Metrics监控

「爱情、让人受尽委屈。」 2022-02-23 14:40 276阅读 0赞

Metrics是一个给JAVA服务的各项指标提供度量工具的包，在JAVA代码中嵌入Metrics代码，可以方便的对业务代码的各个指标进行监控，同时，Metrics能够很好的跟Ganlia、Graphite结合，方便的提供图形化接口。基本使用方式直接将core包（目前稳定版本3.0.1）导入pom文件即可，配置如下：

<dependency>
      <groupId>com.codahale.metrics</groupId>
      <artifactId>metrics-core</artifactId>
      <version>3.0.1</version>
    </dependency>

提供的监控工具：

Gauges：用于某一个值的测量。

Counters：计数器是一个AtomicLong的实例，可以增加或减少其值。

Histograms: 统计数值的分布，例如最大值、最小值和平均值等。

Meters：测量一段时间内的事件发生率，例如TPS。

Timers：测量一段代码被调用的速率和持续时间的分布。

Health Checks：检测服务健康的状况。

提供的多种渠道输出：

ConsoleReporter：控制台

CsvReporter：CSV文件

Slf4jReporter：Logback、Log4j等日志输出

JmxReporter：基于JMX的输出

GangliaReporter：监控工具Ganglia

GraphiteReporter：监控工具Graphite

# 使用方式总结 #

## 1. 启动项目类 ##

package demo.metrics;
    
    import com.codahale.metrics.ConsoleReporter;
    import org.springframework.boot.SpringApplication;
    import org.springframework.boot.autoconfigure.SpringBootApplication;
    import org.springframework.context.ApplicationContext;
    
    import java.util.concurrent.TimeUnit;
    
    @SpringBootApplication
    public class DemoApplication {
    
        public static void main(String[] args) {
            ApplicationContext ctx = SpringApplication.run(DemoApplication.class, args);
    
            // 启动Reporter
            ConsoleReporter reporter = ctx.getBean(ConsoleReporter.class);
            reporter.start(1, TimeUnit.SECONDS);
            
        }
    }

## 2. 接受请求的类 ##

package demo.metrics.action;
    
    import com.codahale.metrics.Counter;
    import com.codahale.metrics.Histogram;
    import com.codahale.metrics.Meter;
    import com.codahale.metrics.Timer;
    import demo.metrics.config.ListManager;
    import org.springframework.beans.factory.annotation.Autowired;
    import org.springframework.stereotype.Controller;
    import org.springframework.web.bind.annotation.RequestMapping;
    import org.springframework.web.bind.annotation.ResponseBody;
    
    import java.util.Random;
    
    @Controller
    @RequestMapping("/")
    public class MainController {
    
        @Autowired
        private Meter requestMeter;
    
        @Autowired
        private Histogram responseSizes;
    
        @Autowired
        private Counter pendingJobs;
    
        @Autowired
        private Timer responses;
    
        @Autowired
        private ListManager listManager;
    
        @RequestMapping("/hello")
        @ResponseBody
        public String helloWorld() {
    
            requestMeter.mark();
    
            pendingJobs.inc();
    
            responseSizes.update(new Random().nextInt(10));
    
            listManager.getList().add(1);
    
            final Timer.Context context = responses.time();
            try {
                return "Hello World";
            } finally {
                context.stop();
            }
        }
    }

##  3. 配置类 ##

package demo.metrics.config;
    
    import com.codahale.metrics.*;
    import org.slf4j.LoggerFactory;
    import org.springframework.context.annotation.Bean;
    import org.springframework.context.annotation.Configuration;
    
    import java.util.concurrent.TimeUnit;
    
    @Configuration
    public class MetricConfig {
    
        @Bean
        public MetricRegistry metrics() {
            return new MetricRegistry();
        }
    
        /**
         * Reporter 数据的展现位置
         *
         * @param metrics
         * @return
         */
        @Bean
        public ConsoleReporter consoleReporter(MetricRegistry metrics) {
            return ConsoleReporter.forRegistry(metrics)
                    .convertRatesTo(TimeUnit.SECONDS)
                    .convertDurationsTo(TimeUnit.MILLISECONDS)
                    .build();
        }
    
