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refactor: Enhance architecture documentation with detailed sections on purpose, constraints, runtime view, deployment, and key concepts; add implementation plan and update quickstart reference

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zwitschi
2025-10-23 16:59:15 +02:00
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docs/architecture/04_solution_strategy.md
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---
title: "04 — Solution Strategy"
description: "High-level solution strategy describing major approaches, technology choices, and trade-offs."
status: draft
---
# 04 — Solution Strategy
Status: skeleton
This section outlines the high-level solution strategy for implementing the CalMiner system, focusing on major approaches, technology choices, and trade-offs.
High-level solution strategy describing major approaches, technology choices, and trade-offs.
## Client-Server Architecture
## Monte Carlo engine & persistence
- **Backend**: FastAPI serves as the backend framework, providing RESTful APIs for data management, simulation execution, and reporting. It leverages SQLAlchemy for ORM-based database interactions with PostgreSQL.
- **Frontend**: Server-rendered Jinja2 templates deliver dynamic HTML views, enhanced with Chart.js for interactive data visualizations. This approach balances performance and simplicity, avoiding the complexity of a full SPA.
- **Middleware**: Custom middleware handles JSON validation to ensure data integrity before processing requests.
- **Monte Carlo engine**: `services/simulation.py` will incorporate stochastic sampling (e.g., NumPy, SciPy) to populate `simulation_result` and feed reporting.
- **Persistence of simulation results**: plan to extend `/api/simulations/run` to persist iterations to `models/simulation_result` and provide a retrieval endpoint for historical runs.
## Technology Choices
## Simulation Roadmap
- **FastAPI**: Chosen for its high performance, ease of use, and modern features like async support and automatic OpenAPI documentation.
- **PostgreSQL**: Selected for its robustness, scalability, and support for complex queries, making it suitable for handling the diverse data needs of mining project management.
- **SQLAlchemy**: Provides a flexible and powerful ORM layer, facilitating database interactions while maintaining code readability and maintainability.
- **Chart.js**: Utilized for its simplicity and effectiveness in rendering interactive charts, enhancing the user experience on the dashboard.
- **Jinja2**: Enables server-side rendering of HTML templates, allowing for dynamic content generation while keeping the frontend lightweight.
- **Pydantic**: Used for data validation and serialization, ensuring that incoming request payloads conform to expected schemas.
- **Docker**: Employed for containerization, ensuring consistent deployment across different environments and simplifying dependency management.
- **Redis**: Used as an in-memory data store to cache frequently accessed data, improving application performance and reducing database load.
- Implement stochastic sampling in `services/simulation.py` (e.g., NumPy random draws based on parameter distributions).
- Store iterations in `models/simulation_result.py` via `/api/simulations/run`.
- Feed persisted results into reporting for downstream analytics and historical comparisons.
## Trade-offs
### Status update (2025-10-21)
- **Server-Rendered vs. SPA**: Opted for server-rendered templates over a single-page application (SPA) to reduce complexity and improve initial load times, at the cost of some interactivity.
- **Synchronous vs. Asynchronous**: While FastAPI supports async operations, the initial implementation focuses on synchronous request handling for simplicity, with plans to introduce async features as needed.
- **Monolithic vs. Microservices**: The initial architecture follows a monolithic approach for ease of development and deployment, with the possibility of refactoring into microservices as the system scales.
- **In-Memory Caching**: Implementing Redis for caching introduces additional infrastructure complexity but significantly enhances performance for read-heavy operations.
- **Database Choice**: PostgreSQL was chosen over NoSQL alternatives due to the structured nature of the data and the need for complex querying capabilities, despite potential scalability challenges.
- **Technology Familiarity**: Selected technologies align with the team's existing skill set to minimize the learning curve and accelerate development, even if some alternatives may offer marginally better performance or features.
- **Extensibility vs. Simplicity**: The architecture is designed to be extensible for future features (e.g., Monte Carlo simulation engine) while maintaining simplicity in the initial implementation to ensure timely delivery of core functionalities.
- A scaffolded simulation service (`services/simulation.py`) and `/api/simulations/run` route exist and return in-memory results. Persisting those iterations to `models/simulation_result` is scheduled for a follow-up change.
## Future Considerations
- **Scalability**: As the user base grows, consider transitioning to a microservices architecture and implementing load balancing strategies.
- **Asynchronous Processing**: Introduce asynchronous task queues (e.g., Celery) for long-running simulations to improve responsiveness.
- **Enhanced Frontend**: Explore the possibility of integrating a frontend framework (e.g., React or Vue.js) for more dynamic user interactions in future iterations.
- **Advanced Analytics**: Plan for integrating advanced analytics and machine learning capabilities to enhance simulation accuracy and reporting insights.
- **Security Enhancements**: Implement robust authentication and authorization mechanisms to protect sensitive data and ensure compliance with industry standards.
- **Continuous Integration/Continuous Deployment (CI/CD)**: Establish CI/CD pipelines to automate testing, building, and deployment processes for faster and more reliable releases.
- **Monitoring and Logging**: Integrate monitoring tools (e.g., Prometheus, Grafana) and centralized logging solutions (e.g., ELK stack) to track application performance and troubleshoot issues effectively.
- **User Feedback Loop**: Implement mechanisms for collecting user feedback to inform future development priorities and improve user experience.
- **Documentation**: Maintain comprehensive documentation for both developers and end-users to facilitate onboarding and effective use of the system.
- **Testing Strategy**: Develop a robust testing strategy, including unit, integration, and end-to-end tests, to ensure code quality and reliability as the system evolves.