sample

This is a web-service built with Python 3.13, created using the Backstage GitHub Repository Generator template.

• Application Type: web-service
• Runtime: Python 3.13
• Port: 3000
• Containerized: Docker support included
• Kubernetes Ready: Helm charts included
• Auto-scaling: Horizontal Pod Autoscaler configured (2-10 replicas)
• Health Checks: Liveness and readiness probes configured
• CI/CD Pipeline: Trunk-based deployment with GitHub Actions (auto-deploy to dev, manual promote to staging/prod)

• Python 3.13+
• pip
• Docker (for containerization)
• Kubernetes cluster (for deployment)
• Helm 3+ (for chart deployment)

1. Clone the repository:

   git clone https://github.com/radaisystems/sample-application-01.git
   cd sample-application-01

2. Navigate to the app directory:

   cd app/python

3. Install dependencies:

   pip install -r requirements.txt

   Adding Extra Packages: Edit app/python/requirements.txt to uncomment and add any additional Python packages you need. Common packages are already listed as examples.

4. Start the application:

   python main.py

The application will be available at http://localhost:3000

cd app/python
pytest

• Health: GET /health - Application health status
• Readiness: GET /ready - Application readiness status

docker build -t radaisystems/sample-application-01:latest .

docker run -p 3000:3000 radaisystems/sample-application-01:latest

1. Install the Helm chart:

   helm install sample-application-01 ./infrastructure/helm

2. Upgrade the deployment:

   helm upgrade sample-application-01 ./infrastructure/helm

3. Uninstall:

   helm uninstall sample-application-01

The Helm chart supports the following key configurations:

• Replicas: 2 (initial)
• Auto-scaling: 2-10 replicas
• CPU Threshold: 70%
• Memory Threshold: 80%
• Resources:
  • CPU: 100m (request), 500m (limit)
  • Memory: 128Mi (request), 512Mi (limit)
• Service Type: ClusterIP

Edit infrastructure/helm/values.yaml to customize the deployment according to your needs.

This project uses trunk-based deployment for continuous delivery:

• Development: Auto-deployed on every push to main branch
• Testing: All PRs are automatically tested before merge

• Staging: Manual trigger via GitHub Actions
• Production: Manual trigger via GitHub Actions (requires approval)

1. To Development (Automatic):

   git push origin main
   # Automatically deploys to development environment

2. To Staging (Manual):

   • Go to GitHub Actions → CI/CD Pipeline → Run workflow
   • Select "staging" environment
   • Click "Run workflow"

3. To Production (Manual):

   • Go to GitHub Actions → CI/CD Pipeline → Run workflow
   • Select "production" environment
   • Click "Run workflow"
   • Requires production environment approval

# Emergency rollback (if needed)
kubectl rollout undo deployment/sample-application-01 -n <environment>

# Or re-run workflow with previous stable version

sample-application-01/
├── README.md
├── catalog-info.yaml          # Backstage catalog definition
├── Dockerfile                 # Container definition
├── .github/                   # CI/CD workflows
│   └── workflows/
│       └── ci-cd.yml          # GitHub Actions pipeline
├── app/                       # Application code
│   └── python/
│       ├── main.py            # Python Flask application
│       ├── requirements.txt   # Python dependencies
│       ├── __init__.py
│       └── tests/             # Test files
│           ├── __init__.py
│           └── test_main.py
└── infrastructure/
    └── helm/                  # Helm chart
        ├── Chart.yaml
        ├── values.yaml
        └── templates/
            ├── deployment.yaml
            ├── service.yaml
            ├── hpa.yaml       # Horizontal Pod Autoscaler
            └── ...

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request