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.feature_service.FeatureService, List[Union[feast.feature_view.FeatureView, feast.on_demand_feature_view.OnDemandFeatureView, feast.request_feature_view.RequestFeatureView, feast.entity.Entity, feast.feature_service.FeatureService, feast.data_source.DataSource]]], 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]]] = 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, ValueType, FileSource, RepoConfig >>> from datetime import timedelta >>> fs = FeatureStore(repo_path="feature_repo") >>> driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id") >>> driver_hourly_stats = FileSource( ... path="feature_repo/data/driver_stats.parquet", ... event_timestamp_column="event_timestamp", ... created_timestamp_column="created", ... ) >>> driver_hourly_stats_view = FeatureView( ... name="driver_hourly_stats", ... entities=["driver_id"], ... ttl=timedelta(seconds=86400 * 1), ... batch_source=driver_hourly_stats, ... ) >>> fs.apply([driver_hourly_stats_view, driver]) # register entity and feature view
- config: feast.repo_config.RepoConfig
- 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, profiler: Optional[feast.dqm.profilers.ge_profiler.GEProfiler] = 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) feast.entity.Entity [source]
Retrieves an entity.
- Parameters
name – Name of entity.
- Returns
The specified entity.
- Raises
EntityNotFoundException – The entity could not be found.
- get_feature_server_endpoint() Optional[str] [source]
Returns endpoint for the feature server, if it exists.
- get_feature_service(name: str, 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) feast.feature_view.FeatureView [source]
Retrieves a feature view.
- Parameters
name – Name of feature view.
- Returns
The specified feature view.
- Raises
FeatureViewNotFoundException – The feature view could not be found.
- get_historical_features(entity_df: Union[pandas.core.frame.DataFrame, str], features: 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 – A list of features, that should be retrieved from the offline store. Either a list of string feature references can be provided or a FeatureService object. Feature references are of the format “feature_view:feature”, e.g., “customer_fv:daily_transactions”.
full_feature_names – A boolean that provides the option to add the feature view prefixes to the feature names, changing them from the format “feature” to “feature_view__feature” (e.g., “daily_transactions” changes to “customer_fv__daily_transactions”). By default, this value is set to False.
- Returns
RetrievalJob which can be used to materialize the results.
- Raises
ValueError – Both or neither of features and feature_refs are specified.
Examples
Retrieve historical features from a local offline store.
>>> from feast import FeatureStore, RepoConfig >>> import pandas as pd >>> fs = FeatureStore(repo_path="feature_repo") >>> entity_df = pd.DataFrame.from_dict( ... { ... "driver_id": [1001, 1002], ... "event_timestamp": [ ... datetime(2021, 4, 12, 10, 59, 42), ... datetime(2021, 4, 12, 8, 12, 10), ... ], ... } ... ) >>> retrieval_job = fs.get_historical_features( ... entity_df=entity_df, ... features=[ ... "driver_hourly_stats:conv_rate", ... "driver_hourly_stats:acc_rate", ... "driver_hourly_stats:avg_daily_trips", ... ], ... ) >>> feature_data = retrieval_job.to_df()
- 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 – List of feature references that will be returned for each entity. Each feature reference should have the following format: “feature_view:feature” where “feature_view” & “feature” refer to the Feature and FeatureView names respectively. Only the feature name is required.
entity_rows – A list of dictionaries where each key-value is an entity-name, entity-value pair.
- Returns
OnlineResponse containing the feature data in records.
- Raises
Exception – No entity with the specified name exists.
Examples
Materialize all features into the online store over the interval from 3 hours ago to 10 minutes ago, and then retrieve these online features.
>>> from feast import FeatureStore, RepoConfig >>> fs = FeatureStore(repo_path="feature_repo") >>> online_response = fs.get_online_features( ... features=[ ... "driver_hourly_stats:conv_rate", ... "driver_hourly_stats:acc_rate", ... "driver_hourly_stats:avg_daily_trips", ... ], ... entity_rows=[{"driver_id": 1001}, {"driver_id": 1002}, {"driver_id": 1003}, {"driver_id": 1004}], ... ) >>> online_response_dict = online_response.to_dict()
- 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
- 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.
- materialize(start_date: datetime.datetime, end_date: datetime.datetime, feature_views: Optional[List[str]] = None) None [source]
Materialize data from the offline store into the online store.
