代码地址

Github地址：https://github.com/bbhhe/thumbs-backend.git

章节内容

完善系统的可观测性能力，实时了解系统的健康状况和性能指标

技术方案

本项目将构建一套基于 Prometheus 生态的可观测性解决方案，所以在此之前我们需要先熟悉下 Prometheus 和 Grafana。

Prometheus：一个开源的监控系统，用于收集和存储时间序列数据。
Grafana：一个开源的可视化工具，用于创建和共享仪表盘。

Prometheus 可以通过 HTTP 接口暴露指标数据，而 Grafana 可以通过 Prometheus 的 HTTP API 来获取数据并创建可视化图表。

开发内容

1. 重启TIDB

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shell
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tiup playground --tag thumb --host 0.0.0.0 --without-monitor

2. Redis指标监控

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shell
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docker run --name my-redis -d -p 6379:6379 redis:6.2.14 --requirepass bbhhe1

docker run --name redis-exporter \
  -p 9121:9121 \
  oliver006/redis_exporter \
  --redis.addr=redis://192.168.234.130:6379 \
  --redis.password=bbhhe1

3. 应用指标监控

3.1 引入pom依赖

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xml
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<!-- 整合 Prometheus + Grafana -->
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
<dependency>
    <groupId>io.micrometer</groupId>
    <artifactId>micrometer-registry-prometheus</artifactId>
</dependency>

3.2 修改配置

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yaml
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# 整合 Prometheus + Grafana
management:
  endpoints:
    web:
      exposure:
        include: health, prometheus
  metrics:
    distribution:
      percentiles:
        http:
          server:
            requests: 0.5, 0.75, 0.9, 0.95, 0.99

3.3 修改代码ThumbController

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java
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package com.bbhhe.thumbsbackend.controller;

import com.bbhhe.thumbsbackend.common.BaseResponse;
import com.bbhhe.thumbsbackend.common.ResultUtils;
import com.bbhhe.thumbsbackend.model.dto.thumb.DoThumbRequest;
import com.bbhhe.thumbsbackend.model.entity.User;
import com.bbhhe.thumbsbackend.service.ThumbService;
import io.micrometer.core.instrument.Counter;
import io.micrometer.core.instrument.MeterRegistry;
import jakarta.annotation.Resource;
import jakarta.servlet.http.HttpServletRequest;
import jakarta.servlet.http.HttpSession;
import org.springframework.beans.factory.annotation.Qualifier;
import org.springframework.web.bind.annotation.*;

import org.springframework.beans.factory.annotation.Autowired;

@RestController
@RequestMapping("/thumb")
public class ThumbController {
    @Autowired
    @Qualifier("thumbServiceMq")
    private ThumbService thumbService;

    private final Counter successCounter;
    private final Counter failureCounter;

    public ThumbController(MeterRegistry registry) {
        this.successCounter = Counter.builder("thumb.success.count")
                .description("Total successful thumb")
                .register(registry);
        this.failureCounter = Counter.builder("thumb.failure.count")
                .description("Total failed thumb")
                .register(registry);
    }


    @PostMapping("/do")
    public BaseResponse<Boolean> doThumb(@RequestBody DoThumbRequest request, HttpServletRequest servletRequest) throws InterruptedException {
        if (request == null || request.getBlogId() == null) {
            failureCounter.increment();
            return ResultUtils.error(40000, "请求参数错误");
        }
        HttpSession session = servletRequest.getSession();
        User loginUser = (User) session.getAttribute("loginUser");
        if (loginUser == null) {
            failureCounter.increment();
            return ResultUtils.error(40100, "用户未登录");
        }
        boolean result = thumbService.doThumb(request.getBlogId(), loginUser.getId());
        if(result){
            successCounter.increment();
        }else {
            failureCounter.increment();
        }
        return ResultUtils.success(result);
    }

    @PostMapping("/undo")
    public BaseResponse<Boolean> undoThumb(@RequestBody DoThumbRequest request, HttpServletRequest servletRequest) throws InterruptedException {
        if (request == null || request.getBlogId() == null) {
            failureCounter.increment();
            return ResultUtils.error(40000, "请求参数错误");
        }
        HttpSession session = servletRequest.getSession();
        User loginUser = (User) session.getAttribute("loginUser");
        if (loginUser == null) {
            failureCounter.increment();
            return ResultUtils.error(40100, "用户未登录");
        }
        boolean result = thumbService.undoThumb(request.getBlogId(), loginUser.getId());
        if(result){
            successCounter.increment();
        }else {
            failureCounter.increment();
        }
        return ResultUtils.success(result);
    }
}

3.4 启动之后测试访问

4. 启动Prometheus

4.1 创建配置文件prometheus.yml

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yaml
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global:  
  scrape_interval:     15s # By default, scrape targets every 15 seconds.  
  
  # Attach these labels to any time series or alerts when communicating with  
  # external systems (federation, remote storage, Alertmanager).  
  external_labels:  
    monitor: 'codelab-monitor'  
  
# A scrape configuration containing exactly one endpoint to scrape:  
# Here it's Prometheus itself.  
scrape_configs:  
  # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config.  
  - job_name: 'prometheus'  
  
    # Override the global default and scrape targets from this job every 5 seconds.  
    scrape_interval: 5s  
  
    static_configs:  
      - targets: ['localhost:9090']  
  - job_name: 'biz'  
    # Override the global default and scrape targets from this job every 5 seconds.  
    scrape_interval: 5s  
    metrics_path: '/api/actuator/prometheus'  
    static_configs:  
      - targets: ['192.168.234.1:8080']  
        labels:  
          group: 'thumb'  
  - job_name: 'redis'  
    scrape_interval: 5s  
    metrics_path: '/metrics'
    static_configs:  
      - targets: ['192.168.234.130:9121']

4.2 启动Prometheus

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java
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docker run -d \
    --name prometheus \
    -p 9090:9090 \
    -v /home/binbin/prometheus.yml:/etc/prometheus/prometheus.yml \
    prom/prometheus

4.4 测试访问

5. 启动Grafana并配置

5.1 docker启动Grafana

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shell
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docker run -d \
    --name grafana \
    -p 3000:3000 \
    grafana/grafana-enterprise

5.2 配置系统QPS监控

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text
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sum(rate(http_server_requests_seconds_count[1m]))
sum(rate(http_server_requests_seconds_count[1m])) by (uri)

5.3 配置系统分位置监控

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text
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avg(http_server_requests_seconds{group="thumb", quantile="0.5"})

5.4 配置系统点赞成功率监控

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text
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rate(thumb_success_count_total[1m]) / (rate(thumb_success_count_total[1m]) + rate(thumb_failure_count_total[1m]))

5.5 配置系统缓存命中率监控

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text
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rate(redis_cache_hits_total[1m]) / (rate(redis_cache_hits_total[1m]) + rate(redis_cache_misses_total[1m]))

5.6 把监控放到同一个视图下

6. 告警配置

总结

有了可视化界面，我们可以更加直观地了解系统的运行情况，实现对系统的监控和告警。这样可以及时发现系统的问题，避免系统出现故障。

7-Ai辅助开发点赞系统笔记-可视化化监控系统