A high-performance, cost-optimized ticket booking system designed to handle massive concurrent traffic during concert releases. Built with serverless architecture and modern cloud-native patterns. Inspired by ticketmaster
Traditional ticket booking systems face critical challenges during high-demand events:
- High Concurrency: Thousands of users competing for limited tickets simultaneously
- Race Conditions: Multiple users attempting to purchase the same seat at the same time
- Cost Optimization: Need to scale efficiently without over-provisioning as a lot of people is using the system
- Performance: Sub-second response times during peak traffic
- Serverless-First: Auto-scaling Lambda functions for unpredictable traffic patterns
- Event-Driven: EventBridge for orchestration and Lambda warming
- Cache-Heavy: Redis/ElastiCache for temporary seat reservations
- Database Optimization: DynamoDB for single-key lookups and to aid for massive scale
- Idempotency: Ensuring safe retries and preventing duplicate purchases
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ React Frontend │────│ API Gateway │────│ Lambda Functions│
│ User Interface │ │ (Rate Limiting)│ │ (Business Logic)│
└─────────────────┘ └──────────────────┘ └─────────────────┘
│
┌──────────────────┐ │
│ EventBridge │────────────┘
│ (Orchestration)│
└──────────────────┘
│
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ DynamoDB │────│ ElastiCache │────│ State Machine │
│ (Persistent) │ │ (Reservations) │ │ (Workflow) │
└─────────────────┘ └──────────────────┘ └─────────────────┘
- Authentication: Secure login/signup with JWT tokens
- Concert Selection: Browse and filter available concerts
- Seat Selection: Real-time seat availability fetched from Redis - users can only click available seats
- Checkout Process: Stripe payment processing with seat reservation
- Account Management: View purchased tickets and booking history
- Seat Reservation System: 5-minute TTL in Redis for checkout process
- Exponential Backoff: Retry mechanisms for failed operations
- State Machine: Orchestrated workflow for ticket purchase lifecycle
- Idempotency Keys: Prevent duplicate transactions
- Lambda Warming: Pre-provisioned capacity for known high-traffic events
- React with TypeScript
- Tailwind CSS for styling
- React Query for state management and caching
- NestJS with TypeScript
- TypeORM for database ORM with PostgreSQL
- class-validator for request validation
- Swagger for API documentation
- JWT for authentication
- Redis for seat reservation caching
- Stripe for payment processing
- AWS Lambda for serverless functions (production)
- API Gateway for request routing and rate limiting (production)
- EventBridge for event orchestration (production)
- DynamoDB for persistent data storage
- ElastiCache (Redis) for temporary seat reservations and real-time seat availability
- Local Development: MongoDB/Redis for development (NoSQL to match production)
- Terraform for Infrastructure as Code
- Docker for local development
- GitHub Actions for CI/CD pipeline
- Lambda: Serverless compute
- API Gateway: API management
- DynamoDB: NoSQL database
- ElastiCache: Redis caching
- EventBridge: Event routing and warming up of lambdas
- CloudWatch: Monitoring and logging
- CloudFront: CDN for global distribution
1. User views seats → Fetch availability from Redis (real-time)
2. User clicks available seat → Reserve in Redis (5min TTL)
3. User proceeds to checkout → Validate reservation
4. Stripe payment success → Move from Redis to DynamoDB
5. Payment failure/timeout → Release reservation
- Eventual Consistency: Redis serves as the source of truth for seat availability
- Strong Consistency: DynamoDB conditional writes ensure no double-booking
- Sync Mechanism: Background jobs sync Redis with DynamoDB changes
- Conflict Resolution: Last-write-wins with timestamp-based versioning
- Optimistic Locking: Version fields in DynamoDB
- Atomic Operations: Conditional writes with existence checks
- Distributed Locks: Redis-based locking mechanism
- Circuit Breaker: Prevent cascade failures
- Redis Cache: Real-time seat availability and temporary reservations
- Lambda Memory: In-memory caching for concert metadata and static data
- DynamoDB: Persistent storage with eventual consistency sync to Redis
- Horizontal Scaling: Auto-scaling Lambda functions
- Vertical Scaling: Provisioned capacity for known events
- Geographic Distribution: Multi-region deployment
- CDN: CloudFront for static assets
- Pay-per-request: DynamoDB on-demand billing
- Spot Instances: For non-critical workloads
- Reserved Capacity: For predictable traffic patterns
- Auto-scaling: Right-size resources based on demand
- Node.js 18+
- Docker & Docker Compose
- AWS CLI configured
- Terraform 1.0+
# Clone repository
git clone <repository-url>
cd ticket-management-system
# Start local services
docker-compose -f docker-compose-dev.yml up -d
# Install dependencies
cd backend && npm install
cd ../frontend && npm install
# Start development servers
cd backend && npm run start:dev
cd frontend && npm run devcd iac
terraform init
terraform plan
terraform applyAfter researching how ticketmaster handle extreme traffic, i discovered that Ticketmaster likely uses Cloudflare Workers and Cloudflare virtual waiting rooms to manage massive concurrent demand. While this current proposed solution provides a solid foundation for handling high concurrency, it may still face challenges during unprecedented events like Taylor Swift concerts where demand can exceed millions of concurrent users. However, its a good start and there is room for improvement.
Key Limitations Identified:
- Scale Ceiling: Even with auto-scaling Lambda functions, AWS has concurrency limits
- Cold Start Latency: Lambda cold starts could impact user experience during traffic spikes
- Redis Bottleneck: Single Redis instance could become a bottleneck at extreme scale