- 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.
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title, description, status
| title | description | status |
|---|---|---|
| 02 — Environmental Constraints | Environmental constraints that affect architecture decisions. | draft |
Environmental Constraints
e.g., deployment environments, cloud provider limitations.
Environmental constraints affecting the architecture include:
- Deployment Environments: The architecture must accommodate various deployment environments (development, testing, production) with differing configurations and resource allocations.
- Cloud Provider Limitations: If deployed on a specific cloud provider, the architecture may need to align with the provider's services, limitations, and best practices, such as using managed databases or specific container orchestration tools.
- Containerization: The use of Docker for containerization imposes constraints on how the application is packaged, deployed, and scaled, influencing the architecture to ensure compatibility with container orchestration platforms.
- Scalability Requirements: The architecture must be designed to scale efficiently based on anticipated load and usage patterns, considering the limitations of the chosen infrastructure.