LuckyJinx

Overview

This project is a collaborative coding platform designed to facilitate real-time pair programming, problem-solving, and communication. Built as part of the CS3219 module, it leverages a microservice-based architecture deployed on a Google Kubernetes Engine (GKE) cluster. The platform provides features such as real-time code collaboration, user matching based on skill and topic preferences, integrated communication, and enhanced code execution functionalities.

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Features

Core Features

1. User Management:

   • User registration and login with email/password.
   • Integration with Google and GitHub for OAuth-based authentication.
   • Profile management, including preferred programming languages and profile pictures.
   • Differentiation between admin and user roles.

2. Matching Service:

   • Real-time matching based on difficulty level and topics.
   • Advanced algorithms to ensure unique and efficient matches.
   • Notifications and options for users to confirm or cancel matches.

3. Question Repository:

   • A comprehensive database of coding problems categorized by difficulty and topic.
   • Filtering, sorting, and viewing detailed problem statements.
   • Admins can add, edit, and delete questions.

4. Collaboration Tools:

   • Real-time code editor with synchronized changes and conflict resolution.
   • Cursor tracking to visualize user activity.
   • Integrated video and chat functionality for seamless communication.

5. Code Execution:

   • Secure and sandboxed environment for code compilation and execution.
   • Support for multiple programming languages.

6. AI Integration:

   • AI-assisted hints to guide problem-solving.
   • Session history and replay features for review and learning.

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Tech Stack

Backend

• Node.js:
  • Frameworks: Express.js, Socket.IO.
  • Purpose: API development and real-time communication.
• Python:
  • Framework: FastAPI.
  • Purpose: Backend microservices for specific functionalities.
• Database:
  • MongoDB (primary database for persistence).
  • SQLite (lightweight in-memory database for certain services).
• Messaging:
  • RabbitMQ (message broker for inter-service communication).
• Authentication:
  • JSON Web Tokens (JWT) for secure and stateless authentication.

Frontend

• React:
  • Framework for building the Single Page Application (SPA).
• Code Editor:
  • CodeMirror (integrated editor for coding functionalities).
• UI/UX:
  • MaterialUI for responsive components.
  • Sass for styling.

Deployment

• Containerization:
  • Docker for packaging applications into containers.
• Orchestration:
  • Kubernetes for managing containerized applications.
• Cloud Provider:
  • Google Cloud Platform (GKE) for scalable and reliable deployment.
• Service Discovery:
  • Kubernetes-native mechanisms for discovering and linking services.

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Architecture

The platform employs a microservices architecture:

• Frontend Service: Built with React for user interaction.
• User Service: Handles authentication and user management.
• Matching Service: Manages real-time matching based on criteria.
• Question Service: Manages the coding problems repository.
• Collaboration Service: Enables real-time document sharing and conflict resolution.
• Code Execution Service: Executes user code securely.
• Communication Service: Video and chat integration.

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Installation and Setup

Prerequisites

• Docker and Kubernetes installed locally.
• Access to Google Cloud Platform for Kubernetes deployment.
• MongoDB and RabbitMQ running.

Steps

1. Clone the repository:

   git clone <repository-url>
   cd <repository-folder>

2. Build Docker images:

   docker-compose build

3. Start services locally:

   docker-compose up

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Contributions

Team Members

• Kumar Prabhat: Backend services, Dockerization, and collaboration refinements.
• Luo Jiale: Frontend integration, matching service components, and session management.
• Sun Yitong: UI development, AI hints service, and test cases.
• Sun Xinyu: Backend deployment, matching service optimizations.
• Nguyen Khoi Nguyen: Collaboration backend, user service, and execution environment.

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Future Enhancements

1. Enhanced analytics and reporting for collaborative sessions.
2. Expansion of the AI assistant capabilities for real-time debugging.
3. Integration of more advanced matching algorithms.
4. Support for additional languages in the execution environment.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any queries or feedback, contact the team at [email protected].

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