This method loads feature data in the specified interval from either the specified feature views, or all feature views if none are specified, into the online store where it is available for online serving.
- Parameters
start_date (datetime) – Start date for time range of data to materialize into the online store
end_date (datetime) – End date for time range of data to materialize into the online store
feature_views (List[str]) – Optional list of feature view names. If selected, will only run materialization for the specified feature views.
Examples
Materialize all features into the online store over the interval from 3 hours ago to 10 minutes ago.
>>> from feast import FeatureStore, RepoConfig >>> from datetime import datetime, timedelta >>> fs = FeatureStore(repo_path="feature_repo") >>> fs.materialize( ... start_date=datetime.utcnow() - timedelta(hours=3), end_date=datetime.utcnow() - timedelta(minutes=10) ... ) Materializing... ...
- materialize_incremental(end_date: datetime.datetime, feature_views: Optional[List[str]] = None) None [source]
Materialize incremental new data from the offline store into the online store.
This method loads incremental new feature data up to the specified end time from either the specified feature views, or all feature views if none are specified, into the online store where it is available for online serving. The start time of the interval materialized is either the most recent end time of a prior materialization or (now - ttl) if no such prior materialization exists.
- Parameters
end_date (datetime) – End date for time range of data to materialize into the online store
feature_views (List[str]) – Optional list of feature view names. If selected, will only run materialization for the specified feature views.
- Raises
Exception – A feature view being materialized does not have a TTL set.
Examples
Materialize all features into the online store up to 5 minutes ago.
>>> from feast import FeatureStore, RepoConfig >>> from datetime import datetime, timedelta >>> fs = FeatureStore(repo_path="feature_repo") >>> fs.materialize_incremental(end_date=datetime.utcnow() - timedelta(minutes=5)) Materializing... ...
- refresh_registry()[source]
Fetches and caches a copy of the feature registry in memory.
Explicitly calling this method allows for direct control of the state of the registry cache. Every time this method is called the complete registry state will be retrieved from the remote registry store backend (e.g., GCS, S3), and the cache timer will be reset. If refresh_registry() is run before get_online_features() is called, then get_online_feature() will use the cached registry instead of retrieving (and caching) the registry itself.
Additionally, the TTL for the registry cache can be set to infinity (by setting it to 0), which means that refresh_registry() will become the only way to update the cached registry. If the TTL is set to a value greater than 0, then once the cache becomes stale (more time than the TTL has passed), a new cache will be downloaded synchronously, which may increase latencies if the triggering method is get_online_features()
- property registry: feast.registry.Registry
Gets the registry of this feature store.
- repo_path: pathlib.Path
- serve(host: str, port: int, no_access_log: bool) None [source]
Start the feature consumption server locally on a given port.
Config
- class feast.repo_config.RegistryConfig(*, registry_store_type: pydantic.types.StrictStr = None, path: pydantic.types.StrictStr, cache_ttl_seconds: pydantic.types.StrictInt = 600, **extra_data: Any)[source]
Metadata Store Configuration. Configuration that relates to reading from and writing to the Feast registry.
- cache_ttl_seconds: pydantic.types.StrictInt
The cache TTL is the amount of time registry state will be cached in memory. If this TTL is exceeded then the registry will be refreshed when any feature store method asks for access to registry state. The TTL can be set to infinity by setting TTL to 0 seconds, which means the cache will only be loaded once and will never expire. Users can manually refresh the cache by calling feature_store.refresh_registry()
- Type
- path: pydantic.types.StrictStr
Path to metadata store. Can be a local path, or remote object storage path, e.g. a GCS URI
- Type
- class feast.repo_config.RepoConfig(*, registry: Union[pydantic.types.StrictStr, feast.repo_config.RegistryConfig] = 'data/registry.db', project: pydantic.types.StrictStr, provider: pydantic.types.StrictStr, online_store: Any = None, offline_store: Any = None, feature_server: Any = None, flags: Any = None, repo_path: pathlib.Path = None, **data: Any)[source]
Repo config. Typically loaded from feature_store.yaml
- feature_server: Optional[Any]
Feature server configuration (optional depending on provider)
- Type
FeatureServerConfig
- flags: Any
Feature flags for experimental features (optional)
- Type
Flags
- offline_store: Any
Offline store configuration (optional depending on provider)
- Type
OfflineStoreConfig
- online_store: Any
Online store configuration (optional depending on provider)
- Type
OnlineStoreConfig
- project: pydantic.types.StrictStr
Feast project id. This can be any alphanumeric string up to 16 characters. You can have multiple independent feature repositories deployed to the same cloud provider account, as long as they have different project ids.
