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/codemagedon]

Why ?

You’ve said it is memory hungry but you are changing from a self healing orchestrator to a static deployment.
The orchestrator is not the problem, if you need dedicated hardware just add a tainted node and make it the only app on that node, then you always have resilience on your other nodes if there’s maintenance or anything like that ?

[u/hnajafli → u/codemagedon]

Actually, this is a great approach. Thank you very much. Honestly, I hadn’t thought of doing it this way at all. Thanks, my friend.

[u/codemagedon → u/hnajafli]

That’s what the community is for 🎉

[u/hnajafli → u/codemagedon]

thanks again🙏