calminer/docs/architecture/07_deployment_view.md

title, description, status

title	description	status
07 — Deployment View	Describe deployment topology, infrastructure components, and environments (dev/stage/prod).	draft

07 — Deployment View

Deployment Topology

The CalMiner application is deployed using a multi-tier architecture consisting of the following layers:

1. Client Layer: This layer consists of web browsers that interact with the application through a user interface rendered by Jinja2 templates and enhanced with JavaScript (Chart.js for dashboards).
2. Web Application Layer: This layer hosts the FastAPI application, which handles API requests, business logic, and serves HTML templates. It communicates with the database layer for data persistence.
3. Database Layer: This layer consists of a PostgreSQL database that stores all application data, including scenarios, parameters, costs, consumption, production outputs, equipment, maintenance logs, and simulation results.

graph TD
    A[Client Layer] --> B[Web Application Layer]
    B --> C[Database Layer]

Infrastructure Components

The infrastructure components for the application include:

• Reverse Proxy (optional): An Nginx or Apache server can be used as a reverse proxy.
• Containerization: Docker images are generated via the repository Dockerfile, using a multi-stage build to keep the final runtime minimal.
• CI/CD Pipeline: Automated pipelines (Gitea Actions) run tests, build/push Docker images, and trigger deployments.
  • Gitea Actions Workflows: Located under .gitea/workflows/, these workflows handle testing, building, pushing, and deploying the application.
  • Gitea Action Runners: Self-hosted runners execute the CI/CD workflows.
  • Testing and Continuous Integration: Automated tests ensure code quality before deployment, also documented in Testing & CI.
• Docker Infrastructure: Docker is used to containerize the application for consistent deployment across environments.
• Portainer: Production deployment environment for managing Docker containers.
  • Web Server: Hosts the FastAPI application and serves API endpoints.
  • Database Server: PostgreSQL database for persisting application data.
  • Static File Server: Serves static assets such as CSS, JavaScript, and image files.
• Cloud Infrastructure (optional): The application can be deployed on cloud platforms.

graph TD
    G[Git Repository] --> C[CI/CD Pipeline]
    C --> GAW[Gitea Action Workflows]
    GAW --> GAR[Gitea Action Runners]
    GAR --> T[Testing]
    GAR --> CI[Continuous Integration]
    T --> G
    CI --> G

    W[Web Server] --> DB[Database Server]
    RP[Reverse Proxy] --> W
    I((Internet)) <--> RP
    PO[Containerization] --> W
    C[CI/CD Pipeline] --> PO
    W --> S[Static File Server]
    S --> RP
    PO --> DB
    PO --> S

Environments

The application can be deployed in multiple environments to support development, testing, and production.

graph TD
    R[Repository] --> DEV[Development Environment]
    R[Repository] --> TEST[Testing Environment]
    R[Repository] --> PROD[Production Environment]

    DEV --> W_DEV[Web Server - Dev]
    DEV --> DB_DEV[Database Server - Dev]
    TEST --> W_TEST[Web Server - Test]
    TEST --> DB_TEST[Database Server - Test]
    PROD --> W_PROD[Web Server - Prod]
    PROD --> DB_PROD[Database Server - Prod]

Development Environment

The development environment is set up for local development and testing. It includes:

• Local PostgreSQL instance (docker compose recommended, script available at docker-compose.postgres.yml)
• FastAPI server running in debug mode

Testing Environment

The testing environment is set up for automated testing and quality assurance. It includes:

• Staging PostgreSQL instance
• FastAPI server running in testing mode
• Automated test suite (e.g., pytest) for running unit and integration tests

Production Environment

The production environment is set up for serving live traffic and includes:

• Production PostgreSQL instance
• FastAPI server running in production mode
• Load balancer (Traefik) for distributing incoming requests
• Monitoring and logging tools for tracking application performance

Containerized Deployment Flow

The Docker-based deployment path aligns with the solution strategy documented in Solution Strategy and the CI practices captured in Testing & CI.

Image Build

• The multi-stage Dockerfile installs dependencies in a builder layer (including system compilers and Python packages) and copies only the required runtime artifacts to the final image.
• Build arguments are minimal; database configuration is supplied at runtime via granular variables (DATABASE_DRIVER, DATABASE_HOST, DATABASE_PORT, DATABASE_USER, DATABASE_PASSWORD, DATABASE_NAME, optional DATABASE_SCHEMA). Secrets and configuration should be passed via environment variables or an orchestrator.
• The resulting image exposes port 8000 and starts uvicorn main:app (see main README.md).

Runtime Environment

• For single-node deployments, run the container alongside PostgreSQL/Redis using Docker Compose or an equivalent orchestrator.
• A reverse proxy (Traefik) terminates TLS and forwards traffic to the container on port 8000.
• Migrations must be applied prior to rolling out a new image; automation can hook into the deploy step to run scripts/run_migrations.py.

CI/CD Integration

• Gitea Actions workflows reside under .gitea/workflows/.
• test.yml executes the pytest suite using cached pip dependencies.
• build-and-push.yml logs into the container registry, rebuilds the Docker image using GitHub Actions cache-backed layers, and pushes latest (and additional tags as required).
• deploy.yml connects to the target host via SSH, pulls the pushed tag, stops any existing container, and launches the new version.
• Required secrets: REGISTRY_URL, REGISTRY_USERNAME, REGISTRY_PASSWORD, SSH_HOST, SSH_USERNAME, SSH_PRIVATE_KEY.
• Extend these workflows when introducing staging/blue-green deployments; keep cross-links with Testing & CI up to date.

Integrations and Future Work (deployment-related)

• Persistence of results: /api/simulations/run currently returns in-memory results; next iteration should persist to simulation_result and reference scenarios.
• Deployment: implement infrastructure-as-code (e.g., Terraform/Ansible) to provision the hosting environment and maintain parity across dev/stage/prod.