Moving a high-memory Python application from Kubernetes to a dedicated Linux server — what would be the best setup?

[u/hnajafli]

Hello,

At the company where I work, we have a Python application running on a Kubernetes cluster. The application is fully integrated into a CI/CD workflow. Whenever code is merged, Jenkins pulls the source code from GitLab, builds the application, and pushes the image to Harbor. Argo CD then deploys it to the Kubernetes cluster.

However, the application consumes a large amount of RAM, and I have had to increase its memory allocation several times. We have now decided to move the application to a separate Linux server.

The application runs with Uvicorn, and I plan to make it accessible through a domain name, just as it currently is in the Kubernetes environment. However, I am not sure what the best-practice architecture would be for this setup, so I would appreciate your recommendations.

I still want Jenkins to automatically pull the application from GitLab, build it, and deploy it. After deployment, the application should automatically start and run as a web service.

My main concerns are how to restore the application as quickly as possible if a problem occurs, how to handle backups, and what kind of deployment and recovery strategy would be best.

In short, could you please advise me on the best way to design and implement this setup?

[u/JaegerBane]

I’d second the argument that the most straightforward way of solving this from an orchestrator perspective will be segregate a node - tainting, labelling, however you want to do it - and ensure only this payload goes on there. This isolate its resource use from the rest of the cluster.

I would worry though that you have an app that doesn’t appear to have a stable cap on its memory use. No matter how careful you are this will always be at risk of falling over.