Feast uses a registry to store all applied Feast objects (e.g. Feature views, entities, etc). The registry exposes methods to apply, list, retrieve and delete these objects, and is an abstraction with multiple implementations.
By default, Feast uses a file-based registry implementation, which stores the protobuf representation of the registry as a serialized file. This registry file can be stored in a local file system, or in cloud storage (in, say, S3 or GCS, or Azure).
The quickstart guides that use
feast initwill use a registry on a local file system. To allow Feast to configure a remote file registry, you need to create a GCS / S3 bucket that Feast can understand:
Example S3 file registry
Example GCS file registry
path: s3://[YOUR BUCKET YOU CREATED]/registry.pb
path: gs://[YOUR BUCKET YOU CREATED]/registry.pb
However, there are inherent limitations with a file-based registry, since changing a single field in the registry requires re-writing the whole registry file. With multiple concurrent writers, this presents a risk of data loss, or bottlenecks writes to the registry since all changes have to be serialized (e.g. when running materialization for multiple feature views or time ranges concurrently).
The configuration roughly looks like:
project: <your project name>
provider: <provider name>
path: postgresql://postgres:[email protected]:55001/feast
This supports any SQLAlchemy compatible database as a backend. The exact schema can be seen in sql.py
We recommend users store their Feast feature definitions in a version controlled repository, which then via CI/CD automatically stays synced with the registry. Users will often also want multiple registries to correspond to different environments (e.g. dev vs staging vs prod), with staging and production registries with locked down write access since they can impact real user traffic. See Running Feast in Production for details on how to set this up.
Users can specify the registry through a
feature_store.yamlconfig file, or programmatically. We often see teams preferring the programmatic approach because it makes notebook driven development very easy:
repo_config = RepoConfig(
offline_store="file", # Could also be the OfflineStoreConfig e.g. FileOfflineStoreConfig
online_store="null", # Could also be the OnlineStoreConfig e.g. RedisOnlineStoreConfig
store = FeatureStore(config=repo_config)
FeatureStoreobject can then point to this:
store = FeatureStore(repo_path=".")