Java程序监控---Metrics

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概念

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

目前最为流行的 metrics 库是来自 Coda Hale 的 dropwizard/metrics,该库被广泛地应用于各个知名的开源项目中。例如 Hadoop,Kafka,Spark,JStorm 中。

有一些优点:

  • 提供了对Ehcache、Apache HttpClient、JDBI、Jersey、Jetty、Log4J、Logback、JVM等的集成
  • 支持多种Metric指标:Gauges、Counters、Meters、Histograms和Timers
  • 支持多种Reporter发布指标
    • JMX、Console,CSV文件和SLF4J loggers
    • Ganglia、Graphite,用于图形化展示

MetricRegistry

MetricRegistry类是Metrics的核心,它是存放应用中所有metrics的容器。也是我们使用 Metrics 库的起点。其中maven依赖添加在文末。

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static final MetricRegistry metrics = new MetricRegistry();

Reporter

指标获取之后需要上传到各种地方,就需要用到Reporter。

控制台

监控指标直接打印在控制台

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pravite static void startReportConsole() {
ConsoleReporter reporter = ConsoleReporter.forRegistry(metrics)
.convertRatesTo(TimeUnit.SECONDS)
.convertDurationsTo(TimeUnit.MILLISECONDS)
.build();
reporter.start(1, TimeUnit.SECONDS);
}

JMX

将监控指标上报到JMX中,后续可以通过其他的开源工具上传到Graphite等供图形化展示。从Jconsole中MBean中能看到。

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pravite static void startReportJmx(){
JmxReporter reporterJmx = JmxReporter.forRegistry(metrics).build();
reporterJmx.start();
}

Graphite

将监控指标上传到Graphite,从Graphite-web中能看到上传的监控指标。

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pravite static void startReportGraphite(){
Graphite graphite = new Graphite(new InetSocketAddress("graphite.xxx.com", 2003));
GraphiteReporter reporter = GraphiteReporter.forRegistry(metrics)
.prefixedWith("test.metrics")
.convertRatesTo(TimeUnit.SECONDS)
.convertDurationsTo(TimeUnit.MILLISECONDS)
.filter(MetricFilter.ALL)
.build(graphite);
reporter.start(1, TimeUnit.MINUTES);
}

封装各种Reporter

调用方式MetricCommon.getMetricAndStartReport();

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public class MetricCommon {
private static final MetricRegistry metricRegistry = new MetricRegistry();
public static MetricRegistry getMetricAndStartReport(){
startReportConsole();
startReportJmx();
startReportGraphite();
return metricRegistry;
}
pravite static void startReportConsole() {...}
pravite static void startReportJmx(){...}
pravite static void startReportGraphite(){...}
}

Metics指标

Metrics 有如下监控指标:

  • Gauges:记录一个瞬时值。例如一个待处理队列的长度。
  • Histograms:统计单个数据的分布情况,最大值、最小值、平均值、中位数,百分比(75%、90%、95%、98%、99%和99.9%)
  • Meters:统计调用的频率(TPS),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
  • Timers:当我们既要统计TPS又要统计耗时分布情况,Timer基于Histograms和Meters来实现
  • Counter:计数器,自带inc()和dec()方法计数,初始为0。
  • 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

Gauges

最简单的度量指标,只有一个简单的返回值,例如,我们想衡量一个待处理队列中任务的个数

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public class GaugeTest {
private static final MetricRegistry registry = MetricCommon.getMetricAndStartReport();
private static final Random random = new Random();
@Test
public void testOneGuage() throws InterruptedException {
Queue queue= new LinkedList<String>();
registry.register(MetricRegistry.name(GaugeTest.class, "testGauges-queue-size", "size"),
(Gauge<Integer>) () -> queue.size());
while(true){
Thread.sleep(1000);
queue.add("Job-xxx");
}
}
@Test
public void testMultiGuage() throws InterruptedException {
Map<Integer, Integer> map = new ConcurrentHashMap<>();
while(true){
int i = random.nextInt(100);
int j = i % 10;
if(!map.containsKey(j)){
map.put(j,i);
registry.register(MetricRegistry.name(GaugeTest.class, "testGauges-number", String.valueOf(j)),
(Gauge<Integer>) () -> map.get(j));
}else{
map.put(j,i);
}
Thread.sleep(1000);
}
}
}

第一个测试用例,是用一个guage记录队列的长度

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-- Gauges ----------------------------------------------------------------------
GaugeTest.testGauges-queue-size.size
value = 4

第二个测试用例,每次产生一个100以内的随机数,将这些数以个位数的数字分组,guage记录每一组现在是什么数。

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-- Gauges ----------------------------------------------------------------------
GaugeTest.testGauges-number.0
value = 60
GaugeTest.testGauges-number.1
value = 1
GaugeTest.testGauges-number.2
value = 82
GaugeTest.testGauges-number.3
value = 23
GaugeTest.testGauges-number.4
value = 74
GaugeTest.testGauges-number.5
value = 25
GaugeTest.testGauges-number.7
value = 17
GaugeTest.testGauges-number.8
value = 78
GaugeTest.testGauges-number.9
value = 69

