1.Spring Boot 工程集成 Micrometer
1.1引入依赖
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-actuator</artifactId>
</dependency>
<dependency>
<groupId>io.micrometer</groupId>
<artifactId>micrometer-registry-prometheus</artifactId>
</dependency>
1.2配置
management.server.port=9003
management.endpoints.web.exposure.include=*
management.endpoint.metrics.enabled=true
management.endpoint.health.show-details=always
management.endpoint.health.probes.enabled=true
management.endpoint.prometheus.enabled=true
management.metrics.export.prometheus.enabled=true
management.metrics.tags.application=voice-qc-backend
这里management.endpoints.web.exposure.include=*配置为开启 Actuator 服务，因为Spring Boot Actuator 会自动配置一个 URL 为/actuator/Prometheus的 HTTP 服务来供 Prometheus 抓取数据，不过默认该服务是关闭的，该配置将打开所有的 Actuator 服务。
management.metrics.tags.application配置会将该工程应用名称添加到计量器注册表的 tag 中去，方便后边 Prometheus 根据应用名称来区分不同的服务。
1.3监控jvm信息
然后在工程启动主类中添加 Bean 如下来监控 JVM 性能指标信息：
@SpringBootApplication
public class GatewayDatumApplication {
public static void main(String[] args) {
SpringApplication.run(GatewayDatumApplication.class, args);
}
@Bean
MeterRegistryCustomizer<MeterRegistry> configurer(
@Value("${spring.application.name}") String applicationName) {
return (registry) -> registry.config().commonTags("application", applicationName);
}
}
1.4创建自定义监控
监控请求次数与响应时间
package com.lianxin.gobot.api.monitor;
import io.micrometer.core.instrument.Counter;
import io.micrometer.core.instrument.MeterRegistry;
import io.micrometer.core.instrument.Timer;
import lombok.Getter;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.stereotype.Component;
import javax.annotation.PostConstruct;
/**
* @Author: GZ
* @CreateTime: 2022-08-30 10:50
* @Description: 自定义监控服务
* @Version: 1.0
*/
@Component
public class PrometheusCustomMonitor {
/**
* 上报拨打请求次数
*/
@Getter
private Counter reportDialRequestCount;
/**
* 上报拨打URL
*/
@Value("${lx.call-result-report.url}")
private String callReportUrl;
/**
* 上报拨打响应时间
*/
@Getter
private Timer reportDialResponseTime;
@Getter
private final MeterRegistry registry;
@Autowired
public PrometheusCustomMonitor(MeterRegistry registry) {
this.registry = registry;
}
@PostConstruct
private void init() {
reportDialRequestCount = registry.counter("go_api_report_dial_request_count", "url",callReportUrl);
reportDialResponseTime= registry.timer("go_api_report_dial_response_time", "url",callReportUrl);
}
}
1.5添加具体业务代码监控
//统计请求次数
prometheusCustomMonitor.getReportDialRequestCount().increment();
long startTime = System.currentTimeMillis();
String company = HttpUtils.post(companyUrl,"");
//统计响应时间
long endTime = System.currentTimeMillis();
prometheusCustomMonitor.getReportDialResponseTime().record(endTime-startTime, TimeUnit.MILLISECONDS);
在浏览器访问http://127.0.0.1:9001/actuator/prometheus，就可以看到服务的一系列不同类型 metrics 信息，例如jvm_memory_used_bytes gauge、jvm_gc_memory_promoted_bytes_total counter，go_api_report_dial_request_count等
到此，Spring Boot 工程集成 Micrometer 就已经完成，接下里就要与 Prometheus 进行集成了。
2.集成 Prometheus
2.1安装
docker pull prom/prometheus
mdkir /usr/local/prometheus
vi prometheus.yml
# my global config
global:
scrape_interval: 15s # Set the scrape interval to every 15 seconds. Default is every 1 minute.
evaluation_interval: 15s # Evaluate rules every 15 seconds. The default is every 1 minute.
# scrape_timeout is set to the global default (10s).
# Alertmanager configuration
alerting:
alertmanagers:
- static_configs:
- targets:
# - alertmanager:9093
# Load rules once and periodically evaluate them according to the global 'evaluation_interval'.
rule_files:
# - "first_rules.yml"
# - "second_rules.yml"
# 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'
# metrics_path defaults to '/metrics'
# scheme defaults to 'http'.
static_configs:
- targets: ['192.168.136.129:9090']
docker run -d --name prometheus -p 9090:9090 -v/usr/local/prometheus/prometheus.yml:/etc/prometheus/prometheus.yml prom/prometheus
2.2集成配置
global:
scrape_interval: 15s
scrape_configs:
- job_name: "prometheus"
static_configs:
- targets: ["localhost:9090"]
- job_name: "metricsLocalTest"
metrics_path: "/actuator/prometheus"
static_configs:
- targets: ["localhost:9003"]
这里localhost:9001就是上边本地启动的服务地址，也就是 Prometheus 要监控的服务地址。同时可以添加一些与应用相关的标签，方便后期执行 PromSQL 查询语句区分。最后重启 Prometheus 服务
3.使用 Grafana Dashboard 展示监控项
3.1安装grafana
docker pull grafana/grafana
docker run -d --name grafana -p 3000:3000 -v /usr/local/grafana:/var/lib/grafana grafana/grafana
默认用户名/密码 admin/admin
3.2配置prometheus数据源
3.3增加jvm面板
模板编号为4701
3.4配置业务接口监控面板
