Java程序监控---Metrics
作者：互联网
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
1234567 | 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。
1234 | 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。
12345678910 | 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();
12345678910111213 | 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
123456789101112131415161718192021222324252627282930313233 | 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
123 | -- 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。
12345678910111213141516171819 | -- 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)。
123456789101112131415 | 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。
12345678910111213 | -- 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。
123456789101112131415161718192021222324 | 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
123 | 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。
1234567 | -- 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。
123456789101112131415161718 | 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。
1234567891011121314151617181920212223242526272829303132333435 | 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。
1234567891011121314151617 | -- 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。
1234567891011121314151617181920212223242526272829303132333435 | 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。
123 | -- 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。
12345678910111213141516171819202122232425262728 | 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：
123456789 | 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
123456789101112131415161718192021222324252627282930
<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