CodeMaster
CodeMaster
Table of Contents
1. Introduction
CodeMaster is a sophisticated competitive programming platform designed to automatically judge and evaluate code submissions from programming languages in real-time. The platform represents a modern approach to online coding challenges, built with a focus on containerization, scalability, and portability using Docker technology.
The core purpose of CodeMaster is to provide a complete end-to-end solution for accepting code submissions from users, executing code against predefined test cases, comparing output with expected results, providing real-time feedback to users, maintaining a persistent database of problems and submissions, and handling concurrent submissions efficiently through a microservices architecture.
Why Containerization?
Traditional approaches to building such platforms often face significant challenges including environment inconsistency, deployment complexity, scalability issues, dependency management problems, and development-production parity issues. Docker containerization solves these problems by packaging everything together, ensuring consistency across all environments, enabling isolation between services, simplifying deployment to a single command, facilitating independent scaling of services, and enabling rapid development cycles.
Building a competitive programming platform requires orchestrating multiple complex services: a Node.js backend API for handling submissions, a Python-based judge worker for executing and testing code, a MongoDB database for persistence, a RabbitMQ message queue for asynchronous processing, a Redis cache for performance optimization, and a React frontend for user interaction. Without containerization, integrating these services would require extensive manual configuration, version management, network setup, and environment-specific troubleshooting.
Docker containerization transforms this complexity into a manageable, reproducible, and scalable system where all services work together seamlessly. The entire platform can be deployed with a single docker-compose up command, making it accessible to developers, students, and organizations without extensive DevOps expertise.
2. Objectives
Part 1: Backend Services Containerization
Primary Goal: Establish the foundational backend infrastructure using Docker containers.
- Create Backend API Container: Containerize the Node.js Express server with Socket.IO support, implement health check endpoints for monitoring, configure environment variables for database and message queue connections, and set up proper error handling and logging.
- Containerize Database Service: Set up MongoDB 7.0 container with authentication, create initialization scripts to seed initial data, implement data persistence using Docker volumes, and configure backup and recovery mechanisms.
- Implement Message Queue: Deploy RabbitMQ 3.13 for asynchronous submission processing, configure queue bindings for submission jobs, implement consumer patterns for the judge worker, and set up monitoring and management interface.
- Set Up Caching Layer: Deploy Redis 7 for performance optimization, implement caching strategies for frequently accessed data, configure persistence for cache data, and set up cache invalidation mechanisms.
- Create Docker Compose Orchestration: Define all backend services in docker-compose.yml, configure service dependencies and startup order, set up networking between containers, and implement health checks and restart policies.
Deliverables:
- Fully functional backend API accessible on port 5001
- MongoDB database with 6 problems loaded
- RabbitMQ message queue processing submissions
- Redis cache improving performance
- All services running with health checks passing
Part 2: Frontend Integration and Real-Time Communication
Primary Goal: Integrate the user interface and establish real-time bidirectional communication.
- Containerize Frontend Application: Create Dockerfile for React + Vite frontend, configure hot reload for development, set up proper build optimization for production, and implement environment variable configuration.
- Establish Real-Time Communication: Implement Socket.IO client in frontend, create room-based messaging for submission updates, handle connection/disconnection events, and implement automatic reconnection logic.
- Integrate Frontend with Backend: Configure API endpoints for problem fetching, implement authentication token management, set up submission flow with real-time status updates, and create error handling and user feedback mechanisms.
- Configure Container Networking: Set up Docker network for service communication, configure port mappings for external access, implement service discovery, and set up reverse proxy if needed.
- Implement Real-Time Updates: Display submission status changes without page refresh, show test case progress in real-time, implement accuracy percentage calculation, and display error messages and debugging information.
Deliverables:
- Frontend accessible on port 5173
- Real-time Socket.IO connection working
- Submission flow fully integrated
- All services communicating correctly
- User interface displaying results in real-time
Part 3: Testing, Validation, and Production Readiness
Primary Goal: Ensure system reliability, correctness, and production readiness through comprehensive testing.
- Create Automated Test Suite: Develop comprehensive end-to-end tests, test correct solutions for multiple problem types, test error handling for wrong answers, and test runtime error detection and reporting.
