TTL
TTL Examples
// Redis TTL
redis.set("product:123", product, Duration.ofMinutes(30));
redis.set("session:abc", session, Duration.ofHours(2));
redis.set("config:global", config, Duration.ofDays(1));
TTL Guidelines
| Data Type | TTL |
|---|---|
| User session | 30 min - 2 hours |
| Product data | 5 - 30 min |
| Static config | 1 - 24 hours |
| Rate limit counter | 1 minute |
Key Points
- Understanding Time-To-Live (TTL) 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
Best Practices
Key Principles
- Follow SOLID principles
- Write clean, readable code
- Test thoroughly
- Document decisions
- Monitor in production
Implementation
- Start simple, refactor as needed
- Use established patterns
- Consider trade-offs
- Review with peers
Continuous Improvement
- Learn from incidents
- Update documentation
- Share knowledge
- Mentor others
Key Points
- Understanding Time-To-Live (TTL) 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 Time-To-Live (TTL) in a backend system. Consider scalability, error handling, and production readiness.
Solution
// Time-To-Live (TTL) implementation
// Key aspects: validation, error handling, logging, testing
public class TimeToLiveTTL {
// Production-ready implementation
}Identify and handle edge cases for Time-To-Live (TTL). 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 Time-To-Live (TTL). 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. TTL means?
2. Too short TTL causes?
3. What is the primary purpose of Time-To-Live (TTL)?
4. What is a common mistake when implementing Time-To-Live (TTL)?
Flashcards
Question
TTL?
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Answer
Time-To-Live - how long entry stays in cache
Question
Too short TTL?
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Answer
More cache misses, more DB queries
Question
What is Time-To-Live (TTL)?
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Answer
Time-To-Live (TTL) is a key concept in backend development.
Question
When to use Time-To-Live (TTL)?
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Answer
Use Time-To-Live (TTL) when building production systems that require reliability, scalability, and maintainability.
Question
Time-To-Live (TTL) best practices
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Answer
Follow SOLID principles, write clean code, test thoroughly, document decisions, and monitor in production.
Revision Notes
Key Takeaways
- 1.TTL auto-expires cache entries
- 2.Balance: too short = many misses, too long = stale data
- 3.Different data types need different TTLs
Interview Tips
- •Set appropriate TTLs
- •Know TTL tradeoffs
Cheat Sheet
TTL
- Time-To-Live: auto-expire entries
- Session: 30min-2hr
- Product: 5-30min
- Config: 1-24hr