50 lines
5.1 KiB
Markdown
50 lines
5.1 KiB
Markdown
---
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title: "04 — Solution Strategy"
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description: "High-level solution strategy describing major approaches, technology choices, and trade-offs."
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status: draft
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---
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# 04 — Solution Strategy
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This section outlines the high-level solution strategy for implementing the CalMiner system, focusing on major approaches, technology choices, and trade-offs.
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## Client-Server Architecture
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- **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.
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- **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.
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- **Middleware**: Custom middleware handles JSON validation to ensure data integrity before processing requests.
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## Technology Choices
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- **FastAPI**: Chosen for its high performance, ease of use, and modern features like async support and automatic OpenAPI documentation.
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- **PostgreSQL**: Selected for its robustness, scalability, and support for complex queries, making it suitable for handling the diverse data needs of mining project management.
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- **SQLAlchemy**: Provides a flexible and powerful ORM layer, facilitating database interactions while maintaining code readability and maintainability.
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- **Chart.js**: Utilized for its simplicity and effectiveness in rendering interactive charts, enhancing the user experience on the dashboard.
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- **Jinja2**: Enables server-side rendering of HTML templates, allowing for dynamic content generation while keeping the frontend lightweight.
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- **Pydantic**: Used for data validation and serialization, ensuring that incoming request payloads conform to expected schemas.
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- **Docker**: Employed for containerization, ensuring consistent deployment across different environments and simplifying dependency management.
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- **Redis**: Used as an in-memory data store to cache frequently accessed data, improving application performance and reducing database load.
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## Trade-offs
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- **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.
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- **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.
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- **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.
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- **In-Memory Caching**: Implementing Redis for caching introduces additional infrastructure complexity but significantly enhances performance for read-heavy operations.
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- **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.
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- **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.
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- **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.
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## Future Considerations
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- **Scalability**: As the user base grows, consider transitioning to a microservices architecture and implementing load balancing strategies.
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- **Asynchronous Processing**: Introduce asynchronous task queues (e.g., Celery) for long-running simulations to improve responsiveness.
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- **Enhanced Frontend**: Explore the possibility of integrating a frontend framework (e.g., React or Vue.js) for more dynamic user interactions in future iterations.
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- **Advanced Analytics**: Plan for integrating advanced analytics and machine learning capabilities to enhance simulation accuracy and reporting insights.
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- **Security Enhancements**: Implement robust authentication and authorization mechanisms to protect sensitive data and ensure compliance with industry standards.
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- **Continuous Integration/Continuous Deployment (CI/CD)**: Establish CI/CD pipelines to automate testing, building, and deployment processes for faster and more reliable releases.
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- **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.
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- **User Feedback Loop**: Implement mechanisms for collecting user feedback to inform future development priorities and improve user experience.
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- **Documentation**: Maintain comprehensive documentation for both developers and end-users to facilitate onboarding and effective use of the system.
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- **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.
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