- Type
- 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
Data Source
- class feast.data_source.DataSource(name: str, event_timestamp_column: Optional[str] = None, created_timestamp_column: Optional[str] = None, field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = None)[source]
DataSource that can be used to source features.
- Parameters
name – Name of data source, which should be unique within a project
event_timestamp_column (optional) – Event timestamp column used for point in time joins of feature values.
created_timestamp_column (optional) – Timestamp column indicating when the row was created, used for deduplicating rows.
field_mapping (optional) – A dictionary mapping of column names in this data source to feature names in a feature table or view. Only used for feature columns, not entity or timestamp columns.
date_partition_column (optional) – Timestamp column used for partitioning.
- 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.RequestDataSource(name: str, schema: Dict[str, feast.value_type.ValueType])[source]
RequestDataSource that can be used to provide input features for on demand transforms
- Parameters
name – Name of the request data source
schema – Schema mapping from the input feature name to a ValueType
- static from_proto(data_source: feast.core.DataSource_pb2.DataSource)[source]
Converts data source config in 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.
- class feast.data_source.SourceType(value)[source]
DataSource value type. Used to define source types in DataSource.
BigQuery Source
- class feast.infra.offline_stores.bigquery_source.BigQuerySource(name: Optional[str] = None, event_timestamp_column: Optional[str] = '', table: Optional[str] = None, table_ref: Optional[str] = None, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = '', query: Optional[str] = None)[source]
- 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.RedshiftSource(name: Optional[str] = None, event_timestamp_column: Optional[str] = '', table: Optional[str] = None, schema: Optional[str] = None, created_timestamp_column: Optional[str] = '', field_mapping: Optional[Dict[str, str]] = None, date_partition_column: Optional[str] = '', query: Optional[str] = None)[source]
- static from_proto(data_source: feast.core.DataSource_pb2.DataSource)[source]
Creates a RedshiftSource from a protobuf representation of a RedshiftSource.
- Parameters
data_source – A protobuf representation of a RedshiftSource
- Returns
A RedshiftSource object based on the data_source protobuf.
- get_table_column_names_and_types(config: feast.repo_config.RepoConfig) Iterable[Tuple[str, str]] [source]
Returns a mapping of column names to types for this Redshift source.
- Parameters
config – A RepoConfig describing the feature repo
- get_table_query_string() str [source]
Returns a string that can directly be used to reference this table in SQL.
- property query
Returns the Redshift options of this Redshift source.
- property 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.
File Source
- class feast.infra.offline_stores.file_source.FileSource(path: str, name: Optional[str] = '', 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)[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(name: str, value_type: feast.value_type.ValueType = ValueType.UNKNOWN, description: str = '', join_key: Optional[str] = None, tags: Dict[str, str] = None, labels: Optional[Dict[str, str]] = None, owner: str = '')[source]
An entity defines a collection of entities for which features can be defined. An entity can also contain associated metadata.
- value_type
The type of the entity, such as string or float.
- 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
- 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]
- 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.
Feature View
- class feast.feature_view.FeatureView(name: str, entities: List[str], ttl: Union[google.protobuf.duration_pb2.Duration, datetime.timedelta], input: Optional[feast.data_source.DataSource] = None, batch_source: Optional[feast.data_source.DataSource] = None, stream_source: Optional[feast.data_source.DataSource] = None, features: Optional[List[feast.feature.Feature]] = None, tags: Optional[Dict[str, str]] = None, online: bool = True)[source]
A FeatureView defines a logical grouping of serveable features.
- Parameters
name – Name of the group of features.
entities – The entities to which this group of features is associated.
ttl – The amount of time this group of features lives. A ttl of 0 indicates that this group of features lives forever. Note that large ttl’s or a ttl of 0 can result in extremely computationally intensive queries.
input – The source of data where this group of features is stored.
batch_source (optional) – The batch source of data where this group of features is stored.
stream_source (optional) – The stream source of data where this group of features is stored.
features (optional) – The set of features defined as part of this FeatureView.
tags (optional) – A dictionary of key-value pairs used for organizing FeatureViews.
- ensure_valid()[source]
Validates the state of this feature view locally.
