监控工具——Metrics的Histogram可配置项

Metrics的Histogram配置参数

这篇是之前监控工具——Metrics 快速入门的补充

代码地址

因为每个例子涉及代码较多，且包含测试用例，如果都贴到文章中内容过多，所以只贴出了部分代码。全部的代码在这里： https://gitee.com/daifyutils/springboot-samples。

此篇文章所属模块为：base-metrics

Histogram

在使用Histogram进行指标统计的时候，可以看到其支持一个Reservoir类型的参数

    public Histogram(Reservoir reservoir) {
        this.reservoir = reservoir;
        this.count = LongAdderProxy.create();
    }

通过查看可以看到此参数支持的实现类有5种

ExponentiallyDecayingReservoir

基于指数级别的抽样算法，根据更新时间与开始时间的差值转化为权重值，权重越大数据被保留的几率越大。内部使用ConcurrentSkipListMap并发跳表来存储数据。它的大小没有限制，因此使用它对高频过程进行采样可能需要大量内存。因为它记录了每次测量。

使用方式

Histogram histogram = new Histogram(new ExponentiallyDecayingReservoir());

使用这种方法可能会有内存问题，官方文档专门提醒过这个问题

While SlidingTimeWindowReservoir is easier to understand than ExponentiallyDecayingReservoir, it is not bounded in size, so using it to sample a high-frequency process can require a significant amount of memory. Because it records every measurement, it’s also the slowest reservoir type.

UniformReservoir

随机抽样，随着更新次数的增加，数据被抽样的几率减少。

使用方式

Histogram histogram = new Histogram(new UniformReservoir());
或者
    
Histogram histogram = new Histogram(new UniformReservoir(1028));

SlidingWindowReservoir

只存最近N条数据的一种采样的指标分析模式

使用方式

    public static void main(String[] args) throws InterruptedException {
        initMetric();
        // SlidingWindowReservoir(只存最近N条数据)
        // 注册一个Histogram
        Histogram histogram = new Histogram(new SlidingWindowReservoir(10));
        registry.register(MetricRegistry.name(HistogramsTest.class, "request", "histogram"), histogram);
        int i = 0;
        while(true){
            Thread.sleep(1000);
            i++;
            System.out.println(((i/10) + 1) * 100000);
            histogram.update(((i/10) + 1) * 100000);
        }
    }

最开始输出内容

-- Histograms ------------------------------------------------------------------
dai.samples.metrics.HistogramsTest.request.histogram
             count = 9
               min = 100000
               max = 100000
              mean = 100000.00
            stddev = 0.00
            median = 100000.00
              75% <= 100000.00
              95% <= 100000.00
              98% <= 100000.00
              99% <= 100000.00
            99.9% <= 100000.00

最后输出内容

dai.samples.metrics.HistogramsTest.request.histogram
             count = 19
               min = 200000
               max = 200000
              mean = 200000.00
            stddev = 0.00
            median = 200000.00
              75% <= 200000.00
              95% <= 200000.00
              98% <= 200000.00
              99% <= 200000.00
            99.9% <= 200000.00

SlidingTimeWindowReservoir

只记录最近的指定时间范围的指标分析模式

使用方式

    public static void main(String[] args) throws InterruptedException {
        initMetric();
        // SlidingTimeWindowReservoir(指定时间窗口重置数据)
        // 注册一个Histogram
        Histogram histogram = new Histogram(new SlidingTimeWindowReservoir(10,TimeUnit.SECONDS));
        registry.register(MetricRegistry.name(HistogramsTest.class, "request", "histogram"), histogram);
        int i = 0;
        while(true){
            Thread.sleep(1000);
            i++;
            System.out.println(((i/10) + 1) * 100000);
            histogram.update(((i/10) + 1) * 100000);
        }
    }

最开始输出内容

-- Histograms ------------------------------------------------------------------
dai.samples.metrics.HistogramsTest.request.histogram
             count = 9
               min = 100000
               max = 100000
              mean = 100000.00
            stddev = 0.00
            median = 100000.00
              75% <= 100000.00
              95% <= 100000.00
              98% <= 100000.00
              99% <= 100000.00
            99.9% <= 100000.00

最后输出内容

dai.samples.metrics.HistogramsTest.request.histogram
             count = 19
               min = 200000
               max = 200000
              mean = 200000.00
            stddev = 0.00
            median = 200000.00
              75% <= 200000.00
              95% <= 200000.00
              98% <= 200000.00
              99% <= 200000.00
            99.9% <= 200000.00

SlidingTimeWindowArrayReservoir

SlidingTimeWindowArrayReservoir是对SlidingTimeWindowReservoir的优化。根据官方描述SlidingTimeWindowArrayReservoir提供了更低的内存开销。其GC开销比SlidingTimeWindow低60-80倍

使用方式

Histogram histogram = new Histogram(new SlidingTimeWindowArrayReservoir(1,TimeUnit.MINUTES));

对于其使用所需内存官方提供了相关的例子

Example: 10K measurements / sec with reservoir storing time of 1 minute will take 10000 * 60 * 128 / 8 = 9600000 bytes ~ 9 megabytes

Timer

Timer提供了直方图的相关数据分析，其内部也初始化了一个Histogram

Timer timer = registry.timer(MetricRegistry.name(TimersTest.class,"get-latency"));

默认情况下其初始化的Timer使用ExponentiallyDecayingReservoir

MetricBuilder TIMERS = new MetricBuilder() {
            @Override
            public Timer newMetric() {
                return new Timer();
            }

            @Override
            public boolean isInstance(Metric metric) {
                return Timer.class.isInstance(metric);
            }
        };
        
     public Timer() {
        this(new ExponentiallyDecayingReservoir());
    }

明白上面不同的参数类型我们可以在使用Timer的时候使用需要的Reservoir

public class ReservoirTimersTest extends MetricBase {

    public static Random random = new Random();

    public static void main(String[] args) throws InterruptedException {
        initMetric();

        MetricRegistry.MetricSupplier metricSupplier = () -> new Timer(new SlidingTimeWindowReservoir(1,TimeUnit.MINUTES));

        Timer timer = registry.timer(MetricRegistry.name(TimersTest.class,"get-latency"),metricSupplier);

        Timer.Context ctx;

        while(true){
            ctx = timer.time();
            Thread.sleep(random.nextInt(1000));
            ctx.stop();
        }
    }


}

监控工具——Metrics的Histogram可配置项

Java相关栏目本月热门文章