My first attempt at running Kubernetes at home: spoiler alert, it was painful

My K8s Homelab Odyssey: From Zero to... Well, Still Learning!

Hey everyone! Today, I want to pull back the curtain on a recent adventure that left me both utterly frustrated and incredibly satisfied: my very first attempt at running Kubernetes in my homelab. If you've been eyeing K8s for your home setup, buckle up, because it's a wild ride!

The Dream vs. The Reality

Like many of you, I've been hearing about Kubernetes for years. The promise of orchestrating containers, self-healing deployments, and efficient resource utilization sounded like pure magic. I envisioned effortlessly deploying my Plex server, Home Assistant, and a dozen other services across a cluster of low-power machines. The reality, as I quickly discovered, was a bit more... humbling.

My setup was pretty modest: a couple of old Intel NUCs running Proxmox, providing me with a few VMs. I decided to go with a `kubeadm` setup, thinking it was the 'standard' way to do it. Spoiler: it's standard, but not necessarily 'easy' for a beginner.

Phase 1: The "Easy" Install (or so I thought)

I started by following a popular online guide. Initial steps felt great! SSHing into my VMs, running `kubeadm init`, `kubectl apply -f [CNI]`, and seeing those first few `kubectl get nodes` results felt like a huge win. "This isn't so bad!" I thought, naively.

Then came the first application deployment. A simple Nginx pod. It deployed! Success! Then I tried to expose it with a Service. And that's when the wheels started to fall off.

Phase 2: The Wall of Networking Tears

Oh, the networking! This was, without a doubt, my biggest hurdle. My pods couldn't talk to each other. My services weren't reachable. I spent countless hours staring at `kubectl get pods -A`, seeing some pods in `ContainerCreating` or `CrashLoopBackOff`, and many others in `Pending` state. The logs were cryptic, often just saying `connection refused` or `network unreachable`.

I tried Flannel, then Calico. I wrestled with firewall rules on my VMs, trying to understand what ports Kubernetes needed open. `iptables` became both my best friend and worst enemy. The concepts of Pod CIDRs, Service CIDRs, ClusterIP, NodePort, and Ingress controllers all swirled around in a confusing vortex. I learned the hard way that a misconfigured CNI (Container Network Interface) means your cluster is effectively dead in the water.

Lesson Learned: Networking in Kubernetes is foundational. Don't skim over it. Understand your chosen CNI, how pods communicate, and how services expose applications. `kubectl describe pod ` and `kubectl logs ` became my most used commands.

Phase 3: Storage: Where Do My Bits Go?

Once I somewhat wrestled the networking into submission (mostly by restarting everything 100 times and copying configurations blindly until something worked), I hit the next wall: persistent storage. Deploying stateless applications was one thing, but what about my databases or file shares?

My initial thought was `hostPath` volumes. Quick and dirty, right? Until I realized that only works if your pod always lands on the same node, which defeats the purpose of Kubernetes' scheduling. I then dove into Persistent Volumes (PVs) and Persistent Volume Claims (PVCs), and the concept of StorageClasses. It was a whole new layer of abstraction!

I experimented with NFS shares exported from another server, using an external provisioner. It was clunky, prone to issues, and felt like a fragile solution for a homelab. This phase was less about tears and more about deep sighs and head-scratching. I eventually landed on exploring solutions like Longhorn, which seemed more integrated, but that was a battle for another day.

Lesson Learned: Stateful applications are significantly harder to manage in Kubernetes than stateless ones. Understand PVs, PVCs, and StorageClasses. For homelabs, dedicated storage solutions like Longhorn or Rook-Ceph are often preferred over basic `hostPath` or external NFS mounts.

Phase 4: YAML Hell and Debugging Nightmares

Between networking and storage, I was also constantly battling YAML syntax errors, incorrect selectors, missing fields, and trying to understand why my deployments weren't scaling or my services weren't routing traffic correctly. The sheer verbosity and strictness of Kubernetes YAML can be daunting.

I spent hours comparing my YAML to examples, trying to understand the subtle differences that made one work and the other fail spectacularly. The iterative process of `kubectl apply -f`, seeing an error, editing, and repeating became a core part of my daily routine.

Lesson Learned: Pay close attention to indentation! Use a good editor with YAML linting. Understand the different Kubernetes objects (Deployment, Service, Pod, Ingress, ConfigMap, Secret) and their relationships. The `kubectl explain` command is your friend!

What I Learned (The Hard Way)

Networking is EVERYTHING: Seriously, dedicate time to understanding your CNI, service IPs, pod CIDRs, and how traffic flows within your cluster.Persistent Storage is a Beast: Don't underestimate it. Start simple, but be prepared to explore more robust solutions for stateful workloads.Read the Docs (and then read them again): The official Kubernetes documentation is dense but incredibly comprehensive and accurate.Start Small, Iterate Often: Don't try to deploy a full microservices architecture on day one. Start with a single Nginx pod, then a simple web app, then add a database.The Community is Gold: Forums, Discord channels, Stack Overflow – don't be afraid to ask questions. There's a huge, helpful community out there.Observability is Key: Learn to use `kubectl describe`, `kubectl logs`, and understand events (`kubectl get events`). These are invaluable debugging tools.

Was It Worth It? Absolutely!

Despite the frustration, the countless hours spent debugging, and the occasional urge to throw my keyboard across the room, it was an incredibly rewarding experience. I gained a deep appreciation for the complexity and power of Kubernetes. I now have a much better understanding of container orchestration, networking principles, and distributed systems.

My homelab K8s cluster is still a work in progress, but it's now actually running a few services reliably. The pain was real, but the learning was invaluable. If you're thinking about diving into Kubernetes at home, I say go for it! Just be prepared for a steep learning curve, and remember that every error message is just an opportunity to learn something new.

Happy homelabbing, and may your pods always be `Running`!