Running in production
These are general recommendations on running Metarank in a production environment.
Production environment overview

Metarank provides several Persistence options, however for production setup we recommend using only Redis persistance as it operates separately from running Metarank instances.
Redis does not depend on Metarank instances being re-deployed and should be configured with disc backup.
At the moment, Metarank stores only processed events in Redis, so we recommend storing all events separately.

Metarank CLI exposes several modes with which you can run Metarank: standalone and serve.
Although standalone mode is great for development purposes, it can't be used for production deployment:
  • standalone mode cannot be scaled as it's not possible to run several instances that point to the same database
  • you cannot re-train the model without restarting Metarank
For production deployment, you should only use the serve mode. You can have as many serve instances as you need, depending on the load you have and you can perform graceful restarts of Metarank with 0 downtime.
Resource consumption of the serve mode is relatively low as it performs minimal computations, so you can use cheaper nodes than when training the model.
At the moment, Metarank does not provide clustering capabilities out of the box, so you will need to use an external load balancer when deploying multiple API instances.

Metarank exposes a train mode that re-trains your model based on the calculated features. Training is a long-running process with high memory consumption, which depends on the amount of data that is stored, so we recommend running this process on-demand. You can re-train your model once a week or once a month, so there's no need to keep a large instance online all the time.
Copy link
Edit on GitHub
On this page
Persistence
API Serving
Re-training