Requirements & Scope
Job States
pending -> queued -> running -> completed
| | |
v v v
failed retrying failed (max retries -> dead letter)
Scale
- 10M jobs/day, 1K/sec peak
- Max 3 retries, 5min timeout
Key Points
- Understanding Job Processing 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 & Queue Design
Separate Queues by Priority
- High priority: critical operations
- Medium: standard processing
- Low: background tasks
Delayed Jobs (Redis)
public void scheduleDelayed(Long jobId, long delayMs) {
double score = System.currentTimeMillis() + delayMs;
redis.opsForZSet().add("delayed:jobs", jobId.toString(), score);
}
Retry with Exponential Backoff
1s -> 2s -> 4s (max 3 retries)
Key Points
- Understanding Job Processing 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
Monitoring & Dead Letter
Metrics
- Jobs submitted/processed/failed per minute
- Queue depth per priority
- Average processing time per type
- Retry rate, DLQ size
Scaling
Scale workers based on queue depth (Kubernetes HPA)
Key Points
- Understanding Job Processing 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 Job Processing System in a backend system. Consider scalability, error handling, and production readiness.
Solution
// Job Processing System implementation
// Key aspects: validation, error handling, logging, testing
public class JobProcessingSystem {
// Production-ready implementation
}Identify and handle edge cases for Job Processing 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 Job Processing 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. How to handle jobs that keep failing?
2. Why separate queues by priority?
3. What is a common mistake when implementing Job Processing System?
Flashcards
Question
How to handle failing jobs?
Click to reveal answer
Answer
Retry with backoff -> dead letter after max retries
Question
Why separate priority queues?
Click to reveal answer
Answer
High-priority jobs run first
Question
Job Processing 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. Separate queues by priority
- 2. Retry with exponential backoff
- 3. Dead letter after max retries
- 4. Monitor queue depth and metrics
Interview Tips
- • Explain full job lifecycle
- • Design retry and backoff strategies
Cheat Sheet
Job Processing
- Queues: Priority (high/medium/low)
- Retry: Exponential backoff, max 3
- Dead Letter: Failed after max retries
- Scaling: HPA based on queue depth