服务监控面板：pig-monitor Grafana自定义仪表盘全指南

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服务监控面板：pig-monitor Grafana自定义仪表盘全指南

【免费下载链接】pig 【免费下载链接】pig 项目地址: https://gitcode.com/gh_mirrors/pig/pig

引言：微服务监控的痛点与解决方案

在分布式系统架构中，服务监控面临三大核心挑战：服务节点分散导致监控盲区、指标碎片化难以统一分析、告警响应滞后影响系统可用性。pig-monitor模块基于Spring Boot Admin构建，整合Grafana可视化能力，提供从服务注册发现到指标分析的全链路监控解决方案。本文将系统讲解如何从零开始配置监控环境、自定义业务仪表盘、设置智能告警规则，最终实现微服务架构的可观测性治理。

读完本文你将掌握：

  • 基于Spring Boot Admin的服务监控平台搭建
  • Grafana仪表盘自定义开发与指标配置
  • 多维度监控指标（JVM/业务/接口）的采集方案
  • 企业级告警策略设计与实施

技术架构：pig-monitor核心组件解析

监控体系架构图

mermaid

核心依赖组件说明

组件名称版本作用核心功能
Spring Boot Admin2.6.x服务监控服务端服务健康检查、元数据管理、日志查看
Prometheus2.30.x时序数据存储指标采集、聚合、持久化
Grafana8.2.x可视化平台自定义仪表盘、指标查询、告警配置
Spring Security5.6.x安全认证监控平台权限控制、用户管理
Nacos Discovery2021.0.1.0服务发现动态服务注册与健康状态感知

环境搭建：从部署到基础配置

1. 监控服务部署流程

# 1. 克隆项目代码
git clone https://gitcode.com/gh_mirrors/pig/pig.git
cd pig

# 2. 构建监控模块
mvn clean package -pl pig-visual/pig-monitor -am -Dmaven.test.skip=true

# 3. 启动监控服务
cd pig-visual/pig-monitor
java -jar target/pig-monitor.jar --spring.profiles.active=prod

2. 核心配置文件解析（application.yml）

# 服务基础配置
server:
  port: 5001
  servlet:
    context-path: /monitor

# Spring Boot Admin配置
spring:
  boot:
    admin:
      ui:
        title: "pig微服务监控平台"
        brand: "<span>pig</span> Monitor"
      monitor:
        status-lifetime: 10000
        connect-timeout: 5000
        read-timeout: 5000
  
  # Nacos注册配置
  cloud:
    nacos:
      discovery:
        server-addr: ${NACOS_SERVER:127.0.0.1:8848}
        namespace: ${NACOS_NAMESPACE:public}
      config:
        server-addr: ${NACOS_SERVER:127.0.0.1:8848}
        file-extension: yaml
        namespace: ${NACOS_NAMESPACE:public}

# 监控指标暴露配置
management:
  endpoints:
    web:
      exposure:
        include: health,info,metrics,prometheus,httptrace
  metrics:
    tags:
      application: ${spring.application.name}
    export:
      prometheus:
        enabled: true

3. 安全认证配置（SecurityConfig）

@Configuration
@EnableWebSecurity
public class SecurityConfig extends WebSecurityConfigurerAdapter {

    private final String adminContextPath;

    public SecurityConfig(AdminServerProperties adminServerProperties) {
        this.adminContextPath = adminServerProperties.getContextPath();
    }

    @Override
    protected void configure(HttpSecurity http) throws Exception {
        SavedRequestAwareAuthenticationSuccessHandler successHandler = 
            new SavedRequestAwareAuthenticationSuccessHandler();
        successHandler.setTargetUrlParameter("redirectTo");
        successHandler.setDefaultTargetUrl(adminContextPath + "/");

        http.authorizeRequests()
            // 静态资源允许访问
            .antMatchers(adminContextPath + "/assets/**").permitAll()
            // 登录页面允许访问
            .antMatchers(adminContextPath + "/login").permitAll()
            // 其他请求需要认证
            .anyRequest().authenticated()
            .and()
            // 配置登录表单
            .formLogin().loginPage(adminContextPath + "/login")
            .successHandler(successHandler).and()
            // 配置登出
            .logout().logoutUrl(adminContextPath + "/logout")
            .and()
            // 配置CSRF保护
            .csrf().csrfTokenRepository(CookieCsrfTokenRepository.withHttpOnlyFalse())
            // 支持iframe
            .and().headers().frameOptions().disable();
    }
}