        @Bean
        public Slf4jReporter slf4jReporter(MetricRegistry metrics) {
            return Slf4jReporter.forRegistry(metrics)
                    .outputTo(LoggerFactory.getLogger("demo.metrics"))
                    .convertRatesTo(TimeUnit.SECONDS)
                    .convertDurationsTo(TimeUnit.MILLISECONDS)
                    .build();
        }
    
        @Bean
        public JmxReporter jmxReporter(MetricRegistry metrics) {
            return JmxReporter.forRegistry(metrics).build();
        }
    
        /**
         * 自定义单位
         *
         * @param metrics
         * @return
         */
        @Bean
        public ListManager listManager(MetricRegistry metrics) {
            return new ListManager(metrics);
        }
    
        /**
         * TPS 计算器
         *
         * @param metrics
         * @return
         */
        @Bean
        public Meter requestMeter(MetricRegistry metrics) {
            return metrics.meter("request");
        }
    
        /**
         * 直方图
         *
         * @param metrics
         * @return
         */
        @Bean
        public Histogram responseSizes(MetricRegistry metrics) {
            return metrics.histogram("response-sizes");
        }
    
        /**
         * 计数器
         *
         * @param metrics
         * @return
         */
        @Bean
        public Counter pendingJobs(MetricRegistry metrics) {
            return metrics.counter("requestCount");
        }
    
        /**
         * 计时器
         *
         * @param metrics
         * @return
         */
        @Bean
        public Timer responses(MetricRegistry metrics) {
            return metrics.timer("executeTime");
        }
    
    }

# **五种Metrics类型示例**： #

## 1. Gauges ##

Gauges是一个最简单的计量，一般用来统计瞬时状态的数据信息，比如系统中处于pending状态的job。测试代码

package com.netease.test.metrics;
    
    import com.codahale.metrics.ConsoleReporter;
    import com.codahale.metrics.Gauge;
    import com.codahale.metrics.JmxReporter;
    import com.codahale.metrics.MetricRegistry;
    
    import java.util.Queue;
    import java.util.concurrent.LinkedBlockingDeque;
    import java.util.concurrent.TimeUnit;
    
    /**
     * User: hzwangxx
     * Date: 14-2-17
     * Time: 14:47
     * 测试Gauges，实时统计pending状态的job个数
     */
    public class TestGauges {
        /**
         * 实例化一个registry，最核心的一个模块，相当于一个应用程序的metrics系统的容器，维护一个Map
         */
        private static final MetricRegistry metrics = new MetricRegistry();
    
        private static Queue<String> queue = new LinkedBlockingDeque<String>();
    
        /**
         * 在控制台上打印输出
         */
        private static ConsoleReporter reporter = ConsoleReporter.forRegistry(metrics).build();
    
        public static void main(String[] args) throws InterruptedException {
            reporter.start(3, TimeUnit.SECONDS);
    
            //实例化一个Gauge
            Gauge<Integer> gauge = new Gauge<Integer>() {
                @Override
                public Integer getValue() {
                    return queue.size();
                }
            };
    
            //注册到容器中
            metrics.register(MetricRegistry.name(TestGauges.class, "pending-job", "size"), gauge);
    
            //测试JMX
            JmxReporter jmxReporter = JmxReporter.forRegistry(metrics).build();
            jmxReporter.start();
    
            //模拟数据
            for (int i=0; i<20; i++){
                queue.add("a");
                Thread.sleep(1000);
            }
    
        }
    }
    
    /*
    console output:
    14-2-17 15:29:35 ===============================================================
    
    -- Gauges ----------------------------------------------------------------------
    com.netease.test.metrics.TestGauges.pending-job.size
                 value = 4
    
    
    14-2-17 15:29:38 ===============================================================
    
    -- Gauges ----------------------------------------------------------------------
    com.netease.test.metrics.TestGauges.pending-job.size
                 value = 6
    
    
    14-2-17 15:29:41 ===============================================================
    
    -- Gauges ----------------------------------------------------------------------
    com.netease.test.metrics.TestGauges.pending-job.size
                 value = 9
     */

通过以上步骤将会向MetricsRegistry容器中注册一个名字为*com.netease.test.metrics .TestGauges.pending-job.size*的metrics，实时获取队列长度的指标。另外，Core包种还扩展了几种特定的Gauge：

