How to customize monitoring indicators in Spring Boot

1. Create a project

pom.xml and introduce related dependencies

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd">
	<modelVersion>4.0.0</modelVersion>
	<groupId>com.olive</groupId>
	<artifactId>prometheus-meter-demo</artifactId>
	<version>0.0.1-SNAPSHOT</version>
	<parent>
		<groupId>org.springframework.boot</groupId>
		<artifactId>spring-boot-starter-parent</artifactId>
		<version>2.3.7.RELEASE</version>
		<relativePath />
	</parent>
	<properties>
		<java.version>1.8</java.version>
		<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
		<project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
		<spring-boot.version>2.3.7.RELEASE</spring-boot.version>
	</properties>
	<dependencies>
		<dependency>
			<groupId>org.springframework.boot</groupId>
			<artifactId>spring-boot-starter-aop</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-actuator</artifactId>
		</dependency>
		<!-- Micrometer Prometheus registry  -->
		<dependency>
			<groupId>io.micrometer</groupId>
			<artifactId>micrometer-registry-prometheus</artifactId>
		</dependency>
	</dependencies>
	<dependencyManagement>
		<dependencies>
			<dependency>
				<groupId>org.springframework.boot</groupId>
				<artifactId>spring-boot-dependencies</artifactId>
				<version>${spring-boot.version}</version>
				<type>pom</type>
				<scope>import</scope>
			</dependency>
		</dependencies>
	</dependencyManagement>
</project>

2.Customize indicators

method One

Directly use the class of micrometercore package to define and register indicators

package com.olive.monitor;
 
import javax.annotation.PostConstruct;
 
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Component;
 
import io.micrometer.core.instrument.Counter;
import io.micrometer.core.instrument.DistributionSummary;
import io.micrometer.core.instrument.MeterRegistry;
 
@Component
public class NativeMetricsMontior {
 
	/**
	 * 支付次数
	 */
	private Counter payCount;
 
	/**
	 * 支付金额统计
	 */
	private DistributionSummary payAmountSum;
 
	@Autowired
	private MeterRegistry registry;
 
	@PostConstruct
	private void init() {
		payCount = registry.counter("pay_request_count", "payCount", "pay-count");
		payAmountSum = registry.summary("pay_amount_sum", "payAmountSum", "pay-amount-sum");
	}
 
	public Counter getPayCount() {
		return payCount;
	}
 
	public DistributionSummary getPayAmountSum() {
		return payAmountSum;
	}
 
}

Method two

By introducingmicrometer-registry-prometheus Package, this package combines prometheus to encapsulate micrometer

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

Also defines two metrics

package com.olive.monitor;
 
import javax.annotation.PostConstruct;
 
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Component;
 
import io.prometheus.client.CollectorRegistry;
import io.prometheus.client.Counter;
 
@Component
public class PrometheusMetricsMonitor {
 
	/**
	 * 订单发起次数
	 */
	private Counter orderCount;
 
	/**
	 * 金额统计
	 */
	private Counter orderAmountSum;
	
	@Autowired
	private CollectorRegistry registry;
	@PostConstruct
	private void init() {
		orderCount = Counter.build().name("order_request_count")
				.help("order request count.")
				.labelNames("orderCount")
				.register();
		orderAmountSum = Counter.build().name("order_amount_sum")
				.help("order amount sum.")
				.labelNames("orderAmountSum")
				.register();
		registry.register(orderCount);
		registry.register(orderAmountSum);
	}
 
	public Counter getOrderCount() {
		return orderCount;
	}
 
	public Counter getOrderAmountSum() {
		return orderAmountSum;
	}
 
}

prometheus 4 commonly used Metrics

Counter

Counters that continuously increase but do not decrease can be used to record types that only increase but do not decrease, such as: the number of website visitors, system running time, etc.

For Counter type indicators, there is only one inc() method, which is used for counter 1.

Generally speaking, we use _total for Counter type metric indicators. End, such as http_requests_total.

Gauge

A dashboard that can be increased or decreased, and a curve graph

For this type of indicator that can be increased or decreased, it is used to reflect the current status of the application state.

For example, when monitoring the host, the host's current free memory size, available memory size, etc.

The Gauge indicator object contains two main methods inc() and dec(), which are used to increase and decrease the count.

Histogram

is mainly used to count the distribution of data. This is a special metrics data type, which represents an approximate percentage estimate value. It counts all discrete indicator data in The number of times in each value range. For example: We want to count the data distribution of http request responses less than 0.005 seconds, less than 0.01 seconds, and less than 0.025 seconds within a period of time. Then use Histogram to collect the time of each http request and set the bucket at the same time.

Summary

Summary and Histogram are very similar. They can both count the number or size of events and their distribution. They both provide a count of time and a summary of values. , also provide functions that can calculate statistical sample distribution. The difference is that Histogram can calculate quantiles on the server through the histogram_quantile function. Sumamry's quantile is defined directly on the client. Therefore, for the calculation of quantiles, Summary has better performance when querying through PromQL, while Histogram consumes more resources, but Histogram consumes fewer resources compared to the client. You can use any one, and you can adjust it freely according to the actual scene.

