Project Justification - ORAC Risk Assessments API

1. Layered Architecture
The backend is intentionally organized into clear layers: routing (HTTP endpoints and URL structure), controllers (request handling and orchestration), configuration (database and environment settings), and middleware (cross-cutting concerns such as logging, security headers, and error handling). This separation of concerns makes the codebase easier to reason about and reduces coupling between parts of the system. As the project grows, new features like role-based access control, request validation, or caching can be introduced in the appropriate layer without forcing invasive changes across the entire codebase, which improves maintainability and testability over time.

2. Generic CRUD with Table-Specific Wiring
The core CRUD behavior (listing, filtering, fetching by primary key, inserting, updating, and deleting) is implemented once in a generic controller and is reused for every table via a small configuration of table name and primary key fields. Each table still has its own route file (and can have a specific controller wrapper) so that table-specific logic, such as additional validation or side effects, can be added without duplicating low-level SQL and HTTP boilerplate. This pattern balances DRY (Don’t Repeat Yourself) principles with the flexibility to evolve individual modules independently as business rules become more complex.

3. Full Database Schema Coverage
Every table present in the MySQL dump is mapped to a corresponding REST resource with consistent CRUD semantics. Composite primary keys are respected in the URL design, ensuring that each record can be uniquely addressed and manipulated, even in junction or mapping tables. This approach guarantees that the API reflects the actual relational model of the database rather than a simplified subset, which is important for administrative tools, reporting, and future integrations that might need to work with less commonly used tables.

4. Flexible Querying and Filtering
List endpoints are designed to accept arbitrary query parameters, allowing filtering on any column using equality or simple text-based search via the *_like convention. This generic filtering mechanism leverages the existing schema (including foreign key columns) so that related data can be retrieved by combining table-specific filters without adding handcrafted endpoints for each reporting need. As a result, frontends and external consumers can quickly build rich data views (for example, filtering assessments by status, location, or uploader) while the backend remains compact and consistent.

5. Production-Oriented Infrastructure
By using mysql2 with a connection pool, the API can handle concurrent requests efficiently without opening a new database connection for each call. Centralized error handling ensures that failures are reported in a uniform JSON format, improving both debugging and client-side error management. Middleware such as helmet and morgan introduces sensible security headers and structured request logging out of the box, aligning the project with common production practices for Node.js services and making it easier to deploy behind reverse proxies, API gateways, or monitoring tools.

6. Documentation and Developer Experience
The API_DOCS.md file provides a single, structured reference for all exposed resources, including URL patterns, primary key shapes, filtering rules, and example payloads. This documentation, combined with a predictable naming scheme for endpoints and modules, significantly lowers the learning curve for new developers and external integrators. Clear docs and consistent design reduce ambiguity, cut down on clarification cycles between teams, and support faster iteration when UI requirements change or new consumers start using the API.