- Raises
ValueError – The feature view does not have a name or does not have entities.
- classmethod from_proto(feature_view_proto: feast.core.FeatureView_pb2.FeatureView)[source]
Creates a feature view from a protobuf representation of a feature view.
- Parameters
feature_view_proto – A protobuf representation of a feature view.
- Returns
A FeatureViewProto object based on the feature view protobuf.
- property most_recent_end_time: Optional[datetime.datetime]
Retrieves the latest time up to which the feature view has been materialized.
- Returns
The latest time, or None if the feature view has not been materialized.
- to_proto() feast.core.FeatureView_pb2.FeatureView [source]
Converts a feature view object to its protobuf representation.
- Returns
A FeatureViewProto protobuf.
- with_join_key_map(join_key_map: Dict[str, str])[source]
Sets the join_key_map by returning a copy of this feature view with that field set. This join_key mapping operation is only used as part of query operations and will not modify the underlying FeatureView.
- Parameters
join_key_map – A map of join keys in which the left is the join_key that corresponds with the feature data and the right corresponds with the entity data.
- Returns
A copy of this FeatureView with the join_key_map replaced with the ‘join_key_map’ input.
Examples
Join a location feature data table to both the origin column and destination column of the entity data.
- temperatures_feature_service = FeatureService(
name=”temperatures”, features=[
- location_stats_feature_view
.with_name(“origin_stats”) .with_join_key_map(
{“location_id”: “origin_id”}
),
- location_stats_feature_view
.with_name(“destination_stats”) .with_join_key_map(
{“location_id”: “destination_id”}
),
],
)
- with_name(name: str)[source]
Renames this feature view by returning a copy of this feature view with an alias set for the feature view name. This rename operation is only used as part of query operations and will not modify the underlying FeatureView.
- Parameters
name – Name to assign to the FeatureView copy.
- Returns
A copy of this FeatureView with the name replaced with the ‘name’ input.
- with_projection(feature_view_projection: feast.feature_view_projection.FeatureViewProjection)[source]
Sets the feature view projection by returning a copy of this feature view with its projection set to the given projection. A projection is an object that stores the modifications to a feature view that is used during query operations.
- Parameters
feature_view_projection – The FeatureViewProjection object to link to this OnDemandFeatureView.
- Returns
A copy of this FeatureView with its projection replaced with the ‘feature_view_projection’ argument.
On Demand Feature View
- class feast.on_demand_feature_view.OnDemandFeatureView(name: str, features: List[feast.feature.Feature], inputs: Dict[str, Union[feast.feature_view.FeatureView, feast.feature_view_projection.FeatureViewProjection, feast.data_source.RequestDataSource]], udf: method)[source]
[Experimental] An OnDemandFeatureView defines on demand transformations on existing feature view values and request data.
- Parameters
name – Name of the group of features.
features – Output schema of transformation with feature names
inputs – The input feature views passed into the transform.
udf – User defined transformation function that takes as input pandas dataframes
- classmethod from_proto(on_demand_feature_view_proto: feast.core.OnDemandFeatureView_pb2.OnDemandFeatureView)[source]
Creates an on demand feature view from a protobuf representation.
- Parameters
on_demand_feature_view_proto – A protobuf representation of an on-demand feature view.
- Returns
A OnDemandFeatureView object based on the on-demand feature view protobuf.
- infer_features()[source]
Infers the set of features associated to this feature view from the input source.
- Raises
RegistryInferenceFailure – The set of features could not be inferred.
- feast.on_demand_feature_view.on_demand_feature_view(features: List[feast.feature.Feature], inputs: Dict[str, Union[feast.feature_view.FeatureView, feast.data_source.RequestDataSource]])[source]
Declare an on-demand feature view
- Parameters
features – Output schema with feature names
inputs – The inputs passed into the transform.
- Returns
An On Demand Feature View.
Feature
- class feast.feature.Feature(name: str, dtype: feast.value_type.ValueType, labels: Optional[Dict[str, str]] = None)[source]
A Feature represents a class of serveable feature.
- Parameters
name – Name of the feature.
dtype – The type of the feature, such as string or float.
labels (optional) – User-defined metadata in dictionary form.
- property dtype: feast.value_type.ValueType
Gets the data type of this feature.
