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