🖥️ LeetCode serves over 15 million users globally, processing millions of code submissions daily. This document outlines the comprehensive architecture that enables LeetCode to provide secure code execution, real-time feedback, and scalable interview experiences with 99.9% availability.

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:

Security-first design with multiple layers of isolation
Horizontal scalability for handling traffic spikes
Multi-region deployment for global performance
Comprehensive monitoring for operational excellence
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.

LeetCode

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