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Metrics-Java版的指标度量

热度：61   发布时间：2023-12-13 17:05:17.0

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
 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
 
  
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