Histogram

Histogram统计数据的分布情况。比如最小值,最大值,中间值,还有中位数,75百分位, 90百分位, 95百分位, 98百分位, 99百分位, 和 99.9百分位的值(percentiles)。

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public class HistogramTest {
private static final MetricRegistry registry = MetricCommon.getMetricAndStartReport();
public static Random random = new Random();
@Test
public void test() throws InterruptedException {
Histogram histogram = new Histogram(new ExponentiallyDecayingReservoir());
registry.register(MetricRegistry.name(HistogramTest.class, "request", "histogram"), histogram);
while(true){
Thread.sleep(1000);
histogram.update(random.nextInt(100000));
}
}
}

运行很长时间之后,相当于随机值取极限,会趋向于统计值,75%肯定是要<=75000,99.9%肯定是要<=999000。

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-- Histograms ------------------------------------------------------------------
HistogramTest.request.histogram
count = 1336
min = 97
max = 99930
mean = 49816.49
stddev = 29435.27
median = 49368.00
75% <= 75803.00
95% <= 95340.00
98% <= 98096.00
99% <= 98724.00
99.9% <= 99930.00

Meters

Meter度量一系列事件发生的速率(rate),例如TPS。Meters会统计最近1分钟,5分钟,15分钟,还有全部时间的速率。

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public class MetersTest {
MetricRegistry registry = MetricCommon.getMetricAndStartAllReport("nc110x.corp.youdao.com","test.metrics");
public static Random random = new Random();
@Test
public void testOne() throws InterruptedException {
Meter meterTps = registry.meter(MetricRegistry.name(MetersTest.class,"request","tps"));
while(true){
meterTps.mark();
Thread.sleep(random.nextInt(1000));
}
}
@Test
public void testMulti() throws InterruptedException {
while(true){
int i = random.nextInt(100);
int j = i % 10;
Meter meterTps = registry.meter(MetricRegistry.name(MetersTest.class,"request","tps",String.valueOf(j)));
meterTps.mark();
Thread.sleep(10);
}
}
}

这里,多个注册多个meter与注册多个guage、Histograms用法会有不同,meter方法是getOrAdd

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public Meter meter(String name) {
return (Meter)this.getOrAdd(name, MetricRegistry.MetricBuilder.METERS);
}

一个meter的测试用例,运行结果如下。可以看到随着次数的增多,各种rate无限趋近于2次。

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-- Meters ------------------------------- 大专栏  Java程序监控---Metrics---------------------------------------
MetersTest.request.tps
count = 452
mean rate = 1.99 events/second
1-minute rate = 2.03 events/second
5-minute rate = 2.00 events/second
15-minute rate = 2.00 events/second

多个meter的测试用例,运行结果取了数字个位数为6/7/8的三个如下。最后都会无限趋近于10。sleep时间为10ms,每秒有100份,平均到尾数不同的,每组就有10份。

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MetersTest.request.tps.6
count = 905
mean rate = 9.74 events/second
1-minute rate = 9.76 events/second
5-minute rate = 9.94 events/second
15-minute rate = 9.98 events/second
MetersTest.request.tps.7
count = 935
mean rate = 10.07 events/second
1-minute rate = 10.62 events/second
5-minute rate = 11.82 events/second
15-minute rate = 12.19 events/second
MetersTest.request.tps.8
count = 937
mean rate = 10.09 events/second
1-minute rate = 10.09 events/second
5-minute rate = 10.31 events/second
15-minute rate = 10.37 events/second

Timer

Timer其实是 Histogram 和 Meter 的结合, histogram 某部分代码/调用的耗时, meter统计TPS。

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public class TimerTest {
public static Random random = new Random();
private static final MetricRegistry registry = MetricCommon.getMetricAndStartAllReport("nc110x.corp.youdao.com","test.metrics");
private static final Map<Integer,Timer> timerMap = new ConcurrentHashMap<>();
@Test
public void testOneTimer() throws InterruptedException {
Timer timer = registry.timer(MetricRegistry.name(TestTimer.class,"get-latency"));
Timer.Context ctx;
while(true){
ctx = timer.time();
Thread.sleep(random.nextInt(1000));
ctx.stop();
}
}
@Test
public void testMultiTimer() throws InterruptedException {
while(true){
int i = random.nextInt(100);
int j = i % 10;
Timer timer = registry.timer(MetricRegistry.name(TestTimer.class,"get-latency",String.valueOf(j)));
Timer.Context ctx;
ctx = timer.time();
Thread.sleep(random.nextInt(1000));
ctx.stop();
Thread.sleep(1000);
}
}
}

