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TTL

Use Time-To-Live to automatically expire cached data.

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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

  1. Validation: Always validate input at the boundary
  2. Error Handling: Use structured error responses
  3. Logging: Log key events for debugging
  4. Testing: Unit, integration, and load tests
  5. Documentation: Keep docs updated with code changes

Best Practices

Key Principles

  1. Follow SOLID principles
  2. Write clean, readable code
  3. Test thoroughly
  4. Document decisions
  5. 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

  1. Validation: Always validate input at the boundary
  2. Error Handling: Use structured error responses
  3. Logging: Log key events for debugging
  4. Testing: Unit, integration, and load tests
  5. Documentation: Keep docs updated with code changes

Practice Problems

0/3solved
Implement Time-To-Live (TTL)

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
}
Time-To-Live (TTL) Edge Cases

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, idempotency
Time-To-Live (TTL) Testing Strategy

Write 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 injection

Quiz

1. TTL means?

Question 1 options

2. Too short TTL causes?

Question 2 options

3. What is the primary purpose of Time-To-Live (TTL)?

Question 3 options

4. What is a common mistake when implementing Time-To-Live (TTL)?

Question 4 options

Flashcards

Question

TTL?

Answer

Time-To-Live - how long entry stays in cache

Question

Too short TTL?

Answer

More cache misses, more DB queries

Question

What is Time-To-Live (TTL)?

Answer

Time-To-Live (TTL) is a key concept in backend development.

Question

When to use Time-To-Live (TTL)?

Answer

Use Time-To-Live (TTL) when building production systems that require reliability, scalability, and maintainability.

Question

Time-To-Live (TTL) best practices

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