Prometheus+Grafana监控SpringBoot

Prometheus+Grafana监控SpringBoot

• 一、Prometheus监控SpringBoot
• 1.1 pom.xml添加依赖
• 1.2 修改application.yml配置文件
• 1.3 设置启动类Application
• 1.4 Prometheus配置
• 1.4.1 prometheus.yml
• 1.4.2 java_springboot.yml
• 1.4.3 node_down.yml
• 1.4.4 memory_over.yml
• 1.4.5 cpu_over.yml
• 二、Rest接口的编写
• 2.1 模拟调用
• 2.2 Grafana监控视图的制作

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相关内容原文地址：

CSDN：carson0408：使用prometheus和grafana监控springboot项目

CSDN：大老杨：SpringBoot使用prometheus监控

思否：泥瓦匠：Spring-Boot-应用可视化监控

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Grafana模板：10280

一、Prometheus监控SpringBoot

1.1 pom.xml添加依赖

<dependencies>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-test</artifactId>
<scope>test</scope>
</dependency>
<dependency>
<groupId>io.micrometer</groupId>
<artifactId>micrometer-registry-prometheus</artifactId>
<version>1.1.3</version>
</dependency>
</dependencies>

1.2 修改application.yml配置文件

server:
  port: 8002   #  配置启动端口号
spring:
  application:
    name: mydemo
  metrics:
    servo:
      enabled: false
management:
  endpoints:
    web:
      exposure:
        include: info, health, beans, env, metrics, mappings, scheduledtasks, sessions, threaddump, docs, logfile, jolokia,prometheus
      base-path: /actuator #默认/actuator 不更改可不用配置
      #CORS跨域支持
      cors:
        allowed-origins: http：//example.com
        allowed-methods: GET,PUT,POST,DELETE
    prometheus:
      id: springmetrics
  endpoint:
    beans:
      cache:
        time-to-live: 10s #端点缓存响应的时间量
    health:
      show-details: always #详细信息显示给所有用户
  server:
    port: 8001 #默认8080
    address: 127.0.0.1 #配置此项表示不允许远程连接
  #监测
  metrics:
    export:
      datadog:
        application-key: ${spring.application.name}
    web:
      server:
        auto-time-requests: false

这里涉及两个port，一个是server port，一个是prometheus port，其中server port则是调用接口使用的端口，而prometheus port则与该服务在prometheus.yml中的port是一致的，不一致的话则会使该服务down。

1.3 设置启动类Application

@SpringBootApplication
public class Springboot2PrometheusApplication {

	public static void main(String[] args) {
		SpringApplication.run(Springboot2PrometheusApplication.class, args);
	}
	@Bean
	MeterRegistryCustomizer<MeterRegistry> configurer(
			@Value("${spring.application.name}") String applicationName) {
		return (registry) -> registry.config().commonTags("application", applicationName);
	}
}

SpringBoot项目到这里就配置完成了，启动项目，访问http://localhost:8080/actuator/prometheus，如图所示，可以看到一些度量指标。

1.4 Prometheus配置

1.4.1 prometheus.yml

# 全局配置
global:
  scrape_interval:     15s
  evaluation_interval: 15s

scrape_configs:
  # 监控prometheus本身
  - job_name: 'prometheus监控'
    static_configs:
    - targets: ['ip:9090']
  # 通过node_exporter将监控数据传给prometheus，如果要监控多台服务器，只要在每个服务器上安装node_exporter，指定不同多ip地址就好了
  - job_name: '服务器监控'
    static_configs:
    - targets: ['ip:9100']
  - job_name: "redis集群监控"
    static_configs:
    - targets: ['ip:9121']
  - job_name: 'docker监控'
    # 静态添加
    static_configs:
    # 指定监控实例
    - targets: ['ip:8081']
  - job_name: 'Java项目监控'
    metrics_path: '/actuator/prometheus'
    file_sd_configs:
    - refresh_interval: 1m
      files:
      - "/home/prometheus/java_springboot.yml"
alerting:
  alertmanagers:
  - static_configs:
    - targets:
      - ip:9093
rule_files:
  - "/home/prometheus/rules/node_down.yml"                 # 实例存活报警规则文件
  - "/home/prometheus/rules/memory_over.yml"               # 内存报警规则文件
  - "/home/prometheus/rules/cpu_over.yml"                  # cpu报警规则文件

1.4.2 java_springboot.yml

java_springboot.yml：

- targets:
  - "ip.50:8085"
  labels:
    instance: 服务A
- targets:
  - "ip0.50:8086"
  labels:
    instance: 服务B

1.4.3 node_down.yml

node_down.yml：

groups:
- name: 实例存活告警规则
  rules:
  - alert: 实例存活告警
    expr: up == 0
    for: 1m
    labels:
      user: prometheus
      severity: warning
    annotations:
      description: "{{ $labels.instance }} of job {{ $labels.job }} has been down for more than 1 minutes."

1.4.4 memory_over.yml

memory_over.yml：

groups:
- name: 内存报警规则
  rules:
  - alert: 内存使用率告警
    expr: (node_memory_MemTotal_bytes - (node_memory_MemFree_bytes+node_memory_Buffers_bytes+node_memory_Cached_bytes )) / node_memory_MemTotal_bytes * 100 > 80
    for: 1m
    labels:
      user: prometheus
      severity: warning
    annotations:
      description: "服务器: 内存使用超过80%！(当前值: {{ $value }}%)"

1.4.5 cpu_over.yml

cpu_over.yml：

groups:
- name: CPU报警规则
  rules:
  - alert: CPU使用率告警
    expr: 100 - (avg by (instance)(irate(node_cpu_seconds_total{mode="idle"}[1m]) )) * 100 > 90
    for: 1m
    labels:
      user: prometheus
      severity: warning
    annotations:
      description: "服务器: CPU使用超过90%！(当前值: {{ $value }}%)"

二、Rest接口的编写

编写了一个接口，接口是http rest风格的add接口，具体代码如下所示：

import io.micrometer.core.instrument.Counter;
import io.micrometer.core.instrument.MeterRegistry;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.validation.annotation.Validated;
import org.springframework.web.bind.annotation.*;
import javax.annotation.PostConstruct;
@RestController
@RequestMapping("/api")
public class OperationController {
    @Autowired
    MeterRegistry registry;
    private Counter counter;
    private Counter failCounter;
    @PostConstruct
    private void init(){
        failCounter=  registry.counter("requests_add_fail_total","save","carson");
        counter = registry.counter("requests_add_total","save","carson");
    }
    @RequestMapping(value = "/add",method = RequestMethod.POST)
    public String add(@Validated String firstName,@Validated String secondName) throws Exception {
        try{
            String name = firstName+secondName;
            counter.increment();
            return name;
        }catch (Exception e){
            failCounter.increment();
            throw new Exception("异常");
        }
    }
}

其中init方法则是对prometheus中counter组件进行初始化，而在add接口中则可以直接使用，这里两个指标分别为调用成功的次数与调用失败的次数。

2.1 模拟调用

通过postman进行调用接口，如下图所示：

2.2 Grafana监控视图的制作

在grafana页面新增dashboard之后，便进入下图所示：



然后选中数据源，并进行metrics语句编写，如下图所示，sum(request_add_total) ,其中sum函数中的字段可以模糊搜索，只要prometheus中的服务是up的。然后图就如下所示，可以看出，调用情况：