LeetCode

High-Level Architecture

Core Components

1. User Management System

Responsibilities:

• User registration and authentication
• Profile management and preferences
• Progress tracking and statistics
• Social authentication integration
• Technologies: Java Spring Boot, JWT, OAuth 2.0

2. Problem Management System

Key Features:

• 3000+ problems across difficulty levels
• Rich text editor with markdown support
• Multi-language problem descriptions
• Automated difficulty rating algorithm
• Technologies: Python Django, Elasticsearch, Redis

3. Code Execution Engine

Architecture Details:

Docker-based Execution

Supported Languages:

• Python (3.x)
• Java (8, 11, 17)
• C++ (GCC, Clang)
• JavaScript (Node.js)
• C# (.NET)
• Go
• Rust
• Swift
• Kotlin
• TypeScript

Security Measures:

• Container isolation with minimal attack surface
• Resource limits (CPU, memory, disk, network)
• Execution timeouts
• System call filtering
• Network isolation

4. Real-time Collaboration System

Real-time Features:

• Collaborative code editing with conflict resolution
• Live cursor positions and selections
• Real-time compilation and execution
• Video/audio communication via WebRTC
• Session recording and playback

5. Contest Management System

Contest Features:

• Real-time leaderboards with live updates
• Anti-cheating measures and plagiarism detection
• Dynamic problem difficulty adjustment
• Global and regional rankings
• Prize distribution and rating calculations

Data Architecture

1. PostgreSQL (Primary Database)

Schema Design:

• Users: Profile, preferences, subscription data
• Problems: Metadata, test cases, editorial content
• Submissions: Code, results, performance metrics
• Contests: Rules, participants, rankings

2. Cassandra (Submission Storage)

Data Modeling:

• Partition by user_id and problem_id
• Time-series data for submission history
• Efficient range queries for analytics
• Replication factor of 3 across regions

3. Redis Cache Layer

Cache Strategies:

• Session management and authentication tokens
• Problem metadata and test cases
• Real-time contest rankings
• Rate limiting counters
• API response caching

4. MongoDB (Interview Data)

Document Structure:

• Interview sessions with participant data
• Real-time collaboration events
• Video/audio recording metadata
• Feedback and evaluation data

Execution Infrastructure

1. Code Execution Pipeline

2. Container Management

Container Specifications:

• Base images for each language runtime
• Resource limits: 1 CPU core, 256MB RAM, 100MB disk
• Network isolation and no internet access
• Execution timeout: 30 seconds maximum
• File system restrictions and read-only access

3. Judge System Architecture

Judge Verdict Types:

• Accepted (AC): Correct solution
• Wrong Answer (WA): Incorrect output
• Time Limit Exceeded (TLE): Execution timeout
• Memory Limit Exceeded (MLE): Memory overflow
• Runtime Error (RE): Program crashed
• Compilation Error (CE): Code compilation failed
• Presentation Error (PE): Output format issue

Scalability and Performance

1. Auto-scaling Strategy

Scaling Policies:

• Horizontal scaling based on queue depth
• Vertical scaling for compute-intensive tasks
• Predictive scaling for contest traffic
• Multi-region deployment for global reach

2. Caching Strategy

Cache Hit Ratios:

• Static assets: 95%+
• Problem data: 85%+
• User sessions: 90%+
• API responses: 70%+

3. Database Optimization

Security Architecture

1. Multi-layer Security

2. Code Execution Security

Real-time Features

1. WebSocket Architecture

2. Operational Transform

Contest System

1. Contest Infrastructure

2. Anti-cheat Measures

Anti-cheat Features:

• Code similarity detection using AST comparison
• Typing pattern analysis and behavioral biometrics
• Multiple account detection via device fingerprinting
• Real-time monitoring during contests
• Machine learning models for cheating prediction

Analytics and Machine Learning

1. Data Pipeline

2. Machine Learning Applications

ML Models Used:

• Collaborative filtering for problem recommendations
• Gradient boosting for difficulty prediction
• Neural networks for code similarity detection
• NLP models for problem classification
• Reinforcement learning for adaptive learning paths

3. Analytics Dashboard

Monitoring and Observability

1. Comprehensive Monitoring Stack

2. Key Performance Indicators

3. Alerting Strategy

Deployment and DevOps

1. CI/CD Pipeline

2. Infrastructure as Code

3. Container Orchestration

Global Infrastructure

1. Multi-Region Deployment

2. Traffic Routing Strategy

Performance Optimization

1. Database Optimization

2. API Performance

Cost Optimization

1. Infrastructure Cost Management

2. Resource Efficiency

Future Architecture Considerations

1. Emerging Technologies

2. Scalability Roadmap

Disaster Recovery and Business Continuity

1. Backup and Recovery Strategy

2. High Availability Design

Conclusion

LeetCode's architecture represents a sophisticated distributed system designed to handle the unique challenges of online coding platforms. The system successfully manages:

• Secure code execution at massive scale with container isolation
• Real-time collaboration for technical interviews
• High-throughput submission processing during contests
• Global availability with regional optimization
• Advanced anti-cheat mechanisms for fair competition
• Intelligent recommendation systems for personalized learning

The architecture continues to evolve with emerging technologies like AI-powered coding assistance, WebAssembly execution, and immersive VR interview experiences. The platform's success lies in its ability to balance performance, security, and user experience while maintaining cost efficiency and operational reliability.

Key architectural principles that make LeetCode successful:

1. Security-first design with multiple layers of isolation
2. Horizontal scalability for handling traffic spikes
3. Multi-region deployment for global performance
4. Comprehensive monitoring for operational excellence
5. Continuous optimization based on data-driven insights

The platform serves as an excellent example of how to build and operate a large-scale technical platform that combines education, assessment, and competitive programming in a unified, secure, and performant system.

There might be iterations needed, current data is as close I could get.