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Add comprehensive documentation for deployment architecture, environm…
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‎README.md‎

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# DevOps Technical Assignment
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This project demonstrates practical DevOps implementation including structured Git workflow, CI/CD automation, Docker-based deployment, environment separation, monitoring strategy and production debugging approach.
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---
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## 🔧 Tech Stack
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- FastAPI (Python)
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- Docker (Containerization)
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- GitHub Actions (CI/CD)
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- Azure Virtual Machine (Cloud Deployment)
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- Nginx (Reverse Proxy)
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- Git Branching Strategy (Feature → Develop → Main)
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---
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## 🌿 Branching Strategy
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This repository follows a structured Git branching model:
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- `main` → Production environment
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- `develop` → Staging / integration environment
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- `feature/*` → Feature development branches
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### Workflow
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1. Feature branch is created from `develop`
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2. Pull Request is raised to `develop`
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3. CI pipeline validates build & container health
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4. Feature is merged into `develop` (staging deploy triggered)
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5. Release PR is created from `develop` to `main`
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6. Merge to `main` triggers production deployment
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Branch protection practices:
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- No direct push to `main`
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- CI must pass before merge
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- PR review required
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---
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## ⚙️ CI/CD Pipeline
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CI/CD is implemented using GitHub Actions.
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### CI Stage
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- Dependency installation
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- Syntax validation
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- Docker image build verification
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- Container health endpoint validation
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### CD Stage
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- `develop` branch → Deploys staging container
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- `main` branch → Deploys production container
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- Deployment happens via secure SSH automation into Azure VM
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- Containers are rebuilt and restarted on each deployment
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---
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## 🚀 Deployment Architecture
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Application is deployed on a single Azure VM using Docker containers.
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### Runtime Architecture
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- Production container runs on port **8000**
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- Staging container runs on port **8001**
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- Nginx reverse proxy routes external traffic
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- CI pipeline performs automated remote deployment
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### Live URLs
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- Staging → `http://20.244.44.228/staging/health`
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- Production → `http://20.244.44.228/prod/health`
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---
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## 🔐 Environment & Secrets Management
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- SSH credentials stored in GitHub Secrets
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- Environment variables passed during container runtime
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- Azure Key Vault recommended for production scale deployments
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---
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## 🔁 Rollback Strategy
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- Revert to previous Git commit
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- Redeploy previous Docker image tag
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- Restart container with last stable version
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- Re-run CI/CD workflow
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---
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## 📊 Monitoring & Infrastructure Strategy
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- Container logs monitored using `docker logs`
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- Nginx access logs help identify traffic patterns
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- Azure Monitor can track CPU / memory utilization
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- Health endpoint alerts can detect service failure
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- Horizontal container scaling can be implemented for load handling
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---
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## 🛠 Production Debugging Approach
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If production deployment fails:
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1. Identify blast radius (staging vs production impact)
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2. Check GitHub Actions logs
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3. SSH into Azure VM
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4. Validate container status and logs
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5. Verify environment configuration
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6. Rollback to last stable release
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7. Redeploy and monitor
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---
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## 🌟 Bonus Implementation
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### Dockerization
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The application is fully containerized using Docker with a production-ready image design:
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- Dockerized application deployment
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- Environment separation using container ports
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- Reverse proxy routing via Nginx
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- Cloud VM deployment on Azure
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- CI validated container health checks
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- Uses slim Python base image for reduced size
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- Runs as non-root user for better security
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- Includes container healthcheck endpoint
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- Supports environment-based deployments
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- Enables reproducible builds across environments
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### Kubernetes Deployment Approach (High Level)
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For large-scale production deployment, this architecture can be migrated to Kubernetes:
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- Docker image can be deployed using Kubernetes Deployment resource
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- Service object can expose application internally
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- Ingress controller can replace Nginx reverse proxy
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- Horizontal Pod Autoscaler can scale pods based on CPU / memory usage
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- ConfigMaps and Secrets can manage runtime configuration
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- Rolling updates can ensure zero downtime deployments
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### AWS Exposure
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Although this implementation uses Azure VM for deployment, similar architecture can be implemented on AWS using:
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- EC2 instances for container hosting
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- Application Load Balancer for routing
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- CloudWatch for logs and metrics
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- Auto Scaling Groups for infrastructure scaling
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- ECS or EKS for container orchestration
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