- Validate System Functionality: Verify all 3 problem types work (array-based, string-based, class-based), confirm accuracy calculation is correct, validate test case evaluation, and ensure error messages are clear and helpful.
- Test Real-Time Updates: Verify Socket.IO connection establishment, confirm status updates without page refresh, test concurrent submissions, and validate message delivery reliability.
- Perform Database Verification: Confirm 6 problems loaded correctly, verify test cases have proper structure, test data persistence across container restarts, and validate backup and recovery procedures.
- Ensure Production Readiness: Verify all services have health checks, confirm resource limits are set, test container restart policies, and validate logging and monitoring.
Deliverables:
- Real-time updates verified
- Database integrity confirmed
- System ready for production deployment
3. Technical Components
Docker Containers and Official Images
Container 1 Backend API Container
Base Image: node:18-alpine
Docker Hub Link: https://hub.docker.com/_/node
Purpose: Runs the Express.js backend server with Socket.IO support for handling HTTP requests, WebSocket connections, user authentication with JWT tokens, RabbitMQ coordination, and MongoDB data persistence.
Port Mapping: 5001:5001
Key Features: Health check endpoint, environment variable configuration, multi-stage build optimization
Container 2 Judge Worker Container
Base Image: node:18-alpine (with Python3 added)
Docker Hub Link: https://hub.docker.com/_/node
Purpose: Executes submitted code and evaluates test cases by consuming submission jobs from RabbitMQ queue, executing Python code in isolated environment, comparing output with expected results, calculating accuracy percentage, and sending results back via Socket.IO.
Key Features: Python3 runtime, isolated code execution, comprehensive error handling
Container 3 Frontend Container
Base Image: node:20-alpine
Docker Hub Link: https://hub.docker.com/_/node
Purpose: Serves React + Vite frontend application providing user interface for problem browsing, code submission, real-time submission results display, user authentication management, and communication with backend via REST API and WebSocket.
Port Mapping: 5173:5173
Key Features: Hot reload support, environment variable configuration, optimized build process
Container 4 MongoDB Container
Base Image: mongo:7.0
Docker Hub Link: https://hub.docker.com/_/mongo
Purpose: Persistent data storage for 6 competitive programming problems, submission history, user accounts and authentication data, and query interface for data retrieval.
Port Mapping: 27017:27017
Volume: mongodb_data for persistence
Key Features: Authentication enabled, initialization scripts, health checks
Container 5 RabbitMQ Container
Base Image: rabbitmq:3.13-management-alpine
Docker Hub Link: https://hub.docker.com/_/rabbitmq
Purpose: Message queue for asynchronous submission processing, receiving submission jobs from backend API, queuing jobs for judge worker processing, ensuring reliable message delivery, and providing management interface for monitoring.
Port Mapping: 5672:5672 (AMQP), 15672:15672 (Management UI)
Key Features: Management plugin, persistent queues, health checks
Container 6 Redis Container
Base Image: redis:7-alpine
Docker Hub Link: https://hub.docker.com/_/redis
Purpose: In-memory cache for performance optimization, caching frequently accessed problems, storing submission results for quick retrieval, implementing cache invalidation strategies, and providing session storage.
Port Mapping: 6379:6379
Volume: redis_data for persistence
Key Features: AOF persistence, health checks, memory optimization
Additional Software and Technologies
| Technology | Version | Purpose |
|---|---|---|
| Node.js | 18.x & 20.x | JavaScript runtime for backend (v18) and frontend (v20). Enables Express.js server, Socket.IO, and React development. |
| Python 3 | 3.x | Code execution environment for submitted solutions. Installed in judge worker container for safe, sandboxed execution. |
| Docker Compose | 3.8 | Orchestrates all 6 containers as a single system. Manages networking, volumes, dependencies, and health checks. |
| React | 19.1.1 | Component-based UI framework for building responsive, interactive user interface. |
| Vite | 7.1.2 | Lightning-fast build tool with hot module replacement for rapid development. |
| Express.js | 4.19.2 | Lightweight Node.js web framework for HTTP routing, middleware, and REST API endpoints. |
| Socket.IO | 4.8.1 | Real-time bidirectional communication with WebSocket support, room-based messaging, and automatic reconnection. |
| Mongoose | 8.4.1 | MongoDB object modeling for Node.js with schema validation and query building. |
| amqplib | 0.10.4 | RabbitMQ client library for Node.js enabling message queue operations. |
| Redis Client | 5.8.2 | Node.js interface to Redis cache for performance optimization. |
4. Architecture
Overall Architecture
The CodeMaster platform implements a modern microservices architecture using Docker containers, providing several key advantages through separation of concerns, loose coupling, and high cohesion. Each service has a single, well-defined responsibility: the Backend API handles HTTP requests and WebSocket connections, the Judge Worker executes code and evaluates results, MongoDB stores persistent data, RabbitMQ manages asynchronous job processing, Redis provides caching, and the Frontend delivers the user interface.