*  JMX Gauges—提供给第三方库只通过JMX将指标暴露出来。
 *  Ratio Gauges—简单地通过创建一个gauge计算两个数的比值。
 *  Cached Gauges—对某些计量指标提供缓存

Derivative Gauges—提供Gauge的值是基于其他Gauge值的接口。

## 2.    Counter ##

Counter是Gauge的一个特例，维护一个计数器，可以通过inc()和dec()方法对计数器做修改。使用步骤与Gauge基本类似，在MetricRegistry中提供了静态方法可以直接实例化一个Counter。

package com.netease.test.metrics;
    
    import com.codahale.metrics.ConsoleReporter;
    import com.codahale.metrics.Counter;
    import com.codahale.metrics.MetricRegistry;
    
    import java.util.LinkedList;
    import java.util.Queue;
    import java.util.concurrent.TimeUnit;
    import static com.codahale.metrics.MetricRegistry.*;
    /**
     * User: hzwangxx
     * Date: 14-2-14
     * Time: 14:02
     * 测试Counter
     */
    public class TestCounter {
    
        /**
         * 实例化一个registry，最核心的一个模块，相当于一个应用程序的metrics系统的容器，维护一个Map
         */
        private static final MetricRegistry metrics = new MetricRegistry();
    
        /**
         * 在控制台上打印输出
         */
        private static ConsoleReporter reporter = ConsoleReporter.forRegistry(metrics).build();
    
        /**
         * 实例化一个counter,同样可以通过如下方式进行实例化再注册进去
         * pendingJobs = new Counter();
         * metrics.register(MetricRegistry.name(TestCounter.class, "pending-jobs"), pendingJobs);
         */
        private static Counter pendingJobs = metrics.counter(name(TestCounter.class, "pedding-jobs"));
    //    private static Counter pendingJobs = metrics.counter(MetricRegistry.name(TestCounter.class, "pedding-jobs"));
    
    
    
        private static Queue<String> queue = new LinkedList<String>();
    
        public static void add(String str) {
            pendingJobs.inc();
            queue.offer(str);
        }
    
        public String take() {
            pendingJobs.dec();
            return queue.poll();
        }
    
        public static void main(String[]args) throws InterruptedException {
            reporter.start(3, TimeUnit.SECONDS);
            while(true){
                add("1");
                Thread.sleep(1000);
            }
    
        }
    }
    
    /*
    console output：
    14-2-17 17:52:34 ===============================================================
    
    -- Counters --------------------------------------------------------------------
    com.netease.test.metrics.TestCounter.pedding-jobs
                 count = 4
    
    
    14-2-17 17:52:37 ===============================================================
    
    -- Counters --------------------------------------------------------------------
    com.netease.test.metrics.TestCounter.pedding-jobs
                 count = 6
    
    
    14-2-17 17:52:40 ===============================================================
    
    -- Counters --------------------------------------------------------------------
    com.netease.test.metrics.TestCounter.pedding-jobs
                 count = 9
    
     */

## 3.       Meters ##

Meters用来度量某个时间段的平均处理次数（request per second），每1、5、15分钟的TPS。比如一个service的请求数，通过metrics.meter()实例化一个Meter之后，然后通过meter.mark()方法就能将本次请求记录下来。统计结果有总的请求数，平均每秒的请求数，以及最近的1、5、15分钟的平均TPS。

package com.netease.test.metrics;
    
    import com.codahale.metrics.ConsoleReporter;
    import com.codahale.metrics.Meter;
    import com.codahale.metrics.MetricRegistry;
    
    import java.util.concurrent.TimeUnit;
    
    import static com.codahale.metrics.MetricRegistry.*;
    
    /**
     * User: hzwangxx
     * Date: 14-2-17
     * Time: 18:34
     * 测试Meters
     */
    public class TestMeters {
        /**
         * 实例化一个registry，最核心的一个模块，相当于一个应用程序的metrics系统的容器，维护一个Map
         */
        private static final MetricRegistry metrics = new MetricRegistry();
    
