As part of my DevOps learning journey, I recently worked on deploying a three-tier application on Kubernetes. This project helped me gain hands-on experience with containerization, orchestration, and scaling real-world applications. In this blog, I’ll walk you through how I designed and deployed the setup.

🔹 What is a Three-Tier Application?

A three-tier architecture divides an application into three logical layers:

Frontend (UI Layer) – The user interface, built with React/Angular/HTML-CSS.
Backend (Application Layer) – The business logic, often built with Node.js, Java, or Python.
Database (Data Layer) – Stores and retrieves application data (MySQL, MongoDB, PostgreSQL, etc.).

This separation makes the system more scalable, manageable, and fault-tolerant.

🔹 Tools & Technologies Used

Docker – For containerizing the app.
Kubernetes (K8s) – For deployment, scaling, and service management.
YAML Manifests – For defining deployments, services, and configs.
NodePort Service – For exposing the application externally.
Horizontal Pod Autoscaler (HPA) – For auto-scaling based on load.

🔹 Project Setup

1️⃣ Frontend Deployment

Containerized the frontend app using Dockerfile.
Created a Deployment YAML to run multiple replicas.
Exposed it using a NodePort Service so users can access it via browser.

2️⃣ Backend Deployment

Built a backend container image (Node.js API).
Defined a Deployment YAML for backend pods.
Exposed the backend internally with a ClusterIP Service, so only frontend communicates with it.

3️⃣ Database Deployment

Deployed MongoDB/MySQL using a StatefulSet (or Deployment).
Configured PersistentVolume (PV) & PersistentVolumeClaim (PVC) for data persistence.
Connected backend to the database via internal ClusterIP service.

🔹 Horizontal Pod Autoscaling (HPA)

To make the system production-ready, I implemented HPA.

The frontend and backend deployments automatically scale pods up/down based on CPU usage.
This ensures the app remains highly available even under heavy traffic.

🔹 Accessing the Application

Finally, I accessed the application using the NodePort external IP. The three layers communicated smoothly:

Frontend → Backend → Database
Verified that data was stored and retrieved correctly.

🔹 Key Learnings

✅ How to design and deploy a real-time multi-tier architecture in Kubernetes.
✅ Using NodePort vs ClusterIP for internal/external communication.
✅ Setting up Persistent Volumes for database storage.
✅ Implementing HPA for automatic scaling.

🔹 Final Thoughts

This project gave me real-world exposure to how enterprises deploy scalable applications on Kubernetes. Going forward, I plan to integrate Ingress, CI/CD pipelines, and monitoring (Prometheus + Grafana) to make it even more production-grade.

If you’re starting with Kubernetes, I highly recommend trying out a three-tier app project—it’s a complete package for learning orchestration.

👉 Hashtags for LinkedIn/Medium:
#DevOps #Kubernetes #Docker #CloudComputing #Projects #LearningByDoing

Do you want me to also write YAML snippets (frontend, backend, DB, HPA) inside the blog so it looks more technical and attracts recruiters/learners on LinkedIn?

You said:

make it in detail mentation all the thinks

ChatGPT said:

Perfect 🔥 Let’s create a detailed, step-by-step blog for your Three-Tier Application Deployment on Kubernetes, with all the YAMLs, commands, and explanations. This will look professional on LinkedIn / Medium / GitHub and show your DevOps skills clearly.

🚀 Step-by-Step Guide: Deploying a Three-Tier Application on Kubernetes

As part of my DevOps learning journey, I recently deployed a three-tier application (Frontend, Backend, Database) on Kubernetes. This project gave me hands-on experience with containerization, orchestration, and autoscaling. In this blog, I’ll explain each step in detail along with YAML manifests.

🔹 1. Understanding the Three-Tier Architecture

A three-tier architecture separates an app into three layers:

Frontend (UI Layer): React / Angular / HTML-CSS app.
Backend (API Layer): Node.js / Java / Python app with business logic.
Database (Data Layer): MySQL / MongoDB storing application data.

This separation ensures scalability, maintainability, and fault tolerance.

🔹 2. Tools & Technologies Used

Docker → Containerization
Kubernetes → Deployment & orchestration
kubectl → Command-line tool to manage clusters
NodePort & ClusterIP Services → For networking
Persistent Volumes → For database storage
Horizontal Pod Autoscaler (HPA) → For autoscaling

🔹 3. Project Workflow

Containerize frontend, backend, and database.
Write Kubernetes YAMLs for each tier.
Deploy them using kubectl apply -f.
Expose services (NodePort for external access, ClusterIP for internal).
Set up HPA for scaling.

