Requirements & Scope
Order States
pending -> confirmed -> processing -> shipped -> delivered
| | | |
v v v v
cancelled cancelled cancelled returned
Scale
- 5K orders/sec peak
- All state changes tracked in audit log
- Search latency p99 < 500ms
Key Points
- Understanding Order Management System is essential for production systems
- Always consider scalability and maintainability
- Test thoroughly before deploying to production
- Monitor performance and set up alerting
Common Patterns
- Validation: Always validate input at the boundary
- Error Handling: Use structured error responses
- Logging: Log key events for debugging
- Testing: Unit, integration, and load tests
- Documentation: Keep docs updated with code changes
Architecture & State Machine
Event-Driven Architecture
Order Service -> Event Bus -> Payment, Shipping, Notify services
State Machine
- Enforces valid transitions
- Event sourcing for audit trail
- Idempotency keys for duplicate prevention
- Optimistic locking (version field) for concurrency
Event Publishing
@Transactional
public void updateStatus(Long orderId, OrderStatus newStatus) {
Order order = orderRepository.findById(orderId);
order.transition(newStatus);
orderRepository.save(order);
eventPublisher.publish(new OrderStatusChanged(orderId, newStatus));
}
Database & Events
Tables
- orders(id, user_id, status, version, items JSONB, total, idempotency_key)
- order_events(id, order_id, event_type, payload JSONB)
Optimistic Locking
UPDATE orders SET status = ?, version = version + 1
WHERE id = ? AND version = ?;
Key Points
- Understanding Order Management System is essential for production systems
- Always consider scalability and maintainability
- Test thoroughly before deploying to production
- Monitor performance and set up alerting
Common Patterns
- Validation: Always validate input at the boundary
- Error Handling: Use structured error responses
- Logging: Log key events for debugging
- Testing: Unit, integration, and load tests
- Documentation: Keep docs updated with code changes
Practice Problems
Design and implement a solution for Order Management System in a backend system. Consider scalability, error handling, and production readiness.
Solution
// Order Management System implementation
// Key aspects: validation, error handling, logging, testing
public class OrderManagementSystem {
// Production-ready implementation
}Identify and handle edge cases for Order Management System. What happens under high load, with invalid input, or during failures?
Solution
// Edge case handling:
// 1. Null/empty input -> validation
// 2. High load -> rate limiting, queuing
// 3. Failures -> retries, circuit breaker
// 4. Concurrent access -> locks, idempotencyWrite a testing strategy for Order Management System. Include unit tests, integration tests, and performance tests.
Solution
// Test plan:
// - Unit: 80% coverage target
// - Integration: API contracts
// - Performance: latency, throughput
// - Chaos: failure injectionQuiz
1. Why use event sourcing for orders?
2. How to prevent duplicate order updates?
3. What is a common mistake when implementing Order Management System?
Flashcards
Question
What is event sourcing?
Click to reveal answer
Answer
Storing state changes as events for audit and replay
Question
How to prevent duplicate updates?
Click to reveal answer
Answer
Idempotency keys + version checking
Question
Order Management System best practices
Click to reveal answer
Answer
Follow SOLID principles, write clean code, test thoroughly, document decisions, and monitor in production.
Revision Notes
Key Takeaways
- 1. State machine enforces valid transitions
- 2. Event sourcing provides audit trail
- 3. Idempotency keys prevent duplicates
- 4. Optimistic locking handles concurrency
Interview Tips
- • Draw the state machine diagram
- • Explain why event sourcing over CRUD
Cheat Sheet
Order Management
- States: pending -> confirmed -> processing -> shipped -> delivered
- Events: Audit trail and decoupling
- Idempotency: Keys + version check
- Locking: Optimistic (version field)