Dragonfly is a modern in-memory datastore that implements novel algorithms and data structures on top of a multi-threaded, share-nothing architecture. Thanks to its API compatibility, Dragonfly can act as a drop-in replacement for Redis. Due to Dragonfly's hardware efficiency, you can run a single node instance on a small 8GB instance or scale vertically to large 768GB machines with 64 cores. This greatly reduces infrastructure costs as well as architectural complexity.

Similar to Redis, Dragonfly can be used as an online feature store for Feast.

Using Dragonfly 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 Dragonfly 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_dragonfly
cd feast_dragonfly/feature_repo

Update feature_repo/feature_store.yaml with the below contents:

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

2. Start Dragonfly

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

docker run --network=host --ulimit memlock=-1 docker.dragonflydb.io/dragonflydb/dragonfly

3. Register feature definitions and deploy your feature store

feast apply

The apply command scans python files in the current directory (example_repo.py in this case) 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.


write feature values to the online store


read feature values from the online store


update infrastructure (e.g. tables) in the online store


teardown infrastructure (e.g. tables) in the online store


generate a plan of infrastructure changes


support for on-demand transforms


readable by Python SDK


readable by Java


readable by Go


support for entityless feature views


support for concurrent writing to the same key


support for ttl (time to live) at retrieval


support for deleting expired data


collocated by feature view


collocated by feature service


collocated by entity key


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

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