The containers communicate through a Docker-created network called codemaster-network, enabling seamless inter-service communication while maintaining isolation from the host system. The Frontend Container communicates with the Backend API via HTTP/REST API for synchronous operations and WebSocket (Socket.IO) for real-time updates. The Backend API connects to MongoDB for data persistence, RabbitMQ for asynchronous job queuing, and Redis for caching frequently accessed data. The Judge Worker consumes jobs from RabbitMQ, executes submitted code in an isolated Python environment, and sends results back to the Backend API via Socket.IO, which then broadcasts updates to the Frontend in real-time. The overall architecture is illustrated in Figure 1.
Figure 1: CodeMaster Microservices Architecture Diagram showing all six containers and their interconnections
Data Flow and Processing Pipeline
Step 1: User Interaction - User browses problems on the frontend, selects a problem, writes code in the integrated editor, and clicks submit.
Step 2: Frontend Processing - Frontend validates the code submission, sends HTTP POST request to Backend API with problem ID, code, and language, joins Socket.IO room for real-time updates, and displays loading state to user.
Step 3: Backend API Processing - Backend receives submission, validates authentication token, retrieves problem details from MongoDB (or Redis cache), creates submission record in database, publishes submission job to RabbitMQ queue, and returns submission ID to frontend.
Step 4: Asynchronous Job Processing - RabbitMQ queues the submission job, Judge Worker consumes job from queue, retrieves problem test cases from MongoDB, and begins code execution process.
Step 5: Code Execution - Judge Worker creates isolated temporary directory, writes user code to file, executes code against each test case using Python3, captures output and errors, compares actual output with expected output, calculates accuracy percentage, and emits progress updates via Socket.IO.
Step 6: Result Delivery - Judge Worker sends final results to Backend API via Socket.IO, Backend updates submission record in MongoDB, Backend broadcasts results to frontend clients in submission room, and Frontend displays results to user in real-time without page refresh.
Step 7: Caching and Optimization - Redis caches frequently accessed problems to reduce MongoDB queries, stores recent submission results for quick retrieval, implements TTL (Time To Live) for automatic cache expiration, and invalidates cache when data is updated.
5. Procedures
Part 1: Backend Services Containerization
Step 1 Initial Setup and Prerequisites
Prerequisites:
- Docker Desktop installed (version 20.10 or higher)
- Docker Compose installed (version 2.0 or higher)
- Git for version control
- Text editor or IDE (VS Code recommended)
- Minimum 8GB RAM and 20GB free disk space
The Docker version verification is shown in Figure 2, confirming the installed versions of Docker and Docker Compose meet the requirements.
Figure 2: Docker and Docker Compose version verification
Step 2 Configure Backend Dockerfile
The Backend Dockerfile configuration is shown in Figure 3, which demonstrates the multi-stage build process and the addition of Python3 runtime for code execution capabilities.
Figure 3: Backend Dockerfile configuration with Python3 runtime and multi-stage build
Step 3 Create docker-compose.yml
The docker-compose.yml file orchestrates all six containers as shown in Figure 4. This configuration defines service dependencies, network settings, volume mappings, and health check configurations.
Figure 4: Docker Compose YAML configuration file defining all services
Step 4 Build and Start Backend Services
The backend services startup process is illustrated in Figures 5 and 6. Figure 5 shows the initial container building and startup sequence, while Figure 6 displays all backend services running successfully with their health status.
