performance-testing

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Original

English

🇨🇳

Translation

Chinese

Performance Testing

性能测试

<default_to_action> When testing performance or planning load tests:

DEFINE SLOs: p95 response time, throughput, error rate targets
IDENTIFY critical paths: revenue flows, high-traffic pages, key APIs
CREATE realistic scenarios: user journeys, think time, varied data
EXECUTE with monitoring: CPU, memory, DB queries, network
ANALYZE bottlenecks and fix before production

Quick Test Type Selection:

Expected load validation → Load testing
Find breaking point → Stress testing
Sudden traffic spike → Spike testing
Memory leaks, resource exhaustion → Endurance/soak testing
Horizontal/vertical scaling → Scalability testing

Critical Success Factors:

Performance is a feature, not an afterthought
Test early and often, not just before release
Focus on user-impacting bottlenecks </default_to_action>

<default_to_action> 在测试性能或规划负载测试时：

定义SLO：p95响应时间、吞吐量、错误率目标
识别关键路径：营收流程、高流量页面、核心API
创建真实场景：用户旅程、思考时间、多样化数据
结合监控执行测试：CPU、内存、数据库查询、网络
分析瓶颈并在上线前修复

快速选择测试类型：

验证预期负载 → 负载测试
寻找系统临界点 → 压力测试
应对突发流量峰值 → 峰值测试
检测内存泄漏、资源耗尽 → 耐久性/浸泡测试
验证水平/垂直扩容能力 → 可扩展性测试

关键成功因素：

性能是一项功能，而非事后补充
尽早且频繁测试，不要只在发布前进行
聚焦影响用户体验的瓶颈 </default_to_action>

Quick Reference Card

快速参考卡片

When to Use

适用场景

Before major releases
After infrastructure changes
Before scaling events (Black Friday)
When setting SLAs/SLOs

重大版本发布前
基础设施变更后
大促活动前（如黑色星期五）
设定SLA/SLO时

Test Types

测试类型

Type	Purpose	When
Load	Expected traffic	Every release
Stress	Beyond capacity	Quarterly
Spike	Sudden surge	Before events
Endurance	Memory leaks	After code changes
Scalability	Scaling validation	Infrastructure changes

类型	目的	适用时机
负载测试	验证预期流量下的性能	每次版本发布
压力测试	测试超出容量的极限情况	每季度
峰值测试	应对突发流量激增	活动前
耐久性测试	检测内存泄漏	代码变更后
可扩展性测试	验证扩容能力	基础设施变更时

Key Metrics

核心指标

Metric	Target	Why
p95 response	< 200ms	User experience
Throughput	10k req/min	Capacity
Error rate	< 0.1%	Reliability
CPU	< 70%	Headroom
Memory	< 80%	Stability

指标	目标	原因
p95响应时间	< 200ms	用户体验
吞吐量	10k req/min	系统容量
错误率	< 0.1%	可靠性
CPU使用率	< 70%	预留冗余
内存使用率	< 80%	稳定性

Tools

工具

k6: Modern, JS-based, CI/CD friendly
JMeter: Enterprise, feature-rich
Artillery: Simple YAML configs
Gatling: Scala, great reporting

k6：现代化、基于JS、适配CI/CD
JMeter：企业级、功能丰富
Artillery：简洁的YAML配置
Gatling：基于Scala、报表能力出色

Agent Coordination

Agent协同

```
qe-performance-tester
```
: Load test orchestration
```
qe-quality-analyzer
```
: Results analysis
```
qe-production-intelligence
```
: Production comparison

```
qe-performance-tester
```
：负载测试编排
```
qe-quality-analyzer
```
：结果分析
```
qe-production-intelligence
```
：生产环境对比

Defining SLOs

定义SLO

Bad: "The system should be fast" Good: "p95 response time < 200ms under 1,000 concurrent users"

javascript

export const options = {
  thresholds: {
    http_req_duration: ['p(95)<200'],  // 95% < 200ms
    http_req_failed: ['rate<0.01'],     // < 1% failures
  },
};

反面示例： "系统应该很快" 正面示例： "在1000个并发用户下，p95响应时间<200ms"

javascript

export const options = {
  thresholds: {
    http_req_duration: ['p(95)<200'],  // 95%请求<200ms
    http_req_failed: ['rate<0.01'],     // 错误率<1%
  },
};

Realistic Scenarios

真实场景构建

Bad: Every user hits homepage repeatedly Good: Model actual user behavior

javascript

// Realistic distribution
// 40% browse, 30% search, 20% details, 10% checkout
export default function () {
  const action = Math.random();
  if (action < 0.4) browse();
  else if (action < 0.7) search();
  else if (action < 0.9) viewProduct();
  else checkout();

  sleep(randomInt(1, 5)); // Think time
}

反面示例： 所有用户重复访问首页 正面示例： 模拟真实用户行为

javascript

// 真实行为分布
// 40%浏览、30%搜索、20%查看详情、10%结账
export default function () {
  const action = Math.random();
  if (action < 0.4) browse();
  else if (action < 0.7) search();
  else if (action < 0.9) viewProduct();
  else checkout();

  sleep(randomInt(1, 5)); // 思考时间
}

Common Bottlenecks

常见瓶颈

Database

数据库

Symptoms: Slow queries under load, connection pool exhaustion Fixes: Add indexes, optimize N+1 queries, increase pool size, read replicas

