	
    <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>io.micrometer</groupId>
        <artifactId>micrometer-registry-prometheus</artifactId>
    </dependency>

3.2参数配置

3.2.1 Spring boot程序yaml配置文件

spring:
  application:
    name: prometheus-example
management:
  endpoints:
    web:
      exposure:
        include: health,prometheus
  metrics:
    tags:
      application: ${spring.application.name}  #为应用设置tag ，方便区分不同的应用

4. Micrometer指标开发

4.1 计数器 Counter

Counter 只会报告单个指标，可以增加一个可选的值，且该值必须是正数，默认为 1。Counter常用来记录某些将一直正增长的数据，如请求次数、变化次数，通过在对应监控系统使用聚合函数，如 rate、increase 等，可以计算出指标在指定时间范围的变化率，也就是通常说的 QPS

4.1.1 定义

import io.micrometer.core.instrument.MeterRegistry;
import io.micrometer.core.instrument.Counter;

@Data
@RestController
public class PrometheusController {

    private Counter counter;

    private final MeterRegistry registry;

    @Autowired
    public PrometheusCustomMonitor(MeterRegistry registry) {
        this.registry = registry;
        counter = registry.counter("order_counter", "orderCounter", "test-auth");
    }

}

4.1.2 使用

@RequestMapping("/counter")
public String orderCounter() {
    // 统计下单次数     
    counter.increment();
    Random random = new Random();
    int amount = random.nextInt(100);
    return "下单成功, 金额: " + amount;
}

4.2 总计 DistributionSummary

Summary用于跟踪事件的分发。它在结构上类似于计时器，但记录的值不代表时间单位。例如，分发摘要可用于衡量命中服务器的请求的有效负载大小。

4.2.1 定义

import io.micrometer.core.instrument.MeterRegistry;
import io.micrometer.core.instrument.DistributionSummary;

@Data
@RestController
public class PrometheusController {

    private DistributionSummary summary;

    private final MeterRegistry registry;

    @Autowired
    public PrometheusCustomMonitor(MeterRegistry registry) {
        this.registry = registry;
   	summary = registry.summary("order_summary", "orderSummary", "test-auth");
    }

}

4.2.2 使用

@RequestMapping("/summary")
public String orderSummary() {
   Random random = new Random();
    int amount = random.nextInt(100);
    // 统计下单记录 
    summary.record(amount);
    return "下单成功, 金额: " + amount;
}

4.3 量规 Gauge

一般用于监测有自然上限的事件或任务，而 Counter 一般用于无自然上限的事件或任务。有自然上限:如内存使用大小、容器大小、运行中的线程数量等。

4.3.1 定义

import io.micrometer.core.instrument.Tags;
import io.micrometer.core.instrument.MeterRegistry;

@Data
@RestController
public class PrometheusController {

   private AtomicInteger gauge;

    private final MeterRegistry registry;

    @Autowired
    public PrometheusCustomMonitor(MeterRegistry registry) {
        this.registry = registry;
    	gauge = registry.gauge("order_gauge", Tags.of("orderGauge", "test-auth"), new AtomicInteger(1000));
    }

}

4.3.2 使用

 
@RequestMapping("/gauge")
public String orderGauge（) {
   Random random = new Random();
    int amount = random.nextInt(100);
    // 记录余额
    gauge.decrementAndGet();
    return "下单成功, 金额: " + amount;
}

4.4 计时器 Timer

Timer是计时器，用来测量短时间的代码块的执行时间的分布。Timer记录代码块的执行时间后，可以对执行时间进行统计，分析记录执行的最大时间、总时间、平均时间、执行完成的总任务等。

4.4.1 定义

import io.micrometer.core.instrument.Timer;
import io.micrometer.core.instrument.MeterRegistry;

@Data
@RestController
public class PrometheusController {

   private Timer timer;

    private final MeterRegistry registry;

    @Autowired
    public PrometheusCustomMonitor(MeterRegistry registry) {
        this.registry = registry;
     	timer = registry.timer("order_timer", "orderTimer", "test-auth");
    }

}

4.4.2 使用

@RequestMapping("/timer")
public String orderTimer（) {
   Random random = new Random();
    int amount = random.nextInt(100);
    // 记录接口用时
    timer.record(100, TimeUnit.MILLISECONDS);
    timer.record(Duration.ofMillis(100));
    timer.record(() -> {
        try {
            Thread.sleep(amount2 * amount2 / 10);
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
    });   
    return "下单成功, 金额: " + amount;
}

4.5 在线计时器 LongTaskTimer

LongTaskTimer一般监控长时间执行的程序，可以实时观测任务的耗时和正在执行的任务数量

4.5.1 定义

import io.micrometer.core.instrument.LongTaskTimer;
import io.micrometer.core.instrument.MeterRegistry;

@Data
@RestController
public class PrometheusController {

    private LongTaskTimer longTaskTimer;

    private final MeterRegistry registry;

    @Autowired
    public PrometheusCustomMonitor(MeterRegistry registry) {
        this.registry = registry;
     	longTaskTimer = registry.more().longTaskTimer("order_long_task_timer", "order_long_task_time", "test-auth");
    }

}

4.5.2 使用

@RequestMapping("/long/timer")
public String orderTimer（) {
    Random random = new Random();
     int amount2 = random.nextInt(100);
     longTaskTimer.record(() -> {
     	try {
            Thread.sleep(amount2 * amount2 * 10);
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
     });
     return "下单成功, 金额， amount2: " + amount2;
}

5. Prometheus client指标开发

5.1 计数器 Counter

5.1.1 定义

import io.prometheus.client.Counter;
import io.prometheus.client.CollectorRegistry;

