From Docker to Kubernetes

Introduction

Kubernetes is the de facto orchestrator. Same images, same Dockerfiles — just a richer scheduler with self-healing, autoscaling, rolling deploys and a vast ecosystem.

Purpose of this lesson

This lesson teaches From Docker to Kubernetes as an engineering decision: what problem it solves, when to use it, how to implement it safely, and what signals tell you it is failing.

Understanding the topic

Use this as foundation knowledge before moving into image builds, Compose stacks, CI/CD, and production hardening. The goal is to understand the runtime model rather than memorize commands.

Core concepts to understand:

• Pod = one or more containers sharing a network namespace.
• Deployment = desired state for replica pods.
• Service = stable network endpoint in front of pods.
• Ingress = HTTP(S) router from the internet.

Visual explanation

Architecture or command flow to keep in mind:

# deployment.yaml (excerpt)
apiVersion: apps/v1
kind: Deployment
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: api
        image: ghcr.io/acme/api:1.2.3
        ports: [{ containerPort: 8080 }]

Step-by-step explanation

1. Model each service with its own image, ports, environment, healthcheck, networks, and volumes.
2. Keep internal service-to-service traffic on the Compose network and publish only the edge service to the host.
3. Use named volumes for state, bind mounts for local source code, and profiles for optional developer tools.
4. Bring the stack up, read health status and logs, then restart one dependency to see whether the app recovers.
5. Create a production override file with pinned images, restart policies, resource limits, logging, backup, and rollback guidance.

Informative example

Use the example below as a working baseline, then verify the runtime behavior instead of assuming the command or file is correct.

# deployment.yaml (excerpt)
apiVersion: apps/v1
kind: Deployment
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: api
        image: ghcr.io/acme/api:1.2.3
        ports: [{ containerPort: 8080 }]

A production-minded check usually includes docker ps, docker logs, docker inspect, and one validation from outside the container such as curl, a database connection, or a registry pull.

# Verification loop for From Docker to Kubernetes
docker ps -a
docker logs --tail=100 <container-name>
docker inspect <container-or-image-name>
docker system df

Real-world use

If you can write a clean Dockerfile and a Compose file, you're 70% of the way to writing a Kubernetes Deployment. The other 30% is the YAML and the cluster.

Enterprise use cases

In a mature engineering organization, From Docker to Kubernetes is documented as a repeatable pattern with approved base images, ownership labels, CI checks, security expectations, rollback notes, and troubleshooting commands. The difference between a tutorial and production practice is that every container decision must be observable, reviewable, and reversible.

Best practices

• Start on a managed cluster (EKS / GKE / AKS) — don't self-host.
• Reuse the same image you use locally — that's the whole point.

Debugging tips

• Read logs before restarting; a restart often removes the timing context you need for root cause analysis.
• Use docker inspect to compare configured state with actual runtime state.
• Check daemon health, disk usage, image tags, exit code, and port mappings before blaming application code.

Optimization strategies

• Prefer explicit names, labels, tags, and networks so cleanup and debugging stay predictable.
• Pin versions for repeatability, then update intentionally through a scheduled base-image refresh.
• Use docker system df and targeted prune commands to control local and CI disk growth.

Advanced interview questions

Hands-on exercise

Create a small lab for From Docker to Kubernetes: run the example, inspect the created Docker object, intentionally introduce one mistake, and record the command that reveals the failure. The goal is not just to make the happy path work; it is to build operational reflexes.

# Hands-on lab scaffold
mkdir -p docker-from-docker-to-kubernetes-lab
cd docker-from-docker-to-kubernetes-lab

# Add the Dockerfile, compose.yml, or command from this lesson.
# Then run one happy-path test and one broken-path test.
docker version
docker info
docker system df

Summary

From Docker to Kubernetes matters because Docker is not only a packaging tool; it is a runtime, build, networking, storage, and delivery workflow. Treat each lesson as a production habit: make it repeatable, inspectable, secure, and easy to debug.