Spring Boot/Cloud 集成 Prometheus 和 Grafana 完整监控方案

字数

1401 字

阅读时间

8 分钟

1 概述

1.1 技术栈版本

Spring Boot: 2.7. x
Spring Cloud: 2021. x
JDK: 1.8+
Prometheus: 2.45.0
Grafana: 9.5. x
Maven: 3.6. x

1.2 监控架构

单体架构：
[Spring Boot Application] --> [Prometheus] --> [Grafana]

微服务架构：
[Gateway Service] -----> [Prometheus] --> [Grafana]
[Service A     ] -----> 
[Service B     ] -----> 
[Service C     ] ----->

2 基础环境配置

2.1 Maven 依赖配置

2.1.1 Spring Boot 项目

xml

<dependencies>
    <!-- Spring Boot Starter -->
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
    
    <!-- Actuator -->
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-actuator</artifactId>
    </dependency>
    
    <!-- Prometheus -->
    <dependency>
        <groupId>io.micrometer</groupId>
        <artifactId>micrometer-registry-prometheus</artifactId>
    </dependency>
</dependencies>

2.1.2 Spring Cloud 项目（common 模块）

xml

<dependencies>
    <!-- Spring Cloud Dependencies -->
    <dependencyManagement>
        <dependencies>
            <dependency>
                <groupId>org.springframework.cloud</groupId>
                <artifactId>spring-cloud-dependencies</artifactId>
                <version>${spring-cloud.version}</version>
                <type>pom</type>
                <scope>import</scope>
            </dependency>
        </dependencies>
    </dependencyManagement>
    
    <!-- Monitoring Dependencies -->
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-actuator</artifactId>
    </dependency>
    <dependency>
        <groupId>io.micrometer</groupId>
        <artifactId>micrometer-registry-prometheus</artifactId>
    </dependency>
</dependencies>

2.2 应用配置

2.2.1 Spring Boot 配置 (application. yml)

yaml

server:
  port: 8080

spring:
  application:
    name: springboot-prometheus-demo

management:
  endpoints:
    web:
      exposure:
        include: prometheus,health,info,metrics
  endpoint:
    health:
      show-details: always
    metrics:
      enabled: true
    prometheus:
      enabled: true
  metrics:
    tags:
      application: ${spring.application.name}
    export:
      prometheus:
        enabled: true

2.2.2 Spring Cloud 配置 (bootstrap. yml)

yaml

spring:
  application:
    name: ${SERVICE_NAME}
  cloud:
    config:
      enabled: true

management:
  endpoints:
    web:
      exposure:
        include: prometheus,health,info,metrics
  metrics:
    tags:
      application: ${spring.application.name}
      environment: ${spring.profiles.active:default}
    enable:
      all: true
    export:
      prometheus:
        enabled: true
  endpoint:
    prometheus:
      enabled: true

3 监控指标配置

3.1 通用监控指标

java

@Configuration
public class MetricsConfig {
    
    @Bean
    MeterRegistry meterRegistry() {
        return new SimpleMeterRegistry();
    }
    
    @Bean
    public Counter requestCounter(MeterRegistry registry) {
        return Counter.builder("app_requests_total")
                .description("应用请求总数")
                .tags("type", "total")
                .register(registry);
    }
    
    @Bean
    public Timer requestLatencyTimer(MeterRegistry registry) {
        return Timer.builder("app_request_latency")
                .description("请求延迟")
                .tags("type", "latency")
                .register(registry);
    }
    
    @Bean
    public Gauge queueSize(MeterRegistry registry, Queue<?> queue) {
        return Gauge.builder("app_queue_size", queue::size)
                .description("队列大小")
                .register(registry);
    }
}

3.2 业务监控指标

java

@Service
@RequiredArgsConstructor
public class BusinessService {
    private final MeterRegistry registry;
    private final Counter businessCounter;
    
    public void processBusinessLogic() {
        Timer.Sample sample = Timer.start(registry);
        try {
            // 业务逻辑
            businessCounter.increment();
        } finally {
            sample.stop(registry.timer("business.process.time"));
        }
    }
}

3.3 网关监控指标（Spring Cloud）

java

@Configuration
public class GatewayMetricsConfig {
    
    @Bean
    public RouteDefinitionMetrics routeMetrics(MeterRegistry registry) {
        return new RouteDefinitionMetrics(registry);
    }
    
