使用Metrics指标度量工具监控Java应用程序性能(Gauges, Counters, Histograms, Meters和 Timers实例)

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 */

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 9