你们公司的QPS是怎么统计出来的？这5种常见方法我踩过一半的坑！

http {
    # 开启状态监控页面
    server {
        listen 8080;
        location /nginx-status {
            stub_status on;
            allow 192.168.0.0/24; # 只允许内网访问
            deny all;
        }
    }

    # 记录详细请求日志（用于离线分析）
    log_format main '$remote_addr [$time_local] "$request" $status $request_time';
    server {
        listen 80;
        server_name api.example.com;
        access_log /var/log/nginx/api-access.log main; # 日志路径

        # 转发到后端服务
        location / {
            proxy_pass http://backend-service;
        }
    }
}

Active connections:200
serveracceptshandledrequests
10000  10000  80000
Reading: 0 Writing: 10 Waiting:190

whiletrue; do
    # 取当前请求数
    current=$(curl -s http://192.168.0.100:8080/nginx-status | awk 'NR==3 {print $3}')
    sleep 1
    # 取1秒后请求数
    next=$(curl -s http://192.168.0.100:8080/nginx-status | awk 'NR==3 {print $3}')
    qps=$((next - current))
    echo"当前QPS: $qps"
done

@Component
publicclassQpsStatisticsFilterimplementsGlobalFilter, Ordered{
    // 存储接口QPS：key=接口路径，value=原子计数器
    privatefinal Map<String, AtomicLong> pathQpsMap = new ConcurrentHashMap<>();

    // 定时1秒清零计数器（避免数值过大）
    @PostConstruct
    publicvoidinit(){
        ScheduledExecutorService executor = Executors.newSingleThreadScheduledExecutor();
        executor.scheduleAtFixedRate(() -> {
            // 遍历所有接口，打印QPS后清零
            pathQpsMap.forEach((path, counter) -> {
                long qps = counter.getAndSet(0);
                log.info("接口[{}] QPS: {}", path, qps);
            });
        }, 0, 1, TimeUnit.SECONDS);
    }

    @Override
    public Mono<Void> filter(ServerWebExchange exchange, GatewayFilterChain chain){
        // 获取请求路径（如/order/seckill）
        String path = exchange.getRequest().getPath().value();
        // 计数器自增（线程安全）
        pathQpsMap.computeIfAbsent(path, k -> new AtomicLong()).incrementAndGet();
        // 继续转发请求
        return chain.filter(exchange);
    }

    @Override
    publicintgetOrder(){
        return -1; // 过滤器优先级：数字越小越先执行
    }
}

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-aop</artifactId>
</dependency>

@Aspect
@Component
@Slf4j
publicclassApiQpsAspect{
    // 存储接口QPS：key=接口名（如com.example.OrderController.createOrder），value=计数器
    privatefinal Map<String, AtomicLong> apiQpsMap = new ConcurrentHashMap<>();

    // 定时1秒打印QPS并清零
    @PostConstruct
    publicvoidscheduleQpsPrint(){
        Executors.newSingleThreadScheduledExecutor().scheduleAtFixedRate(() -> {
            apiQpsMap.forEach((api, counter) -> {
                long qps = counter.getAndSet(0);
                if (qps > 0) { // 只打印有请求的接口
                    log.info("[QPS统计] 接口: {}, QPS: {}", api, qps);
                }
            });
        }, 0, 1, TimeUnit.SECONDS);
    }

    // 切入点：拦截所有Controller方法
    @Pointcut("execution(* com.example.*.controller..*(..))")
    publicvoidapiPointcut(){}

    // 环绕通知：统计请求数
    @Around("apiPointcut()")
    public Object countQps(ProceedingJoinPoint joinPoint)throws Throwable {
        // 获取接口名（类名+方法名）
        String apiName = joinPoint.getSignature().getDeclaringTypeName() + "." + joinPoint.getSignature().getName();
        // 计数器自增
        apiQpsMap.computeIfAbsent(apiName, k -> new AtomicLong()).incrementAndGet();
        // 执行原方法
        return joinPoint.proceed();
    }
}