测试用例1是单个timer,结果如下。最后的时间都趋近于统计值。

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-- Timers ----------------------------------------------------------------------
com.testmetrics.TestTimer.get-latency
count = 657
mean rate = 2.05 calls/second
1-minute rate = 1.98 calls/second
5-minute rate = 2.02 calls/second
15-minute rate = 2.01 calls/second
min = 4.98 milliseconds
max = 998.93 milliseconds
mean = 496.79 milliseconds
stddev = 297.46 milliseconds
median = 501.02 milliseconds
75% <= 765.09 milliseconds
95% <= 952.03 milliseconds
98% <= 974.12 milliseconds
99% <= 989.02 milliseconds
99.9% <= 998.93 milliseconds

Counters

Counter 就是计数器,Counter 只是用 Gauge 封装了 AtomicLong 。我们可以使用如下的方法,使得获得队列大小更加高效。

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public class CounterTest {
private static final MetricRegistry registry = MetricCommon.getMetricAndStartReport();
public static Queue<String> q = new LinkedBlockingQueue<String>();
public static Counter pendingJobs;
public static Random random = new Random();
public static void addJob(String job) {
pendingJobs.inc();
q.offer(job);
}
public static String takeJob() {
pendingJobs.dec();
return q.poll();
}
@Test
public void test() throws InterruptedException {
pendingJobs = registry.counter(MetricRegistry.name(Queue.class,"pending-jobs","size"));
int num = 1;
while(true){
Thread.sleep(200);
if (random.nextDouble() > 0.7){
String job = takeJob();
System.out.println("take job : "+job);
}else{
String job = "Job-"+num;
addJob(job);
System.out.println("add job : "+job);
}
num++;
}
}
}

job会越来越多,因为每次取走只取一个job,但是加入job是加入num个,num会一直增加,而概率是7:3。

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-- Counters --------------------------------------------------------------------
java.util.Queue.pending-jobs.size
count = 36

HeathChecks

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。

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public class HeathChecksTest extends HealthCheck {
@Override
protected Result check() throws Exception {
Random random = new Random();
if(random.nextInt(10)!=9){
return Result.healthy();
}else{
return Result.unhealthy("oh,unhealthy");
}
}
@Test
public void test() throws InterruptedException {
HealthCheckRegistry registry = new HealthCheckRegistry();
registry.register("check1",new HeathChecksTest());
registry.register("check2", new HeathChecksTest());
while (true) {
for (Map.Entry<String, Result> entry : registry.runHealthChecks().entrySet()) {
if (entry.getValue().isHealthy()) {
System.out.println(entry.getKey() + ": OK, message:"+entry.getValue());
} else {
System.err.println(entry.getKey() + ": FAIL, error message: " + entry.getValue());
}
}
Thread.sleep(1000);
}
}
}

注册两个HeathChecks,重写其check()方法为取随机数,只要不是9就为healthy,输出结果如下:

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check1: OK, message:Result{isHealthy=true}
check2: FAIL, error message: Result{isHealthy=false, message=oh,unhealthy}
check1: OK, message:Result{isHealthy=true}
check2: OK, message:Result{isHealthy=true}
check1: OK, message:Result{isHealthy=true}
check2: OK, message:Result{isHealthy=true}
check1: OK, message:Result{isHealthy=true}
check2: OK, message:Result{isHealthy=true}
check1: OK, message:Result{isHealthy=true}

maven依赖

  • metrics-core:必须添加
  • metrics-healthchecks:用到healthchecks时添加
  • metrics-graphite:用到graphite时添加
  • org.slf4j:不添加看不到metrics-graphite包出错的log
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    <properties>
    <metrics.version>3.1.0</metrics.version>
    <sl4j.version>1.7.22</sl4j.version>
    </properties>
    <dependency>
    <groupId>io.dropwizard.metrics</groupId>
    <artifactId>metrics-core</artifactId>
    <version>${metrics.version}</version>
    </dependency>
    <dependency>
    <groupId>io.dropwizard.metrics</groupId>
    <artifactId>metrics-healthchecks</artifactId>
    <version>${metrics.version}</version>
    </dependency>
    <dependency>
    <groupId>io.dropwizard.metrics</groupId>
    <artifactId>metrics-graphite</artifactId>
    <version>${metrics.version}</version>
    </dependency>
    <dependency>
    <groupId>org.slf4j</groupId>
    <artifactId>slf4j-api</artifactId>
    <version>${sl4j.version}</version>
    </dependency>
    <dependency>
    <groupId>org.slf4j</groupId>
    <artifactId>slf4j-simple</artifactId>
    <version>${sl4j.version}</version>
    </dependency>

参考

http://metrics.dropwizard.io/3.1.0/getting-started/
http://www.cnblogs.com/nexiyi/p/metrics_sample_1.html
http://wuchong.me/blog/2015/08/01/getting-started-with-metrics/

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