3. Test

Define two controllers and use NativeMetricsMontior and PrometheusMetricsMonitor

package com.olive.controller;
 
import java.util.Random;
 
import javax.annotation.Resource;
 
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
 
import com.olive.monitor.NativeMetricsMontior;
 
@RestController
public class PayController {
 
	@Resource
	private NativeMetricsMontior monitor;
 
	@RequestMapping("/pay")
	public String pay(@RequestParam("amount") Double amount) throws Exception {
		// 统计支付次数
		monitor.getPayCount().increment();
 
		Random random = new Random();
		//int amount = random.nextInt(100);
		if(amount==null) {
			amount = 0.0;
		}
		// 统计支付总金额
		monitor.getPayAmountSum().record(amount);
		return "支付成功, 支付金额: " + amount;
	}
 
}
package com.olive.controller;
 
import java.util.Random;
 
import javax.annotation.Resource;
 
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
 
import com.olive.monitor.PrometheusMetricsMonitor;
 
@RestController
public class OrderController {
 
	@Resource
	private PrometheusMetricsMonitor monitor;
 
	@RequestMapping("/order")
	public String order(@RequestParam("amount") Double amount) throws Exception {
		// 订单总数
		monitor.getOrderCount()
			.labels("orderCount")
			.inc();
 
		Random random = new Random();
		//int amount = random.nextInt(100);
		if(amount==null) {
			amount = 0.0;
		}
		// 统计订单总金额
		monitor.getOrderAmountSum()
			.labels("orderAmountSum")
			.inc(amount);
		return "下单成功, 订单金额: " + amount;
	}
 
}

to start the service

Accesshttp://127.0.0.1:9595/actuator/prometheus; Normally see the monitoring data

Change the amount multiple times After http://127.0.0.1:8080/order?amount=100 and http://127.0.0.1:8080/pay?amount=10; then visit http:/ /127.0.0.1:9595/actuator/prometheus. Check the monitoring data

4. Application in the project

It is not practical to monitor the data buried points in the way mentioned above; in the spring project Basically, buried point monitoring is carried out through AOP. For example, write an aspectAspect; this method is very friendly. Data buried point monitoring can be done at the entrance without the need to write code in the controller.

package com.olive.aspect;
 
import java.time.LocalDate;
import java.util.concurrent.TimeUnit;
 
import javax.servlet.http.HttpServletRequest;
 
import org.aspectj.lang.ProceedingJoinPoint;
import org.aspectj.lang.annotation.Around;
import org.aspectj.lang.annotation.Aspect;
import org.aspectj.lang.annotation.Pointcut;
import org.springframework.stereotype.Component;
import org.springframework.util.StringUtils;
import org.springframework.web.context.request.RequestContextHolder;
import org.springframework.web.context.request.ServletRequestAttributes;
 
import io.micrometer.core.instrument.Metrics;
 
@Aspect
@Component
public class PrometheusMetricsAspect {
 
    // 切入所有controller包下的请求方法
    @Pointcut("execution(* com.olive.controller..*.*(..))")
    public void controllerPointcut() {
    }
 
    @Around("controllerPointcut()")
    public Object MetricsCollector(ProceedingJoinPoint joinPoint) throws Throwable {
 
        HttpServletRequest request = ((ServletRequestAttributes) RequestContextHolder.getRequestAttributes()).getRequest();
        String userId = StringUtils.hasText(request.getParameter("userId")) ? 
        		request.getParameter("userId") : "no userId";
        
        // 获取api url
        String api = request.getServletPath();
        // 获取请求方法
        String method = request.getMethod();
        long startTs = System.currentTimeMillis();
        LocalDate now = LocalDate.now();
        String[] tags = new String[10];
        tags[0] = "api";
        tags[1] = api;
        tags[2] = "method";
        tags[3] = method;
        tags[4] = "day";
        tags[5] = now.toString();
        tags[6] = "userId";
        tags[7] = userId;
        
        String amount = StringUtils.hasText(request.getParameter("amount")) ? 
        		request.getParameter("amount") : "0.0";
        
        tags[8] = "amount";
        tags[9] = amount;
        // 请求次数加1
        //自定义的指标名称：custom_http_request_all，指标包含数据
        Metrics.counter("custom_http_request_all", tags).increment();
        Object object = null;
        try {
            object = joinPoint.proceed();
        } catch (Exception e) {
            //请求失败次数加1
            Metrics.counter("custom_http_request_error", tags).increment();
            throw e;
        } finally {
            long endTs = System.currentTimeMillis() - startTs;
            //记录请求响应时间
           Metrics.timer("custom_http_request_time", tags).record(endTs, TimeUnit.MILLISECONDS);
        }
        return object;
    }
}

After writing the aspect, restart the service; access the controller interface, and you can also bury custom monitoring indicators

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