🔹 4. Deployment Manifests

✅ 4.1 Frontend Deployment (NodePort Service)

apiVersion: apps/v1
kind: Deployment
metadata:
  name: frontend-deploy
  labels:
    app: frontend
spec:
  replicas: 2
  selector:
    matchLabels:
      app: frontend
  template:
    metadata:
      labels:
        app: frontend
    spec:
      containers:
      - name: frontend
        image: your-dockerhub-username/frontend-app:latest
        ports:
        - containerPort: 3000
---
apiVersion: v1
kind: Service
metadata:
  name: frontend-svc
spec:
  type: NodePort
  selector:
    app: frontend
  ports:
  - port: 3000
    targetPort: 3000
    nodePort: 30080

📌 The frontend is exposed on NodePort 30080 so users can access it via browser.

✅ 4.2 Backend Deployment (ClusterIP Service)

apiVersion: apps/v1
kind: Deployment
metadata:
  name: backend-deploy
  labels:
    app: backend
spec:
  replicas: 2
  selector:
    matchLabels:
      app: backend
  template:
    metadata:
      labels:
        app: backend
    spec:
      containers:
      - name: backend
        image: your-dockerhub-username/backend-app:latest
        ports:
        - containerPort: 3500
---
apiVersion: v1
kind: Service
metadata:
  name: backend-svc
spec:
  type: ClusterIP
  selector:
    app: backend
  ports:
  - port: 3500
    targetPort: 3500

📌 Backend is ClusterIP, so only frontend can talk to it internally.

✅ 4.3 Database Deployment (Persistent Storage + ClusterIP)

apiVersion: v1
kind: PersistentVolume
metadata:
  name: db-pv
spec:
  capacity:
    storage: 1Gi
  accessModes:
    - ReadWriteOnce
  hostPath:
    path: "/data/db"
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: db-pvc
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 1Gi
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: db-deploy
spec:
  replicas: 1
  selector:
    matchLabels:
      app: database
  template:
    metadata:
      labels:
        app: database
    spec:
      containers:
      - name: mongo
        image: mongo:latest
        ports:
        - containerPort: 27017
        volumeMounts:
        - name: db-storage
          mountPath: /data/db
      volumes:
      - name: db-storage
        persistentVolumeClaim:
          claimName: db-pvc
---
apiVersion: v1
kind: Service
metadata:
  name: db-svc
spec:
  type: ClusterIP
  selector:
    app: database
  ports:
  - port: 27017
    targetPort: 27017

📌 Database uses PV + PVC to persist data even if the pod restarts.

✅ 4.4 Horizontal Pod Autoscaler (HPA)

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: backend-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: backend-deploy
  minReplicas: 2
  maxReplicas: 5
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 50

📌 The backend scales between 2 to 5 replicas depending on CPU usage.

🔹 5. Deployment Commands

# Apply all YAMLs
kubectl apply -f frontend.yml
kubectl apply -f backend.yml
kubectl apply -f database.yml
kubectl apply -f hpa.yml

# Verify pods & services
kubectl get pods
kubectl get svc
kubectl get hpa

🔹 6. Testing the Application

Get Node IP using:
```
 kubectl get nodes -o wide
```
Access the frontend in browser:
```
 http://<node-ip>:30080
```
Check connections:
- Frontend → Backend → Database
- Insert and fetch data to confirm.

🔹 7. Key Learnings

✅ Designed and deployed a three-tier application in Kubernetes.
✅ Used NodePort for external access and ClusterIP for internal services.
✅ Implemented Persistent Volumes for database storage.
✅ Enabled HPA for automatic scaling under load.

🔹 8. Future Enhancements

Use Ingress Controller instead of NodePort.
Implement CI/CD with Jenkins + GitHub Actions.
Add Monitoring with Prometheus + Grafana.
Deploy on Cloud (AWS/GCP/Azure) instead of local cluster.

🎯 Final Thoughts

This project was a great hands-on DevOps practice. I got real-time experience in handling multi-tier architecture on Kubernetes with scalability and persistence.

If you’re learning DevOps, I highly recommend building and deploying a similar project—it gives end-to-end exposure to how real applications run in production.

🚀 Deploying a Three-Tier Application on Kubernetes: My Real-Time Project Experience

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