Advanced Microservices-based Code Compilation and Execution Platform

An advanced, highly scalable platform for code compilation and execution, inspired by coding platforms like Leetcode and Codeforces. This project leverages a robust microservices architecture and AWS deployment to ensure seamless scalability, high availability, and exceptional performance.

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Project Overview

This platform is designed to manage complex coding challenges, compile and execute user code in multiple languages, and provide real-time feedback. Key features include dynamic problem administration, asynchronous submission handling, and real-time updates, all supported by AWS for high availability and fault tolerance.

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Features

Microservices Architecture & Scalability

• Architected with a microservices-based design for scalability and modularity.
• Deployed on AWS with auto-scaling capabilities and load balancing for seamless scaling and high availability.
• Each service operates independently, ensuring optimal resource usage and fault tolerance.

Dynamic Problem Administration

• Built a Problem Admin Service using JavaScript, Express, and MongoDB.
• Supports CRUD operations for problems, enabling admins to manage coding challenges dynamically.
• Handles complex test cases and code stubs, ensuring accurate and comprehensive evaluations of user submissions.

Advanced Code Execution

• Developed an Executor Service in TypeScript and Express to support multi-language code execution (Java, Python, C++).
• Utilizes Docker containers to create isolated environments for safe code execution.
• Employs Strategy and Factory design patterns to efficiently manage execution environments and handle time-limit conditions (TLE), enhancing system performance and reliability.

High-performance Asynchronous Communication

• Designed a Submission Service using Fastify to handle high request volumes.
• Utilized Redis message queues for asynchronous communication between the submission and executor services.
• Implemented WebSocket services to provide real-time feedback to users, enhancing interactivity and responsiveness.

AWS Deployment & Operational Excellence

• Deployed on AWS using:
  • Auto-scaling groups and Load Balancers for traffic management and fault tolerance.
  • Monitoring tools for performance tracking to ensure operational excellence and reliability.

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Tech Stack

• Backend Frameworks: Node.js (Express, Fastify)
• Programming Languages: JavaScript, TypeScript, Python, Java, C++
• Database: MongoDB
• Messaging & Queues: Redis
• Containerization: Docker
• Real-time Communication: WebSocket
• Cloud Platform: AWS (EC2, Load Balancer, Auto Scaling)

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Steps

1. Clone the repository:

   git clone https://github.com/hannanaarif/AlgocodeBackend.git
   cd AlgocodeBackend
   

2. Install dependencies for each service:

   • Navigate to each service folder and run:

          npm install
     

3.Deploy to AWS:

• Configure your AWS CLI credentials and deploy using your deployment script or CI/CD pipeline.
• Ensure that auto-scaling groups, load balancers, and monitoring configurations are set up for each service.

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Complexity Analysis

Code Execution Optimization

The platform is designed to efficiently handle large volumes of requests with optimized code execution through:

• Containerization: Isolates and manages resources for each language.
• Design Patterns: Utilizes Strategy and Factory patterns to ensure efficient resource allocation and scalability.

Asynchronous Communication

Efficiently manages high request volumes with:

• Redis Queues: Ensures asynchronous, non-blocking communication between services.
• WebSocket Integration: Provides real-time updates, enhancing user experience and response times.