// 记录响应时间
long start = System.currentTimeMillis();
Object result = joinPoint.proceed();
long cost = System.currentTimeMillis() - start;
// 存储响应时间（key=接口名，value=时间列表）
timeMap.computeIfAbsent(apiName, k -> new CopyOnWriteArrayList<>()).add(cost);
// 计算平均响应时间
double avgTime = timeMap.get(apiName).stream().mapToLong(Long::longValue).average().orElse(0);

<!-- Micrometer：对接Prometheus的工具 -->
<dependency>
    <groupId>io.micrometer</groupId>
    <artifactId>micrometer-registry-prometheus</artifactId>
</dependency>
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-actuator</artifactId>
</dependency>

spring:
  application:
    name:order-service# 服务名，用于Prometheus识别

management:
endpoints:
    web:
      exposure:
        include:prometheus# 暴露/prometheus端点
metrics:
    tags:
      application:${spring.application.name}# 给指标加服务名标签
    distribution:
      percentiles-histogram:
        http:
          server:
            requests:true# 开启响应时间分位数统计

@RestController
@RequestMapping("/order")
publicclassOrderController{
    // 注入MeterRegistry
    privatefinal MeterRegistry meterRegistry;

    @Autowired
    publicOrderController(MeterRegistry meterRegistry){
        this.meterRegistry = meterRegistry;
    }

    @PostMapping("/create")
    public String createOrder(){
        // 统计/create接口的QPS：meterRegistry会自动按秒聚合
        Counter.builder("order.create.qps") // 指标名
                .description("订单创建接口QPS") // 描述
                .register(meterRegistry)
                .increment(); // 计数器自增

        // 业务逻辑
        return"success";
    }
}

scrape_configs:
  -job_name:'order-service'
    scrape_interval:1s# 每秒拉取一次（实时性高）
    static_configs:
      -targets:['192.168.0.101:8080']# 应用地址（暴露的actuator端口）

<!-- logback-spring.xml -->
<appendername="JSON_FILE"class="ch.qos.logback.core.rolling.RollingFileAppender">
    <file>/var/log/order-service/request.log</file>
    <rollingPolicyclass="ch.qos.logback.core.rolling.TimeBasedRollingPolicy">
        <fileNamePattern>/var/log/order-service/request.%d{yyyy-MM-dd}.log</fileNamePattern>
    </rollingPolicy>
    <!-- 输出JSON格式日志 -->
    <encoderclass="net.logstash.logback.encoder.LogstashEncoder">
        <includeMdcKeyName>requestPath</includeMdcKeyName>
        <includeMdcKeyName>requestTime</includeMdcKeyName>
    </encoder>
</appender>

<rootlevel="INFO">
    <appender-refref="JSON_FILE" />
</root>

@Component
publicclassRequestLogFilterextendsOncePerRequestFilter{
    @Override
    protectedvoiddoFilterInternal(HttpServletRequest request, HttpServletResponse response, FilterChain chain)throws ServletException, IOException {
        try {
            // 记录请求路径到MDC
            MDC.put("requestPath", request.getRequestURI());
            // 记录请求时间
            MDC.put("requestTime", String.valueOf(System.currentTimeMillis()));
            chain.doFilter(request, response);
        } finally {
            // 清除MDC，避免线程复用导致数据污染
            MDC.clear();
        }
    }
}

input {
    file {
        path => "/var/log/order-service/request.*.log" # 日志路径
        start_position => "beginning"
        sincedb_path => "/dev/null" # 每次重启都重新读取所有日志
    }
}

filter {
    json {
        source => "message" # 解析JSON格式日志
    }
    # 提取时间字段（转为Elasticsearch时间格式）
    date {
        match => ["requestTime", "yyyy-MM-dd HH:mm:ss"]
        target => "@timestamp"
    }
}

output {
    elasticsearch {
        hosts => ["192.168.0.102:9200"] # Elasticsearch地址
        index => "order-request-%{+YYYY.MM.dd}" # 索引名
    }
}

-- 查看当前连接数（QPS高时连接数会增长）
showstatuslike'Threads_connected';
-- 查看每秒查询数（DB层QPS，可反推应用QPS）
showstatuslike'Queries';

slow_query_log = 1
slow_query_log_file = /var/log/mysql/slow.log
long_query_time = 1 # 超过1秒的查询记录为慢查询