        /**
         * 在控制台上打印输出
         */
        private static ConsoleReporter reporter = ConsoleReporter.forRegistry(metrics).build();
    
        /**
         * 实例化一个Meter
         */
        private static final Meter requests = metrics.meter(name(TestMeters.class, "request"));
    
        public static void handleRequest() {
            requests.mark();
        }
    
        public static void main(String[] args) throws InterruptedException {
            reporter.start(3, TimeUnit.SECONDS);
            while(true){
                handleRequest();
                Thread.sleep(100);
            }
        }
    
    }
    
    /*
    14-2-17 18:43:08 ===============================================================
    
    -- Meters ----------------------------------------------------------------------
    com.netease.test.metrics.TestMeters.request
                 count = 30
             mean rate = 9.95 events/second
         1-minute rate = 0.00 events/second
         5-minute rate = 0.00 events/second
        15-minute rate = 0.00 events/second
    
    
    14-2-17 18:43:11 ===============================================================
    
    -- Meters ----------------------------------------------------------------------
    com.netease.test.metrics.TestMeters.request
                 count = 60
             mean rate = 9.99 events/second
         1-minute rate = 10.00 events/second
         5-minute rate = 10.00 events/second
        15-minute rate = 10.00 events/second
    
    
    14-2-17 18:43:14 ===============================================================
    
    -- Meters ----------------------------------------------------------------------
    com.netease.test.metrics.TestMeters.request
                 count = 90
             mean rate = 9.99 events/second
         1-minute rate = 10.00 events/second
         5-minute rate = 10.00 events/second
        15-minute rate = 10.00 events/second
    */

## 4.       Histograms ##

Histograms主要使用来统计数据的分布情况，最大值、最小值、平均值、中位数，百分比（75%、90%、95%、98%、99%和99.9%）。例如，需要统计某个页面的请求响应时间分布情况，可以使用该种类型的Metrics进行统计。具体的样例代码如下：

package com.netease.test.metrics;
    
    import com.codahale.metrics.ConsoleReporter;
    import com.codahale.metrics.Histogram;
    import com.codahale.metrics.MetricRegistry;
    
    import java.util.Random;
    import java.util.concurrent.TimeUnit;
    
    import static com.codahale.metrics.MetricRegistry.name;
    
    /**
     * User: hzwangxx
     * Date: 14-2-17
     * Time: 18:34
     * 测试Histograms
     */
    public class TestHistograms {
        /**
         * 实例化一个registry，最核心的一个模块，相当于一个应用程序的metrics系统的容器，维护一个Map
         */
        private static final MetricRegistry metrics = new MetricRegistry();
    
        /**
         * 在控制台上打印输出
         */
        private static ConsoleReporter reporter = ConsoleReporter.forRegistry(metrics).build();
    
        /**
         * 实例化一个Histograms
         */
        private static final Histogram randomNums = metrics.histogram(name(TestHistograms.class, "random"));
    
        public static void handleRequest(double random) {
            randomNums.update((int) (random*100));
        }
    
        public static void main(String[] args) throws InterruptedException {
            reporter.start(3, TimeUnit.SECONDS);
            Random rand = new Random();
            while(true){
                handleRequest(rand.nextDouble());
                Thread.sleep(100);
            }
        }
    
    }
    
    /*
    14-2-17 19:39:11 ===============================================================
    
    -- Histograms ------------------------------------------------------------------
    com.netease.test.metrics.TestHistograms.random
                 count = 30
                   min = 1
                   max = 97
                  mean = 45.93
                stddev = 29.12
                median = 39.50
                  75% <= 71.00
                  95% <= 95.90
                  98% <= 97.00
                  99% <= 97.00
                99.9% <= 97.00
    
    
    14-2-17 19:39:14 ===============================================================
    
    -- Histograms ------------------------------------------------------------------
    com.netease.test.metrics.TestHistograms.random
                 count = 60
                   min = 0
                   max = 97
                  mean = 41.17
                stddev = 28.60
                median = 34.50
                  75% <= 69.75
                  95% <= 92.90
                  98% <= 96.56
                  99% <= 97.00
                99.9% <= 97.00
    
