The post opens with a raw admission many engineers feel but rarely voice: "I’ve been working with Docker, k3s, and Rancher for a while now, but there’s one thing that’s haunted me forever: I never really understood what I was doing or why it made sense." This engineer, with Linux IT support experience, hit a wall during a DevOps interview when asked foundational questions about containerization vs virtualization, Kubernetes pod limits, and Traefik’s role. Their struggle highlights a critical gap between tool usage and deep comprehension in modern infrastructure.

The Core Confusion: Where’s the OS?

The poster’s central dilemma resonates: "When people say ‘there’s an OS in a virtual machine but no host OS in Kubernetes,’ it just doesn’t click. Every pod needs an OS underneath, right?" This reveals a common misconception. Let's clarify:

  1. Virtualization (VMs): Hypervisors (VirtualBox, Hyper-V, VMware) emulate full hardware stacks. Each VM runs a complete guest OS kernel (Windows, Linux) on top of this virtual hardware. Overhead is high (CPU, memory, boot time) because you’re running multiple independent kernels.

  2. Containerization (Docker/Kubernetes): Containers leverage kernel features (cgroups for resource limits, namespaces for isolation) on a single host OS kernel. A container shares this kernel but has its own isolated:

    • Filesystem (via layered images)
    • Process tree
    • Network stack
    • Users/Groups

    The Key Insight: Containers don’t boot an OS kernel. They run isolated user-space processes on the host’s existing kernel. A container image includes binaries/libs (like /bin/bash, glibc) but not a kernel. Kubernetes schedules these containers onto nodes running a single host OS.

Kubernetes Capacity: It’s Not (Just) About IPs

The poster guessed pod limits relate to k3s’s default subnet (10.42.0.0/16 implying ~65k IPs). While IP range is a factor, it’s rarely the primary constraint. Determining pod/deployment limits involves:

  1. Node Resources: CPU, Memory, Disk I/O, Network bandwidth per worker node.
  2. Pod Resource Requests/Limits: Kubernetes uses requests for scheduling and limits for capping. A node can run N pods where N = (Node Allocatable Resources) / (Average Pod Request).
  3. Control Plane Scalability: The Kubernetes API server, etcd database, and scheduler handle state. Too many pods/deployments can overwhelm them, causing slow responses or failures. k3s is lighter than full K8s but still has limits.
  4. Networking: IP exhaustion (as noted) can hit with large clusters using smaller CIDRs. Network plugin overhead (e.g., Calico, Flannel) also consumes resources.
  5. Practical Limits: k3s documentation suggests tested limits around 50 nodes and 150 pods/node. Exceeding requires careful tuning.

Traefik: More Than Just "API Gateway"

The poster described Traefik as an "API Gateway" – partially correct but incomplete. Traefik is primarily a dynamic reverse proxy and ingress controller designed for microservices:

  • Ingress Controller: Watches Kubernetes API for Ingress resources and configures itself automatically to route external HTTP(S) traffic to services/pods.
  • Reverse Proxy: Routes requests based on hostname, path, headers.
  • SSL/TLS Termination: Handles HTTPS decryption at the edge.
  • API Gateway Features: Can do path rewriting, load balancing, circuit breaking, rate limiting.

Building Foundational DevOps Knowledge: Where to Focus

The poster quit their job to study CS fundamentals – a bold move. Here’s a targeted roadmap:

  1. Linux Internals: Deep dive into processes, namespaces, cgroups, filesystems, and the kernel/user-space boundary. man pages and books like The Linux Programming Interface are gold.
  2. Networking: TCP/IP stack, routing, DNS, firewalls (iptables/nftables), HTTP/S. Kubernetes networking models (CNI, service meshes) build on this.
  3. Container Runtimes: Understand Docker/containerd mechanics: image layers, union filesystems (OverlayFS), runtime specs (OCI).
  4. Kubernetes Components: Master the roles of the API Server, etcd, Scheduler, Controller Manager, Kubelet, Kube-Proxy, and CNI plugins. kubectl explain is your friend.
  5. Infrastructure as Code (IaC) & GitOps: Principles of declarative configuration (YAML), tools like Helm, and GitOps workflows (ArgoCD, Flux).

The Path Forward: From Tools to Principles

The poster’s journey underscores a vital truth: DevOps mastery isn’t about memorizing tools but understanding the principles that birthed them. Grasping why namespaces and cgroups enable containers, how schedulers balance trade-offs, and where networking bottlenecks arise transforms confusion into the ability to design, troubleshoot, and innovate. Their quest for fundamentals – OS, networking – is precisely the right path. The tools will change; the principles underpinning scalable, resilient systems endure.

Source: Hacker News User Post (https://news.ycombinator.com/item?id=45081850)