Figure 5: Backend services building and starting process
Figure 6: All backend services successfully running
The Docker Compose process status is shown in Figure 7, displaying the running state of all containers with their port mappings and health status.
Figure 7: Docker Compose ps output showing all running containers
Step 5 Verify Backend Services
Each backend service was verified for proper functionality. Figure 8 shows the Backend API health check endpoint returning a successful response with MongoDB connection status.
Figure 8: Backend API health check endpoint verification
MongoDB connectivity was tested as shown in Figure 9, confirming successful authentication and database access.
Figure 9: MongoDB connection and authentication verification
Redis cache functionality was confirmed with a ping test as shown in Figure 10, returning PONG to verify the service is operational.
Figure 10: Redis cache service verification with ping command
Part 2: Frontend Integration and Real-Time Communication
Step 6 Create Frontend Dockerfile
The Frontend Dockerfile configuration is shown in Figure 11, demonstrating the use of Node.js 20 for Vite compatibility and the network configuration for external accessibility.
Figure 11: Frontend Dockerfile with Node.js 20 and Vite configuration
Step 7 Configure Vite for Docker
The Vite configuration for Docker deployment is shown in Figure 12. This configuration enables the development server to accept connections from outside the container by binding to 0.0.0.0 while maintaining hot module replacement functionality.
Figure 12: Vite configuration file for Docker container networking
Step 8 Add Frontend to docker-compose.yml
The frontend service addition to the docker-compose.yml file is displayed in Figure 13, showing the service definition with volume mounts for hot reload and proper network configuration.
Figure 13: Frontend service configuration in docker-compose.yml
Step 9 Build and Start Complete System
Figure 14 demonstrates all six containers running simultaneously, completing the full-stack deployment. The output confirms that the Frontend, Backend API, Judge Worker, MongoDB, RabbitMQ, and Redis containers are all operational and healthy.
Figure 14: Complete system with all six containers running successfully
Step 10 Access Frontend Application
The CodeMaster frontend user interface, accessible at http://localhost:5173, is shown in Figure 15. This interface provides users with the ability to browse problems, submit code solutions, and view real-time evaluation results.
Figure 15: CodeMaster frontend user interface
Part 3: Testing, Validation, and Production Readiness
Step 11 Seed Database with Problems
The database seeding process is shown in Figure 16, where the initialization script successfully loaded all 6 competitive programming problems into MongoDB with their respective test cases and metadata.
Figure 16: Database seeding process with problem data initialization
Step 12 Test Correct Solution Submission
Figure 17 demonstrates a successful code submission where the solution passed all test cases, achieving 100% accuracy. The real-time status updates and test case results are displayed to the user without requiring a page refresh.
Figure 17: Successful code submission with 100% test case accuracy
Step 13 Test Wrong Answer Handling
The system's error detection capability is illustrated in Figure 18, where an incorrect solution was properly identified. The judge displays which test cases failed and provides the expected versus actual output comparison.
Figure 18: Wrong answer detection with detailed test case failure information
Step 14 Test Runtime Error Detection
Runtime error detection is demonstrated in Figure 19, where code with syntax or execution errors is caught by the judge worker and reported back to the user with detailed error messages.
Figure 19: Runtime error detection with comprehensive error messaging
Step 15 Run Automated Test Suite
The comprehensive automated test suite results are shown in Figures 20 and 21. All five end-to-end tests passed successfully, validating correct submissions, wrong answer detection, and runtime error handling across different problem types.
Figure 20: Automated test suite execution - Part 1
Figure 21: Automated test suite execution - Part 2 showing 100% pass rate
Step 16 Verify Container Health Checks
The final verification of container health checks is presented in Figure 22, confirming all six containers are running with healthy status, proper restart policies, and configured health check endpoints.
Figure 22: Container health check status verification showing all services healthy
6. Modifications Made to Containers After Downloading
This section details all modifications performed on the Docker containers after downloading the base images from Docker Hub. These modifications were essential for implementing the specific requirements of the CodeMaster platform and ensuring proper functionality across all services.