    @Bean
    public FilteringWebHandler filteringWebHandler(List<GlobalFilter> globalFilters,
                                                 MeterRegistry registry) {
        return new MetricsFilteringWebHandler(globalFilters, registry);
    }
}

4 Prometheus 配置

4.1 单体应用配置

yaml

global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: 'springboot'
    metrics_path: '/actuator/prometheus'
    static_configs:
      - targets: ['localhost:8080']
    scrape_interval: 5s

4.2 微服务配置

yaml

global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  # 网关监控
  - job_name: 'spring-cloud-gateway'
    metrics_path: '/actuator/prometheus'
    static_configs:
      - targets: ['gateway:8080']
    
  # 服务发现监控
  - job_name: 'spring-cloud-services'
    eureka_sd_configs:
      - server: http://eureka-server:8761/eureka
    metrics_path: '/actuator/prometheus'
    relabel_configs:
      - source_labels: [__meta_eureka_app_name]
        target_label: application

  # 自定义业务监控
  - job_name: 'business-metrics'
    metrics_path: '/actuator/prometheus'
    static_configs:
      - targets: ['service-a:8081', 'service-b:8082']
    metric_relabel_configs:
      - source_labels: [__name__]
        regex: 'business_.*'
        action: keep

4.3 告警规则配置

yaml

groups:
  - name: SpringAlerts
    rules:
      # 实例存活告警
      - alert: InstanceDown
        expr: up == 0
        for: 1m
        labels:
          severity: critical
        annotations:
          summary: "Instance {{ $labels.instance }} down"
          
      # 高错误率告警
      - alert: HighErrorRate
        expr: rate(http_server_requests_seconds_count{status="5xx"}[5m]) > 1
        for: 2m
        labels:
          severity: warning
        annotations:
          summary: "High error rate on {{ $labels.instance }}"
          
      # JVM内存告警
      - alert: HighJVMMemoryUsage
        expr: jvm_memory_used_bytes / jvm_memory_max_bytes * 100 > 85
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "High JVM memory usage on {{ $labels.instance }}"

5 Grafana 配置

5.1 数据源配置

json

{
  "name": "Prometheus",
  "type": "prometheus",
  "url": "http://localhost:9090",
  "access": "proxy",
  "basicAuth": false
}

5.2 Dashboard 配置

5.2.1 JVM 监控面板

json

{
  "panels": [
    {
      "title": "JVM堆内存使用",
      "type": "graph",
      "datasource": "Prometheus",
      "targets": [
        {
          "expr": "sum(jvm_memory_used_bytes{area=\"heap\"}) by (instance)",
          "legendFormat": "{{instance}}"
        }
      ]
    },
    {
      "title": "GC暂停时间",
      "type": "graph",
      "datasource": "Prometheus",
      "targets": [
        {
          "expr": "rate(jvm_gc_pause_seconds_sum[5m])",
          "legendFormat": "GC暂停"
        }
      ]
    }
  ]
}

5.2.2 微服务监控面板

json

{
  "panels": [
    {
      "title": "服务健康状态",
      "type": "stat",
      "datasource": "Prometheus",
      "targets": [
        {
          "expr": "up",
          "legendFormat": "{{application}}"
        }
      ]
    },
    {
      "title": "服务请求量",
      "type": "graph",
      "datasource": "Prometheus",
      "targets": [
        {
          "expr": "sum(rate(http_server_requests_seconds_count[5m])) by (application)",
          "legendFormat": "{{application}}"
        }
      ]
    },
    {
      "title": "服务响应时间",
      "type": "heatmap",
      "datasource": "Prometheus",
      "targets": [
        {
          "expr": "rate(http_server_requests_seconds_sum[5m]) / rate(http_server_requests_seconds_count[5m])",
          "legendFormat": "{{application}}"
        }
      ]
    }
  ]
}

6 最佳实践

6.1 监控指标分类

系统指标
- CPU 使用率
- 内存使用
- 磁盘 IO
- 网络流量
JVM 指标
- 堆内存使用
- 非堆内存使用
- GC 情况
- 线程状态
应用指标
- 请求量
- 响应时间
- 错误率
- 业务指标
中间件指标
- 连接池状态
- 队列大小
- 缓存命中率

