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.
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]'
Bootstrap a new feature repository:
Update feature_repo/feature_store.yaml
with the below contents:
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
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:
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.
To compare this set of functionality against other online stores, please see the full functionality matrix.
Redis | |
---|---|
write feature values to the online store
yes
read feature values from the online store
yes
update infrastructure (e.g. tables) in the online store
yes
teardown infrastructure (e.g. tables) in the online store
yes
generate a plan of infrastructure changes
no
support for on-demand transforms
yes
readable by Python SDK
yes
readable by Java
yes
readable by Go
yes
support for entityless feature views
yes
support for concurrent writing to the same key
yes
support for ttl (time to live) at retrieval
yes
support for deleting expired data
yes
collocated by feature view
no
collocated by feature service
no
collocated by entity key
yes