    
    14-2-17 19:39:17 ===============================================================
    
    -- Histograms ------------------------------------------------------------------
    com.netease.test.metrics.TestHistograms.random
                 count = 90
                   min = 0
                   max = 97
                  mean = 44.67
                stddev = 28.47
                median = 43.00
                  75% <= 71.00
                  95% <= 91.90
                  98% <= 96.18
                  99% <= 97.00
                99.9% <= 97.00
    */

## 5.       Timers ##

Timers主要是用来统计某一块代码段的执行时间以及其分布情况，具体是基于Histograms和Meters来实现的。样例代码如下：

package com.netease.test.metrics;
    
    import com.codahale.metrics.ConsoleReporter;
    import com.codahale.metrics.MetricRegistry;
    import com.codahale.metrics.Timer;
    
    import java.util.Random;
    import java.util.concurrent.TimeUnit;
    
    import static com.codahale.metrics.MetricRegistry.name;
    
    /**
     * User: hzwangxx
     * Date: 14-2-17
     * Time: 18:34
     * 测试Timers
     */
    public class TestTimers {
        /**
         * 实例化一个registry，最核心的一个模块，相当于一个应用程序的metrics系统的容器，维护一个Map
         */
        private static final MetricRegistry metrics = new MetricRegistry();
    
        /**
         * 在控制台上打印输出
         */
        private static ConsoleReporter reporter = ConsoleReporter.forRegistry(metrics).build();
    
        /**
         * 实例化一个Meter
         */
    //    private static final Timer requests = metrics.timer(name(TestTimers.class, "request"));
        private static final Timer requests = metrics.timer(name(TestTimers.class, "request"));
    
        public static void handleRequest(int sleep) {
            Timer.Context context = requests.time();
            try {
                //some operator
                Thread.sleep(sleep);
            } catch (InterruptedException e) {
                e.printStackTrace();
            } finally {
                context.stop();
            }
    
        }
    
        public static void main(String[] args) throws InterruptedException {
            reporter.start(3, TimeUnit.SECONDS);
            Random random = new Random();
            while(true){
                handleRequest(random.nextInt(1000));
            }
        }
    
    }
    
    /*
    14-2-18 9:31:54 ================================================================
    
    -- Timers ----------------------------------------------------------------------
    com.netease.test.metrics.TestTimers.request
                 count = 4
             mean rate = 1.33 calls/second
         1-minute rate = 0.00 calls/second
         5-minute rate = 0.00 calls/second
        15-minute rate = 0.00 calls/second
                   min = 483.07 milliseconds
                   max = 901.92 milliseconds
                  mean = 612.64 milliseconds
                stddev = 196.32 milliseconds
                median = 532.79 milliseconds
                  75% <= 818.31 milliseconds
                  95% <= 901.92 milliseconds
                  98% <= 901.92 milliseconds
                  99% <= 901.92 milliseconds
                99.9% <= 901.92 milliseconds
    
    
    14-2-18 9:31:57 ================================================================
    
    -- Timers ----------------------------------------------------------------------
    com.netease.test.metrics.TestTimers.request
                 count = 8
             mean rate = 1.33 calls/second
         1-minute rate = 1.40 calls/second
         5-minute rate = 1.40 calls/second
        15-minute rate = 1.40 calls/second
                   min = 41.07 milliseconds
                   max = 968.19 milliseconds
                  mean = 639.50 milliseconds
                stddev = 306.12 milliseconds
                median = 692.77 milliseconds
                  75% <= 885.96 milliseconds
                  95% <= 968.19 milliseconds
                  98% <= 968.19 milliseconds
                  99% <= 968.19 milliseconds
                99.9% <= 968.19 milliseconds
    
    
    14-2-18 9:32:00 ================================================================
    
    -- Timers ----------------------------------------------------------------------
    com.netease.test.metrics.TestTimers.request
                 count = 15
             mean rate = 1.67 calls/second
         1-minute rate = 1.40 calls/second
         5-minute rate = 1.40 calls/second
        15-minute rate = 1.40 calls/second
                   min = 41.07 milliseconds
                   max = 968.19 milliseconds
                  mean = 591.35 milliseconds
                stddev = 302.96 milliseconds
                median = 650.56 milliseconds
                  75% <= 838.07 milliseconds
                  95% <= 968.19 milliseconds
                  98% <= 968.19 milliseconds
                  99% <= 968.19 milliseconds
                99.9% <= 968.19 milliseconds
    
