optional[str] = None, description: Optional[str] = '', tags: Optional[Dict[str, str]] = None, owner: Optional[str] = '', timestamp_field: Optional[str] = None)[source]
static from_proto(data_source: feast.core.DataSource_pb2.DataSource)[source]

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

Parameters

data_source – A protobuf representation of a DataSource.

Returns

A DataSource class object.

Raises

ValueError – The type of DataSource could not be identified.

get_table_column_names_and_types(config: 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

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 a 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.

Redshift Source

class feast.infra.offline_stores.redshift_source.RedshiftLoggingDestination(*, table_name: str)[source]
to_data_source() feast.data_source.DataSource[source]

Convert this object into a data source to read logs from an offline store.

class feast.infra.offline_stores.redshift_source.RedshiftSource(*, event_timestamp_column: Optional[str] = '', table: Optional[str] = None, schema: Optional[str] = None, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = None, query: Optional[str] = None, name: Optional[str] = None, description: Optional[str] = '', tags: Optional[Dict[str, str]] = None, owner: Optional[str] = '', database: Optional[str] = '', timestamp_field: Optional[str] = '')[source]
property database

Returns the Redshift database of this Redshift source.

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

Creates a RedshiftSource from a protobuf representation of a RedshiftSource.

Parameters

data_source – A protobuf representation of a RedshiftSource

Returns

A RedshiftSource object based on the data_source protobuf.

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

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

Parameters

config – A RepoConfig describing the feature repo

get_table_query_string() str[source]

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

property query

Returns the Redshift query of this Redshift source.

property schema

Returns the schema of this Redshift source.

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

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

property table

Returns the table of this Redshift source.

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

Converts a RedshiftSource object to its protobuf representation.

Returns

A DataSourceProto object.

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

Validates the underlying data source.

Parameters

config – Configuration object used to configure a feature store.

Snowflake Source

class feast.infra.offline_stores.snowflake_source.SnowflakeLoggingDestination(*, table_name: str)[source]
to_data_source() feast.data_source.DataSource[source]

Convert this object into a data source to read logs from an offline store.

class feast.infra.offline_stores.snowflake_source.SnowflakeSource(*, database: Optional[str] = None, warehouse: Optional[str] = None, schema: Optional[str] = None, table: Optional[str] = None, query: Optional[str] = None, event_timestamp_column: Optional[str] = '', date_partition_column: Optional[str] = None, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, name: Optional[str] = None, description: Optional[str] = '', tags: Optional[Dict[str, str]] = None, owner: Optional[str] = '', timestamp_field: Optional[str] = '')[source]
property database

Returns the database of this snowflake source.

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

Creates a SnowflakeSource from a protobuf representation of a SnowflakeSource.

Parameters

data_source – A protobuf representation of a SnowflakeSource

Returns

A SnowflakeSource object based on the data_source protobuf.

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

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

Parameters

config – A RepoConfig describing the feature repo

get_table_query_string() str[source]

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

property query

Returns the snowflake options of this snowflake source.

property schema

Returns the schema of this snowflake source.

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

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

property table

Returns the table of this snowflake source.

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

Converts a SnowflakeSource object to its protobuf representation.

Returns

A DataSourceProto object.

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

Validates the underlying data source.

Parameters

config – Configuration object used to configure a feature store.

property warehouse

Returns the warehouse of this snowflake source.

Spark Source

class feast.infra.offline_stores.contrib.spark_offline_store.spark_source.SparkSource(*, name: Optional[str] = None, table: Optional[str] = None, query: Optional[str] = None, path: Optional[str] = None, file_format: Optional[str] = None, 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, description: Optional[str] = '', tags: Optional[Dict[str, str]] = None, owner: Optional[str] = '', timestamp_field: Optional[str] = None)[source]
property file_format

Returns the file format of this feature data source.

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

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

Parameters

data_source – A protobuf representation of a DataSource.

Returns

A DataSource class object.

Raises

ValueError – The type of DataSource could not be identified.

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

Returns the list of column names and raw column types.

Parameters

config – Configuration object used to configure a feature store.

get_table_query_string() str[source]

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

property path

Returns the path of the spark data source file.

property query

Returns the query of this feature data source

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

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

property table

Returns the table of this feature data source

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

Converts a 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.infra.offline_stores.contrib.spark_offline_store.spark_source.SparkSourceFormat(value)[source]

An enumeration.

Trino Source

class feast.infra.offline_stores.contrib.trino_offline_store.trino_source.TrinoSource(*, event_timestamp_column: Optional[str] = '', table: Optional[str] = None, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, query: Optional[str] = None, name: Optional[str] = None, description: Optional[str] = '', tags: Optional[Dict[str, str]] = None, owner: Optional[str] = '', timestamp_field: Optional[str] = None)[source]
static from_proto(data_source: feast.core.DataSource_pb2.DataSource)[source]

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

Parameters

data_source – A protobuf representation of a DataSource.

Returns

A DataSource class object.

Raises

ValueError – The type of DataSource could not be identified.

get_table_column_names_and_types(config: 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

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 a DataSourceProto object to its protobuf representation.

property trino_options

Returns the Trino options of this data source

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

Validates the underlying data source.

Parameters

config – Configuration object used to configure a feature store.

PostgreSQL Source

class feast.infra.offline_stores.contrib.postgres_offline_store.postgres_source.PostgreSQLSource(name: str, query: str, timestamp_field: Optional[str] = '', created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = '', description: Optional[str] = '', tags: Optional[Dict[str, str]] = None, owner: Optional[str] = '')[source]
static from_proto(data_source: feast.core.DataSource_pb2.DataSource)[source]

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

Parameters

data_source – A protobuf representation of a DataSource.

Returns

A DataSource class object.

Raises

ValueError – The type of DataSource could not be identified.

get_table_column_names_and_types(config: 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.

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 a 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.

File Source

class feast.infra.offline_stores.file_source.FileLoggingDestination(*, path: str, s3_endpoint_override='', partition_by: Optional[List[str]] = None)[source]
to_data_source() feast.data_source.DataSource[source]

Convert this object into a data source to read logs from an offline store.

class feast.infra.offline_stores.file_source.FileSource(*args, path: Optional[str] = None, event_timestamp_column: Optional[str] = '', file_format: Optional[feast.data_format.FileFormat] = None, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = '', s3_endpoint_override: Optional[str] = None, name: Optional[str] = '', description: Optional[str] = '', tags: Optional[Dict[str, str]] = None, owner: Optional[str] = '', timestamp_field: Optional[str] = '')[source]
static from_proto(data_source: feast.core.DataSource_pb2.DataSource)[source]

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

Parameters

data_source – A protobuf representation of a DataSource.

Returns

A DataSource class object.

Raises

ValueError – The type of DataSource could not be identified.

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

Returns the list of column names and raw column types.

Parameters

config – Configuration object used to configure a feature store.

get_table_query_string() str[source]

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

property path

Returns the path of this file 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 a 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.

Entity

class feast.entity.Entity(*args, name: Optional[str] = None, value_type: Optional[feast.value_type.ValueType] = None, description: str = '', join_key: Optional[str] = None, tags: Optional[Dict[str, str]] = None, owner: str = '', join_keys: Optional[List[str]] = None)[source]

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

name

The unique name of the entity.

Type

str

value_type

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

Type

deprecated

join_key

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

Type

str

description

A human-readable description.

Type

str

tags

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

Type

Dict[str, str]

owner

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

Type

str

created_timestamp

The time when the entity was created.

Type

Optional[datetime.datetime]

last_updated_timestamp

The time when the entity was last updated.

Type

Optional[datetime.datetime]

join_keys

A list of properties that uniquely identifies different entities within the collection. This is meant to replace the join_key parameter, but currently only supports a list of size one.

Type

List[str]

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 Entity object based on the entity protobuf.

is_valid()[source]

Validates the state of this entity locally.

Raises

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

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

Converts an entity object to its protobuf representation.

Returns

An EntityProto protobuf.

Feature View

class feast.feature_view.FeatureView(*args, name: Optional[str] = None, entities: Optional[Union[List[feast.entity.Entity], List[str]]] = None, ttl: Optional[Union[google.protobuf.duration_pb2.Duration, datetime.timedelta]] = 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, description: str = '', owner: str = '', schema: Optional[List[feast.field.Field]] = None, source: Optional[feast.data_source.DataSource] = None)[source]

A FeatureView defines a logical group of features.

name

The unique name of the feature view.

Type

str

entities

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

Type

List[str]

ttl

The amount of time this group of features lives. A ttl of 0 indicates that this group of features lives forever. Note that large ttl’s or a ttl of 0 can result in extremely computationally intensive queries.

