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Travel Booking App System Design ​

1. Business Requirements ​

Functional Requirements ​

  • User registration and authentication (travelers, agents, admins)
  • Search and booking for flights, hotels, cars, and activities
  • Real-time availability and pricing
  • Booking management (view, modify, cancel)
  • Payment processing and confirmation
  • Alerts/notifications (urgent changes, reminders, cancellations)
  • Mobile-ready responsive UI and API
  • Role-based access control
  • Analytics and reporting (booking trends, revenue)
  • API for third-party integrations (e.g., airlines, hotels)

Non-Functional Requirements ​

  • 99.9% availability (max ~8.76 hours downtime/year)
  • Scalability to handle high search and booking volume
  • Secure data storage and access control (PCI DSS for payments)
  • Fast response times (<300ms for most requests)
  • Audit logging and monitoring
  • Backup and disaster recovery
  • GDPR/data privacy compliance
  • Mobile responsiveness

Out of Scope ​

  • In-person travel agent support
  • Loyalty/rewards program (unless specified)
  • Built-in travel insurance management

2. Estimation & Back-of-the-Envelope Calculations ​

  • Users: 1M
  • Bookings/day: 100,000 (flights, hotels, cars, activities)
  • Peak concurrent users: ~20,000
  • Data size:
    • User data: 1M × 2 KB ≈ 2 GB
    • Bookings: 10M × 1 KB ≈ 10 GB
    • Search logs: 100M × 0.2 KB ≈ 20 GB
    • Partner data (hotels, flights): 1M × 2 KB ≈ 2 GB
    • Audit logs: 100M × 0.2 KB ≈ 20 GB
    • Total DB size: ~54 GB (excluding logs, backups)
  • Availability:
    • 99.9% = 8.76 hours/year downtime max
    • Use managed DB, multi-AZ deployment, health checks, auto-scaling

3. High Level Design (Mermaid Diagrams) ​

Component Diagram ​

mermaid
flowchart LR
  User[User (Web/Mobile)]
  LB[Load Balancer]
  App[Application Server]
  DB[(Database)]
  Cache[Cache (Redis)]
  Partner[Partner API Gateway]
  Payment[Payment Gateway]
  Alert[Alert/Notification Service]
  Analytics[Analytics Engine]

  User --> LB --> App
  App --> DB
  App --> Cache
  App --> Partner
  App --> Payment
  App --> Alert
  App --> Analytics
  Analytics --> DB

Data Flow Diagram ​

mermaid
sequenceDiagram
  participant U as User
  participant A as App Server
  participant D as Database
  participant C as Cache
  participant P as Partner API
  participant G as Payment
  participant L as Alert Service

  U->>A: Book Travel
  A->>C: Check Availability
  C-->>A: Hit/Miss
  A->>P: Fetch Real-Time Data
  P-->>A: Availability/Pricing
  A->>D: Create Booking Record
  D-->>A: Success/Fail
  A->>G: Process Payment
  G-->>A: Payment Status
  A->>L: Send Confirmation/Urgent Alert
  A-->>U: Response

Key Design Decisions ​

  • Database: Relational DB (e.g., PostgreSQL) for transactional data, strong consistency
  • Cache: Redis for fast lookups (availability, sessions)
  • Partner API Gateway: For real-time integration with airlines, hotels, etc.
  • Payment Gateway: Secure payment processing (PCI DSS compliant)
  • Alerting/Notifications: Email/SMS/push via third-party service (e.g., Twilio, Firebase)
  • Analytics: Batch or streaming (e.g., Kafka + Spark, or managed cloud analytics)
  • Deployment: Cloud-based, multi-AZ, managed services for high availability
  • API: REST/GraphQL for mobile and web clients

4. Conceptual Design ​

Entities ​

  • User: id, name, email, password_hash, role, registration_date, status
  • Booking: id, user_id, type (flight/hotel/car/activity), details, status, created_at, updated_at
  • PartnerData: id, partner_type, data, last_synced
  • Payment: id, booking_id, amount, status, method, transaction_id, created_at
  • Alert: id, user_id, booking_id, type (urgent/confirmation/cancellation), message, created_at, status
  • AuditLog: id, user_id, action, entity, entity_id, timestamp

Key Flows ​

  • Booking:
    1. User searches and selects travel option
    2. App checks cache, fetches real-time data from partner API
    3. Creates booking record, processes payment
    4. Sends confirmation/urgent alert
  • Alerts:
    • System triggers urgent alerts for changes, cancellations, reminders
  • Analytics:
    • Periodic jobs aggregate booking, revenue, and trend data

Security ​

  • Role-based access control (RBAC)
  • Input validation, rate limiting
  • Encrypted connections (HTTPS)
  • PCI DSS compliance for payments
  • Regular backups and audit logs

5. Bottlenecks and Refinement ​

Potential Bottlenecks ​

  • Partner API latency:
    • Use async requests, retries, and fallback data
  • Database contention:
    • Use read replicas, caching, and DB connection pooling
  • Payment processing:
    • Use async queues and retries for payment gateway integration
  • Alert delivery:
    • Use async queues for urgent notifications
  • Cache invalidation:
    • Ensure cache is updated on booking/partner data changes
  • Single region failure:
    • Deploy across multiple availability zones/regions

Refinement ​

  • Monitor system metrics and auto-scale app servers
  • Regularly test failover and backup restores
  • Optimize queries and indexes for frequent operations
  • Consider sharding if user/booking volume grows significantly

This design provides a scalable, highly available, and mobile-ready travel booking system with robust urgent alerts, analytics, and operational best practices.