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Cache Consistency

Maintain consistency between cache and database.

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Cache Consistency

Consistency Models

Model Description
Strong Cache and DB always match
Eventual Eventually consistent
Read-your-writes You see your own writes

Invalidation on Write

@Transactional
public Product updateProduct(Long id, UpdateRequest req) {
    Product product = productRepository.save(...);
    redis.delete("product:" + id);  // Invalidate
    return product;
}

Race Condition

Thread 1: Read from DB (price = $150)
Thread 2: Read from DB (price = $150)
Thread 1: Write to cache (price = $150)
Thread 2: Write to cache (price = $100)  ← WRONG! Stale!

Fix: Use locks or single-writer pattern.

Cache Best Practices

Strategies

  • Cache-Aside: Application manages cache
  • Write-Through: Sync write to cache and DB
  • Write-Behind: Async write to DB
  • Read-Through: Cache loads from DB

Invalidation

  • Time-based TTL
  • Event-based invalidation
  • Version-based keys
  • Tag-based grouping

Monitoring

  • Hit rate > 80% is good
  • Monitor eviction rates
  • Track cache size
  • Alert on anomalies

Key Points

  • Understanding Cache Consistency 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 / 3 solved
Implement Cache Consistency

Design and implement a solution for Cache Consistency in a backend system. Consider scalability, error handling, and production readiness.

Solution 
// Cache Consistency implementation
// Key aspects: validation, error handling, logging, testing

public class CacheConsistency {
    // Production-ready implementation
}
Cache Consistency Edge Cases

Identify and handle edge cases for Cache Consistency. 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
Cache Consistency Testing Strategy

Write a testing strategy for Cache Consistency. 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. Strong consistency means?

Question 1 options

2. Race condition fix?

Question 2 options

3. What is a common mistake when implementing Cache Consistency?

Question 3 options

Flashcards

Question

Strong consistency?

Click to reveal answer

Answer

Cache and DB always match

Question

Race condition fix?

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Answer

Locks or single-writer pattern

Question

Cache Consistency 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. Strong: always matches, Eventual: eventually matches
  • 2. Invalidate cache on write
  • 3. Use locks to prevent race conditions

Interview Tips

  • • Handle cache consistency
  • • Prevent race conditions

Cheat Sheet

Cache Consistency

  • Strong: always matches
  • Eventual: eventually matches
  • Invalidate on write
  • Race conditions: locks or single-writer