Java程序监控---Metrics

时间：2020-02-04 13:42:14 阅读：96 评论：0 收藏：0 [点我收藏+]

标签：false timers 最大值 jdb hit job kafka table cer

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。

1	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

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。

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。

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();

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

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。

-- 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)。

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。

-- 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。

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。

-- 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。

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。

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。

-- 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。

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。

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：

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

<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/

Java程序监控---Metrics

标签：false timers 最大值 jdb hit job kafka table cer

原文地址：https://www.cnblogs.com/lijianming180/p/12259003.html

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