Containers and Orchestration with Kubernetes
Learn how containers package services and how Kubernetes schedules, scales, and heals them — the foundation that makes microservices and serverless deployments manageable.
Containers and Orchestration with Kubernetes is a free API Rate Limiting & Scalability Patterns lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the API Rate Limiting & Scalability Patterns learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Why Containers
Microservices multiply the number of deployable units. Containers package each service with its dependencies into a portable, isolated image that runs the same everywhere.
Image vs. Container
An image is the immutable blueprint; a container is a running instance of it. You build an image once and run many identical containers from it.
FROM node:20-alpine
WORKDIR /app
COPY . .
RUN npm ci
CMD ["node", "server.js"]The Orchestration Problem
Running a few containers by hand is easy. Running hundreds across many machines — restarting crashes, balancing load, rolling out updates — needs an orchestrator.
Kubernetes Basics
Kubernetes schedules containers onto a cluster of machines and keeps them running. Core objects:
- Pod — one or more containers that run together
- Deployment — declares desired replicas
- Service — stable network endpoint
Declarative Desired State
You declare what you want; Kubernetes makes reality match. Ask for 3 replicas and it keeps 3 alive, restarting any that die.
apiVersion: apps/v1
kind: Deployment
spec:
replicas: 3
selector:
matchLabels:
app: ordersSelf-Healing
If a node fails or a container crashes, Kubernetes reschedules the pod elsewhere automatically. The system converges back to the declared state without manual intervention.
Health Probes
Kubernetes uses probes to know container state:
- liveness — restart if it is hung
- readiness — only send traffic when ready
readinessProbe:
httpGet:
path: /healthz
port: 8080
initialDelaySeconds: 5Horizontal Pod Autoscaling
The Horizontal Pod Autoscaler adds or removes pods based on metrics like CPU, letting microservices scale out under load and back in when quiet.
minReplicas: 2
maxReplicas: 20
targetCPUUtilizationPercentage: 70Rolling Updates
Deploying a new version replaces pods gradually, keeping the service available throughout. If the new version misbehaves, Kubernetes can roll back to the previous one.
How This Powers Serverless
Many serverless and Functions-as-a-Service platforms run on top of Kubernetes. Understanding pods, autoscaling, and probes demystifies what happens beneath a deployed function.
Resource Requests and Limits
Each container declares a CPU and memory request (what it needs to be scheduled) and a limit (its hard ceiling). Correct values let the scheduler pack nodes efficiently and stop one greedy pod from starving its neighbors.
resources:
requests:
cpu: 250m
memory: 256Mi
limits:
cpu: 500m
memory: 512MiQuick Check
Test your orchestration knowledge.
Recap
You learned the container platform behind scalable services:
- Containers package services portably
- Kubernetes schedules and self-heals them
- Probes guide restarts and traffic
- Autoscaling and rolling updates keep services elastic and available
Frequently asked questions
Is the “Containers and Orchestration with Kubernetes” lesson free?
Yes — the full text of “Containers and Orchestration with Kubernetes” is free to read here on the web, and the API Rate Limiting & Scalability Patterns course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the API Rate Limiting & Scalability Patterns course, upgrade to CoddyKit PRO.
What will I learn in “Containers and Orchestration with Kubernetes”?
Learn how containers package services and how Kubernetes schedules, scales, and heals them — the foundation that makes microservices and serverless deployments manageable. You practise API Rate Limiting & Scalability Patterns with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start API Rate Limiting & Scalability Patterns?
No prior experience is required. API Rate Limiting & Scalability Patterns on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Containers and Orchestration with Kubernetes” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this API Rate Limiting & Scalability Patterns lesson?
Yes. Every API Rate Limiting & Scalability Patterns lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Scaling with Microservices Architecture
- Serverless Functions for Event-Driven APIs
- Service Mesh Concepts and Benefits
- Containers and Orchestration with Kubernetes