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The top-level namespace within Feast is a project. Users define one or more feature views within a project. Each feature view contains one or more features. These features typically relate to one or more entities. A feature view must always have a data source, which in turn is used during the generation of training datasets and when materializing feature values into the online store.
Projects provide complete isolation of feature stores at the infrastructure level. This is accomplished through resource namespacing, e.g., prefixing table names with the associated project. Each project should be considered a completely separate universe of entities and features. It is not possible to retrieve features from multiple projects in a single request. We recommend having a single feature store and a single project per environment (dev
, staging
, prod
).
Projects are currently being supported for backward compatibility reasons. Projects may change in the future as we simplify the Feast API.
A feature view is an object that represents a logical group of time-series feature data as it is found in a . Feature views consist of zero or more , one or more , and a . Feature views allow Feast to model your existing feature data in a consistent way in both an offline (training) and online (serving) environment. Feature views generally contain features that are properties of a specific object, in which case that object is defined as an entity and included in the feature view. If the features are not related to a specific object, the feature view might not have entities; see below.
Feature views are used during
The generation of training datasets by querying the data source of feature views in order to find historical feature values. A single training dataset may consist of features from multiple feature views.
Loading of feature values into an online store. Feature views determine the storage schema in the online store. Feature values can be loaded from batch sources or from .
Retrieval of features from the online store. Feature views provide the schema definition to Feast in order to look up features from the online store.
Feast does not generate feature values. It acts as the ingestion and serving system. The data sources described within feature views should reference feature values in their already computed form.
If a feature view contains features that are not related to a specific entity, the feature view can be defined without entities (only event timestamps are needed for this feature view).
If the features
parameter is not specified in the feature view creation, Feast will infer the features during feast apply
by creating a feature for each column in the underlying data source except the columns corresponding to the entities of the feature view or the columns corresponding to the timestamp columns of the feature view's data source. The names and value types of the inferred features will use the names and data types of the columns from which the features were inferred.
"Entity aliases" can be specified to join entity_dataframe
columns that do not match the column names in the source table of a FeatureView.
This could be used if a user has no control over these column names or if there are multiple entities are a subclass of a more general entity. For example, "spammer" and "reporter" could be aliases of a "user" entity, and "origin" and "destination" could be aliases of a "location" entity as shown below.
It is suggested that you dynamically specify the new FeatureView name using .with_name
and join_key_map
override using .with_join_key_map
instead of needing to register each new copy.
A feature is an individual measurable property. It is typically a property observed on a specific entity, but does not have to be associated with an entity. For example, a feature of a customer
entity could be the number of transactions they have made on an average month, while a feature that is not observed on a specific entity could be the total number of posts made by all users in the last month.
Features are defined as part of feature views. Since Feast does not transform data, a feature is essentially a schema that only contains a name and a type:
On demand feature views allows users to use existing features and request time data (features only available at request time) to transform and create new features. Users define python transformation logic which is executed in both historical retrieval and online retrieval paths:
Together with , they indicate to Feast where to find your feature values, e.g., in a specific parquet file or BigQuery table. Feature definitions are also used when reading features from the feature store, using .
Feature names must be unique within a .