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：

com.codahale.metricsgroupId> metrics-coreartifactId> 3.0.1version> 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

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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 queue = new LinkedBlockingDeque();

/**

* 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 gauge = new Gauge() {

@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。

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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 queue = new LinkedList();

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。

点击(此处)折叠或打开

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：

点击(此处)折叠或打开

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：

点击(此处)折叠或打开

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

*/

6  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。

com.codahale.metricsgroupId> metrics-healthchecksartifactId> 3.0.1version> 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：

点击(此处)折叠或打开

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 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。

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