Kubernetes has become the de facto standard for container orchestration, enabling teams to deploy, scale, and manage containerized applications with ease. In this guide, we'll explore the fundamentals of building scalable infrastructure with Kubernetes.

What is Kubernetes?

Kubernetes (K8s) is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. Originally developed by Google, it's now maintained by the Cloud Native Computing Foundation (CNCF).

Core Concepts

Pods

The smallest deployable unit in Kubernetes. A Pod represents one or more containers that share storage, network, and specifications for how to run.

Deployments

Deployments manage the desired state of your Pods, handling rolling updates and rollbacks automatically.

Services

Services provide stable networking for Pods, enabling load balancing and service discovery.

Getting Started

Here's a simple Deployment manifest: 

apiVersion: apps/v1
kind: Deployment
metadata:
  name: web-app
spec:
  replicas: 3
  selector:
    matchLabels:
      app: web
  template:
    metadata:
      labels:
        app: web
    spec:
      containers:
      - name: web
        image: nginx:latest
        ports:
        - containerPort: 80

Best Practices

  • Use namespaces to organize resources
  • Implement resource limits and requests
  • Set up health checks with liveness and readiness probes
  • Use ConfigMaps and Secrets for configuration
  • Implement proper RBAC (Role-Based Access Control)
  • Monitor with Prometheus and Grafana

Scaling Your Applications

Kubernetes makes scaling trivial. You can manually scale: 

kubectl scale deployment web-app --replicas=5

Or use the Horizontal Pod Autoscaler (HPA) for automatic scaling based on CPU, memory, or custom metrics.

Conclusion

Kubernetes provides a powerful platform for building and managing scalable infrastructure. While there's a learning curve, the benefits of automated deployments, self-healing, and horizontal scaling make it worthwhile for production workloads.