zwitschi
ef4fb7dcf0
- Updated architecture constraints documentation to include detailed sections on technical, organizational, regulatory, environmental, and performance constraints. - Created separate markdown files for each type of constraint for better organization and clarity. - Revised the architecture scope section to provide a clearer overview of the system's key areas. - Enhanced the solution strategy documentation with detailed explanations of the client-server architecture, technology choices, trade-offs, and future considerations. - Added comprehensive descriptions of backend and frontend components, middleware, and utilities in the architecture documentation. - Migrated UI, templates, and styling notes to a dedicated section for better structure. - Updated requirements.txt to include missing dependencies. - Refactored user authentication logic in the users.py and security.py files to improve code organization and maintainability, including the integration of OAuth2 password bearer token handling.
1.6 KiB
1.6 KiB
title, description
| title | description |
|---|---|
| 04.03 — Trade-offs | Discussion of trade-offs made in the CalMiner architecture. |
04.03 — 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.