Type

Optional[datetime.timedelta]

batch_source

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

Type

optional

stream_source

The stream source of data where this group of features is stored. This is deprecated in favor of source.

Type

optional

schema

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

Type

List[feast.field.Field]

entity_columns

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

Type

List[feast.field.Field]

features

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

Type

List[feast.field.Field]

online

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

Type

bool

description

A human-readable description.

Type

str

tags

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

Type

Dict[str, str]

owner

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

Type

str

source

The source of data for this group of features. May be a stream source, or a batch source. If a stream source, the source should contain a batch_source for backfills & batch materialization.

Type

optional

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.

property join_keys: List[str]

Returns a list of all the join keys.

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]

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

Parameters

join_key_map – A map of join keys in which the left is the join_key that corresponds with the feature data and the right corresponds with the entity data.

Examples

Join a location feature data table to both the origin column and destination column of the entity data.

temperatures_feature_service = FeatureService(

name=”temperatures”, features=[

location_stats_feature_view

.with_name(“origin_stats”) .with_join_key_map(

{“location_id”: “origin_id”}

),

location_stats_feature_view

.with_name(“destination_stats”) .with_join_key_map(

{“location_id”: “destination_id”}

),

],

)

On Demand Feature View

class feast.on_demand_feature_view.OnDemandFeatureView(*args, name: Optional[str] = None, features: Optional[List[feast.feature.Feature]] = None, sources: Optional[List[Any]] = None, udf: Optional[function] = None, inputs: Optional[Dict[str, Union[feast.feature_view.FeatureView, feast.feature_view_projection.FeatureViewProjection, feast.data_source.RequestSource]]] = None, schema: Optional[List[feast.field.Field]] = None, description: str = '', tags: Optional[Dict[str, str]] = None, owner: str = '')[source]

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

name

The unique name of the on demand feature view.

Type

str

features

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

Type

List[feast.field.Field]

source_feature_view_projections

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

Type

Dict[str, feast.feature_view_projection.FeatureViewProjection]

source_request_sources

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

Type

Dict[str, feast.data_source.RequestSource]

udf

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

Type

function

description

A human-readable description.

Type

str

tags

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

Type

Dict[str, str]

owner

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

Type

str

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(*args, features: Optional[List[feast.feature.Feature]] = None, sources: Optional[List[Union[feast.batch_feature_view.BatchFeatureView, feast.stream_feature_view.StreamFeatureView, feast.data_source.RequestSource, feast.feature_view_projection.FeatureViewProjection]]] = None, inputs: Optional[Dict[str, Union[feast.feature_view.FeatureView, feast.data_source.RequestSource]]] = None, schema: Optional[List[feast.field.Field]] = None, description: str = '', tags: Optional[Dict[str, str]] = None, owner: str = '')[source]

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

Parameters
  • features (deprecated) – The list of features in the output of the on demand feature view, after the transformation has been applied.

  • sources (optional) – A map from input source names to the actual input sources, which may be feature views, or request data sources. These sources serve as inputs to the udf, which will refer to them by name.

  • inputs (optional) – A map from input source names to the actual input sources, which may be feature views, feature view projections, or request data sources. These sources serve as inputs to the udf, which will refer to them by name.

  • schema (optional) – The list of features in the output of the on demand feature view, after the transformation has been applied.

  • description (optional) – A human-readable description.

  • tags (optional) – A dictionary of key-value pairs to store arbitrary metadata.

  • owner (optional) – The owner of the on demand feature view, typically the email of the primary maintainer.

Stream Feature View

class feast.stream_feature_view.StreamFeatureView(*, name: Optional[str] = None, entities: Optional[Union[List[feast.entity.Entity], List[str]]] = None, ttl: Optional[datetime.timedelta] = None, tags: Optional[Dict[str, str]] = None, online: Optional[bool] = True, description: Optional[str] = '', owner: Optional[str] = '', schema: Optional[List[feast.field.Field]] = None, source: Optional[feast.data_source.DataSource] = None, aggregations: Optional[List[feast.aggregation.Aggregation]] = None, mode: Optional[str] = 'spark', timestamp_field: Optional[str] = '', udf: Optional[function] = None)[source]

NOTE: Stream Feature Views are not yet fully implemented and exist to allow users to register their stream sources and schemas with Feast.

name

The unique name of the stream feature view.

Type

str

entities

List of entities or entity join keys.

Type

List[str]

ttl

The amount of time this group of features lives. A ttl of 0 indicates that this group of features lives forever. Note that large ttl’s or a ttl of 0 can result in extremely computationally intensive queries.

Type

Optional[datetime.timedelta]

schema

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

Type

List[feast.field.Field]

source

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

Type

feast.data_source.DataSource

aggregations

List of aggregations registered with the stream feature view.

Type

List[feast.aggregation.Aggregation]

mode

The mode of execution.

Type

str

timestamp_field

Must be specified if aggregations are specified. Defines the timestamp column on which to aggregate windows.

Type

str

online

Defines whether this stream feature view is used in online feature retrieval.

Type

bool

description

A human-readable description.

Type

str

tags

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

Type

Dict[str, str]

owner

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

Type

str

udf

The user defined transformation function. This transformation function should have all of the corresponding imports imported within the function.

Type

Optional[function]

classmethod from_proto(sfv_proto)[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.

to_proto()[source]

Converts a feature view object to its protobuf representation.

Returns

A FeatureViewProto protobuf.

feast.stream_feature_view.stream_feature_view(*, entities: Optional[Union[List[feast.entity.Entity], List[str]]] = None, ttl: Optional[datetime.timedelta] = None, tags: Optional[Dict[str, str]] = None, online: Optional[bool] = True, description: Optional[str] = '', owner: Optional[str] = '', schema: Optional[List[feast.field.Field]] = None, source: Optional[feast.data_source.DataSource] = None, aggregations: Optional[List[feast.aggregation.Aggregation]] = None, mode: Optional[str] = 'spark', timestamp_field: Optional[str] = '')[source]

Creates an StreamFeatureView object with the given user function as udf. Please make sure that the udf contains all non-built in imports within the function to ensure that the execution of a deserialized function does not miss imports.

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.

Feature Service

class feast.feature_service.FeatureService(*args, name: Optional[str] = None, features: Optional[List[Union[feast.feature_view.FeatureView, feast.on_demand_feature_view.OnDemandFeatureView]]] = None, tags: Dict[str, str] = None, description: str = '', owner: str = '', logging_config: Optional[feast.feature_logging.LoggingConfig] = None)[source]

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

name

The unique name of the feature service.

Type

str

feature_view_projections

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

Type

List[feast.feature_view_projection.FeatureViewProjection]

description

A human-readable description.

Type

str

tags

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

Type

Dict[str, str]

owner

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

Type

str

created_timestamp

The time when the feature service was created.

Type

Optional[datetime.datetime]

last_updated_timestamp

The time when the feature service was last updated.

Type

Optional[datetime.datetime]

classmethod 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 feature service to its protobuf representation.

Returns

A FeatureServiceProto protobuf.

Registry

class feast.registry.BaseRegistry[source]
abstract apply_data_source(data_source: feast.data_source.DataSource, project: str, commit: bool = True)[source]

Registers a single data source with Feast

Parameters
  • data_source – A data source that will be registered

  • project – Feast project that this data source belongs to

  • commit – Whether to immediately commit to the registry

abstract 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

abstract 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

abstract 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

abstract 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

abstract apply_saved_dataset(saved_dataset: feast.saved_dataset.SavedDataset, project: str, commit: bool = True)[source]

Stores a saved dataset metadata with Feast

Parameters
  • saved_dataset – SavedDataset that will be added / updated to registry

  • project – Feast project that this dataset belongs to

  • commit – Whether the change should be persisted immediately

abstract apply_validation_reference(validation_reference: feast.saved_dataset.ValidationReference, project: str, commit: bool = True)[source]

Persist a validation reference

Parameters
  • validation_reference – ValidationReference that will be added / updated to registry

  • project – Feast project that this dataset belongs to

  • commit – Whether the change should be persisted immediately

abstract commit()[source]

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

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

Deletes a data source or raises an exception if not found.

Parameters
  • name – Name of data source

  • project – Feast project that this data source belongs to

  • commit – Whether the change should be persisted immediately

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

Deletes an entity or raises an exception if not found.

Parameters
  • name – Name of entity

  • project – Feast project that this entity belongs to

  • commit – Whether the change should be persisted immediately

abstract 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

abstract 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

delete_saved_dataset(name: str, project: str, allow_cache: bool = False)[source]

Delete a saved dataset.