Overview of Container Modifications
Ten significant modifications were made to the downloaded Docker containers to transform them from their base configurations into a fully functional competitive programming platform. These modifications addressed compatibility issues, added required functionality, optimized performance, and ensured production readiness. The following subsections provide detailed explanations of each modification, including the original state, the changes made, the reasoning behind each modification, and the resulting impact on the system.
Detailed Modifications
Modification 1: Backend Container - Added Python3 Runtime
Original State: Base node:18-alpine image contains only Node.js runtime.
Modification: Added Python3 and pip to the container.
Reason: The judge worker needs to execute user-submitted Python code. Without Python3 installed in the container, code execution would fail. This modification enables the judge worker to run Python scripts in an isolated environment.
Impact: Increased image size by approximately 50MB but enabled core functionality of code execution.
Modification 2: Backend Container - Multi-Stage Build Implementation
Original State: Single-stage build including all build dependencies in final image.
Modification: Implemented multi-stage build to separate build and runtime stages.
FROM node:18-alpine AS base
RUN npm ci --only=production
# Stage 2: Runtime stage
FROM node:18-alpine
COPY --from=base /app/node_modules ./node_modules
Reason: Multi-stage builds reduce final image size by excluding build tools (gcc, g++, make) from the production image. Only production dependencies are included.
Impact: Reduced final image size by approximately 100MB, faster deployment, and improved security by removing unnecessary build tools.
Modification 3: Backend Container - Health Check Implementation
Original State: No health check mechanism.
Modification: Added Docker health check with custom endpoint.
CMD curl -f http://localhost:5001/api/health || exit 1
Backend Code Addition:
Reason: Docker uses health checks to monitor service availability and automatically restart unhealthy containers. This ensures high availability and automatic recovery from failures.
Impact: Improved system reliability with automatic failure detection and recovery.
Modification 4: Frontend Container - Node.js Version Upgrade
Original State: node:18-alpine
Modification: Upgraded to node:20-alpine
Reason: Vite 7.x requires Node.js 20.19+ or 22.12+. Using Node 18 caused compatibility errors and build failures. Node 20 provides the required features while remaining lightweight.
Impact: Resolved Vite compatibility issues, enabled hot module replacement, and improved build performance.
Modification 5: Frontend Container - Vite Network Configuration
Original State: Default Vite configuration listens only on localhost.
Modification: Configured Vite to listen on all network interfaces.
Dockerfile Command:
Reason: Default Vite configuration only accepts connections from localhost, making it inaccessible from outside the container. Setting host to 0.0.0.0 allows external connections while maintaining HMR functionality.
Impact: Frontend accessible from host machine and other containers, hot reload working correctly.
Modification 6: MongoDB Container - Initialization Script
Original State: Empty MongoDB container without initial data.
Modification: Added initialization script and database seeding.
Seeding Script: Created backend/scripts/seed-all-problems.js to load 6 problems.
Reason: Platform requires pre-loaded problems for users to solve. Initialization script creates indexes and seeds data automatically on first startup.
Impact: Automated database setup, consistent initial state across deployments, no manual data entry required.
Modification 7: Judge Worker - 3-Tier Input Parsing Fallback
Original State: Simple JSON parsing for all inputs.
Modification: Implemented 3-tier fallback parsing system.
try:
arr = json.loads(input_lines[0])
except:
# Tier 2: Try eval() for Python literals
try:
arr = eval(input_lines[0])
except:
# Tier 3: Treat as strings
arr = input_lines[0]
Reason: Different problems use different input formats (JSON arrays, Python literals, plain strings). Single parsing method caused failures for string-based problems like "Add Binary".
Impact: All 3 problem types (array-based, string-based, class-based) now work correctly.
Modification 8: Judge Worker - Class-Based Problem Wrapper
Original State: Only function-based problems supported.
Modification: Created Python wrapper for class-based problems.
commands = ["MinStack", "push", "push", "top", "getMin"]
args = [[], [1], [2], [], []]
results = []
obj = None
for i, cmd in enumerate(commands):
if cmd == "MinStack":
obj = MinStack()
results.append(None)
else:
method = getattr(obj, cmd.lower())
result = method(*args[i]) if args[i] else method()
results.append(result)
Reason: LeetCode-style class-based problems require instantiation and method calls, not simple function calls. Without this wrapper, class-based problems would fail.
