This repository contains a backend API built with Flask, designed to simulate a real-world backend service rather than a simple CRUD demo.
The project focuses on backend fundamentals such as API structure, authentication, error handling, configuration management, and readiness for scaling.
The goal of this project is to provide a backend service capable of handling structured data through REST APIs while considering real-world constraints such as invalid input, authentication, and future scalability.
- Python
- Flask
- REST APIs
- Environment-based configuration
- JWT-style authentication (or session-based, if applicable)
- Relational / NoSQL database (as used in the project)
The application is structured to separate concerns clearly:
- Routing layer for HTTP endpoints
- Service / business logic layer
- Data access layer
- Configuration and environment handling
This separation allows the application to scale as new features are added without tightly coupling components.
- RESTful API design
- Input validation and structured error responses
- Authentication-protected endpoints
- Modular project structure
- Environment-based configuration using
.env - Ready for containerization and deployment
The API explicitly handles:
- Invalid request payloads
- Authentication failures
- Resource-not-found scenarios
Consistent HTTP status codes and error messages are returned to clients.
While the domain is intentionally simple, the project is designed with scalability in mind:
- Stateless API design
- Authentication via tokens
- Clear boundaries between layers
- Easily extendable to add caching, rate limiting, or background tasks
To keep the project focused, advanced features such as distributed caching, message queues, and observability tooling are not implemented here.
- Redis-backed rate limiting to protect APIs from abuse
- Environment-driven configuration for limits and Redis connection
- Graceful fallback when Redis is unavailable
Given more time, the following enhancements would be added:
- Redis-based caching for read-heavy endpoints
- Rate limiting to prevent abuse
- Structured logging and request tracing
- Background jobs for long-running tasks
- Dockerized production deployment
pip install -r requirements.txt
python app.py