java结合prometheus实现自定义数据监控

一、配置prometheus

prometheus.yml

...

- job_name: 'my-service'
  metrics_path: /metrics
  static_configs:
  - targets: ['xxx.xxx.xxx.xxx:yyyy'] //被监控应用的url

...

二、被监控应用

思路

1. 引入相关依赖
2. 配置监控指标暴露的endpoint
3. 自定义监控指标

关键代码

1. 引入相关依赖

pom.xml

<!-- prometheus -->
<dependency>
		<groupId>io.prometheus</groupId>
		<artifactId>simpleclient</artifactId>
		<version>0.3.0</version>
</dependency>
<dependency>
		<groupId>io.prometheus</groupId>
		<artifactId>simpleclient_hotspot</artifactId>
		<version>0.3.0</version>
</dependency>
<dependency>
		<groupId>io.prometheus</groupId>
		<artifactId>simpleclient_servlet</artifactId>
		<version>0.3.0</version>
</dependency>

2. 将监控指标暴露到’/metrics’

PrometheusConfig.java

import io.prometheus.client.exporter.MetricsServlet;
import io.prometheus.client.hotspot.DefaultExports;
import org.springframework.boot.web.servlet.ServletRegistrationBean;
...

@Configuration
public class PrometheusConfig {

		// ...

		@Bean
		public ServletRegistrationBean servletRegistrationBean(){
			DefaultExports.initialize();
			return new ServletRegistrationBean(new MetricsServlet(), "/metrics");
		}
}

3. 自定义监控指标

MyMetrics.java

import io.prometheus.client.Counter;
import io.prometheus.client.Gauge;
...

// counter只增不减
static final Counter customizeCounter = Counter.build()
        .name("customize_counter") //定义名称，名称可用于搜索
        .help("customize counter") //定义描述
        .register();
customizeCounter.inc(); //当前对象加1

// gauge可增可减
static final Gauge customizeGauge = Gauge.build()
        .name("customize_gauge")
        .help("customize gauge")
        .labelNames("label1", "label2", "label3") //定义标签名称，可定义多个
        .register();
customizeGauge.inc(); //当前对象加1
customizeGauge.dec(); //当前对象减1
customizeGauge.labels("value1","value2","value3").set(1100); //设置标签值，标签可用于条件筛选

// 此外还有histogram和summary两种指标

三、监控应用

思路

1. 引入相关依赖
2. 调用prometheus API获取数据
3. 整理数据并返回

关键代码

1. 引入相关依赖

pom.xml

<!-- prometheus -->
<dependency>
		<groupId>io.prometheus</groupId>
		<artifactId>simpleclient</artifactId>
		<version>0.3.0</version>
</dependency>
<dependency>
		<groupId>io.prometheus</groupId>
		<artifactId>simpleclient_hotspot</artifactId>
		<version>0.3.0</version>
</dependency>
<dependency>
		<groupId>io.prometheus</groupId>
		<artifactId>simpleclient_servlet</artifactId>
		<version>0.3.0</version>
</dependency>

2. 调用prometheus API

获取某个时间点的数据

String query = "customize_counter";
String url = "http://[ip]:[port]/api/v1/query?query=" + query + "&time=2019-05-01T20:10:51.781Z";
HttpGet get = new HttpGet(url);
CloseableHttpClient httpClient = HttpClients.custom().build();
CloseableHttpResponse response = httpClient.execute(get);

获取某个时间段的数据

String query = "rate(customize_counter{label1 = value1}[30s])";
String url = "http://[ip]:[port]/api/v1/query_range?query=" + query + "&start=2019-05-01T20:10:51.781Z&end=2019-05-02T20:10:51.781Z";
HttpGet get = new HttpGet(url);
CloseableHttpClient httpClient = HttpClients.custom().build();
CloseableHttpResponse response = httpClient.execute(get);

更多使用方法请参考Prometheus - 查询

3. 处理数据

prometheus有时会返回乱序的结果，以下代码可以按时间戳排序

public class ComparatorPromData implements Comparator {

    @Override
    public int compare(Object o1, Object o2) {
        List list1 = (List)o1;
        List list2 = (List)o2;
        return ((Integer) list1.get(0)).compareTo(((Integer) list2.get(0)));
    }
}

public class SortUtil {

    public static List sortPromData(List<List> list) {
        ComparatorPromData comparator = new ComparatorPromData();
        Collections.sort(list, comparator);
        return list;
    }
}