Grafana仪表盘开发实战

1. 数据采集与指标暴露

Spring Boot应用通过Actuator暴露监控端点，配合Micrometer实现指标采集：

<!-- pom.xml添加依赖 -->
<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
<dependency>
    <groupId>io.micrometer</groupId>
    <artifactId>micrometer-registry-prometheus</artifactId>
</dependency>

业务指标自定义实现：

@Component
public class BusinessMetricsCollector {
    private final MeterRegistry meterRegistry;
    private Counter orderSuccessCounter;
    private Counter orderFailedCounter;
    private Timer apiResponseTimer;

    public BusinessMetricsCollector(MeterRegistry meterRegistry) {
        this.meterRegistry = meterRegistry;
        initMetrics();
    }

    private void initMetrics() {
        // 订单成功计数器
        orderSuccessCounter = Counter.builder("business.order.success.count")
                .description("订单成功总数")
                .register(meterRegistry);
                
        // 订单失败计数器
        orderFailedCounter = Counter.builder("business.order.failed.count")
                .description("订单失败总数")
                .register(meterRegistry);
                
        // API响应时间计时器
        apiResponseTimer = Timer.builder("business.api.response.time")
                .description("API接口响应时间")
                .register(meterRegistry);
    }

    // 订单成功计数
    public void incrementOrderSuccess() {
        orderSuccessCounter.increment();
    }
    
    // 订单失败计数
    public void incrementOrderFailed() {
        orderFailedCounter.increment();
    }
    
    // 记录API响应时间
    public <T> T recordApiResponseTime(Supplier<T> supplier) {
        return Timer.start(meterRegistry).record(supplier);
    }
}

2. 自定义仪表盘JSON配置

以下是订单业务监控仪表盘的核心配置（关键部分节选）：

{
  "annotations": {
    "list": [
      {
        "builtIn": 1,
        "datasource": "-- Grafana --",
        "enable": true,
        "hide": true,
        "iconColor": "rgba(0, 211, 255, 1)",
        "name": "Annotations & Alerts",
        "type": "dashboard"
      }
    ]
  },
  "editable": true,
  "gnetId": null,
  "graphTooltip": 0,
  "id": 1,
  "iteration": 1629267345463,
  "links": [],
  "panels": [
    {
      "aliasColors": {},
      "bars": false,
      "dashLength": 10,
      "dashes": false,
      "datasource": "Prometheus",
      "fieldConfig": {
        "defaults": {
          "links": []
        },
        "overrides": []
      },
      "fill": 1,
      "fillGradient": 0,
      "gridPos": {
        "h": 8,
        "w": 12,
        "x": 0,
        "y": 0
      },
      "hiddenSeries": false,
      "id": 2,
      "legend": {
        "avg": false,
        "current": false,
        "max": false,
        "min": false,
        "show": true,
        "total": false,
        "values": false
      },
      "lines": true,
      "linewidth": 1,
      "nullPointMode": "null",
      "options": {
        "alertThreshold": true
      },
      "percentage": false,
      "pluginVersion": "8.2.0",
      "pointradius": 2,
      "points": false,
      "renderer": "flot",
      "seriesOverrides": [],
      "spaceLength": 10,
      "stack": false,
      "steppedLine": false,
      "targets": [
        {
          "expr": "increase(business.order.success.count[5m])",
          "interval": "",
          "legendFormat": "订单成功数",
          "refId": "A"
        },
        {
          "expr": "increase(business.order.failed.count[5m])",
          "interval": "",
          "legendFormat": "订单失败数",
          "refId": "B"
        }
      ],
      "thresholds": [],
      "timeFrom": null,
      "timeRegions": [],
      "timeShift": null,
      "title": "订单交易量趋势",
      "tooltip": {
        "shared": true,
        "sort": 0,
        "value_type": "individual"
      },
      "type": "graph",
      "xaxis": {
        "buckets": null,
        "mode": "time",
        "name": null,
        "show": true,
        "values": []
      },
      "yaxes": [
        {
          "format": "short",
          "label": "订单数",
          "logBase": 1,
          "max": null,
          "min": "0",
          "show": true
        },
        {
          "format": "short",
          "label": null,
          "logBase": 1,
          "max": null,
          "min": null,
          "show": true
        }
      ],
      "yaxis": {
        "align": false,
        "alignLevel": null
      }
    }
  ],
  "refresh": "5s",
  "schemaVersion": 27,
  "style": "dark",
  "tags": [],
  "templating": {
    "list": []
  },
  "time": {
    "from": "now-6h",
    "to": "now"
  },
  "timepicker": {},
  "timezone": "",
  "title": "订单业务监控仪表盘",
  "uid": "order-business-dashboard",
  "version": 1
}