Parameters
  • name – Name of dataset

  • project – Feast project that this dataset belongs to

  • allow_cache – Whether to allow returning this dataset from a cached registry

Returns

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

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

Deletes a validation reference or raises an exception if not found.

Parameters
  • name – Name of validation reference

  • project – Feast project that this object belongs to

  • commit – Whether the change should be persisted immediately

abstract get_data_source(name: str, project: str, allow_cache: bool = False) feast.data_source.DataSource[source]

Retrieves a data source.

Parameters
  • name – Name of data source

  • project – Feast project that this data source belongs to

  • allow_cache – Whether to allow returning this data source from a cached registry

Returns

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

abstract 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

  • allow_cache – Whether to allow returning this entity from a cached registry

Returns

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

abstract 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

  • allow_cache – Whether to allow returning this feature service from a cached registry

Returns

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

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

Retrieves a feature view.

Parameters
  • name – Name of feature view

  • project – Feast project that this feature view belongs to

  • allow_cache – Allow returning feature view from the cached registry

Returns

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

abstract get_infra(project: str, allow_cache: bool = False) feast.infra.infra_object.Infra[source]

Retrieves the stored Infra object.

Parameters
  • project – Feast project that the Infra object refers to

  • allow_cache – Whether to allow returning this entity from a cached registry

Returns

The stored Infra object.

abstract 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 view belongs to

  • allow_cache – Whether to allow returning this on demand feature view from a cached registry

Returns

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

abstract get_request_feature_view(name: str, project: str) feast.request_feature_view.RequestFeatureView[source]

Retrieves a request feature view.

Parameters
  • name – Name of request feature view

  • project – Feast project that this feature view belongs to

  • allow_cache – Allow returning feature view from the cached registry

Returns

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

abstract get_saved_dataset(name: str, project: str, allow_cache: bool = False) feast.saved_dataset.SavedDataset[source]

Retrieves a saved dataset.

Parameters
  • name – Name of dataset

  • project – Feast project that this dataset belongs to

  • allow_cache – Whether to allow returning this dataset from a cached registry

Returns

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

abstract get_stream_feature_view(name: str, project: str, allow_cache: bool = False)[source]

Retrieves a stream feature view.

Parameters
  • name – Name of stream feature view

  • project – Feast project that this feature view belongs to

  • allow_cache – Allow returning feature view from the cached registry

Returns

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

abstract get_validation_reference(name: str, project: str, allow_cache: bool = False) feast.saved_dataset.ValidationReference[source]

Retrieves a validation reference.

Parameters
  • name – Name of dataset

  • project – Feast project that this dataset belongs to

  • allow_cache – Whether to allow returning this dataset from a cached registry

Returns

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

abstract list_data_sources(project: str, allow_cache: bool = False) List[feast.data_source.DataSource][source]

Retrieve a list of data sources from the registry

Parameters
  • project – Filter data source based on project name

  • allow_cache – Whether to allow returning data sources from a cached registry

Returns

List of data sources

abstract 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

abstract 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

abstract 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

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

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

Returns

List of on demand feature views

abstract 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 request feature views

abstract list_saved_datasets(project: str, allow_cache: bool = False) List[feast.saved_dataset.SavedDataset][source]

Retrieves a list of all saved datasets in specified project

Parameters
  • project – Feast project

  • allow_cache – Whether to allow returning this dataset from a cached registry

Returns

Returns the list of SavedDatasets

abstract list_stream_feature_views(project: str, allow_cache: bool = False) List[feast.stream_feature_view.StreamFeatureView][source]

Retrieve a list of stream feature views from the registry

Parameters
  • project – Filter stream feature views based on project name

  • allow_cache – Whether to allow returning stream feature views from a cached registry

Returns

List of stream feature views

list_validation_references(project: str, allow_cache: bool = False) List[feast.saved_dataset.ValidationReference][source]

Retrieve a list of validation references 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 request feature views

abstract proto() feast.core.Registry_pb2.Registry[source]

Retrieves a proto version of the registry.

Returns

The registry proto object.

abstract refresh()[source]

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

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

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

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

Parameters

project – Feast project to convert to a dict

abstract update_infra(infra: feast.infra.infra_object.Infra, project: str, commit: bool = True)[source]

Updates the stored Infra object.

Parameters
  • infra – The new Infra object to be stored.

  • project – Feast project that the Infra object refers to

  • commit – Whether the change should be persisted immediately

class feast.registry.FeastObjectType(value)[source]

An enumeration.

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

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

apply_data_source(data_source: feast.data_source.DataSource, project: str, commit: bool = True)[source]

Registers a single data source with Feast

Parameters
  • data_source – A data source that will be registered

  • project – Feast project that this data source belongs to

  • commit – Whether to immediately commit to the registry

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_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

apply_saved_dataset(saved_dataset: feast.saved_dataset.SavedDataset, project: str, commit: bool = True)[source]

Stores a saved dataset metadata with Feast

Parameters
  • saved_dataset – SavedDataset that will be added / updated to registry

  • project – Feast project that this dataset belongs to

  • commit – Whether the change should be persisted immediately

apply_validation_reference(validation_reference: feast.saved_dataset.ValidationReference, project: str, commit: bool = True)[source]

Persist a validation reference

Parameters
  • validation_reference – ValidationReference that will be added / updated to registry

  • project – Feast project that this dataset belongs to

  • commit – Whether the change should be persisted immediately

commit()[source]

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

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

Deletes a data source or raises an exception if not found.

Parameters
  • name – Name of data source

  • project – Feast project that this data source belongs to

  • commit – Whether the change should be persisted immediately

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

Deletes an entity or raises an exception if not found.

Parameters
  • name – Name of entity

  • project – Feast project that this entity belongs to

  • commit – Whether the change should be persisted immediately

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

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

Parameters
  • name – Name of feature service

  • project – Feast project that this feature service belongs to

  • commit – Whether the change should be persisted immediately

delete_feature_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

delete_saved_dataset(name: str, project: str, allow_cache: bool = False)

Delete a saved dataset.

Parameters
  • name – Name of dataset

  • project – Feast project that this dataset belongs to

  • allow_cache – Whether to allow returning this dataset from a cached registry

Returns

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

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

Deletes a validation reference or raises an exception if not found.

Parameters
  • name – Name of validation reference

  • project – Feast project that this object belongs to

  • commit – Whether the change should be persisted immediately

get_data_source(name: str, project: str, allow_cache: bool = False) feast.data_source.DataSource[source]

Retrieves a data source.

Parameters
  • name – Name of data source

  • project – Feast project that this data source belongs to

  • allow_cache – Whether to allow returning this data source from a cached registry

Returns

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

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

  • allow_cache – Whether to allow returning this entity from a cached registry

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

  • allow_cache – Whether to allow returning this feature service from a cached registry

Returns

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

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

Retrieves a feature view.

Parameters
  • name – Name of feature view

  • project – Feast project that this feature view belongs to

  • allow_cache – Allow returning feature view from the cached registry

Returns

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

get_infra(project: str, allow_cache: bool = False) feast.infra.infra_object.Infra[source]

Retrieves the stored Infra object.

Parameters
  • project – Feast project that the Infra object refers to

  • allow_cache – Whether to allow returning this entity from a cached registry

Returns

The stored Infra object.

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 view belongs to

  • allow_cache – Whether to allow returning this on demand feature view from a cached registry

Returns

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

get_request_feature_view(name: str, project: str)[source]

Retrieves a feature view.

Parameters
  • name – Name of feature view

  • project – Feast project that this feature view belongs to

  • allow_cache – Allow returning feature view from the cached registry

Returns

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

get_saved_dataset(name: str, project: str, allow_cache: bool = False) feast.saved_dataset.SavedDataset[source]

Retrieves a saved dataset.

Parameters
  • name – Name of dataset

  • project – Feast project that this dataset belongs to

  • allow_cache – Whether to allow returning this dataset from a cached registry

Returns

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

get_stream_feature_view(name: str, project: str, allow_cache: bool = False) feast.stream_feature_view.StreamFeatureView[source]

Retrieves a stream feature view.

Parameters
  • name – Name of stream feature view

  • project – Feast project that this stream feature view belongs to

  • allow_cache – Allow returning feature view from the cached registry

Returns

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

get_validation_reference(name: str, project: str, allow_cache: bool = False) feast.saved_dataset.ValidationReference[source]

Retrieves a validation reference.

