Performance Optimization
Identify Bottlenecks
- Measure baseline (latency, throughput, errors)
- Profile (CPU, memory, I/O)
- Check DB queries (EXPLAIN)
- Check external API latency
- Check resource utilization
N+1 Query Problem
1 query for list + N queries for each item.
Fix: Eager loading, batch queries, JOINs.
Quick Wins
- Connection pooling (HikariCP)
- Caching (Redis)
- Async processing (queues)
- Rate limiting
Key Points
- Understanding Performance Questions 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
Profiling & Monitoring
Java Profiling
- jprofiler / VisualVM
- async-profiler (production)
- JFR (Java Flight Recorder)
Key Metrics
- Response time: p50, p95, p99
- Error rate
- Throughput: req/sec
- Thread pool utilization
Anti-Patterns
| Pattern | Fix |
|---|---|
| N+1 queries | Eager loading |
| No pooling | HikariCP |
| Sync I/O | Async |
| No caching | Redis |
Key Points
- Understanding Performance Questions 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 Performance Questions in a backend system. Consider scalability, error handling, and production readiness.
Solution
// Performance Questions implementation
// Key aspects: validation, error handling, logging, testing
public class PerformanceQuestions {
// Production-ready implementation
}Identify and handle edge cases for Performance Questions. 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 Performance Questions. 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. What is N+1 query problem?
2. What is p99 latency?
3. What is the primary purpose of Performance Questions?
4. What is a common mistake when implementing Performance Questions?
Flashcards
Question
N+1 query problem?
Click to reveal answer
Answer
1 query for list + N queries for details
Question
p99 latency?
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Answer
99% of requests complete within this time
Question
What is Performance Questions?
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Answer
Performance Questions is a key concept in backend development.
Question
When to use Performance Questions?
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Answer
Use Performance Questions when building production systems that require reliability, scalability, and maintainability.
Question
Performance Questions 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.Profile before optimizing
- 2.N+1 queries are common performance killer
- 3.p99 matters more than average
- 4.Connection pooling and caching are high-impact
Interview Tips
- •Walk through optimization process
- •Explain N+1 query identification and fix
- •Discuss latency vs throughput tradeoffs
Cheat Sheet
Performance Interview
- N+1: 1 query + N detail queries -> eager loading
- p99: 99% of requests complete within this time
- Quick Wins: Connection pooling, caching, async