3. 多维度监控仪表盘设计

JVM监控仪表盘

核心监控指标：

  • 堆内存使用情况（used/committed/max）
  • 非堆内存使用情况
  • 垃圾回收次数与耗时
  • 线程状态分布（活跃/等待/阻塞）
  • 类加载统计（已加载/未加载）

mermaid

接口性能监控仪表盘

关键指标与SQL查询：

指标名称PromQL查询单位阈值告警
接口平均响应时间avg(rate(http_server_requests_seconds_sum[5m]) / rate(http_server_requests_seconds_count[5m])) by (uri)秒>0.5
接口错误率sum(rate(http_server_requests_seconds_count{status=~"5.."}[5m])) / sum(rate(http_server_requests_seconds_count[5m])) by (uri)%>1
接口吞吐量sum(rate(http_server_requests_seconds_count[5m])) by (uri)请求/秒<10

告警策略设计与实施

1. Prometheus告警规则配置

groups:
- name: business_alerts
  rules:
  - alert: HighOrderFailureRate
    expr: sum(rate(business.order.failed.count[5m])) / sum(rate(business.order.success.count[5m]) + rate(business.order.failed.count[5m])) > 0.05
    for: 2m
    labels:
      severity: critical
      service: order-service
    annotations:
      summary: "订单失败率过高"
      description: "订单失败率已连续2分钟超过5% (当前值: {{ $value }})"
      value: "{{ $value | humanizePercentage }}"

  - alert: ApiResponseTimeSlow
    expr: histogram_quantile(0.95, sum(rate(business.api.response.time_seconds_bucket[5m])) by (le, api)) > 1
    for: 5m
    labels:
      severity: warning
      service: api-service
    annotations:
      summary: "API响应时间缓慢"
      description: "API {{ $labels.api }} 95%响应时间超过1秒 (当前值: {{ $value | humanizeDuration }})"

2. 告警通知渠道配置

route:
  receiver: 'default-receiver'
  group_by: ['alertname', 'service']
  group_wait: 30s
  group_interval: 5m
  repeat_interval: 4h
  
receivers:
- name: 'default-receiver'
  email_configs:
  - to: 'admin@example.com'
    send_resolved: true
    smarthost: 'smtp.example.com:587'
    from: 'monitor@example.com'
    auth_username: 'monitor@example.com'
    auth_password: 'password'
    auth_identity: 'monitor@example.com'
  
  webhook_configs:
  - url: 'http://dingtalk-webhook-service:8080/send'
    send_resolved: true

高级特性：自定义业务监控指标

1. 方法执行耗时监控

使用AOP实现无侵入式方法耗时监控：

@Aspect
@Component
public class MethodExecutionTimeAspect {
    private final MeterRegistry meterRegistry;

    public MethodExecutionTimeAspect(MeterRegistry meterRegistry) {
        this.meterRegistry = meterRegistry;
    }

    @Around("@annotation(MonitorMethodTime)")
    public Object monitorMethodExecutionTime(ProceedingJoinPoint joinPoint) throws Throwable {
        MethodSignature signature = (MethodSignature) joinPoint.getSignature();
        MonitorMethodTime annotation = signature.getMethod().getAnnotation(MonitorMethodTime.class);
        