Impact: Enabled support for class-based problems like MinStack, expanding problem variety.
Modification 9: Test Cases - Added Expected Output Field
Original State: Test cases only had input field.
Modification: Added output field to all test cases.
Reason: Judge worker needs expected output to compare against actual output. Without this field, test case evaluation was impossible.
Impact: Enabled proper test case evaluation and accuracy calculation.
Modification 10: Docker Compose - Service Dependencies and Health Checks
Original State: Services started in random order without health checks.
Modification: Added depends_on with health check conditions.
depends_on:
mongodb:
condition: service_healthy
rabbitmq:
condition: service_healthy
redis:
condition: service_healthy
Reason: Backend API requires MongoDB, RabbitMQ, and Redis to be fully operational before starting. Without proper ordering, backend would fail to connect and crash.
Impact: Reliable startup sequence, no connection failures, automatic retry on service unavailability.
Summary of All Modifications
| Component | Modification | Reason | Impact |
|---|---|---|---|
| Backend Container | Added Python3 | Code execution requirement | Enabled judge functionality |
| Backend Container | Multi-stage build | Reduce image size | -100MB, faster deployment |
| Backend Container | Health checks | Automatic failure recovery | Improved reliability |
| Frontend Container | Node 20 upgrade | Vite compatibility | Resolved build errors |
| Frontend Container | Network configuration | External accessibility | Frontend accessible from host |
| MongoDB | Initialization script | Automated setup | Consistent initial state |
| Judge Worker | 3-tier parsing | Multiple input formats | All problem types work |
| Judge Worker | Class wrapper | Class-based problems | Expanded problem variety |
| Test Cases | Added output field | Enable evaluation | Proper accuracy calculation |
| Docker Compose | Service dependencies | Reliable startup | No connection failures |
7. Repository Links
GitHub Repository
Main Project Repository
Repository URL: https://github.com/wolfie1e/CodeMaster
Description: Complete source code for the CodeMaster platform including backend, frontend, Docker configurations, and documentation.
Branch: main
Last Updated: October 2025
Docker Hub Repositories
Custom Docker Images
Backend API and Judge Worker Image: wol1sdm/codemaster-backend
Frontend Image: wol1sdm/codemaster-frontend
Official Base Images Used
- Node.js 18: https://hub.docker.com/_/node (Backend & Judge Worker)
- Node.js 20: https://hub.docker.com/_/node (Frontend)
- MongoDB 7.0: https://hub.docker.com/_/mongo
- RabbitMQ 3.13: https://hub.docker.com/_/rabbitmq
- Redis 7: https://hub.docker.com/_/redis
Quick Start Instructions
# Start all services
docker-compose up -d --build
# Wait 30-60 seconds for services to initialize
# Access the application
# Frontend: http://localhost:5173
# Backend API: http://localhost:5001
# RabbitMQ Management: http://localhost:15672 (guest/guest)
# View logs
docker-compose logs -f
# Stop all services
docker-compose down
# Complete reset (including data)
docker-compose down -v
8. Outcomes
Final Deliverables
Fully Functional Containerized Platform
Complete competitive programming platform with 6 containerized services running seamlessly together, deployable with a single command, accessible via web browser, and handling code submissions with real-time feedback.
Comprehensive Problem Database
6 competitive programming problems loaded and verified, covering 3 difficulty levels, supporting 3 problem types (array-based, string-based, class-based), and each problem with complete test cases including input and expected output.
Automated Code Judging System
Python code execution in isolated environment, automatic test case evaluation, accuracy percentage calculation, real-time status updates via Socket.IO, and comprehensive error handling for wrong answers and runtime errors.
Production-Ready Infrastructure
All services with health checks and automatic restart policies, data persistence across container restarts using Docker volumes, resource limits configured for optimal performance, comprehensive logging and monitoring, and scalable architecture supporting horizontal scaling.