6.2 性能优化建议

采集频率优化
- 系统指标：15 s
- 应用指标：5 s
- 业务指标：30 s
存储优化
- 合理设置数据保留时间
- 使用适当的压缩算法
- 定期清理无用数据
查询优化
- 使用高效的 PromQL
- 避免过度使用正则
- 合理使用标签

6.3 告警策略

告警级别
- Critical：影响业务
- Warning：需要关注
- Info：提示信息
告警规则
- 实例存活
- 资源使用率
- 业务指标
- 错误率
告警通道
- 邮件
- 短信
- 钉钉/企业微信
- 自定义 webhook

7 常见问题排查

7.1 数据采集问题

检查 actuator 端点配置
验证网络连通性
查看 Prometheus target 状态
检查防火墙设置

7.2 性能问题

调整采集间隔
优化指标过滤
配置数据压缩
清理历史数据

7.3 集成问题

版本兼容性检查
依赖冲突解决
配置文件验证
服务注册检查

8 开发建议

8.1 代码实践

java

@RestController
@RequiredArgsConstructor
public class DemoController {
    
    private final MeterRegistry meterRegistry;
    
    @GetMapping("/api/demo")
    public String demo() {
        // 记录请求计数
        meterRegistry.counter("api.requests", "endpoint", "demo").increment();
        
        // 记录处理时间
        Timer.Sample sample = Timer.start(meterRegistry);
        try {
            // 业务逻辑
            return "success";
        } finally {
            sample.stop(meterRegistry.timer("api.response.time", "endpoint", "demo"));
        }
    }
}

8.2 监控实践

合理使用标签
避免高基数指标
关注重要指标
定期检查告警
持续优化配置

8.3 运维实践

配置备份
定期验证
容量规划
安全加固
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Spring Boot/Cloud 集成 Prometheus 和 Grafana 完整监控方案 ​

1 概述 ​

1.1 技术栈版本 ​

1.2 监控架构 ​

2 基础环境配置 ​

2.1 Maven 依赖配置 ​

2.1.1 Spring Boot 项目 ​

2.1.2 Spring Cloud 项目（common 模块） ​

2.2 应用配置 ​

2.2.1 Spring Boot 配置 (application. yml) ​

2.2.2 Spring Cloud 配置 (bootstrap. yml) ​

3 监控指标配置 ​

3.1 通用监控指标 ​

3.2 业务监控指标 ​

3.3 网关监控指标（Spring Cloud） ​

4 Prometheus 配置 ​

4.1 单体应用配置 ​

4.2 微服务配置 ​

4.3 告警规则配置 ​

5 Grafana 配置 ​

5.1 数据源配置 ​

5.2 Dashboard 配置 ​

5.2.1 JVM 监控面板 ​

5.2.2 微服务监控面板 ​

6 最佳实践 ​

6.1 监控指标分类 ​

6.2 性能优化建议 ​

6.3 告警策略 ​

7 常见问题排查 ​

7.1 数据采集问题 ​

7.2 性能问题 ​

7.3 集成问题 ​

8 开发建议 ​

8.1 代码实践 ​

8.2 监控实践 ​

8.3 运维实践 ​

Spring Boot/Cloud 集成 Prometheus 和 Grafana 完整监控方案

1 概述

1.1 技术栈版本

1.2 监控架构

2 基础环境配置

2.1 Maven 依赖配置

2.1.1 Spring Boot 项目

2.1.2 Spring Cloud 项目（common 模块）

2.2 应用配置

2.2.1 Spring Boot 配置 (application. yml)

2.2.2 Spring Cloud 配置 (bootstrap. yml)

3 监控指标配置

3.1 通用监控指标

3.2 业务监控指标

3.3 网关监控指标（Spring Cloud）

4 Prometheus 配置

4.1 单体应用配置

4.2 微服务配置

4.3 告警规则配置

5 Grafana 配置

5.1 数据源配置

5.2 Dashboard 配置

5.2.1 JVM 监控面板

5.2.2 微服务监控面板

6 最佳实践

6.1 监控指标分类

6.2 性能优化建议

6.3 告警策略

7 常见问题排查

7.1 数据采集问题

7.2 性能问题

7.3 集成问题

8 开发建议

8.1 代码实践

8.2 监控实践

8.3 运维实践