What Are Your Go-To Tools for Automated Kubernetes Problem Fixes?

I agree—while some issues can be automated, not all should be. Automation should complement the troubleshooting processes, especially for repetitive tasks. For example, things like PVC resizing or restarting stuck pods could be automated to save time, letting teams focus on more critical problems.

For me, the key strategies are: 1) Proper load testing in staging environments to catch issues early, 2) Have robust monitoring in place with alerts, 3) Utilize Cluster API for features like alpha rollouts, and 4) Set sensible resource limits to avoid future issues.

For automating node drains and reboots, I recommend using Cluster API or tools like Karpenter that have this feature built-in. As for other issues like HPA saturation or OOMKilled, those require a deeper look before any drastic automation. It's crucial to understand the underlying problems to avoid future disruptions.

When it comes to OOMKilled pods, auto-scaling memory might seem tempting, but it defeats the purpose of defined resource limits. It's important that alerts get to the developers so they can assess if memory needs to be increased. For CrashLoopBackOff, we shouldn't expect automated fixes for code bugs—developers need to investigate those errors. Disk pressure might allow for some automation since you could scale up the volume.