Metrics-Java版的指标度量

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浪尖聊大数据 2021-07-23 17:53:25

文章标签 Java 文章分类 Java 后端开发

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.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

Metrics-Java版的指标度量_Java