Guide to DevSecOps

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Event Management System Design ​

1. Business Requirements ​

Functional Requirements ​

  • User registration and authentication (attendees, organizers, admins)
  • Event creation, editing, and deletion by organizers
  • Event discovery and search (by date, location, type, etc.)
  • Ticket booking and management (purchase, cancel, transfer)
  • Real-time seat availability and updates
  • Alerts and notifications (event reminders, changes, cancellations)
  • Mobile-ready responsive UI
  • Analytics and trends (popular events, booking rates)
  • Role-based access control
  • Feedback and rating for events

Non-Functional Requirements ​

  • 99.9% availability (max ~8.76 hours downtime/year)
  • Scalability to handle high traffic (e.g., ticket sales opening)
  • Secure data storage and access control
  • 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 payment processing at event venues
  • Integration with external ticketing platforms
  • Physical access control (e.g., turnstile integration)

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

  • Users: 100,000 attendees, 1,000 organizers, 50 admins
  • Events: 10,000 active at any time
  • Daily transactions: ~50,000 (browsing, bookings, notifications)
  • Peak concurrent users: ~5,000
  • Data size:
    • User data: 101,050 × 2 KB ≈ 200 MB
    • Events: 10,000 × 2 KB ≈ 20 MB
    • Tickets: 1M × 0.5 KB ≈ 500 MB
    • Alerts/notifications: 5M × 0.2 KB ≈ 1 GB
    • Feedback: 500,000 × 0.2 KB ≈ 100 MB
    • Total DB size: ~2 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)]
  Analytics[Analytics Engine]
  Alert[Alert/Notification Service]

  User --> LB --> App
  App --> DB
  App --> Cache
  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 L as Alert Service

  U->>A: Book Ticket
  A->>C: Check Seat Availability
  C-->>A: Hit/Miss
  A->>D: Create Ticket Record
  D-->>A: Success/Fail
  A->>L: Send Booking Confirmation
  A-->>U: Response

Key Design Decisions ​

  • Database: Relational DB (e.g., PostgreSQL) for transactional data, strong consistency
  • Cache: Redis for fast lookups (seat availability, sessions)
  • Analytics: Batch or streaming (e.g., Kafka + Spark, or managed cloud analytics)
  • Deployment: Cloud-based, multi-AZ, managed services for high availability
  • Alerting/Notifications: Email/SMS/push via third-party service (e.g., Twilio, Firebase)

4. Conceptual Design ​

Entities ​

  • User: id, name, email, password_hash, role, registration_date, status
  • Event: id, organizer_id, name, description, location, date, time, category, status
  • Ticket: id, event_id, user_id, seat_number, status, purchase_date
  • Alert: id, user_id, event_id, type (reminder/cancellation/update), message, created_at, status
  • Feedback: id, user_id, event_id, rating, comment, created_at

Key Flows ​

  • Ticket Booking:
    1. User books ticket
    2. App checks cache for seat availability, falls back to DB
    3. Creates Ticket record
    4. Sends booking confirmation alert
  • Event Alerts:
    • System triggers alerts for reminders, changes, or cancellations
  • Analytics:
    • Periodic jobs aggregate bookings, trends, and feedback

Security ​

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

5. Bottlenecks and Refinement ​

Potential Bottlenecks ​

  • Database contention (high read/write during ticket sales):
    • Use read replicas, caching, and DB connection pooling
  • Alert delivery:
    • Use async queues for notifications
  • Analytics workload:
    • Offload to separate analytics engine, run during off-peak
  • 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/event/ticket volume grows significantly

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