Parameters
  • name – Name of dataset

  • project – Feast project that this dataset belongs to

  • allow_cache – Whether to allow returning this dataset from a cached registry

Returns

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

list_data_sources(project: str, allow_cache: bool = False) List[feast.data_source.DataSource][source]

Retrieve a list of data sources from the registry

Parameters
  • project – Filter data source based on project name

  • allow_cache – Whether to allow returning data sources from a cached registry

Returns

List of data sources

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

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

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

list_saved_datasets(project: str, allow_cache: bool = False) List[feast.saved_dataset.SavedDataset][source]

Retrieves a list of all saved datasets in specified project

Parameters
  • project – Feast project

  • allow_cache – Whether to allow returning this dataset from a cached registry

Returns

Returns the list of SavedDatasets

list_stream_feature_views(project: str, allow_cache: bool = False) List[feast.stream_feature_view.StreamFeatureView][source]

Retrieve a list of stream feature views from the registry

Parameters
  • project – Filter stream feature views based on project name

  • allow_cache – Whether to allow returning stream feature views from a cached registry

Returns

List of stream feature views

list_validation_references(project: str, allow_cache: bool = False) List[feast.saved_dataset.ValidationReference]

Retrieve a list of validation references 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 request feature views

proto() feast.core.Registry_pb2.Registry[source]

Retrieves a proto version of the registry.

Returns

The registry proto object.

refresh()[source]

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

teardown()[source]

Tears down (removes) the registry.

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

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

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

Parameters

project – Feast project to convert to a dict

update_infra(infra: feast.infra.infra_object.Infra, project: str, commit: bool = True)[source]

Updates the stored Infra object.

Parameters
  • infra – The new Infra object to be stored.

  • project – Feast project that the Infra object refers to

  • commit – Whether the change should be persisted immediately

Registry Store

class feast.registry_store.RegistryStore[source]

A registry store is a storage backend for the Feast registry.

abstract get_registry_proto() feast.core.Registry_pb2.Registry[source]

Retrieves the registry proto from the registry path. If there is no file at that path, raises a FileNotFoundError.

Returns

Returns either the registry proto stored at the registry path, or an empty registry proto.

abstract teardown()[source]

Tear down the registry.

abstract update_registry_proto(registry_proto: feast.core.Registry_pb2.Registry)[source]

Overwrites the current registry proto with the proto passed in. This method writes to the registry path.

Parameters

registry_proto – the new RegistryProto

SQL Registry Store

class feast.infra.registry_stores.sql.SqlRegistry(registry_config: Optional[feast.repo_config.RegistryConfig], repo_path: Optional[pathlib.Path])[source]
apply_data_source(data_source: feast.data_source.DataSource, project: str, commit: bool = True)[source]

Registers a single data source with Feast

Parameters
  • data_source – A data source that will be registered

  • project – Feast project that this data source belongs to

  • commit – Whether to immediately commit to the registry

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_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

apply_saved_dataset(saved_dataset: feast.saved_dataset.SavedDataset, project: str, commit: bool = True)[source]

Stores a saved dataset metadata with Feast

Parameters
  • saved_dataset – SavedDataset that will be added / updated to registry

  • project – Feast project that this dataset belongs to

  • commit – Whether the change should be persisted immediately

apply_validation_reference(validation_reference: feast.saved_dataset.ValidationReference, project: str, commit: bool = True)[source]

Persist a validation reference

Parameters
  • validation_reference – ValidationReference that will be added / updated to registry

  • project – Feast project that this dataset belongs to

  • commit – Whether the change should be persisted immediately

commit()[source]

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

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

Deletes a data source or raises an exception if not found.

Parameters
  • name – Name of data source

  • project – Feast project that this data source belongs to

  • commit – Whether the change should be persisted immediately

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

Deletes an entity or raises an exception if not found.

Parameters
  • name – Name of entity

  • project – Feast project that this entity belongs to

  • commit – Whether the change should be persisted immediately

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

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

Parameters
  • name – Name of feature service

  • project – Feast project that this feature service belongs to

  • commit – Whether the change should be persisted immediately

delete_feature_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

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

Deletes a validation reference or raises an exception if not found.

Parameters
  • name – Name of validation reference

  • project – Feast project that this object belongs to

  • commit – Whether the change should be persisted immediately

get_data_source(name: str, project: str, allow_cache: bool = False) feast.data_source.DataSource[source]

Retrieves a data source.

Parameters
  • name – Name of data source

  • project – Feast project that this data source belongs to

  • allow_cache – Whether to allow returning this data source from a cached registry

Returns

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

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

  • allow_cache – Whether to allow returning this entity from a cached registry

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

  • allow_cache – Whether to allow returning this feature service from a cached registry

Returns

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

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

Retrieves a feature view.

Parameters
  • name – Name of feature view

  • project – Feast project that this feature view belongs to

  • allow_cache – Allow returning feature view from the cached registry

Returns

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

get_infra(project: str, allow_cache: bool = False) feast.infra.infra_object.Infra[source]

Retrieves the stored Infra object.

Parameters
  • project – Feast project that the Infra object refers to

  • allow_cache – Whether to allow returning this entity from a cached registry

Returns

The stored Infra object.

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 view belongs to

  • allow_cache – Whether to allow returning this on demand feature view from a cached registry

Returns

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

get_request_feature_view(name: str, project: str)[source]

Retrieves a request feature view.

Parameters
  • name – Name of request feature view

  • project – Feast project that this feature view belongs to

  • allow_cache – Allow returning feature view from the cached registry

Returns

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

get_saved_dataset(name: str, project: str, allow_cache: bool = False) feast.saved_dataset.SavedDataset[source]

Retrieves a saved dataset.

Parameters
  • name – Name of dataset

  • project – Feast project that this dataset belongs to

  • allow_cache – Whether to allow returning this dataset from a cached registry

Returns

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

get_stream_feature_view(name: str, project: str, allow_cache: bool = False)[source]

Retrieves a stream feature view.

Parameters
  • name – Name of stream feature view

  • project – Feast project that this feature view belongs to

  • allow_cache – Allow returning feature view from the cached registry

Returns

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

get_validation_reference(name: str, project: str, allow_cache: bool = False) feast.saved_dataset.ValidationReference[source]

Retrieves a validation reference.

Parameters
  • name – Name of dataset

  • project – Feast project that this dataset belongs to

  • allow_cache – Whether to allow returning this dataset from a cached registry

Returns

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

list_data_sources(project: str, allow_cache: bool = False) List[feast.data_source.DataSource][source]

Retrieve a list of data sources from the registry

Parameters
  • project – Filter data source based on project name

  • allow_cache – Whether to allow returning data sources from a cached registry

Returns

List of data sources

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

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

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 request feature views

list_saved_datasets(project: str, allow_cache: bool = False) List[feast.saved_dataset.SavedDataset][source]

Retrieves a list of all saved datasets in specified project

Parameters
  • project – Feast project

  • allow_cache – Whether to allow returning this dataset from a cached registry

Returns

Returns the list of SavedDatasets

list_stream_feature_views(project: str, allow_cache: bool = False) List[feast.stream_feature_view.StreamFeatureView][source]

Retrieve a list of stream feature views from the registry

Parameters
  • project – Filter stream feature views based on project name

  • allow_cache – Whether to allow returning stream feature views from a cached registry

Returns

List of stream feature views

proto() feast.core.Registry_pb2.Registry[source]

Retrieves a proto version of the registry.

Returns

The registry proto object.

refresh()[source]

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

update_infra(infra: feast.infra.infra_object.Infra, project: str, commit: bool = True)[source]

Updates the stored Infra object.

Parameters
  • infra – The new Infra object to be stored.

  • project – Feast project that the Infra object refers to

  • commit – Whether the change should be persisted immediately

PostgreSQL Registry Store

class feast.infra.registry_stores.contrib.postgres.registry_store.PostgreSQLRegistryStore(config: feast.infra.registry_stores.contrib.postgres.registry_store.PostgresRegistryConfig, registry_path: str)[source]
get_registry_proto() feast.core.Registry_pb2.Registry[source]

Retrieves the registry proto from the registry path. If there is no file at that path, raises a FileNotFoundError.

Returns

Returns either the registry proto stored at the registry path, or an empty registry proto.

teardown()[source]

Tear down the registry.

update_registry_proto(registry_proto: feast.core.Registry_pb2.Registry)[source]

Overwrites the current registry proto with the proto passed in. This method writes to the registry path.

