Guide to DevSecOps

Appearance

School Library System Design ​

1. Business Requirements ​

Functional Requirements ​

  • User registration and authentication (students, staff, admins)
  • Search and browse books (by title, author, genre, etc.)
  • Borrow and return books (with due dates, renewals, and holds)
  • Track borrowing history per user
  • Generate borrowing trends and analytics (e.g., most borrowed books, peak times)
  • Notifications (due reminders, overdue alerts)
  • Admin features: add/remove books, manage inventory, view analytics
  • Support for multiple library branches

Non-Functional Requirements ​

  • 99.9% availability (max ~8.76 hours downtime/year)
  • Scalability to support thousands of users and books
  • Data consistency for inventory and transactions
  • Secure user data and access control
  • Responsive UI (web/mobile)
  • Audit logging for all transactions
  • Backup and disaster recovery

Out of Scope ​

  • E-book lending or digital content management
  • Payment processing (e.g., fines)
  • Integration with external library networks

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

  • Users: 5,000 students + 200 staff/admins
  • Books: 50,000 items
  • Daily transactions: ~2,000 (borrows/returns/searches)
  • Peak concurrent users: ~200
  • Data size:
    • Book metadata: 50,000 × 1 KB ≈ 50 MB
    • User data: 5,200 × 2 KB ≈ 10 MB
    • Borrowing history: 1M records × 0.5 KB ≈ 500 MB
    • Total DB size: < 1 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]
  Notif[Notification Service]

  User --> LB --> App
  App --> DB
  App --> Cache
  App --> Notif
  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 N as Notification

  U->>A: Borrow Book Request
  A->>C: Check Book Availability
  C-->>A: Hit/Miss
  A->>D: Update Borrow Record
  D-->>A: Success/Fail
  A->>N: Send Notification
  A-->>U: Response

Key Design Decisions ​

  • Database: Relational DB (e.g., PostgreSQL) for ACID transactions, strong consistency
  • Cache: Redis for fast lookups (book availability, user sessions)
  • Analytics: Batch jobs or streaming (e.g., using Apache Kafka + Spark or managed cloud analytics)
  • Deployment: Cloud-based, multi-AZ, managed services for high availability
  • Notifications: Email/SMS via third-party service (e.g., SendGrid, Twilio)

4. Conceptual Design ​

Entities ​

  • User: id, name, email, role, registration_date, status
  • Book: id, title, author, genre, isbn, branch_id, status, added_date
  • Branch: id, name, address
  • BorrowRecord: id, user_id, book_id, borrow_date, due_date, return_date, status
  • Notification: id, user_id, type, message, sent_at

Key Flows ​

  • Borrow Book:
    1. User requests to borrow
    2. App checks cache for availability, falls back to DB
    3. If available, creates BorrowRecord, updates Book status
    4. Sends notification
  • Return Book:
    1. User returns book
    2. App updates BorrowRecord and Book status
    3. Sends notification
  • Analytics:
    • Periodic jobs aggregate BorrowRecords for trends (e.g., most borrowed books, active users)

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 write/read during peak):
    • Use read replicas, caching, and DB connection pooling
  • Cache invalidation:
    • Ensure cache is updated on borrow/return
  • Analytics workload:
    • Offload to separate analytics engine, run during off-peak
  • Notification delays:
    • Use async queues for notification delivery
  • 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/book volume grows significantly

This design provides a scalable, highly available school library system with analytics and robust operational practices.