example-deployment

Mithril Deployment Example

This example demonstrates Phase 16: DevOps & Deployment, showcasing production-ready deployment configurations for Mithril applications.

Features Demonstrated

🐳 Docker & Containerization

• Multi-stage Dockerfile: Optimized build and runtime stages
• Docker Compose: Development, staging, testing, and production environments
• Health Checks: Container health monitoring
• Security: Non-root user, minimal attack surface

☸️ Kubernetes Orchestration

• Deployments: Scalable application deployment
• Services: Load balancing and service discovery
• Ingress: External access with SSL termination
• HPA: Horizontal Pod Autoscaling
• PVC: Persistent volume claims for data storage
• ConfigMaps & Secrets: Configuration management

🔄 CI/CD Pipeline

• GitHub Actions: Automated testing, building, and deployment
• Multi-environment: Staging and production deployments
• Security Scanning: Vulnerability assessment with Trivy
• Code Quality: Linting and testing automation

📊 Monitoring & Observability

• Prometheus: Metrics collection and alerting
• Grafana: Visualization and dashboards
• Alertmanager: Alert routing and notification
• Health Checks: Application and infrastructure monitoring

Quick Start

Development Environment

# Start development environment
docker-compose -f docker-compose.dev.yml up -d

# View logs
docker-compose -f docker-compose.dev.yml logs -f app

# Stop environment
docker-compose -f docker-compose.dev.yml down

Staging Environment

# Deploy to staging
docker-compose -f docker-compose.staging.yml up -d

# View staging logs
docker-compose -f docker-compose.staging.yml logs -f app

Production Environment

# Deploy to production
docker-compose -f docker-compose.prod.yml up -d

# Scale application
docker-compose -f docker-compose.prod.yml up -d --scale app=5

Kubernetes Deployment

Prerequisites

• Kubernetes cluster (1.20+)
• kubectl configured
• Helm (optional)

Deploy to Kubernetes

# Create namespace
kubectl apply -f k8s/namespace.yaml

# Apply configurations
kubectl apply -f k8s/configmap.yaml
kubectl apply -f k8s/secret.yaml
kubectl apply -f k8s/pvc.yaml

# Deploy application
kubectl apply -f k8s/deployment.yaml
kubectl apply -f k8s/service.yaml
kubectl apply -f k8s/ingress.yaml
kubectl apply -f k8s/hpa.yaml

# Check deployment status
kubectl get pods -n mithril
kubectl get services -n mithril
kubectl get ingress -n mithril

Scale Application

# Scale manually
kubectl scale deployment mithril-app --replicas=5 -n mithril

# Or let HPA handle it automatically
kubectl get hpa -n mithril

Environment Configuration

Environment Variables

Configuration Files

• Development: .env.development
• Staging: .env.staging
• Production: .env.production

Monitoring & Health Checks

Health Endpoints

• Health Check: GET /health - Basic health status
• Readiness: GET /ready - Readiness for traffic
• Metrics: GET /metrics - Prometheus metrics
• Monitor: GET /monitor - System monitoring dashboard

Prometheus Metrics

The application exposes metrics at /metrics endpoint:

• HTTP request metrics
• Database connection metrics
• Cache metrics
• Custom business metrics

Grafana Dashboards

Pre-configured dashboards for:

• Application performance
• Infrastructure metrics
• Database performance
• Cache performance
• Error rates and response times

CI/CD Pipeline

GitHub Actions Workflow

The CI/CD pipeline includes:

1. Test Stage:

   • Code linting with golangci-lint
   • Unit tests with coverage
   • Integration tests with test databases

2. Build Stage:

   • Multi-architecture Docker builds
   • Container registry publishing
   • Image vulnerability scanning

3. Deploy Stage:

   • Staging deployment (on develop branch)
   • Production deployment (on main branch)
   • Database migrations
   • Health checks

Security Scanning

• Trivy: Container vulnerability scanning
• CodeQL: Code security analysis
• Dependency scanning: Package vulnerability detection

Production Considerations

Security

• Non-root containers: Applications run as non-root user
• Secrets management: Kubernetes secrets for sensitive data
• Network policies: Restrictive network access
• RBAC: Role-based access control
• Image scanning: Regular vulnerability assessments

Performance

• Resource limits: CPU and memory constraints
• Horizontal scaling: Auto-scaling based on metrics
• Load balancing: Nginx load balancer
• Caching: Redis for application caching
• CDN: Static asset delivery

Reliability

• Health checks: Liveness and readiness probes
• Graceful shutdown: Proper signal handling
• Circuit breakers: Failure isolation
• Retry logic: Automatic retry for transient failures
• Backup strategies: Database and file backups

Monitoring

• Application metrics: Custom business metrics
• Infrastructure metrics: System resource usage
• Log aggregation: Centralized logging
• Alerting: Proactive issue detection
• Dashboards: Real-time visibility

Troubleshooting

Common Issues

1. Container won't start:

   docker logs <container_id>
   kubectl describe pod <pod_name> -n mithril
   

2. Database connection issues:

   kubectl exec -it <pod_name> -n mithril -- env | grep DB
   

3. Memory issues:

   kubectl top pods -n mithril
   kubectl describe pod <pod_name> -n mithril
   

Debug Commands

# Check pod status
kubectl get pods -n mithril

# View pod logs
kubectl logs -f deployment/mithril-app -n mithril

# Execute commands in pod
kubectl exec -it <pod_name> -n mithril -- /bin/sh

# Check service endpoints
kubectl get endpoints -n mithril

# View ingress status
kubectl describe ingress mithril-ingress -n mithril

Best Practices

1. Use specific image tags instead of latest
2. Set resource limits for all containers
3. Use health checks for all services
4. Implement proper logging with structured logs
5. Monitor all critical metrics
6. Use secrets management for sensitive data
7. Implement backup strategies for data persistence
8. Test deployments in staging before production
9. Use blue-green deployments for zero-downtime updates
10. Implement proper security scanning and policies

This deployment example provides a solid foundation for running Mithril applications in production environments with proper monitoring, scaling, and security measures.