Ride-Hailing System 🚗

This is a personal side project to build an Uber-like ride-hailing system using Django. The project allows users to book rides and drivers to view and accept them. The system will feature dynamic pricing based on real-time demand.

The project is in development and serves as a way to learn about scaling infrastructure, system optimization, and several essential aspects of large-scale system design, including:

• Distributed systems
• Performance optimization
• Cloud deployment and monitoring
• Data structures and algorithms

Features (Planned)

• User registration and authentication
• Driver registration and verification
• Ride booking and matching system
• Dynamic pricing based on demand and supply
• Ride history for users and drivers
• Real-time updates on ride status
• Notifications for ride acceptance and completion

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Tech Stack

• Backend: Django, Django REST Framework
• Database: MySQL & PostgreSQL (planning to explore horizontal scaling)
• Microservices Communication: RabbitMQ
• Caching & Queuing: Redis (for performance optimizations)
• Real-time Functionality: WebSockets (for live ride tracking)
• Deployment: Docker, AWS (Cloud setup in future development stages)

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Learning Goals

This project will evolve to include:

• Scalable infrastructure: Learning to scale a system to handle millions of users, drivers, and transactions
• System optimization: Improving performance with caching, query optimization, and load balancing
• Distributed systems: Implementing microservices and service-oriented architecture
• Cloud deployment: Deploying and managing the application on cloud platforms with proper monitoring and auto-scaling
• Algorithms & Data structures: Implementing efficient data structures for ride matching, demand forecasting, and pricing algorithms

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Future Plans

The following features and optimizations are planned for the future:

• Integration with third-party services for payments
• Real-time ride tracking using Google Maps API
• A recommendation engine to suggest rides or drivers
• A robust monitoring system with logging and error tracking
• Full cloud deployment with CI/CD pipelines for continuous integration

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