    */

## Health Checks ##

Metrics提供了一个独立的模块：Health Checks，用于对Application、其子模块或者关联模块的运行是否正常做检测。该模块是独立metrics-core模块的，使用时则导入metrics-healthchecks包。使用起来和与上述几种类型的Metrics有点类似，但是需要重新实例化一个Metrics容器HealthCheckRegistry，待检测模块继承抽象类HealthCheck并实现check()方法即可，然后将该模块注册到HealthCheckRegistry中，判断的时候通过isHealthy()接口即可。如下示例代码：

package com.netease.test.metrics;
    
    import com.codahale.metrics.health.HealthCheck;
    import com.codahale.metrics.health.HealthCheckRegistry;
    
    import java.util.Map;
    import java.util.Random;
    
    /**
     * User: hzwangxx
     * Date: 14-2-18
     * Time: 9:57
     */
    public class DatabaseHealthCheck extends HealthCheck{
        private final Database database;
    
        public DatabaseHealthCheck(Database database) {
            this.database = database;
        }
    
        @Override
        protected Result check() throws Exception {
            if (database.ping()) {
                return Result.healthy();
            }
            return Result.unhealthy("Can't ping database.");
        }
    
        /**
         * 模拟Database对象
         */
        static class Database {
            /**
             * 模拟database的ping方法
             * @return 随机返回boolean值
             */
            public boolean ping() {
                Random random = new Random();
                return random.nextBoolean();
            }
        }
    
        public static void main(String[] args) {
    //        MetricRegistry metrics = new MetricRegistry();
    //        ConsoleReporter reporter = ConsoleReporter.forRegistry(metrics).build();
            HealthCheckRegistry registry = new HealthCheckRegistry();
            registry.register("database1", new DatabaseHealthCheck(new Database()));
            registry.register("database2", new DatabaseHealthCheck(new Database()));
            while (true) {
                for (Map.Entry<String, Result> entry : registry.runHealthChecks().entrySet()) {
                    if (entry.getValue().isHealthy()) {
                        System.out.println(entry.getKey() + ": OK");
                    } else {
                        System.err.println(entry.getKey() + ": FAIL, error message: " + entry.getValue().getMessage());
                        final Throwable e = entry.getValue().getError();
                        if (e != null) {
                            e.printStackTrace();
                        }
                    }
                }
                try {
                    Thread.sleep(1000);
                } catch (InterruptedException e) {
    
                }
            }
        }
    }
    
    /*
    console output:
    database1: OK
    database2: FAIL, error message: Can't ping database.
    database1: FAIL, error message: Can't ping database.
    database2: OK
    database1: OK
    database2: FAIL, error message: Can't ping database.
    database1: FAIL, error message: Can't ping database.
    database2: OK
    database1: FAIL, error message: Can't ping database.
    database2: FAIL, error message: Can't ping database.
    database1: FAIL, error message: Can't ping database.
    database2: FAIL, error message: Can't ping database.
    database1: OK
    database2: OK
    database1: OK
    database2: FAIL, error message: Can't ping database.
    database1: FAIL, error message: Can't ping database.
    database2: OK
    database1: OK
    database2: OK
    database1: FAIL, error message: Can't ping database.
    database2: OK
    database1: OK
    database2: OK
    database1: OK
    database2: OK
    database1: OK
    database2: FAIL, error message: Can't ping database.
    database1: FAIL, error message: Can't ping database.
    database2: FAIL, error message: Can't ping database.
    
     */

本文内容转载自： [http://www.cnblogs.com/nexiyi/p/metrics\_sample\_2.html][http_www.cnblogs.com_nexiyi_p_metrics_sample_2.html],

[https://www.jianshu.com/p/e4f70ddbc287][https_www.jianshu.com_p_e4f70ddbc287]

[http_www.cnblogs.com_nexiyi_p_metrics_sample_2.html]: http://www.cnblogs.com/nexiyi/p/metrics_sample_2.html
[https_www.jianshu.com_p_e4f70ddbc287]: https://www.jianshu.com/p/e4f70ddbc287