Cache Performance
Cache Metrics
| Metric | Target |
|---|---|
| Hit rate | > 80% |
| Miss rate | < 20% |
| Eviction rate | Low |
| Memory usage | < 80% |
Improving Hit Rate
- Right TTL (not too short)
- Right key design
- Pre-warm cache
- Cache frequent queries
Key Points
- Understanding Caching Performance 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
Performance Optimization
Areas
- Database: Indexes, queries, connection pooling
- Caching: Multi-level, appropriate TTL
- Network: Compression, CDN, HTTP/2
- Code: Profiling, async, batch
Measurement
- Load testing
- Profiling
- APM tools
- Real user monitoring
Best Practices
- Set performance budgets
- Monitor in production
- Optimize hot paths
- Use appropriate data structures
Key Points
- Understanding Caching Performance 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 Caching Performance in a backend system. Consider scalability, error handling, and production readiness.
Solution
// Caching Performance implementation
// Key aspects: validation, error handling, logging, testing
public class CachingPerformance {
// Production-ready implementation
}Identify and handle edge cases for Caching Performance. 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 Caching Performance. 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. Good cache hit rate?
2. Low hit rate fix?
3. What is the primary purpose of Caching Performance?
4. What is a common mistake when implementing Caching Performance?
Flashcards
Question
Target hit rate?
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Answer
> 80%
Question
Low hit rate fixes?
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Answer
Adjust TTL, pre-warm, optimize keys
Question
What is Caching Performance?
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Answer
Caching Performance is a key concept in backend development.
Question
When to use Caching Performance?
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Answer
Use Caching Performance when building production systems that require reliability, scalability, and maintainability.
Question
Caching Performance 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.Target > 80% hit rate
- 2.Monitor: hit rate, miss rate, eviction rate
- 3.Improve: right TTL, pre-warm, key design
Interview Tips
- •Measure cache performance
- •Improve hit rate
Cheat Sheet
Cache Performance
- Hit rate: > 80%
- Monitor: hit, miss, eviction rates
- Improve: TTL, pre-warm, key design