🚀 Fluid DevOps Platform

This project was developed as part of the Fluid AI DevOps Engineer Infrastructure Challenge.

The objective was to build and deploy a production-style application stack demonstrating:

• Containerization
• Kubernetes Deployment
• CI/CD Automation
• Observability
• Operational Debugging

The application consists of a Flask backend connected to Redis and deployed on a K3s Kubernetes cluster running on AWS EC2.

Developer
    │
    ▼
GitHub Repository
    │
    ▼
GitHub Actions CI/CD
    │
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Docker Image Build
    │
    ▼
Docker Hub
    │
    ▼
K3s Kubernetes Cluster (AWS EC2)
    │
 ┌──┴──┐
 ▼     ▼
Flask  Redis
 App   Cache
    │
    ▼
NodePort Service
    │
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End Users

• AWS EC2
• Docker
• Kubernetes (K3s)
• GitHub Actions
• Python Flask
• Redis
• Git & GitHub
• Amazon Linux 2023

• Flask web application
• Redis-backed visit counter
• Health endpoint (/health)
• Dockerized deployment
• Kubernetes orchestration
• CI/CD automation using GitHub Actions
• Rolling updates
• Failure simulation and recovery

Build Docker image:

docker build -t fluid-app:v1 .

Run locally:

docker run -p 5000:5000 fluid-app:v1

Deploy Redis:

kubectl apply -f redis-deployment.yaml
kubectl apply -f redis-service.yaml

Deploy Application:

kubectl apply -f app-deployment.yaml
kubectl apply -f app-service.yaml

Verify:

kubectl get pods
kubectl get svc
kubectl get deployments

The GitHub Actions pipeline performs:

1. Source Code Checkout
2. Docker Image Build
3. Docker Image Push
4. Kubernetes Deployment Update
5. Rolling Deployment

Pipeline Flow:

Git Push
   │
   ▼
GitHub Actions
   │
   ▼
Docker Build
   │
   ▼
Docker Hub Push
   │
   ▼
Kubernetes Deploy

Health endpoint:

/health

Response:

{
  "status": "healthy"
}

Used for application monitoring and operational validation.

Implemented Health Monitoring Endpoint.

Benefits:

• Early failure detection
• Better observability
• Faster troubleshooting
• Production readiness

Simulated Redis connectivity failure by modifying:

REDIS_HOST=invalid-host

Observed application failure through logs and Kubernetes diagnostics.

Debugging Commands:

kubectl logs <pod-name>

kubectl describe pod <pod-name>

kubectl get svc

Recovery:

kubectl rollout restart deployment fluid-app

Application recovered successfully.

• Docker Containerization
• Kubernetes Deployments
• Service Discovery
• CI/CD Automation
• Rolling Updates
• Health Monitoring
• Operational Debugging
• Infrastructure Management
• High Availability

Successfully built and deployed a production-style application stack on Kubernetes with automated deployment, observability, and operational debugging workflows.

This project demonstrates practical DevOps skills in Docker, Kubernetes, CI/CD, cloud infrastructure, monitoring, and troubleshooting.

Deeksha Chouhan

GitHub: https://github.com/Deeksha-chouhan