Overview

This project demonstrates a complete DevOps pipeline for a cloud-based FastAPI microservice built in SWE-455: Cloud Applications Engineering at KFUPM. The system performs CRUD operations on quotes and is fully automated through GitHub Actions for continuous integration, testing, and deployment to Render Cloud.

1. Architecture

• Framework: FastAPI + Uvicorn
• Containerization: Docker for reproducible builds
• CI/CD: GitHub Actions (multi-stage workflows)
• Testing: Pytest for unit and integration testing
• Security: CodeQL for static vulnerability scanning
• Deployment: Render Cloud (auto from main branch)

2. Pipeline Flow

The pipeline automates the full lifecycle from commit to deployment:

1. Run flake8 to ensure PEP8 compliance.
2. Execute pytest for automated unit/integration tests.
3. Perform CodeQL security analysis.
4. If on main branch → deploy automatically to Render.

name: CI/CD Pipeline
on:
  push:
    branches: [ main, dev, staging ]
jobs:
  lint-test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Install dependencies
        run: pip install -r requirements.txt
      - name: Run Linter
        run: flake8 .
      - name: Run Tests
        run: pytest
  deploy:
    needs: lint-test
    if: github.ref == 'refs/heads/main'
    runs-on: ubuntu-latest
    steps:
      - name: Deploy to Render
        run: curl -X POST ${{ secrets.RENDER_DEPLOY_HOOK }}

3. Branch Strategy

Branch	Purpose	Pipeline Stage
dev	Development and feature testing	CI only (lint, test)
staging	Integration and pre-production	CI + preview
main	Production release	Full CI/CD + deploy

4. Quality and Security

• 100% automated tests using Pytest for CRUD API endpoints.
• CodeQL ensures zero known vulnerabilities before merge.
• Branch protection prevents merging without passing CI.
• Artifacts and logs automatically stored for traceability.

5. DevOps Practices

• GitHub Project Board for agile tracking (To-Do → In-Progress → Done).
• Pull requests tied to specific issues for accountability.
• Review-based merges with automated checks.

Key Achievements

• Implemented full CI/CD with GitHub Actions and Render Cloud.
• Achieved secure, automated deployment pipeline.
• Integrated static analysis and testing into every commit.
• Applied agile and DevOps best practices in a real project.

Conclusion

This project showcased the end-to-end principles of DevOps automation — continuous integration, delivery, and security in a reproducible, cloud-based environment. It provided practical experience with pipelines, microservices, and secure deployment workflows that mirror modern industry standards.