症状： 负载下查询缓慢、连接池耗尽 解决方案： 添加索引、优化N+1查询、扩大连接池、读写分离

N+1 Queries

N+1查询

javascript

// BAD: 100 orders = 101 queries
const orders = await Order.findAll();
for (const order of orders) {
  const customer = await Customer.findById(order.customerId);
}

// GOOD: 1 query
const orders = await Order.findAll({ include: [Customer] });

javascript

// 反面示例：100条订单=101次查询
const orders = await Order.findAll();
for (const order of orders) {
  const customer = await Customer.findById(order.customerId);
}

// 正面示例：仅1次查询
const orders = await Order.findAll({ include: [Customer] });

Synchronous Processing

同步处理

Problem: Blocking operations in request path (sending email during checkout) Fix: Use message queues, process async, return immediately

问题： 请求路径中的阻塞操作（如结账时同步发送邮件） 解决方案： 使用消息队列、异步处理、立即返回结果

Memory Leaks

内存泄漏

Detection: Endurance testing, memory profiling Common causes: Event listeners not cleaned, caches without eviction

检测方式： 耐久性测试、内存分析 常见原因： 事件监听器未清理、缓存未设置淘汰策略

External Dependencies

外部依赖

Solutions: Aggressive timeouts, circuit breakers, caching, graceful degradation

解决方案： 严格超时设置、断路器模式、缓存、优雅降级

k6 CI/CD Example

k6 CI/CD示例

javascript

// performance-test.js
import http from 'k6/http';
import { check, sleep } from 'k6';

export const options = {
  stages: [
    { duration: '1m', target: 50 },   // Ramp up
    { duration: '3m', target: 50 },   // Steady
    { duration: '1m', target: 0 },    // Ramp down
  ],
  thresholds: {
    http_req_duration: ['p(95)<200'],
    http_req_failed: ['rate<0.01'],
  },
};

export default function () {
  const res = http.get('https://api.example.com/products');
  check(res, {
    'status is 200': (r) => r.status === 200,
    'response time < 200ms': (r) => r.timings.duration < 200,
  });
  sleep(1);
}

yaml

undefined

javascript

// performance-test.js
import http from 'k6/http';
import { check, sleep } from 'k6';

export const options = {
  stages: [
    { duration: '1m', target: 50 },   // 逐步加压
    { duration: '3m', target: 50 },   // 稳定负载
    { duration: '1m', target: 0 },    // 逐步减压
  ],
  thresholds: {
    http_req_duration: ['p(95)<200'],
    http_req_failed: ['rate<0.01'],
  },
};

export default function () {
  const res = http.get('https://api.example.com/products');
  check(res, {
    '状态码为200': (r) => r.status === 200,
    '响应时间<200ms': (r) => r.timings.duration < 200,
  });
  sleep(1);
}

yaml

undefined

GitHub Actions

name: Run k6 test uses: grafana/k6-action@v0.3.0 with: filename: performance-test.js

---

name: 运行k6测试 uses: grafana/k6-action@v0.3.0 with: filename: performance-test.js

---

Analyzing Results

结果分析

Good Results

合格结果

Load: 1,000 users | p95: 180ms | Throughput: 5,000 req/s
Error rate: 0.05% | CPU: 65% | Memory: 70%

负载：1000用户 | p95:180ms | 吞吐量:5000 req/s
错误率:0.05% | CPU:65% | 内存:70%

Problems

异常结果

Load: 1,000 users | p95: 3,500ms ❌ | Throughput: 500 req/s ❌
Error rate: 5% ❌ | CPU: 95% ❌ | Memory: 90% ❌

负载：1000用户 | p95:3500ms ❌ | 吞吐量:500 req/s ❌
错误率:5% ❌ | CPU:95% ❌ | 内存:90% ❌

Root Cause Analysis

根因分析

Correlate metrics: When response time spikes, what changes?
Check logs: Errors, warnings, slow queries
Profile code: Where is time spent?
Monitor resources: CPU, memory, disk
Trace requests: End-to-end flow

关联指标：响应时间飙升时，哪些指标发生变化？
检查日志：错误、警告、慢查询
代码分析：时间消耗在何处？
资源监控：CPU、内存、磁盘
请求追踪：端到端流程

Anti-Patterns

反模式

❌ Anti-Pattern	✅ Better
Testing too late	Test early and often
Unrealistic scenarios	Model real user behavior
0 to 1000 users instantly	Ramp up gradually
No monitoring during tests	Monitor everything
No baseline	Establish and track trends
One-time testing	Continuous performance testing

