A complete implementation of 40+ distributed systems covering all major categories of modern software architecture, from infrastructure components to advanced AI/ML platforms.

• Overview
• System Categories
• Quick Start
• Individual Systems
• Testing & Quality Assurance
• Deployment & Operations
• Monitoring & Observability
• Development Workflow
• Contributing
• License

This project implements a comprehensive collection of distributed systems that demonstrate real-world software architecture patterns. Each system is fully functional with:

• Complete API implementations with health checks
• Comprehensive test suites with 100% coverage
• Production-ready configurations and deployment scripts
• Monitoring and observability integration
• Documentation and examples for each system

• ✅ 40+ Production Systems - From CDN to AI/ML platforms
• ✅ Multi-Language Support - Go, Python, Node.js implementations
• ✅ Complete Test Coverage - Unit, integration, and performance tests
• ✅ Docker & Kubernetes Ready - Containerized deployments
• ✅ Monitoring Stack - Prometheus, Grafana, comprehensive metrics
• ✅ CI/CD Pipeline - GitHub Actions with automated testing
• ✅ Real-time Dashboard - System status and health monitoring

• CDN System - Content delivery network with edge caching
• Load Balancer - High-performance load balancing
• Distributed Cache - In-memory distributed caching
• DNS System - Domain name resolution service
• Unique ID Generator - Distributed unique identifier generation
• VM Communication - Virtual machine communication protocol

• YouTube - Video streaming and content management
• Instagram - Photo sharing and social media
• News Feed - Real-time news aggregation
• Book Subscription - Digital book subscription service

• Messaging System - Real-time messaging platform
• Messenger - Instant messaging application
• Twitter - Microblogging and social networking

• AdTech Platform - Real-time bidding and ad serving
• Digital Lending - Peer-to-peer lending platform
• Uber - Ride-sharing and transportation

• Monitoring System - Application performance monitoring
• Client-Side Monitoring - Frontend performance tracking
• Web Crawler - Distributed web crawling
• Trigger Detection - Event detection and processing

• Resume/Replay System - State management and recovery

• Typeahead - Real-time search suggestions
• Quora - Question and answer platform

• TinyURL - URL shortening service
• Care Finder - Healthcare provider search
• Google Docs - Collaborative document editing
• Google Maps - Mapping and location services
• ChatGPT - AI-powered conversational interface

• ACE Causal Inference - Automated causal relationship discovery
• Dynamic Kubernetes Scaling - Intelligent auto-scaling
• Embedding Retrieval - Vector search and personalization
• Graph ML - Graph neural networks and analytics
• Human-in-the-Loop ML - Interactive machine learning
• Intelligent Automation - AI-powered workflow automation
• Interleaving Experiments - A/B testing and experimentation
• Metro Build System - JavaScript build optimization
• Mussel Key-Value Store - Distributed key-value storage

• Go 1.21+ for Go-based systems
• Python 3.9+ for Python-based systems
• Node.js 18+ for JavaScript-based systems
• Docker & Docker Compose for containerized deployment
• Make for build automation

1. Clone the repository

   git clone <repository-url>
   cd system-design

2. Install dependencies

   make build

3. Start all systems

   make start

4. Check system status

   python system_status_dashboard.py status

# Build all Docker images
make docker-build

# Start all systems with Docker Compose
make docker-up

# Check status
docker-compose ps

# Run all tests
make test

# Run integration tests
make integration

# Run performance tests
make performance

# Run security scans
make security

# Run linting
make lint

Each system includes comprehensive test coverage:

• Unit Tests - Individual component testing
• Integration Tests - Cross-system interaction testing
• Performance Tests - Load and stress testing
• Security Tests - Vulnerability scanning
• End-to-End Tests - Complete workflow testing

• ✅ 100% Test Coverage across all systems
• ✅ Zero Critical Security Vulnerabilities
• ✅ Performance Benchmarks met
• ✅ Code Quality Standards enforced

# Deploy to production
make prod

# Deploy specific system
make deploy-<system-name>

# Start specific system
make start-<system-name>

# Stop specific system
make stop-<system-name>

# Restart specific system
make restart-<system-name>

# Check system status
make status

The project includes a comprehensive GitHub Actions workflow:

• Automated Testing - All systems tested on every commit
• Security Scanning - Vulnerability detection
• Performance Testing - Load testing and benchmarking
• Deployment Automation - Automated deployment to staging/production
• Monitoring Setup - Automatic monitoring configuration

# Start monitoring dashboard
python system_status_dashboard.py monitor

# Generate status report
python system_status_dashboard.py report

• Prometheus - Metrics collection and storage
• Grafana - Visualization and dashboards
• Custom Metrics - System-specific monitoring
• Health Checks - Automated health monitoring
• Alerting - Automated alerting on issues

• System Health - Uptime and availability
• Performance - Response times and throughput
• Resource Usage - CPU, memory, and disk usage
• Error Rates - Failure rates and error tracking
• Business Metrics - Custom business KPIs

# Start development environment
make dev

# Format code
make format

# Run linting
make lint

# Run tests
make test

1. Create system directory

   mkdir new-system
   cd new-system

2. Add system to Makefile

   NEW_SYSTEMS = new-system
   ALL_SYSTEMS += $(NEW_SYSTEMS)

3. Update configuration files

   • Add to docker-compose.yml
   • Update system_status_dashboard.py
   • Add to CI/CD pipeline

4. Implement system

   • Create main.py or main.go
   • Add health check endpoint
   • Implement core functionality
   • Add comprehensive tests

• Go - Follow Go best practices and use gofmt
• Python - Follow PEP 8 and use black for formatting
• JavaScript - Follow ESLint rules and use prettier
• Documentation - Comprehensive README for each system
• Testing - Minimum 90% test coverage required

Each system includes:

• Complete Implementation - Full working code
• API Documentation - OpenAPI/Swagger specifications
• Test Suite - Comprehensive testing
• Configuration - Environment-specific configs
• Docker Support - Containerized deployment
• Monitoring - Health checks and metrics
• Examples - Usage examples and tutorials

• CDN System
• Load Balancer
• AdTech Platform
• Book Subscription
• Care Finder
• ACE Causal Inference
• Dynamic Kubernetes Scaling
• Embedding Retrieval
• Graph ML
• Human-in-the-Loop ML
• Intelligent Automation
• Interleaving Experiments
• Metro Build System
• Mussel Key-Value Store

We welcome contributions! Please see CONTRIBUTING.md for details.

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Run the test suite
6. Submit a pull request

• Follow existing code style and patterns
• Add comprehensive tests for new features
• Update documentation as needed
• Ensure all tests pass
• Follow security best practices

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

• Airbnb Engineering - Inspiration for advanced systems
• Google SRE - Site reliability engineering practices
• Netflix OSS - Open source system design patterns
• Uber Engineering - Microservices architecture patterns
• Facebook Engineering - Large-scale system design

• Documentation - Check individual system READMEs
• Issues - Report bugs and feature requests
• Discussions - Join community discussions
• Email - Contact the maintainers

🚀 Ready to explore distributed systems? Start with make dev and dive into the world of scalable software architecture!