100% Test Pass Rate
5 out of 5 automated end-to-end tests passing, correct solution tests achieving 100% accuracy, wrong answer tests properly detecting errors, runtime error tests catching syntax issues, and real-time updates verified working correctly.
Quantifiable Results
| Metric | Result | Description |
|---|---|---|
| Total Containers | 6 | Frontend, Backend API, Judge Worker, MongoDB, RabbitMQ, Redis. |
| Total Problems | 6 | Loaded from database. |
| Problem Types Supported | 3 | Array-based, String-based, Class-based. |
| Automated Tests | 5/5 Passing | 100% pass rate on E2E test suite. |
| Deployment Time | ~2 minutes | From docker-compose up --build to fully running system. |
| Real-time Update Latency | < 500ms | Time from test case completion to UI update. |
9. Conclusion
The CodeMaster project successfully achieved all its objectives, culminating in a robust, scalable, and fully containerized competitive programming platform. By leveraging Docker and a microservices architecture, the project effectively addressed common challenges in software development, including environment inconsistency, dependency management, and deployment complexity. The separation of concerns into six distinct services—Frontend, Backend API, Judge Worker, MongoDB, RabbitMQ, and Redis—resulted in a highly maintainable and fault-tolerant system.
Key achievements include the implementation of an asynchronous code judging pipeline using RabbitMQ, which ensures the platform remains responsive under load, and a real-time feedback system using Socket.IO, which provides users with instant submission updates. The extensive testing and validation phase confirmed the system's correctness, with 100% of automated tests passing and all 6 problems across three different types (array, string, and class-based) being judged correctly.
The final platform is not only fully functional but also production-ready, featuring persistent data, automated health checks, and a reliable startup sequence. The entire application can be launched with a single docker-compose up command, demonstrating the power and simplicity of Docker for orchestrating complex, multi-service applications. This project serves as a comprehensive case study in building modern, real-world web applications using containerization and microservices.
10. References
- Docker Official Documentation: https://docs.docker.com/
- Node.js Official Documentation: https://nodejs.org/
- MongoDB Official Documentation: https://www.mongodb.com/docs/
- RabbitMQ Official Documentation: https://www.rabbitmq.com/documentation.html
- Redis Official Documentation: https://redis.io/documentation
- Socket.IO Official Documentation: https://socket.io/docs/
- React Official Documentation: https://react.dev/
- Vite Official Documentation: https://vitejs.dev/
- Express.js Official Documentation: https://expressjs.com/
- Docker Compose Documentation: https://docs.docker.com/compose/
- IITB Spoken Tutorial - Docker: https://spoken-tutorial.org/tutorial-search/?search_foss=Docker&search_language=English
11. Acknowledgements
I would like to express my sincere gratitude to all those who contributed to the successful completion of the CodeMaster project. First and foremost, I extend my deepest appreciation to my Cloud Computing course instructor at VIT, SCOPE for providing invaluable guidance, support, and constructive feedback throughout the Academic Year 2025. Their expertise in containerization technologies and microservices architecture proved instrumental in shaping the direction and quality of this project.
I am deeply grateful to the open-source community for developing and maintaining the robust tools and libraries that formed the foundation of this platform. The creators and maintainers of Docker, Node.js, MongoDB, RabbitMQ, Redis, React, Vite, Express.js, Socket.IO, and all other technologies utilized in this project have made significant contributions to the software development ecosystem, enabling projects like CodeMaster to be realized efficiently and effectively.
Special acknowledgment is due to the comprehensive documentation provided by each of these technologies, which served as essential references during the implementation phase. In particular, the IITB Spoken Tutorial on Docker provided a practical and accessible learning resource.The detailed guides, examples, and best practices documented by these communities, including the tutorial, significantly accelerated the development process and ensured adherence to industry standards.
I would also like to thank my peers and colleagues who provided valuable feedback, suggestions, and support throughout the project lifecycle. Their insights and encouragement were invaluable in overcoming technical challenges and improving the overall quality of the implementation.
Finally, I acknowledge the contributions of the broader academic community in advancing research and knowledge in containerization, microservices architecture, and real-time web applications. The collective wisdom and shared experiences within this community have been an essential resource in the successful execution of this project.
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