- classmethod from_proto(feature_proto: feast.core.Feature_pb2.FeatureSpecV2)[source]
- Parameters
feature_proto – FeatureSpecV2 protobuf object
- Returns
Feature object
- property name
Gets the name of this feature.
Feature Service
- class feast.feature_service.FeatureService(name: str, features: List[Union[feast.feature_view.FeatureView, feast.on_demand_feature_view.OnDemandFeatureView]], tags: Dict[str, str] = None, description: str = '', owner: str = '')[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.
- 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]
- 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]
Registry
- 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]
Registers a single entity 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
- 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
- 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_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
- 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
- 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
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
- 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_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.
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
- 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_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.
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.
- 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], event_timestamp_column: str, start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob [source]
Note that join_key_columns, feature_name_columns, event_timestamp_column, and created_timestamp_column have all already been mapped to column names of the source table and those column names are the values passed into this function.
- abstract static pull_latest_from_table_or_query(config: feast.repo_config.RepoConfig, data_source: feast.data_source.DataSource, join_key_columns: List[str], feature_name_columns: List[str], event_timestamp_column: str, created_timestamp_column: Optional[str], start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob [source]
Note that join_key_columns, feature_name_columns, event_timestamp_column, and created_timestamp_column have all already been mapped to column names of the source table and those column names are the values passed into this function.
- class feast.infra.offline_stores.offline_store.RetrievalJob[source]
RetrievalJob is used to manage the execution of a historical feature retrieval
- 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.
File Offline Store
- class feast.infra.offline_stores.file.FileOfflineStore[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], event_timestamp_column: str, start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob [source]
Note that join_key_columns, feature_name_columns, event_timestamp_column, and created_timestamp_column have all already been mapped to column names of the source table and those column names are the values passed into this function.
- static pull_latest_from_table_or_query(config: feast.repo_config.RepoConfig, data_source: feast.data_source.DataSource, join_key_columns: List[str], feature_name_columns: List[str], event_timestamp_column: str, created_timestamp_column: Optional[str], start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob [source]
Note that join_key_columns, feature_name_columns, event_timestamp_column, and created_timestamp_column have all already been mapped to column names of the source table and those column names are the values passed into this function.
- class feast.infra.offline_stores.file.FileOfflineStoreConfig(*, type: typing_extensions.Literal[file] = 'file')[source]
Offline store config for local (file-based) store
- type: typing_extensions.Literal[file]
Offline store type selector
- class feast.infra.offline_stores.file.FileRetrievalJob(evaluation_function: Callable, full_feature_names: bool, on_demand_feature_views: Optional[List[feast.on_demand_feature_view.OnDemandFeatureView]] = 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.
BigQuery Offline Store
- class feast.infra.offline_stores.bigquery.BigQueryOfflineStore[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], event_timestamp_column: str, start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob [source]
Note that join_key_columns, feature_name_columns, event_timestamp_column, and created_timestamp_column have all already been mapped to column names of the source table and those column names are the values passed into this function.
- static pull_latest_from_table_or_query(config: feast.repo_config.RepoConfig, data_source: feast.data_source.DataSource, join_key_columns: List[str], feature_name_columns: List[str], event_timestamp_column: str, created_timestamp_column: Optional[str], start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob [source]
Note that join_key_columns, feature_name_columns, event_timestamp_column, and created_timestamp_column have all already been mapped to column names of the source table and those column names are the values passed into this function.
- class feast.infra.offline_stores.bigquery.BigQueryOfflineStoreConfig(*, type: typing_extensions.Literal[bigquery] = 'bigquery', dataset: pydantic.types.StrictStr = 'feast', project_id: pydantic.types.StrictStr = None, location: pydantic.types.StrictStr = None)[source]
Offline store config for GCP BigQuery
- dataset: pydantic.types.StrictStr
(optional) BigQuery Dataset name for temporary tables
- location: Optional[pydantic.types.StrictStr]
(optional) GCP location name used for the BigQuery offline store. Examples of location names include
US
,EU
,us-central1
,us-west4
. If a location is not specified, the location defaults to theUS
multi-regional location. For more information on BigQuery data locations see: https://cloud.google.com/bigquery/docs/locations
- project_id: Optional[pydantic.types.StrictStr]
(optional) GCP project name used for the BigQuery offline store
- type: typing_extensions.Literal[bigquery]
Offline store type selector
- class feast.infra.offline_stores.bigquery.BigQueryRetrievalJob(query: Union[str, Callable[[], AbstractContextManager[str]]], client: google.cloud.bigquery.client.Client, config: feast.repo_config.RepoConfig, full_feature_names: bool, on_demand_feature_views: Optional[List[feast.on_demand_feature_view.OnDemandFeatureView]] = 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.