Parameters

registry_proto – the new RegistryProto

class feast.infra.registry_stores.contrib.postgres.registry_store.PostgresRegistryConfig(*, registry_type: pydantic.types.StrictStr = 'file', registry_store_type: pydantic.types.StrictStr = None, path: pydantic.types.StrictStr, cache_ttl_seconds: pydantic.types.StrictInt = 600, host: str, port: int, database: str, db_schema: str, user: str, password: str, sslmode: str = None, sslkey_path: str = None, sslcert_path: str = None, sslrootcert_path: str = None, **extra_data: Any)[source]

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.

ingest_df(feature_view: feast.feature_view.FeatureView, entities: List[feast.entity.Entity], df: pandas.core.frame.DataFrame)[source]

Ingests a DataFrame directly into the online store

ingest_df_to_offline_store(feature_view: feast.feature_view.FeatureView, df: pyarrow.lib.Table)[source]

Ingests a DataFrame directly into the offline store

abstract online_read(config: feast.repo_config.RepoConfig, table: 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: 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 FeatureView

  • 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.

plan_infra(config: feast.repo_config.RepoConfig, desired_registry_proto: feast.core.Registry_pb2.Registry) feast.infra.infra_object.Infra[source]

Returns the Infra required to support the desired registry.

Parameters
  • config – The RepoConfig for the current FeatureStore.

  • desired_registry_proto – The desired registry, in proto form.

abstract retrieve_feature_service_logs(feature_service: feast.feature_service.FeatureService, start_date: datetime.datetime, end_date: datetime.datetime, config: feast.repo_config.RepoConfig, registry: feast.registry.BaseRegistry) feast.infra.offline_stores.offline_store.RetrievalJob[source]

Read logged features from an offline store for a given time window [from, to). Target table is determined based on logging configuration from the feature service.

Returns

RetrievalJob object, which wraps the query to the offline store.

abstract retrieve_saved_dataset(config: feast.repo_config.RepoConfig, dataset: feast.saved_dataset.SavedDataset) feast.infra.offline_stores.offline_store.RetrievalJob[source]

Read saved dataset from offline store. All parameters for retrieval (like path, datetime boundaries, column names for both keys and features, etc) are determined from SavedDataset object.

Returns

RetrievalJob object, which is lazy wrapper for actual query performed under the hood.

abstract teardown_infra(project: str, tables: Sequence[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[feast.feature_view.FeatureView], tables_to_keep: Sequence[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.

abstract write_feature_service_logs(feature_service: feast.feature_service.FeatureService, logs: Union[pyarrow.lib.Table, pathlib.Path], config: feast.repo_config.RepoConfig, registry: feast.registry.BaseRegistry)[source]

Write features and entities logged by a feature server to an offline store.

Schema of logs table is being inferred from the provided feature service. Only feature services with configured logging are accepted.

Logs dataset can be passed as Arrow Table or path to parquet directory.

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.

ingest_df(feature_view: feast.feature_view.FeatureView, entities: List[feast.entity.Entity], df: pandas.core.frame.DataFrame)[source]

Ingests a DataFrame directly into the online store

ingest_df_to_offline_store(feature_view: feast.feature_view.FeatureView, table: pyarrow.lib.Table)[source]

Ingests a DataFrame directly into the offline store

online_read(config: feast.repo_config.RepoConfig, table: feast.feature_view.FeatureView, entity_keys: List[feast.types.EntityKey_pb2.EntityKey], requested_features: List[str] = None) List[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: 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 FeatureView

  • 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.

retrieve_feature_service_logs(feature_service: feast.feature_service.FeatureService, start_date: datetime.datetime, end_date: datetime.datetime, config: feast.repo_config.RepoConfig, registry: feast.registry.BaseRegistry) feast.infra.offline_stores.offline_store.RetrievalJob[source]

Read logged features from an offline store for a given time window [from, to). Target table is determined based on logging configuration from the feature service.

Returns

RetrievalJob object, which wraps the query to the offline store.

retrieve_saved_dataset(config: feast.repo_config.RepoConfig, dataset: feast.saved_dataset.SavedDataset) feast.infra.offline_stores.offline_store.RetrievalJob[source]

Read saved dataset from offline store. All parameters for retrieval (like path, datetime boundaries, column names for both keys and features, etc) are determined from SavedDataset object.

Returns

RetrievalJob object, which is lazy wrapper for actual query performed under the hood.

teardown_infra(project: str, tables: Sequence[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[feast.feature_view.FeatureView], tables_to_keep: Sequence[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.

write_feature_service_logs(feature_service: feast.feature_service.FeatureService, logs: Union[pyarrow.lib.Table, str], config: feast.repo_config.RepoConfig, registry: feast.registry.BaseRegistry)[source]

Write features and entities logged by a feature server to an offline store.

Schema of logs table is being inferred from the provided feature service. Only feature services with configured logging are accepted.

Logs dataset can be passed as Arrow Table or path to parquet directory.

Local Provider

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

This class only exists for backwards compatibility.

plan_infra(config: feast.repo_config.RepoConfig, desired_registry_proto: feast.core.Registry_pb2.Registry) feast.infra.infra_object.Infra[source]

Returns the Infra required to support the desired registry.

Parameters
  • config – The RepoConfig for the current FeatureStore.

  • desired_registry_proto – The desired registry, in proto form.

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[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[feast.feature_view.FeatureView], tables_to_keep: Sequence[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.

static offline_write_batch(config: feast.repo_config.RepoConfig, feature_view: feast.feature_view.FeatureView, table: pyarrow.lib.Table, progress: Optional[Callable[[int], Any]])[source]

Write features to a specified destination in the offline store. Data can be appended to an existing table (destination) or a new one will be created automatically

(if it doesn’t exist).

Hence, this function can be called repeatedly with the same destination config to write features.

Parameters
  • config – Repo configuration object

  • feature_view – FeatureView to write the data to.

  • table – pyarrow table containing feature data and timestamp column for historical feature retrieval

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

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

abstract static pull_all_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], timestamp_field: str, start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob[source]

Returns a Retrieval Job for all join key columns, feature name columns, and the event timestamp columns that occur between the start_date and end_date.

Note that join_key_columns, feature_name_columns, timestamp_field, 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.

Parameters
  • config – Repo configuration object

  • data_source – Data source to pull all of the columns from

  • join_key_columns – Columns of the join keys

  • feature_name_columns – Columns of the feature names needed

  • timestamp_field – Timestamp column

  • start_date – Starting date of query

  • end_date – Ending date of query

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], timestamp_field: str, created_timestamp_column: Optional[str], start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob[source]

This method pulls data from the offline store, and the FeatureStore class is used to write this data into the online store. This method is invoked when running materialization (using the feast materialize or feast materialize-incremental commands, or the corresponding FeatureStore.materialize() method. This method pulls data from the offline store, and the FeatureStore class is used to write this data into the online store.

Note that join_key_columns, feature_name_columns, timestamp_field, 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.

Parameters
  • config – Repo configuration object

  • data_source – Data source to pull all of the columns from

  • join_key_columns – Columns of the join keys

  • feature_name_columns – Columns of the feature names needed

  • timestamp_field – Timestamp column

  • start_date – Starting date of query

  • end_date – Ending date of query

static write_logged_features(config: feast.repo_config.RepoConfig, data: Union[pyarrow.lib.Table, pathlib.Path], source: feast.feature_logging.LoggingSource, logging_config: feast.feature_logging.LoggingConfig, registry: feast.registry.BaseRegistry)[source]

Write logged features to a specified destination (taken from logging_config) in the offline store. Data can be appended to an existing table (destination) or a new one will be created automatically

(if it doesn’t exist).

Hence, this function can be called repeatedly with the same destination to flush logs in chunks.

Parameters
  • config – Repo configuration object

  • data – Arrow table or path to parquet directory that contains logs dataset.

  • source – Logging source that provides schema and some additional metadata.

  • logging_config – used to determine destination

  • registry – Feast registry

This is an optional method that could be supported only be some stores.

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

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

abstract property metadata: Optional[feast.infra.offline_stores.offline_store.RetrievalMetadata]

Return metadata information about retrieval. Should be available even before materializing the dataset itself.

abstract persist(storage: feast.saved_dataset.SavedDatasetStorage)[source]

Run the retrieval and persist the results in the same offline store used for read.

to_arrow(validation_reference: Optional[ValidationReference] = None) pyarrow.lib.Table[source]

Return dataset as pyarrow Table synchronously :param validation_reference: If provided resulting dataset will be validated against this reference profile.

to_df(validation_reference: Optional[ValidationReference] = None) pandas.core.frame.DataFrame[source]

Return dataset as Pandas DataFrame synchronously including on demand transforms :param validation_reference: If provided resulting dataset will be validated against this reference profile.

