🌟 Linktree/Bento-like Backend with Referral System

🚀 Overview

This project is a backend system for a platform similar to Linktr.ee or Bento.me, built using Django and Django REST Framework (DRF). It features a referral system, secure authentication, API endpoints, and scalability enhancements to handle high traffic.

🎯 Features

✅ User Authentication & Registration

• User sign-up via email, username, and password.
• JWT authentication for secure login.
• Password hashing using Argon2 (Django default).
• Forgot password flow via email verification.

🔗 Referral System

• Each user receives a unique referral code, generated using a SHA-256 hash of their email and a secret key.
• Users can invite others using their referral link (e.g., https://yourdomain.com/register?referral=USER_ID).
• Successful referrals are tracked in the database.
• Rewards System: Users earn credits, premium features, or discounts based on the number of successful referrals.
• Celery processes referral calculations asynchronously to ensure scalability.
• Prevents self-referral abuse through validation.

🔐 Security Best Practices

• CSRF & XSS Protection to prevent attacks.
• Rate Limiting to prevent brute-force login attempts.
• JWT tokens stored securely in HttpOnly cookies.
• SQL Injection protection via ORM queries.

📈 Performance & Scalability

• Redis caching for frequently accessed data.
• Celery task queue for background job processing.
• Gunicorn as the primary WSGI server for handling requests efficiently.
• Horizontal scaling support for high user loads.

🛠 API Endpoints

Method	Endpoint	Description
POST	/api/register/	Register a new user with an optional referral code.
POST	/api/login/	Authenticate user and return JWT tokens.
POST	/api/forgot-password/	Handle password recovery requests.
GET	/api/referrals/	Get a list of users referred by the logged-in user.
GET	/api/referral-stats/	Get referral statistics for the logged-in user.

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📂 Database Schema

Users Table

Column	Type	Description
id	UUID	Primary Key
username	String	Unique username
email	String	Unique email address
password_hash	String	Secure password storage
referral_code	String	Unique referral code
referred_by	ForeignKey(User)	Who referred this user
created_at	DateTime	User creation timestamp

Referrals Table

Column	Type	Description
id	UUID	Primary Key
referrer_id	ForeignKey(User)	The referring user
referred_user_id	ForeignKey(User)	The referred user
date_referred	DateTime	Timestamp of referral
status	String	pending, successful

Rewards Table (Optional)

Column	Type	Description
id	UUID	Primary Key
user_id	ForeignKey(User)	User earning the reward
reward_type	String	Type of reward (credits, premium)
amount	Decimal	Reward amount
date_earned	DateTime	Timestamp of reward

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🔥 How Components Work Together

Celery - Used for background task processing, including tracking referrals and updating rewards asynchronously.

Gunicorn - A WSGI server that runs the Django application efficiently.

Redis - Stores frequently accessed data like user sessions, referral stats, and cached queries.

• Fetches data quickly instead of querying the database repeatedly.
• Caches referral stats to improve API performance.

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🚦 Setup & Installation

🔹 1. Clone the Repository

git clone https://github.com/yourusername/linktree-backend.git
cd linktree-backend

🔹 2. Create & Activate a Virtual Environment

python -m venv venv
source venv/bin/activate  # For macOS/Linux
venv\Scripts\activate  # For Windows

🔹 3. Install Dependencies

pip install -r requirements.txt

🔹 4. Start Celery Worker

celery -A linktree_backend worker --loglevel=info

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🛠 Testing

• Unit tests validate each function independently.
• Integration tests check how API endpoints work together.
• End-to-End tests simulate real user actions.

pytest

Test Coverage

• User Registration Tests ✅
• Login Tests ✅
• Referral System Tests ✅
• Edge Cases (Invalid Referral, Duplicate Email, Self-Referral) ✅

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📌 Future Improvements

• Implement reward system for referrals.
• Add OAuth 2.0 authentication (Google, Facebook, etc.).
• Improve caching strategy for user data.
• Enhance rate limiting for better security.

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📄 License

This project is licensed under the MIT License.

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