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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 = 60GaugeTest.testGauges-number.1             value = 1GaugeTest.testGauges-number.2             value = 82GaugeTest.testGauges-number.3             value = 23GaugeTest.testGauges-number.4             value = 74GaugeTest.testGauges-number.5             value = 25GaugeTest.testGauges-number.7             value = 17GaugeTest.testGauges-number.8             value = 78GaugeTest.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/secondMetersTest.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/secondMetersTest.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/

标签:metrics,Java,MetricRegistry,Metrics,rate,static,监控,new,public
来源: https://www.cnblogs.com/lijianming180/p/12259003.html