Instruction file imported from milinddethe15/ticket-booking (.cursor/rules/implementation-summary.mdc). Copyright stays with the author.

Implementation Summary & Key Decisions

Core Implementation Decisions

1. Pessimistic Locking Strategy

Decision: Use PostgreSQL SELECT ... FOR UPDATE instead of optimistic locking Rationale:

• Guarantees no double bookings in high-concurrency scenarios
• Simpler error handling than optimistic collision detection
• Better user experience (immediate feedback vs retry loops) Implementation: server/internal/repository/booking_repository.go

2. Database Isolation Level

Decision: READ_COMMITTED instead of SERIALIZABLE Rationale:

• Better concurrency performance
• Explicit row locking provides needed consistency
• Reduces serialization conflicts under load Location: server/internal/db/database.go

3. Booking Reference Generation

Decision: Nanosecond timestamp (BK{UnixNano}) Rationale:

• Ensures uniqueness in concurrent requests
• Human-readable format
• Avoids database round-trips for ID generation Implementation: generateBookingRef() function

4. Repository Pattern

Decision: Separate repository layer for data access Rationale:

• Clean separation of concerns
• Testable business logic
• Database-agnostic interface design Files: server/internal/repository/

Critical Code Paths

Ticket Booking Flow

1. Validation: Check event existence and timing
2. Locking: Lock event row with FOR UPDATE
3. Availability: Verify sufficient tickets available
4. Reservation: Lock and update specific tickets
5. Booking: Create booking record with expiration
6. Commit: Atomic transaction completion

Error Handling Strategy

• Retry Logic: Automatic retry for deadlocks (3 attempts)
• Timeout Handling: 30-second request timeouts
• Graceful Degradation: Proper error responses with correlation IDs
• Transaction Rollback: Automatic cleanup on failures

Performance Optimizations

1. Connection Pooling

MaxOpenConns:    25  // Prevent connection exhaustion
MaxIdleConns:    5   // Balance resource usage
ConnMaxLifetime: 5*time.Minute  // Prevent stale connections

2. Database Indexing

• Events: start_time, available_tickets
• Tickets: (event_id, status) composite index
• Bookings: user_id, status, expires_at

3. Middleware Optimization

• Rate Limiting: Token bucket algorithm (100 RPS)
• Request Correlation: Unique ID tracking
• Structured Logging: JSON format for log aggregation

Security Considerations

Current Implementation

• Input Validation: Gin binding with validation tags
• SQL Injection: Parameterized queries throughout
• Security Headers: XSS protection, content type validation
• CORS: Configurable cross-origin support

Future Enhancements

• Authentication: JWT-based user authentication
• Authorization: Role-based access control
• Rate Limiting: Per-user limits instead of global
• Audit Logging: Booking event tracking

Scalability Features

Horizontal Scaling

• Stateless Design: No server-side session storage
• Database Connection Pooling: Efficient resource usage
• Health Checks: Kubernetes-ready endpoints
• Configuration: Environment-based settings

Monitoring Ready

• Health Endpoints: /health, /ready
• Structured Logging: JSON with correlation IDs
• Error Tracking: Comprehensive error responses
• Performance Metrics: Request timing and success rates

Testing Strategy

Load Testing Results

• Concurrent Users: 50 simultaneous booking requests
• Success Rate: 100% (no race conditions or double bookings)
• Data Integrity: Perfect sequential ticket allocation
• Performance: Sub-second response times

Test Coverage

• Unit Tests: Repository layer business logic
• Integration Tests: Full API endpoint testing
• Load Tests: Concurrent booking scenarios
• Database Tests: Transaction and locking behavior

Deployment Architecture

Development

• Docker Compose: Local development environment
• Hot Reload: Air tool for development iterations
• Sample Data: Automated test data loading

Production

• Containerized: Docker with multi-stage builds
• Kubernetes Ready: Health checks and graceful shutdown
• Environment Config: 12-factor app compliance
• Database Migrations: Automated schema management

Key Files Reference

• Application Entry: server/main.go
• Core Booking Logic: server/internal/repository/booking_repository.go
• Database Schema: server/migrations/001_initial_schema.up.sql
• API Configuration: server/internal/handlers/
• Environment Setup: server/docker-compose.yml

Critical Bug Fixes Applied

1. Booking Logic Error (FIXED)

Problem: System booked random available seats instead of user's selection Solution: Changed booking query to use status = 'locked' instead of status = 'available' Impact: Users now get the exact seats they selected

2. Auto-cleanup Implementation

Problem: Abandoned seat locks blocked other users indefinitely Solution: Background goroutine runs every 1 minute to unlock seats locked > 3 minutes Impact: Prevents dead locks from disrupting booking flow

3. Real-time Seat Synchronization

Problem: Seat availability not updated in real-time across users Solution: Added polling every 3 seconds + immediate lock/unlock API endpoints Impact: Users see accurate seat availability instantly

Success Metrics

✅ 100% booking success rate under concurrent load ✅ Zero data corruption or double bookings ✅ Sub-second response times for booking operations ✅ Perfect transaction isolation with pessimistic locking ✅ Production-ready with monitoring and health checks ✅ Critical seat selection bug fixed - users get their selected seats ✅ Auto-cleanup prevents abandoned locks from blocking other users