- feast.infra.offline_stores.bigquery.block_until_done(client: google.cloud.bigquery.client.Client, bq_job: Union[google.cloud.bigquery.job.query.QueryJob, google.cloud.bigquery.job.load.LoadJob], timeout: int = 1800, retry_cadence: float = 1)[source]
Waits for bq_job to finish running, up to a maximum amount of time specified by the timeout parameter (defaulting to 30 minutes).
- Parameters
client – A bigquery.client.Client to monitor the bq_job.
bq_job – The bigquery.job.QueryJob that blocks until done runnning.
timeout – An optional number of seconds for setting the time limit of the job.
retry_cadence – An optional number of seconds for setting how long the job should checked for completion.
- Raises
BigQueryJobStillRunning exception if the function has blocked longer than 30 minutes. –
BigQueryJobCancelled exception to signify when that the job has been cancelled (i.e. from timeout or KeyboardInterrupt) –
Redshift Offline Store
- class feast.infra.offline_stores.redshift.RedshiftOfflineStore[source]
- static pull_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], event_timestamp_column: str, start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob [source]
Note that join_key_columns, feature_name_columns, event_timestamp_column, and created_timestamp_column have all already been mapped to column names of the source table and those column names are the values passed into this function.
- static pull_latest_from_table_or_query(config: feast.repo_config.RepoConfig, data_source: feast.data_source.DataSource, join_key_columns: List[str], feature_name_columns: List[str], event_timestamp_column: str, created_timestamp_column: Optional[str], start_date: datetime.datetime, end_date: datetime.datetime) feast.infra.offline_stores.offline_store.RetrievalJob [source]
Note that join_key_columns, feature_name_columns, event_timestamp_column, and created_timestamp_column have all already been mapped to column names of the source table and those column names are the values passed into this function.
- class feast.infra.offline_stores.redshift.RedshiftOfflineStoreConfig(*, type: typing_extensions.Literal[redshift] = 'redshift', cluster_id: pydantic.types.StrictStr, region: pydantic.types.StrictStr, user: pydantic.types.StrictStr, database: pydantic.types.StrictStr, s3_staging_location: pydantic.types.StrictStr, iam_role: pydantic.types.StrictStr)[source]
Offline store config for AWS Redshift
- cluster_id: pydantic.types.StrictStr
Redshift cluster identifier
- database: pydantic.types.StrictStr
Redshift database name
- iam_role: pydantic.types.StrictStr
IAM Role for Redshift, granting it access to S3
- region: pydantic.types.StrictStr
Redshift cluster’s AWS region
- s3_staging_location: pydantic.types.StrictStr
S3 path for importing & exporting data to Redshift
- type: typing_extensions.Literal[redshift]
Offline store type selector
- user: pydantic.types.StrictStr
Redshift user name
- class feast.infra.offline_stores.redshift.RedshiftRetrievalJob(query: Union[str, Callable[[], AbstractContextManager[str]]], redshift_client, s3_resource, config: feast.repo_config.RepoConfig, full_feature_names: bool, on_demand_feature_views: Optional[List[feast.on_demand_feature_view.OnDemandFeatureView]] = 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.
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. 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. 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: typing_extensions.Literal[sqlite, feast.infra.online_stores.sqlite.SqliteOnlineStore] = 'sqlite', path: pydantic.types.StrictStr = 'data/online.db')[source]
Online store config for local (SQLite-based) store
- path: pydantic.types.StrictStr
(optional) Path to sqlite db
- type: typing_extensions.Literal[sqlite, feast.infra.online_stores.sqlite.SqliteOnlineStore]
Online store type selector
- class feast.infra.online_stores.sqlite.SqliteTable(path: str, name: str)[source]
A Sqlite table managed by Feast.
- name
The name of the table.
- conn
SQLite connection.
- Type
- 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.