File Offline Store

class feast.infra.offline_stores.file.FileOfflineStore[source]
static offline_write_batch(config: feast.repo_config.RepoConfig, feature_view: feast.feature_view.FeatureView, table: pyarrow.lib.Table, progress: Optional[Callable[[int], Any]])[source]

Write features to a specified destination in the offline store. Data can be appended to an existing table (destination) or a new one will be created automatically

(if it doesn’t exist).

Hence, this function can be called repeatedly with the same destination config to write features.

Parameters
  • config – Repo configuration object

  • feature_view – FeatureView to write the data to.

  • table – pyarrow table containing feature data and timestamp column for historical feature retrieval

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

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

static pull_all_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], timestamp_field: str, start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob[source]

Returns a Retrieval Job for all join key columns, feature name columns, and the event timestamp columns that occur between the start_date and end_date.

Note that join_key_columns, feature_name_columns, timestamp_field, 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.

Parameters
  • config – Repo configuration object

  • data_source – Data source to pull all of the columns from

  • join_key_columns – Columns of the join keys

  • feature_name_columns – Columns of the feature names needed

  • timestamp_field – Timestamp column

  • start_date – Starting date of query

  • end_date – Ending date of query

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], timestamp_field: str, created_timestamp_column: Optional[str], start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob[source]

This method pulls data from the offline store, and the FeatureStore class is used to write this data into the online store. This method is invoked when running materialization (using the feast materialize or feast materialize-incremental commands, or the corresponding FeatureStore.materialize() method. This method pulls data from the offline store, and the FeatureStore class is used to write this data into the online store.

Note that join_key_columns, feature_name_columns, timestamp_field, 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.

Parameters
  • config – Repo configuration object

  • data_source – Data source to pull all of the columns from

  • join_key_columns – Columns of the join keys

  • feature_name_columns – Columns of the feature names needed

  • timestamp_field – Timestamp column

  • start_date – Starting date of query

  • end_date – Ending date of query

static write_logged_features(config: feast.repo_config.RepoConfig, data: Union[pyarrow.lib.Table, pathlib.Path], source: feast.feature_logging.LoggingSource, logging_config: feast.feature_logging.LoggingConfig, registry: feast.registry.BaseRegistry)[source]

Write logged features to a specified destination (taken from logging_config) in the offline store. Data can be appended to an existing table (destination) or a new one will be created automatically

(if it doesn’t exist).

Hence, this function can be called repeatedly with the same destination to flush logs in chunks.

Parameters
  • config – Repo configuration object

  • data – Arrow table or path to parquet directory that contains logs dataset.

  • source – Logging source that provides schema and some additional metadata.

  • logging_config – used to determine destination

  • registry – Feast registry

This is an optional method that could be supported only be some stores.

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

Offline store config for local (file-based) store

type: 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]] = None, metadata: Optional[feast.infra.offline_stores.offline_store.RetrievalMetadata] = None)[source]
property metadata: Optional[feast.infra.offline_stores.offline_store.RetrievalMetadata]

Return metadata information about retrieval. Should be available even before materializing the dataset itself.

persist(storage: feast.saved_dataset.SavedDatasetStorage)[source]

Run the retrieval and persist the results in the same offline store used for read.

BigQuery Offline Store

class feast.infra.offline_stores.bigquery.BigQueryOfflineStore[source]
static offline_write_batch(config: feast.repo_config.RepoConfig, feature_view: feast.feature_view.FeatureView, table: pyarrow.lib.Table, progress: Optional[Callable[[int], Any]])[source]

Write features to a specified destination in the offline store. Data can be appended to an existing table (destination) or a new one will be created automatically

(if it doesn’t exist).

Hence, this function can be called repeatedly with the same destination config to write features.

Parameters
  • config – Repo configuration object

  • feature_view – FeatureView to write the data to.

  • table – pyarrow table containing feature data and timestamp column for historical feature retrieval

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

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

static pull_all_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], timestamp_field: str, start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob[source]

Returns a Retrieval Job for all join key columns, feature name columns, and the event timestamp columns that occur between the start_date and end_date.

Note that join_key_columns, feature_name_columns, timestamp_field, 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.

Parameters
  • config – Repo configuration object

  • data_source – Data source to pull all of the columns from

  • join_key_columns – Columns of the join keys

  • feature_name_columns – Columns of the feature names needed

  • timestamp_field – Timestamp column

  • start_date – Starting date of query

  • end_date – Ending date of query

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], timestamp_field: str, created_timestamp_column: Optional[str], start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob[source]

This method pulls data from the offline store, and the FeatureStore class is used to write this data into the online store. This method is invoked when running materialization (using the feast materialize or feast materialize-incremental commands, or the corresponding FeatureStore.materialize() method. This method pulls data from the offline store, and the FeatureStore class is used to write this data into the online store.

Note that join_key_columns, feature_name_columns, timestamp_field, 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.

Parameters
  • config – Repo configuration object

  • data_source – Data source to pull all of the columns from

  • join_key_columns – Columns of the join keys

  • feature_name_columns – Columns of the feature names needed

  • timestamp_field – Timestamp column

  • start_date – Starting date of query

  • end_date – Ending date of query

static write_logged_features(config: feast.repo_config.RepoConfig, data: Union[pyarrow.lib.Table, pathlib.Path], source: feast.feature_logging.LoggingSource, logging_config: feast.feature_logging.LoggingConfig, registry: feast.registry.BaseRegistry)[source]

Write logged features to a specified destination (taken from logging_config) in the offline store. Data can be appended to an existing table (destination) or a new one will be created automatically

(if it doesn’t exist).

Hence, this function can be called repeatedly with the same destination to flush logs in chunks.

Parameters
  • config – Repo configuration object

  • data – Arrow table or path to parquet directory that contains logs dataset.

  • source – Logging source that provides schema and some additional metadata.

  • logging_config – used to determine destination

  • registry – Feast registry

This is an optional method that could be supported only be some stores.

class feast.infra.offline_stores.bigquery.BigQueryOfflineStoreConfig(*, type: 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: 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]] = None, metadata: Optional[feast.infra.offline_stores.offline_store.RetrievalMetadata] = None)[source]
property metadata: Optional[feast.infra.offline_stores.offline_store.RetrievalMetadata]

Return metadata information about retrieval. Should be available even before materializing the dataset itself.

persist(storage: feast.saved_dataset.SavedDatasetStorage)[source]

Run the retrieval and persist the results in the same offline store used for read.

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 offline_write_batch(config: feast.repo_config.RepoConfig, feature_view: feast.feature_view.FeatureView, table: pyarrow.lib.Table, progress: Optional[Callable[[int], Any]])[source]

Write features to a specified destination in the offline store. Data can be appended to an existing table (destination) or a new one will be created automatically

(if it doesn’t exist).

Hence, this function can be called repeatedly with the same destination config to write features.

Parameters
  • config – Repo configuration object

  • feature_view – FeatureView to write the data to.

  • table – pyarrow table containing feature data and timestamp column for historical feature retrieval

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

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

static pull_all_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], timestamp_field: str, start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob[source]

Returns a Retrieval Job for all join key columns, feature name columns, and the event timestamp columns that occur between the start_date and end_date.

Note that join_key_columns, feature_name_columns, timestamp_field, 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.

Parameters
  • config – Repo configuration object

  • data_source – Data source to pull all of the columns from

  • join_key_columns – Columns of the join keys

  • feature_name_columns – Columns of the feature names needed

  • timestamp_field – Timestamp column

  • start_date – Starting date of query

  • end_date – Ending date of query

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], timestamp_field: str, created_timestamp_column: Optional[str], start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob[source]

This method pulls data from the offline store, and the FeatureStore class is used to write this data into the online store. This method is invoked when running materialization (using the feast materialize or feast materialize-incremental commands, or the corresponding FeatureStore.materialize() method. This method pulls data from the offline store, and the FeatureStore class is used to write this data into the online store.

Note that join_key_columns, feature_name_columns, timestamp_field, 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.

Parameters
  • config – Repo configuration object

  • data_source – Data source to pull all of the columns from

  • join_key_columns – Columns of the join keys

  • feature_name_columns – Columns of the feature names needed

  • timestamp_field – Timestamp column

  • start_date – Starting date of query

  • end_date – Ending date of query

static write_logged_features(config: feast.repo_config.RepoConfig, data: Union[pyarrow.lib.Table, pathlib.Path], source: feast.feature_logging.LoggingSource, logging_config: feast.feature_logging.LoggingConfig, registry: feast.registry.BaseRegistry)[source]

Write logged features to a specified destination (taken from logging_config) in the offline store. Data can be appended to an existing table (destination) or a new one will be created automatically

(if it doesn’t exist).

