Back to System Architecture
Deployment
Deployment architecture, CI/CD pipelines, environments, and observability (logging, monitoring, tracing).
Deployment Architecture
Environments
- Development: Local development environment with Docker Compose
- Staging: Pre-production environment for testing and validation
- Production: Live production environment with high availability
Containerization
- Docker Containers: All services containerized for consistency
- Multi-stage Builds: Optimized Docker images for smaller size
- Base Images: Official language runtime images (Node.js, Python)
- Container Registry: Docker Hub or private registry for image storage
Orchestration
- Production: Kubernetes for container orchestration and scaling
- Local Development: Docker Compose for easy local setup
- Service Discovery: Kubernetes DNS for service-to-service communication
- Load Balancing: Kubernetes ingress controllers for traffic distribution
CI/CD
- Automated Testing: Unit tests, integration tests run on every commit
- Automated Building: Docker images built automatically on push
- Automated Deployment: Staging auto-deploys, production requires approval
- GitHub Actions: CI/CD pipeline orchestration
- Build Artifacts: Docker images tagged with commit SHA
Deployment Strategies
- Blue-Green Deployment: Zero-downtime deployments with instant rollback
- Canary Releases: Gradual rollout to subset of users
- Feature Flags: Gradual feature rollouts, A/B testing support
- Database Migrations: Versioned migrations with rollback capability
- Health Checks: Automated health checks before traffic routing
Infrastructure as Code
- Terraform: Infrastructure provisioning and management
- Version Control: Infrastructure changes tracked in Git
- Environment Parity: Consistent infrastructure across environments
Observability
Logging
- Structured Logging: JSON format with correlation IDs for traceability
- Log Levels: ERROR, WARN, INFO, DEBUG with appropriate filtering
- Sensitive Data: Never log passwords, tokens, payment data, or PII
- Log Aggregation: Centralized logging via ELK Stack or CloudWatch
- Log Retention: 30 days for production logs, 7 days for development
- Correlation IDs: Unique IDs per request for tracing across services
Monitoring
- Metrics Collection: Request latency, error rates, audio generation time, LLM call latency
- Alerting: Error rate spikes, latency degradation, service downtime alerts
- Dashboards: Service health, user activity, marketplace metrics dashboards
- Service Health: Health check endpoints for all services
- Resource Monitoring: CPU, memory, disk usage monitoring
- Business Metrics: User signups, ritual generation, marketplace transactions
Tracing
- Distributed Tracing: Request tracing across services using Jaeger or Zipkin
- Correlation IDs: Track requests through entire system
- Span Collection: Trace spans for each service call
- Performance Analysis: Identify bottlenecks and slow operations
Error Tracking
- Error Aggregation: Group similar errors for easier debugging
- Stack Traces: Full stack traces for debugging
- Error Context: User ID, request ID, environment context
- Alerting: Immediate alerts for critical errors