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[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. 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) –
- class feast.infra.online_stores.datastore.DatastoreOnlineStoreConfig(*, type: typing_extensions.Literal[datastore] = 'datastore', project_id: pydantic.types.StrictStr = None, namespace: pydantic.types.StrictStr = None, write_concurrency: pydantic.types.PositiveInt = 40, write_batch_size: pydantic.types.PositiveInt = 50)[source]
Online store config for GCP Datastore
- namespace: Optional[pydantic.types.StrictStr]
(optional) Datastore namespace
- project_id: Optional[pydantic.types.StrictStr]
(optional) GCP Project Id
- type: typing_extensions.Literal[datastore]
Online store type selector
- write_batch_size: Optional[pydantic.types.PositiveInt]
(optional) Amount of feature rows per batch being written into Datastore
- write_concurrency: Optional[pydantic.types.PositiveInt]
(optional) Amount of threads to use when writing batches of feature rows into Datastore
- class feast.infra.online_stores.datastore.DatastoreTable(project: str, name: str, project_id: Optional[str] = None, namespace: Optional[str] = None)[source]
A Datastore table managed by Feast.
- name
The name of the table.
- project_id
The GCP project id.
- Type
optional
- namespace
Datastore namespace.
- Type
optional
- 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(datastore_table_proto: feast.core.DatastoreTable_pb2.DatastoreTable) 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.
DynamoDB Online Store
- class feast.infra.online_stores.dynamodb.DynamoDBOnlineStore[source]
Online feature store for AWS DynamoDB.
- _dynamodb_client
Boto3 DynamoDB client.
- _dynamodb_resource
Boto3 DynamoDB resource.
- 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]
Retrieve feature values from the online DynamoDB store.
Note: This method is currently not optimized to retrieve a lot of data at a time as it does sequential gets from the DynamoDB table.
- 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.
- 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 online DynamoDB store.
Note: This method applies a
batch_writer
to automatically handle any unprocessed items and resend them as needed, this is useful if you’re loading a lot of data at a time.- 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) –
- teardown(config: feast.repo_config.RepoConfig, tables: Sequence[feast.feature_view.FeatureView], entities: Sequence[feast.entity.Entity])[source]
Delete tables from the DynamoDB Online Store.
- Parameters
config – The RepoConfig for the current FeatureStore.
tables – Tables to delete from the feature repo.
- update(config: feast.repo_config.RepoConfig, 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]
Update tables from the DynamoDB Online Store.
- Parameters
config – The RepoConfig for the current FeatureStore.
tables_to_delete – Tables to delete from the DynamoDB Online Store.
tables_to_keep – Tables to keep in the DynamoDB Online Store.
- class feast.infra.online_stores.dynamodb.DynamoDBOnlineStoreConfig(*, type: typing_extensions.Literal[dynamodb] = 'dynamodb', region: pydantic.types.StrictStr)[source]
Online store config for DynamoDB store
- region: pydantic.types.StrictStr
AWS Region Name
- type: typing_extensions.Literal[dynamodb]
Online store type selector
- class feast.infra.online_stores.dynamodb.DynamoDBTable(name: str, region: str)[source]
A DynamoDB table managed by Feast.
- name
The name of the table.
- 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(dynamodb_table_proto: feast.core.DynamoDBTable_pb2.DynamoDBTable) 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.
Redis Online Store
- class feast.infra.online_stores.redis.RedisOnlineStore[source]
- 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. 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) –
- teardown(config: feast.repo_config.RepoConfig, tables: Sequence[feast.feature_view.FeatureView], entities: Sequence[feast.entity.Entity])[source]
We delete the keys in redis for tables/views being removed.
- update(config: feast.repo_config.RepoConfig, tables_to_delete: Sequence[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]
Look for join_keys (list of entities) that are not in use anymore (usually this happens when the last feature view that was using specific compound key is deleted) and remove all features attached to this “join_keys”.
- class feast.infra.online_stores.redis.RedisOnlineStoreConfig(*, type: typing_extensions.Literal[redis] = 'redis', redis_type: feast.infra.online_stores.redis.RedisType = RedisType.redis, connection_string: pydantic.types.StrictStr = 'localhost:6379', key_ttl_seconds: int = None)[source]
Online store config for Redis store
- connection_string: pydantic.types.StrictStr
Connection string containing the host, port, and configuration parameters for Redis format: host:port,parameter1,parameter2 eg. redis:6379,db=0
- redis_type: feast.infra.online_stores.redis.RedisType
redis or redis_cluster
- Type
Redis type
- type: typing_extensions.Literal[redis]
Online store type selector