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Valkey online store

docs/reference/online-stores/valkey.md

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Valkey online store

Description

Valkey is an open source (BSD-3-Clause), high-performance key/value datastore hosted by the Linux Foundation, created as a community fork of Redis. It maintains compatibility with the Redis wire protocol, so it can act as a drop-in replacement for Redis. Valkey is also offered as a managed engine by major cloud providers (for example, Amazon ElastiCache for Valkey).

Similar to Redis and Dragonfly, Valkey can be used as an online feature store for Feast: Feast's Redis online store only issues core commands (hash reads/writes, scans, key expiry, pipelines), all of which Valkey implements.

Feast's standard online store operations have been verified against Valkey 8.1: feast apply, feast materialize, online retrieval via get_online_features, feast teardown, and key expiry via the key_ttl_seconds option. Features that depend on Redis modules (such as vector search) are outside the scope of this page.

Using Valkey as a drop-in Feast online store instead of Redis

Make sure you have Python and pip installed.

Install the Feast SDK and CLI

pip install feast

In order to use Valkey as the online store, you'll need to install the redis extra:

pip install 'feast[redis]'

1. Create a feature repository

Bootstrap a new feature repository:

feast init feast_valkey
cd feast_valkey/feature_repo

Update feature_repo/feature_store.yaml with the below contents:

project: feast_valkey
registry: data/registry.db
provider: local
online_store:
  type: redis
  connection_string: "localhost:6379"

Note that the online store type remains redis: Feast talks to Valkey over the Redis protocol, and all options of the Redis online store (such as key_ttl_seconds) apply unchanged.

2. Start Valkey

There are several options available to get Valkey up and running quickly. We will be using Docker for this tutorial.

docker run -d -p 6379:6379 valkey/valkey:8.1

3. Register feature definitions and deploy your feature store

feast apply

The apply command scans python files in the current directory for feature view/entity definitions, registers the objects, and deploys infrastructure. You should see the following output:

....
Created entity driver
Created feature view driver_hourly_stats_fresh
Created feature view driver_hourly_stats
Created on demand feature view transformed_conv_rate
Created on demand feature view transformed_conv_rate_fresh
Created feature service driver_activity_v1
Created feature service driver_activity_v3
Created feature service driver_activity_v2

Functionality Matrix

The set of functionality supported by online stores is described in detail here. Below is a matrix indicating which functionality is supported by the Redis online store, which Feast uses to communicate with Valkey.

Redis
write feature values to the online storeyes
read feature values from the online storeyes
update infrastructure (e.g. tables) in the online storeyes
teardown infrastructure (e.g. tables) in the online storeyes
generate a plan of infrastructure changesno
support for on-demand transformsyes
readable by Python SDKyes
readable by Javayes
readable by Goyes
support for entityless feature viewsyes
support for concurrent writing to the same keyyes
support for ttl (time to live) at retrievalyes
support for deleting expired datayes
collocated by feature viewno
collocated by feature serviceno
collocated by entity keyyes

To compare this set of functionality against other online stores, please see the full functionality matrix.