Upgrading Self-Managed Kubernetes Clusters with kubeadm: Step-by-Step

Upgrading Self-Managed Kubernetes Clusters with kubeadm#

Upgrading a kubeadm-managed cluster is a multi-step procedure that must be executed in a precise order. The control plane upgrades first, then worker nodes one at a time. Skipping steps or upgrading in the wrong order causes version skew violations that can break cluster communication.

This article provides the complete operational sequence. Execute each step in order. Do not skip ahead.

Version Skew Policy#

Kubernetes enforces strict version compatibility rules between components. Violating these rules results in undefined behavior – sometimes things work, sometimes the API server rejects requests, sometimes components silently fail.

Canary Deployments Deep Dive: Argo Rollouts, Flagger, and Metrics-Based Progressive Delivery

Canary Deployments Deep Dive#

A canary deployment sends a small percentage of traffic to a new version of your application while the majority continues hitting the stable version. You monitor the canary for errors, latency regressions, and business metric anomalies. If the canary is healthy, you gradually increase its traffic share until it handles 100%. If something is wrong, you roll back with minimal user impact.

Why Canary Over Rolling Update#

A standard Kubernetes rolling update replaces pods one by one until all pods run the new version. The problem is timing. By the time you notice a bug in your monitoring dashboards, the rolling update may have already replaced most or all pods. Every user is now hitting the broken version.

Kubernetes Disaster Recovery: Runbooks for Common Incidents

Kubernetes Disaster Recovery Runbooks#

These runbooks cover the incidents you will encounter in production Kubernetes environments. Each follows the same structure: detection, diagnosis, recovery, and prevention. Print these out, bookmark them, put them in your on-call wiki. When the alert fires at 2 AM, you want a checklist, not a tutorial.

Incident Response Framework#

Every incident follows the same cycle:

  1. Detect – monitoring alert, user report, or kubectl showing unhealthy state
  2. Assess – determine scope and severity. Is it one pod, one node, or the entire cluster?
  3. Contain – stop the bleeding. Prevent the issue from spreading
  4. Recover – restore normal operation
  5. Post-mortem – document what happened, why, and how to prevent it

Runbook 1: Node Goes NotReady#

Detection: Node condition changes to Ready=False. Pods on the node are rescheduled (if using Deployments). Monitoring alerts on node status.