Hence, this function can be called repeatedly with the same destination to flush logs in chunks.

Parameters
  • config – Repo configuration object

  • data – Arrow table or path to parquet directory that contains logs dataset.

  • source – Logging source that provides schema and some additional metadata.

  • logging_config – used to determine destination

  • registry – Feast registry

This is an optional method that could be supported only be some stores.

class feast.infra.offline_stores.redshift.RedshiftOfflineStoreConfig(*, type: 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: 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]] = None, metadata: Optional[feast.infra.offline_stores.offline_store.RetrievalMetadata] = None)[source]
property metadata: Optional[feast.infra.offline_stores.offline_store.RetrievalMetadata]

Return metadata information about retrieval. Should be available even before materializing the dataset itself.

persist(storage: feast.saved_dataset.SavedDatasetStorage)[source]

Run the retrieval and persist the results in the same offline store used for read.

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

Snowflake Offline Store

class feast.infra.offline_stores.snowflake.SnowflakeOfflineStore[source]
static offline_write_batch(config: feast.repo_config.RepoConfig, feature_view: feast.feature_view.FeatureView, table: pyarrow.lib.Table, progress: Optional[Callable[[int], Any]])[source]

Write features to a specified destination in the offline store. Data can be appended to an existing table (destination) or a new one will be created automatically

(if it doesn’t exist).

Hence, this function can be called repeatedly with the same destination config to write features.

Parameters
  • config – Repo configuration object

  • feature_view – FeatureView to write the data to.

  • table – pyarrow table containing feature data and timestamp column for historical feature retrieval

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

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

static pull_all_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], timestamp_field: str, start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob[source]

Returns a Retrieval Job for all join key columns, feature name columns, and the event timestamp columns that occur between the start_date and end_date.

Note that join_key_columns, feature_name_columns, timestamp_field, 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.

Parameters
  • config – Repo configuration object

  • data_source – Data source to pull all of the columns from

  • join_key_columns – Columns of the join keys

  • feature_name_columns – Columns of the feature names needed

  • timestamp_field – Timestamp column

  • start_date – Starting date of query

  • end_date – Ending date of query

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], timestamp_field: str, created_timestamp_column: Optional[str], start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob[source]

This method pulls data from the offline store, and the FeatureStore class is used to write this data into the online store. This method is invoked when running materialization (using the feast materialize or feast materialize-incremental commands, or the corresponding FeatureStore.materialize() method. This method pulls data from the offline store, and the FeatureStore class is used to write this data into the online store.

Note that join_key_columns, feature_name_columns, timestamp_field, 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.

Parameters
  • config – Repo configuration object

  • data_source – Data source to pull all of the columns from

  • join_key_columns – Columns of the join keys

  • feature_name_columns – Columns of the feature names needed

  • timestamp_field – Timestamp column

  • start_date – Starting date of query

  • end_date – Ending date of query

static write_logged_features(config: feast.repo_config.RepoConfig, data: Union[pyarrow.lib.Table, pathlib.Path], source: feast.feature_logging.LoggingSource, logging_config: feast.feature_logging.LoggingConfig, registry: feast.registry.BaseRegistry)[source]

Write logged features to a specified destination (taken from logging_config) in the offline store. Data can be appended to an existing table (destination) or a new one will be created automatically

(if it doesn’t exist).

Hence, this function can be called repeatedly with the same destination to flush logs in chunks.

Parameters
  • config – Repo configuration object

  • data – Arrow table or path to parquet directory that contains logs dataset.

  • source – Logging source that provides schema and some additional metadata.

  • logging_config – used to determine destination

  • registry – Feast registry

This is an optional method that could be supported only be some stores.

class feast.infra.offline_stores.snowflake.SnowflakeOfflineStoreConfig(*, type: Literal['snowflake.offline'] = 'snowflake.offline', config_path: str = '/home/docs/.snowsql/config', account: str = None, user: str = None, password: str = None, role: str = None, warehouse: str = None, database: str = None, schema: str = None)[source]

Offline store config for Snowflake

account: Optional[str]

Snowflake deployment identifier – drop .snowflakecomputing.com

config_path: Optional[str]

Snowflake config path – absolute path required (Cant use ~)

database: Optional[str]

Snowflake database name

password: Optional[str]

Snowflake password

role: Optional[str]

Snowflake role name

schema_: Optional[str]

Snowflake schema name

type: Literal['snowflake.offline']

Offline store type selector

user: Optional[str]

Snowflake user name

warehouse: Optional[str]

Snowflake warehouse name

class feast.infra.offline_stores.snowflake.SnowflakeRetrievalJob(query: Union[str, Callable[[], AbstractContextManager[str]]], snowflake_conn: snowflake.connector.connection.SnowflakeConnection, config: feast.repo_config.RepoConfig, full_feature_names: bool, on_demand_feature_views: Optional[List[feast.on_demand_feature_view.OnDemandFeatureView]] = None, metadata: Optional[feast.infra.offline_stores.offline_store.RetrievalMetadata] = None)[source]
property metadata: Optional[feast.infra.offline_stores.offline_store.RetrievalMetadata]

Return metadata information about retrieval. Should be available even before materializing the dataset itself.

persist(storage: feast.saved_dataset.SavedDatasetStorage)[source]

Run the retrieval and persist the results in the same offline store used for read.

to_snowflake(table_name: str) None[source]

Save dataset as a new Snowflake table

to_sql() str[source]

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

Spark Offline Store

class feast.infra.offline_stores.contrib.spark_offline_store.spark.SparkOfflineStore[source]
static pull_all_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], timestamp_field: 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, timestamp_field, 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.

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], timestamp_field: str, created_timestamp_column: Optional[str], start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob[source]

This method pulls data from the offline store, and the FeatureStore class is used to write this data into the online store. This method is invoked when running materialization (using the feast materialize or feast materialize-incremental commands, or the corresponding FeatureStore.materialize() method. This method pulls data from the offline store, and the FeatureStore class is used to write this data into the online store.

Note that join_key_columns, feature_name_columns, timestamp_field, 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.

Parameters
  • config – Repo configuration object

  • data_source – Data source to pull all of the columns from

  • join_key_columns – Columns of the join keys

  • feature_name_columns – Columns of the feature names needed

  • timestamp_field – Timestamp column

  • start_date – Starting date of query

  • end_date – Ending date of query

class feast.infra.offline_stores.contrib.spark_offline_store.spark.SparkOfflineStoreConfig(*, type: pydantic.types.StrictStr = 'spark', spark_conf: Dict[str, str] = None)[source]
spark_conf: Optional[Dict[str, str]]

Configuration overlay for the spark session

type: pydantic.types.StrictStr

Offline store type selector

class feast.infra.offline_stores.contrib.spark_offline_store.spark.SparkRetrievalJob(spark_session: pyspark.sql.session.SparkSession, query: str, full_feature_names: bool, on_demand_feature_views: Optional[List[feast.on_demand_feature_view.OnDemandFeatureView]] = None, metadata: Optional[feast.infra.offline_stores.offline_store.RetrievalMetadata] = None)[source]
property metadata: Optional[feast.infra.offline_stores.offline_store.RetrievalMetadata]

Return metadata information about retrieval. Should be available even before materializing the dataset itself.

persist(storage: feast.saved_dataset.SavedDatasetStorage)[source]

Run the retrieval and persist the results in the same offline store used for read. Please note the persisting is done only within the scope of the spark session.

Trino Offline Store

class feast.infra.offline_stores.contrib.trino_offline_store.trino.TrinoOfflineStore[source]
static pull_all_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], timestamp_field: str, start_date: datetime.datetime, end_date: datetime.datetime, user: str = 'user', auth: Optional[trino.auth.Authentication] = None, http_scheme: Optional[str] = None) feast.infra.offline_stores.offline_store.RetrievalJob[source]

Returns a Retrieval Job for all join key columns, feature name columns, and the event timestamp columns that occur between the start_date and end_date.

Note that join_key_columns, feature_name_columns, timestamp_field, 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.

Parameters
  • config – Repo configuration object

  • data_source – Data source to pull all of the columns from

  • join_key_columns – Columns of the join keys

  • feature_name_columns – Columns of the feature names needed

  • timestamp_field – Timestamp column

  • start_date – Starting date of query

  • end_date – Ending date of query

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], timestamp_field: str, created_timestamp_column: Optional[str], start_date: datetime.datetime, end_date: datetime.datetime, user: str = 'user', auth: Optional[trino.auth.Authentication] = None, http_scheme: Optional[str] = None) feast.infra.offline_stores.contrib.trino_offline_store.trino.TrinoRetrievalJob[source]

This method pulls data from the offline store, and the FeatureStore class is used to write this data into the online store. This method is invoked when running materialization (using the feast materialize or feast materialize-incremental commands, or the corresponding FeatureStore.materialize() method. This method pulls data from the offline store, and the FeatureStore class is used to write this data into the online store.

