前言

这一篇是上一篇的继续,如果不了解Sentinel ，请先阅读[Alibaba Sentinel 源码阅读（Part1 执行流程）](Alibaba Sentinel 源码阅读（Part1 执行流程）)

入口

在上一篇我们看到 我们获取的所有信息，都是从StatisticNode 的这两个数据结构中获取的

private transient volatile Metric rollingCounterInSecond = new ArrayMetric(1000 / SampleCountProperty.SAMPLE_COUNT,        IntervalProperty.INTERVAL);    /**     * Holds statistics of the recent 120 seconds. The windowLengthInMs is deliberately set to 1000 milliseconds,     * meaning each bucket per second, in this way we can get accurate statistics of each second.     */    private transient Metric rollingCounterInMinute = new ArrayMetric(1000, 2 * 60);

rollingCounterInMinute 这个两分钟之内的每一秒中数据的一个list，而每一秒中的数据是存储在 MetricBucket,

ArrayMetric

// ArrayMetric 实现了Metric 接口，同时包含了 MetricsLeapArray数据结构，接口的实现就是通过这个MetricsLeapArray来实现的// MetricsLeapArray 是从 LeapArray 继承的，所以这一篇的重点就是LeapArray了public class ArrayMetric implements Metric {    private final MetricsLeapArray data;    /**     * Constructor     *     * @param windowLengthInMs a single window bucket's time length in milliseconds.     * @param intervalInSec    the total time span of this {@link ArrayMetric} in seconds.     */    public ArrayMetric(int windowLengthInMs, int intervalInSec) {        this.data = new MetricsLeapArray(windowLengthInMs, intervalInSec);    }}

LeapArray

实际上就是一个环形数组，来给张官方的图就明白了

看文档其实很清晰，整个是基于时间窗口滑动算法来实现的

新增当前统计数据

@Override    public void addSuccess() {        WindowWrap
    
      wrap = data.currentWindow();        wrap.value().addSuccess();    }
    

获取时间窗口内统计数据

@Override    public long success() {        data.currentWindow();        long success = 0;        List
    
      list = data.values();        for (MetricBucket window : list) {            success += window.success();        }        return success;    }
    

所以重点的方法就是 data.currentWindow()方法了

protected final AtomicReferenceArray
    
     
      > array;public LeapArray(int windowLengthInMs, int intervalInSec) {        this.windowLengthInMs = windowLengthInMs;        this.intervalInMs = intervalInSec * 1000;        this.sampleCount = intervalInMs / windowLengthInMs;        // 初始化容量大小        this.array = new AtomicReferenceArray
      
       
        >(sampleCount);    }
       
      
     
    

/**     * Get window at provided timestamp.     *     * @param time a valid timestamp     * @return the window at provided timestamp     */    public WindowWrap
    
      currentWindow(long time) {        long timeId = time / windowLengthInMs;        // Calculate current index.        int idx = (int)(timeId % array.length());        // Cut the time to current window start.        time = time - time % windowLengthInMs;        while (true) {            WindowWrap
     
       old = array.get(idx);            if (old == null) {                WindowWrap
      
        window = new WindowWrap
       
        (windowLengthInMs, time, newEmptyBucket());                if (array.compareAndSet(idx, null, window)) {                    return window;                } else {                    Thread.yield();                }            } else if (time == old.windowStart()) {                return old;            } else if (time > old.windowStart()) {                if (updateLock.tryLock()) {                    try {                        // if (old is deprecated) then [LOCK] resetTo currentTime.                        return resetWindowTo(old, time);                    } finally {                        updateLock.unlock();                    }                } else {                    Thread.yield();                }            } else if (time < old.windowStart()) {                // Cannot go through here.                return new WindowWrap
        
         (windowLengthInMs, time, newEmptyBucket());            }        }    }
        
       
      
     
    

这部分的内容会维持一个有效的环形数组以统计数据，具体要自己debug 看了。

总结

这里也只是把大致流程梳理了一下方便大家看源码而已，很多地方没有具体分析，这部分还是需要自己亲力亲为。

参考