calminer/docs/architecture/04_solution_strategy.md

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04 — Solution Strategy	High-level solution strategy describing major approaches, technology choices, and trade-offs.	draft

04 — Solution Strategy

This section outlines the high-level solution strategy for implementing the CalMiner system, focusing on major approaches, technology choices, and trade-offs.

Client-Server Architecture

• 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.

Technology Choices

• 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.

Trade-offs

• 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.

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.