在线教育平台大规模并发测试案例_千锋教育

// JMeter 线程组配置
ThreadGroup threadGroup = new ThreadGroup();
threadGroup.setName("模拟学生登录");
threadGroup.setNumThreads(10000); // 1万并发用户
threadGroup.setRampUp(60); // 60秒内全部启动
threadGroup.setDuration(300); // 持续5分钟
threadGroup.setScheduler(true);

// HTTP请求默认值
HTTPSampler httpSampler = new HTTPSampler();
httpSampler.setDomain("api.education.com");
httpSampler.setPort(443);
httpSampler.setProtocol("https");
httpSampler.setPath("/api/v1/login");
httpSampler.setMethod("POST");
httpSampler.addArgument("username", "${__RandomString(8,abcdefgh,)}");
httpSampler.addArgument("password", "test123456");

// 优化前：直接查数据库
User user = userRepository.findByUsername(username);
if (user != null && user.getPassword().equals(password)) {
    // 生成session
}

// 优化后：先查Redis缓存
String cachedUser = redisTemplate.opsForValue().get("user:" + username);
if (cachedUser != null) {
    // 从缓存验证
    User user = JSON.parseObject(cachedUser, User.class);
    if (user.getPassword().equals(password)) {
        // 生成session
    }
} else {
    // 查数据库并写入缓存
    User user = userRepository.findByUsername(username);
    redisTemplate.opsForValue().set("user:" + username, JSON.toJSONString(user), 30, TimeUnit.MINUTES);
}

@HystrixCommand(
    fallbackMethod = "loginFallback",
    commandProperties = {
        @HystrixProperty(name = "circuitBreaker.requestVolumeThreshold", value = "20"),
        @HystrixProperty(name = "circuitBreaker.errorThresholdPercentage", value = "50"),
        @HystrixProperty(name = "circuitBreaker.sleepWindowInMilliseconds", value = "5000")
    }
)
public LoginResponse login(String username, String password) {
    // 登录逻辑
}

// 熔断后的降级方法
public LoginResponse loginFallback(String username, String password) {
    // 返回排队提示，不直接拒绝
    return new LoginResponse(503, "系统繁忙，请稍后重试");
}

// 使用Guava RateLimiter
RateLimiter limiter = RateLimiter.create(10000); // 每秒最多1万请求

public boolean tryAcquire() {
    return limiter.tryAcquire(100, TimeUnit.MILLISECONDS);
}

# Kubernetes HPA 自动扩缩容配置
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: education-api-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: education-api
  minReplicas: 20
  maxReplicas: 200
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80

# 性能测试报告

## 测试目标
- 验证系统在1000万并发用户下的稳定性
- 定位性能瓶颈

## 测试环境
- 服务器：8核16G x 200台（K8s集群）
- 数据库：MySQL 8.0 读写分离 32节点
- 缓存：Redis 集群 64节点

## 测试结果
| 并发数 | 平均响应时间 | 错误率 | CPU使用率 |
|--------|--------------|--------|-----------|
| 10万   | 200ms        | 0.01%  | 30%       |
| 100万  | 300ms        | 0.05%  | 55%       |
| 500万  | 400ms        | 0.10%  | 75%       |
| 1000万 | 450ms        | 0.02%  | 80%       |

## 优化建议
1. 增加CDN节点，减少回源流量
2. 优化WebSocket连接池配置
3. 数据库读写分离比例调整为3:1