@Data
@RestController
public class PrometheusController {

    private Counter counter2;

    private final CollectorRegistry collectorRegistry;
    @Autowired
    public PrometheusCustomMonitor( CollectorRegistry collectorRegistry) {
        this.collectorRegistry = collectorRegistry;  
   	counter2 = io.prometheus.client.Counter.build().name("order_counter2").labelNames("order_counter2", "testCounter2").help("counter").register(collectorRegistry);    
	}

}

5.1.2 使用

@RequestMapping("/order/counter2")
public String orderCounter2() {    
    Random random = new Random();
    int amount = random.nextInt(100);
    // 根据不同标签区分不同数据类型
    counter2.labels("amount1", "test1").inc(amount);
    counter2.labels("amount1", "test3").inc(amount);

    int amount2 = random.nextInt(100);
    counter2.labels("amount2", "test2").inc(amount2);
    return "下单成功, 金额， amount1: " + amount + " amount2: " + amount2;
}

5.2 总计 Summary

Summary用于跟踪事件的分发。它在结构上类似于计时器，但记录的值不代表时间单位。例如，分发摘要可用于衡量命中服务器的请求的有效负载大小。

5.2.1 定义

import io.prometheus.client.Summary;
import io.prometheus.client.CollectorRegistry;

@Data
@RestController
public class PrometheusController {

    private Summary summary2;

    private final CollectorRegistry collectorRegistry;
    @Autowired
    public PrometheusCustomMonitor( CollectorRegistry collectorRegistry) {
        this.collectorRegistry = collectorRegistry;  
    	summary2 = Summary.build().name("order_summary2").labelNames("order_summary2", "testSummary2").help("summary").register(collectorRegistry);    
	}

}

5.2.2 使用

@RequestMapping("/order/summary2")
public String orderSummary2（) {
    Random random = new Random();
    int amount = random.nextInt(100);
    summary2.labels("summary1", "test1").observe(amount);
    int amount2 = random.nextInt(100);
    summary2.labels("summary2", "test2").observe(amount2);
    Summary.Timer timer = summary2.labels("summary4", "timer").startTimer();

    summary2.labels("summary3", "timer").time(() -> {
        try {
            Thread.sleep(amount * amount);
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
    });

    timer.close();
    return "下单成功, 金额， amount1: " + amount + " amount2: " + amount2;
}

5.3 量规 Gauge

一般用于监测有自然上限的事件或任务，而 Counter 一般用于无自然上限的事件或任务。有自然上限:如内存使用大小、容器大小、运行中的线程数量等。

5.3.1 定义

import io.prometheus.client.Gauge;
import io.prometheus.client.CollectorRegistry;

@Data
@RestController
public class PrometheusController {

    private Gauge gauge2;

    private final CollectorRegistry collectorRegistry;
    @Autowired
    public PrometheusCustomMonitor( CollectorRegistry collectorRegistry) {
        this.collectorRegistry = collectorRegistry;  
     	gauge2 = Gauge.build().name("order_gauge2").labelNames("order_gauge2", "testGauge2").help("gauge").register(collectorRegistry);    
	}

}

5.3.2 使用

 
@RequestMapping("/gauge2")
public String orderGauge2() {
    Random random = new Random();
    int amount = random.nextInt(100);
    gauge2.labels("gauge1", "test1").set(amount);
    int amount2 = random.nextInt(100);
    gauge2.labels("gauge2", "test2").set(amount2);

    return "下单成功, 金额， amount1: " + amount + " amount2: " + amount2;
}

5.4 直方图 Histogram

5.4.1 定义

import io.prometheus.client.Histogram;
import io.prometheus.client.CollectorRegistry;

@Data
@RestController
public class PrometheusController {

    private Histogram histogram;
    private Histogram histogram2;
    private Histogram histogram3;;

    private final CollectorRegistry collectorRegistry;
    @Autowired
    public PrometheusCustomMonitor( CollectorRegistry collectorRegistry) {
        this.collectorRegistry = collectorRegistry;  
		// 默认刻度
        histogram = Histogram.build().name("order_histogram").labelNames("order_histogram", "testHistogram").help("histogram").register(collectorRegistry);
		// 自己设定刻度
        histogram2 = Histogram.build().buckets(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10).name("order_histogram2").labelNames("order_histogram", "testHistogram").help("histogram").register(collectorRegistry);
        histogram3 = Histogram.build().buckets(0, 10, 20, 30, 40, 50, 100).name("order_histogram3").labelNames("order_histogram", "testHistogram").help("histogram").register(collectorRegistry);    
	}

}

5.3.2 使用

@RequestMapping("/order/timer")
public String orderTimer() {
    Random random = new Random();
    int amount = random.nextInt(100);
    int amount2 = random.nextInt(100);
    histogram2.labels("histogram2", "test1").observe(amount);
    histogram2.labels("histogram2", "test2").observe(amount2);

    histogram.labels("histogram1", "test3").time(() -> {
        try {
            Thread.sleep(amount * amount / 10);
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
    });
    histogram3.labels("histogram3", "test4").time(() -> {
        try {
            Thread.sleep(amount2 * amount2 / 10);
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
    });

    return "下单成功, 金额， amount1: " + amount + " amount2: " + amount2;
}

6. Prometheus+Grafana配置

6.1 配置数据源

6.1.1 Prometheus yaml文件添加配置

- job_name: 'prometheus-example'
  # 抓取频率
  scrape_interval: 5s
  # 抓取的端点，也就是服务暴露出来的指标接口地址
  metrics_path: '/actuator/prometheus'
  static_configs:
  	# 目标服务地址，数组，也就是说支持集群拉取
    - targets: ['127.0.0.1:8080']