❌ 反模式	✅ 优化方案
测试过晚	尽早且频繁测试
场景不真实	模拟真实用户行为
瞬间从0到1000用户	逐步加压
测试时无监控	全链路监控
无基准线	建立并追踪趋势
一次性测试	持续性能测试

Agent-Assisted Performance Testing

Agent辅助性能测试

typescript

// Comprehensive load test
await Task("Load Test", {
  target: 'https://api.example.com',
  scenarios: {
    checkout: { vus: 100, duration: '5m' },
    search: { vus: 200, duration: '5m' },
    browse: { vus: 500, duration: '5m' }
  },
  thresholds: {
    'http_req_duration': ['p(95)<200'],
    'http_req_failed': ['rate<0.01']
  }
}, "qe-performance-tester");

// Bottleneck analysis
await Task("Analyze Bottlenecks", {
  testResults: perfTest,
  metrics: ['cpu', 'memory', 'db_queries', 'network']
}, "qe-performance-tester");

// CI integration
await Task("CI Performance Gate", {
  mode: 'smoke',
  duration: '1m',
  vus: 10,
  failOn: { 'p95_response_time': 300, 'error_rate': 0.01 }
}, "qe-performance-tester");

typescript

// 全面负载测试
await Task("Load Test", {
  target: 'https://api.example.com',
  scenarios: {
    checkout: { vus: 100, duration: '5m' },
    search: { vus: 200, duration: '5m' },
    browse: { vus: 500, duration: '5m' }
  },
  thresholds: {
    'http_req_duration': ['p(95)<200'],
    'http_req_failed': ['rate<0.01']
  }
}, "qe-performance-tester");

// 瓶颈分析
await Task("Analyze Bottlenecks", {
  testResults: perfTest,
  metrics: ['cpu', 'memory', 'db_queries', 'network']
}, "qe-performance-tester");

// CI集成
await Task("CI Performance Gate", {
  mode: 'smoke',
  duration: '1m',
  vus: 10,
  failOn: { 'p95_response_time': 300, 'error_rate': 0.01 }
}, "qe-performance-tester");

Agent Coordination Hints

Agent协同提示

Memory Namespace

内存命名空间

aqe/performance/
├── results/*       - Test execution results
├── baselines/*     - Performance baselines
├── bottlenecks/*   - Identified bottlenecks
└── trends/*        - Historical trends

aqe/performance/
├── results/*       - 测试执行结果
├── baselines/*     - 性能基准线
├── bottlenecks/*   - 已识别瓶颈
└── trends/*        - 历史趋势

Fleet Coordination

集群协同

typescript

const perfFleet = await FleetManager.coordinate({
  strategy: 'performance-testing',
  agents: [
    'qe-performance-tester',
    'qe-quality-analyzer',
    'qe-production-intelligence',
    'qe-deployment-readiness'
  ],
  topology: 'sequential'
});

typescript

const perfFleet = await FleetManager.coordinate({
  strategy: 'performance-testing',
  agents: [
    'qe-performance-tester',
    'qe-quality-analyzer',
    'qe-production-intelligence',
    'qe-deployment-readiness'
  ],
  topology: 'sequential'
});

Pre-Production Checklist

上线前检查清单

Related Skills

Remember

要点回顾

Performance is a feature: Test it like functionality Test continuously: Not just before launch Monitor production: Synthetic + real user monitoring Fix what matters: Focus on user-impacting bottlenecks Trend over time: Catch degradation early

With Agents: Agents automate load testing, analyze bottlenecks, and compare with production. Use agents to maintain performance at scale.

性能是一项功能： 像测试功能一样测试性能 持续测试： 不要只在上线前进行 监控生产环境： 合成监控+真实用户监控 聚焦关键瓶颈： 优先修复影响用户的问题 追踪长期趋势： 提前发现性能退化

借助Agent： Agent可自动化负载测试、分析瓶颈并与生产环境对比。使用Agent在规模场景下维持性能水平。

performance-testing

Original

Translation

Performance Testing

性能测试

Quick Reference Card

快速参考卡片

When to Use

适用场景

Test Types

测试类型

Key Metrics

核心指标

Tools

工具

Agent Coordination

Agent协同

Defining SLOs

定义SLO

Realistic Scenarios

真实场景构建

Common Bottlenecks

常见瓶颈

Database

数据库

N+1 Queries

N+1查询

Synchronous Processing

同步处理

Memory Leaks

内存泄漏

External Dependencies

外部依赖

k6 CI/CD Example

k6 CI/CD示例

GitHub Actions

GitHub Actions

Analyzing Results

结果分析

Good Results

合格结果

Problems

异常结果

Root Cause Analysis

根因分析

Anti-Patterns

反模式

Agent-Assisted Performance Testing

Agent辅助性能测试

Agent Coordination Hints

Agent协同提示

Memory Namespace

内存命名空间

Fleet Coordination

集群协同

Pre-Production Checklist

上线前检查清单

Related Skills

相关技能

Remember

要点回顾