Note that join_key_columns, feature_name_columns, timestamp_field, 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.

Parameters
  • config – Repo configuration object

  • data_source – Data source to pull all of the columns from

  • join_key_columns – Columns of the join keys

  • feature_name_columns – Columns of the feature names needed

  • timestamp_field – Timestamp column

  • start_date – Starting date of query

  • end_date – Ending date of query

class feast.infra.offline_stores.contrib.trino_offline_store.trino.TrinoOfflineStoreConfig(*, type: pydantic.types.StrictStr = 'trino', host: pydantic.types.StrictStr, port: int, catalog: pydantic.types.StrictStr, connector: Dict[str, str], dataset: pydantic.types.StrictStr = 'feast')[source]

Online store config for Trino

catalog: pydantic.types.StrictStr

Catalog of the Trino cluster

connector: Dict[str, str]

Trino connector to use as well as potential extra parameters. Needs to contain at least the path, for example {“type”: “bigquery”} or {“type”: “hive”, “file_format”: “parquet”}

dataset: pydantic.types.StrictStr

(optional) Trino Dataset name for temporary tables

host: pydantic.types.StrictStr

Host of the Trino cluster

port: int

Port of the Trino cluster

type: pydantic.types.StrictStr

Offline store type selector

class feast.infra.offline_stores.contrib.trino_offline_store.trino.TrinoRetrievalJob(query: str, client: feast.infra.offline_stores.contrib.trino_offline_store.trino_queries.Trino, config: feast.repo_config.RepoConfig, full_feature_names: bool, on_demand_feature_views: Optional[List[feast.on_demand_feature_view.OnDemandFeatureView]] = None, metadata: Optional[feast.infra.offline_stores.offline_store.RetrievalMetadata] = None)[source]
property metadata: Optional[feast.infra.offline_stores.offline_store.RetrievalMetadata]

Return metadata information about retrieval. Should be available even before materializing the dataset itself.

persist(storage: feast.saved_dataset.SavedDatasetStorage)[source]

Run the retrieval and persist the results in the same offline store used for read.

to_sql() str[source]

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

to_trino(destination_table: Optional[str] = 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 Trino table. Runs for a maximum amount of time specified by the timeout parameter (defaulting to 30 minutes). :param timeout: An optional number of seconds for setting the time limit of the QueryJob. :param retry_cadence: An optional number of seconds for setting how long the job should checked for completion.

Returns

Returns the destination table name.

PostgreSQL Offline Store

class feast.infra.offline_stores.contrib.postgres_offline_store.postgres.PostgreSQLOfflineStore[source]
static pull_all_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], timestamp_field: str, start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob[source]

Returns a Retrieval Job for all join key columns, feature name columns, and the event timestamp columns that occur between the start_date and end_date.

Note that join_key_columns, feature_name_columns, timestamp_field, 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.

Parameters
  • config – Repo configuration object

  • data_source – Data source to pull all of the columns from

  • join_key_columns – Columns of the join keys

  • feature_name_columns – Columns of the feature names needed

  • timestamp_field – Timestamp column

  • start_date – Starting date of query

  • end_date – Ending date of query

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], timestamp_field: str, created_timestamp_column: Optional[str], start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob[source]

This method pulls data from the offline store, and the FeatureStore class is used to write this data into the online store. This method is invoked when running materialization (using the feast materialize or feast materialize-incremental commands, or the corresponding FeatureStore.materialize() method. This method pulls data from the offline store, and the FeatureStore class is used to write this data into the online store.

Note that join_key_columns, feature_name_columns, timestamp_field, 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.

Parameters
  • config – Repo configuration object

  • data_source – Data source to pull all of the columns from

  • join_key_columns – Columns of the join keys

  • feature_name_columns – Columns of the feature names needed

  • timestamp_field – Timestamp column

  • start_date – Starting date of query

  • end_date – Ending date of query

class feast.infra.offline_stores.contrib.postgres_offline_store.postgres.PostgreSQLOfflineStoreConfig(*, host: pydantic.types.StrictStr, port: int = 5432, database: pydantic.types.StrictStr, db_schema: pydantic.types.StrictStr = 'public', user: pydantic.types.StrictStr, password: pydantic.types.StrictStr, sslmode: pydantic.types.StrictStr = None, sslkey_path: pydantic.types.StrictStr = None, sslcert_path: pydantic.types.StrictStr = None, sslrootcert_path: pydantic.types.StrictStr = None, type: Literal['postgres'] = 'postgres')[source]
class feast.infra.offline_stores.contrib.postgres_offline_store.postgres.PostgreSQLRetrievalJob(query: Union[str, Callable[[], AbstractContextManager[str]]], config: feast.repo_config.RepoConfig, full_feature_names: bool, on_demand_feature_views: Optional[List[feast.on_demand_feature_view.OnDemandFeatureView]], metadata: Optional[feast.infra.offline_stores.offline_store.RetrievalMetadata] = None)[source]
property metadata: Optional[feast.infra.offline_stores.offline_store.RetrievalMetadata]

Return metadata information about retrieval. Should be available even before materializing the dataset itself.

persist(storage: feast.saved_dataset.SavedDatasetStorage)[source]

Run the retrieval and persist the results in the same offline store used for read.

feast.infra.offline_stores.contrib.postgres_offline_store.postgres.build_point_in_time_query(feature_view_query_contexts: List[dict], left_table_query_string: str, entity_df_event_timestamp_col: str, entity_df_columns: KeysView[str], query_template: str, full_feature_names: bool = False) str[source]

Build point-in-time query between each feature view table and the entity dataframe for PostgreSQL

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: 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 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 in the original order as the entity_keys argument. 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: 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 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) –

plan(config: feast.repo_config.RepoConfig, desired_registry_proto: feast.core.Registry_pb2.Registry) List[feast.infra.infra_object.InfraObject][source]

Returns the set of InfraObjects required to support the desired registry.

Parameters
  • config – The RepoConfig for the current FeatureStore.

  • desired_registry_proto – The desired registry, in proto form.

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.

_conn

SQLite connection.

Type

Optional[sqlite3.Connection]

online_read(config: feast.repo_config.RepoConfig, table: 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 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 in the original order as the entity_keys argument. 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: 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 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) –

plan(config: feast.repo_config.RepoConfig, desired_registry_proto: feast.core.Registry_pb2.Registry) List[feast.infra.infra_object.InfraObject][source]

Returns the set of InfraObjects required to support the desired registry.

Parameters
  • config – The RepoConfig for the current FeatureStore.

  • desired_registry_proto – The desired registry, in proto form.

class feast.infra.online_stores.sqlite.SqliteOnlineStoreConfig(*, type: 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: Literal['sqlite', 'feast.infra.online_stores.sqlite.SqliteOnlineStore']

Online store type selector

class feast.infra.online_stores.sqlite.SqliteTable(path: str, name: str)[source]

A Sqlite table managed by Feast.

path

The absolute path of the Sqlite file.

Type

str

name

The name of the table.

conn

SQLite connection.

Type

sqlite3.Connection

static from_infra_object_proto(infra_object_proto: feast.core.InfraObject_pb2.InfraObject) Any[source]

Returns an InfraObject created from a protobuf representation.

Parameters

infra_object_proto – A protobuf representation of an InfraObject.

Raises

FeastInvalidInfraObjectType – The type of InfraObject could not be identified.

static from_proto(sqlite_table_proto: feast.core.SqliteTable_pb2.SqliteTable) Any[source]

Converts a protobuf representation of a subclass to an object of that subclass.

Parameters

infra_object_proto – A protobuf representation of an InfraObject.

Raises

FeastInvalidInfraObjectType – The type of InfraObject could not be identified.

teardown()[source]

Tears down the infrastructure object.

to_infra_object_proto() feast.core.InfraObject_pb2.InfraObject[source]

Converts an InfraObject to its protobuf representation, wrapped in an InfraObjectProto.

to_proto() Any[source]

Converts an InfraObject to its protobuf representation.

update()[source]

Deploys or updates the infrastructure object.

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: feast.feature_view.FeatureView, entity_keys: List[feast.types.EntityKey_pb2.EntityKey], requested_features: Optional