Metrics-Java版的指标度量工具之一

Metrics-Java版的指标度量工具之一

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>

core包主要提供如下核心功能：

• Metrics Registries类似一个metrics容器，维护一个Map，可以是一个服务一个实例。
• 支持五种metric类型：Gauges、Counters、Meters、Histograms和Timers。
• 可以将metrics值通过JMX、Console，CSV文件和SLF4J loggers发布出来。

五种Metrics类型：

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
*/

接上《Metrics-Java版的指标度量工具之一》

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。

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

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.

 */

其他支持

metrics提供了对Ehcache、Apache HttpClient、JDBI、Jersey、Jetty、Log4J、Logback、JVM等的集成，可以方便地将Metrics输出到Ganglia、Graphite中，供用户图形化展示。

参考资料

http://metrics.codahale.com/

https://github.com/dropwizard/metrics

http://blog.csdn.net/scutshuxue/article/details/8350135

http://blog.synyx.de/2013/09/yammer-metrics-made-easy-part-i/

http://blog.synyx.de/2013/09/yammer-metrics-made-easy-part-ii/

http://wiki.apache.org/hadoop/HADOOP-6728-MetricsV2

分类:  滴水穿石--Java