        String metricName = annotation.metricName();
        if (StringUtils.isEmpty(metricName)) {
            metricName = "method.execution.time." + signature.getDeclaringTypeName() + "." + signature.getName();
        }
        
        Timer.Sample sample = Timer.start(meterRegistry);
        try {
            return joinPoint.proceed();
        } finally {
            sample.stop(meterRegistry.timer(metricName, 
                "class", signature.getDeclaringTypeName(), 
                "method", signature.getName()));
        }
    }
}

// 自定义注解
@Target(ElementType.METHOD)
@Retention(RetentionPolicy.RUNTIME)
public @interface MonitorMethodTime {
    String metricName() default "";
}

// 使用示例
@Service
public class OrderServiceImpl implements OrderService {
    @Override
    @MonitorMethodTime(metricName = "business.order.create.time")
    public OrderVO createOrder(OrderDTO orderDTO) {
        // 订单创建逻辑
    }
}

2. 业务状态监控仪表盘

以电商库存监控为例，实现多级库存预警仪表盘：

mermaid

部署与运维最佳实践

1. Docker容器化部署

pig-monitor Dockerfile:

FROM openjdk:8-jdk-alpine
WORKDIR /app
COPY target/pig-monitor.jar app.jar
EXPOSE 5001
ENTRYPOINT ["java", "-jar", "app.jar", "--spring.profiles.active=prod"]

Docker Compose编排:

version: '3.8'
services:
  pig-monitor:
    build: ./pig-visual/pig-monitor
    ports:
      - "5001:5001"
    environment:
      - NACOS_SERVER=192.168.1.100:8848
      - NACOS_NAMESPACE=prod
    restart: always
    networks:
      - pig-network

  prometheus:
    image: prom/prometheus:v2.30.3
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
      - prometheus-data:/prometheus
    ports:
      - "9090:9090"
    restart: always
    networks:
      - pig-network

  grafana:
    image: grafana/grafana:8.2.0
    volumes:
      - grafana-data:/var/lib/grafana
      - ./grafana/provisioning:/etc/grafana/provisioning
    ports:
      - "3000:3000"
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=secret
    restart: always
    networks:
      - pig-network

networks:
  pig-network:
    driver: bridge

volumes:
  prometheus-data:
  grafana-data:

2. 监控平台性能优化策略

  1. 指标采集优化

    • 合理设置指标采集间隔（默认15s，非核心指标可延长至60s）
    • 对高基数标签（如userId）进行哈希或采样处理
    • 非必要指标通过白名单机制选择性暴露

  2. 存储优化

    • Prometheus配置合理的数据保留时间（--storage.tsdb.retention.time=15d）
    • 实现指标分层存储（近期数据本地存储，历史数据归档至对象存储）
    • 使用remote_write/remote_read实现Prometheus集群联邦

  3. 查询性能优化

    • 避免大范围时间区间的全量数据查询
    • 对常用查询创建Recording Rule预计算
    • 仪表盘按需加载，避免一次性加载过多面板

总结与展望

pig-monitor监控平台基于Spring Boot Admin和Grafana构建，通过本文介绍的方法，你已经掌握了从环境搭建到自定义仪表盘开发的完整流程，并能够针对业务场景设计多维度监控指标与告警策略。随着云原生技术的发展，未来监控体系将向以下方向演进：

  1. 可观测性融合：整合日志（Logging）、指标（Metrics）、追踪（Tracing）实现全链路可观测
  2. 智能化监控：基于机器学习的异常检测与根因分析
  3. ServiceMesh监控：服务网格层面的流量监控与性能分析

建议收藏本文作为监控平台开发参考手册，关注项目GitHub仓库获取最新功能更新。下一篇我们将深入探讨分布式追踪系统的设计与实现，敬请期待。

附录：常用Grafana插件推荐

插件名称功能描述适用场景
Pie Chart饼图可视化资源占比分析
Clock Panel时钟与时间显示多区域部署监控
Alert List告警信息汇总运维监控大屏
Node Graph节点关系可视化服务依赖分析
Status Panel状态指示灯服务健康状态展示

【免费下载链接】pig 【免费下载链接】pig 项目地址: https://gitcode.com/gh_mirrors/pig/pig

创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考

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