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开篇
- sentinel 处理流程是基于slot链(ProcessorSlotChain)来完成的，如限流熔断等，其中重要的一个slot就是StatisticSlot，它是做各种数据统计的，而限流熔断的数据判断来源就是StatisticSlot。
- StatisticSlot的各种数据统计都是基于滑动窗口来完成的，因此本文就重点分析StatisticSlot的滑动窗口统计机制。
- StatisticSlot的滑动窗口需要了解统计指标的数据结构、滑动窗口的窗口定位，指标保存等概念。
StatisticNode
public class StatisticNode implements Node {
// 对每秒指标统计
private transient volatile Metric rollingCounterInSecond = new ArrayMetric(SampleCountProperty.SAMPLE_COUNT,
IntervalProperty.INTERVAL);
// 每分钟指标统计
private transient Metric rollingCounterInMinute = new ArrayMetric(60, 60 * 1000, false);
private LongAdder curThreadNum = new LongAdder();
private long lastFetchTime = -1;
@Override
public void addPassRequest(int count) {
rollingCounterInSecond.addPass(count);
rollingCounterInMinute.addPass(count);
}
}
- 采集指标的统计节点，负责统计相关的采集指标。
- StatisticNode包含rollingCounterInSecond和rollingCounterInMinute。
- rollingCounterInSecond是对每秒指标的统计。
- rollingCounterInMinute是对每分钟指标的统计。
- rollingCounterInSecond和rollingCounterInMinute是ArrayMetric，负责保存统计指标。
统计指标
- 统计指标使用ArrayMetric进行承载。
- ArrayMetric内部是滑动窗口LeapArray对象。
- LeapArray的每个元素为WindowWrap。
- WindowWrap内部包含MetricBucket。
ArrayMetric
public class ArrayMetric implements Metric {
private final LeapArray<MetricBucket> data;
public ArrayMetric(int sampleCount, int intervalInMs) {
this.data = new OccupiableBucketLeapArray(sampleCount, intervalInMs);
}
public ArrayMetric(int sampleCount, int intervalInMs, boolean enableOccupy) {
if (enableOccupy) {
this.data = new OccupiableBucketLeapArray(sampleCount, intervalInMs);
} else {
this.data = new BucketLeapArray(sampleCount, intervalInMs);
}
}
}
- ArrayMetric作为保存指标的数组，通过滑动窗口LeapArray保存MetricBucket。
- MetricBucket代表统计指标，LeapArray代表滑动窗口，滑动窗口的每个窗口是MetricBucket对象。
LeapArray
public class BucketLeapArray extends LeapArray<MetricBucket> {
public BucketLeapArray(int sampleCount, int intervalInMs) {
super(sampleCount, intervalInMs);
}
}
public abstract class LeapArray<T> {
protected int windowLengthInMs;
protected int sampleCount;
protected int intervalInMs;
private double intervalInSecond;
protected final AtomicReferenceArray<WindowWrap<T>> array;
private final ReentrantLock updateLock = new ReentrantLock();
public LeapArray(int sampleCount, int intervalInMs) {
this.windowLengthInMs = intervalInMs / sampleCount;
this.intervalInMs = intervalInMs;
this.intervalInSecond = intervalInMs / 1000.0;
this.sampleCount = sampleCount;
this.array = new AtomicReferenceArray<>(sampleCount);
}
}
- LeapArray作为滑动窗口，BucketLeapArray作为其一种具体的实现。
- LeapArray通过AtomicReferenceArray<WindowWrap<T>> array来实现滑动窗口。
- 滑动窗口的统计指标MetricBucket通过WindowWrap进行包装。
WindowWrap
public class WindowWrap<T> {
private final long windowLengthInMs; // 时间窗口的长度
private long windowStart; // 时间窗口开始时间
private T value; // MetricBucket对象，保存各个指标数据
public WindowWrap(long windowLengthInMs, long windowStart, T value) {
this.windowLengthInMs = windowLengthInMs;
this.windowStart = windowStart;
this.value = value;
}
}
- WindowWrap作为滑动窗口的每个元素的承载，内部保存MetricBucket。
MetricBucket
public class MetricBucket {
private final LongAdder[] counters;
private volatile long minRt;
public MetricBucket() {
MetricEvent[] events = MetricEvent.values();
this.counters = new LongAdder[events.length];
for (MetricEvent event : events) {
counters[event.ordinal()] = new LongAdder();
}
initMinRt();
}
}
public enum MetricEvent {
PASS, // 正常通过
BLOCK, // 阻塞
EXCEPTION, // 异常
SUCCESS, // 成功
RT, // RT统计
OCCUPIED_PASS // 抢占通过
}
- MetricBucket内部保存各个统计指标MetricEvent的LongAdder数组。
- MetricEvent的枚举值代表各个采集指标。
滑动窗口定位
public abstract class LeapArray<T> {
protected int windowLengthInMs; // 时间窗口的长度
protected int sampleCount; // 时间窗口的个数
protected int intervalInMs;
private double intervalInSecond;
protected final AtomicReferenceArray<WindowWrap<T>> array;
public WindowWrap<T> currentWindow() {
return currentWindow(TimeUtil.currentTimeMillis());
}
public WindowWrap<T> currentWindow(long timeMillis) {
if (timeMillis < 0) {
return null;
}
// 根据当前时间和时间窗口的长度进行计算获取窗口下标
int idx = calculateTimeIdx(timeMillis);
// 获取指定下标的时间窗口的开始时间
long windowStart = calculateWindowStart(timeMillis);
/*
* Get bucket item at given time from the array.
*
* (1) Bucket is absent, then just create a new bucket and CAS update to circular array.
* (2) Bucket is up-to-date, then just return the bucket.
* (3) Bucket is deprecated, then reset current bucket and clean all deprecated buckets.
*/
while (true) {
WindowWrap<T> old = array.get(idx);
if (old == null) {
// 1.为空表示当前时间窗口为初始化过，创建WindowWrap并cas设置到array中
/*
* B0 B1 B2 NULL B4
* ||_______|_______|_______|_______|_______||___
* 200 400 600 800 1000 1200 timestamp
* ^
* time=888
* bucket is empty, so create new and update
*
* If the old bucket is absent, then we create a new bucket at {@code windowStart},
* then try to update circular array via a CAS operation. Only one thread can
* succeed to update, while other threads yield its time slice.
*/
WindowWrap<T> window = new WindowWrap<T>(windowLengthInMs, windowStart, newEmptyBucket(timeMillis));
if (array.compareAndSet(idx, null, window)) {
// Successfully updated, return the created bucket.
return window;
} else {
// Contention failed, the thread will yield its time slice to wait for bucket available.
Thread.yield();
}
} else if (windowStart == old.windowStart()) {
// 2.获取的时间窗口正好对应当前时间，直接返回
/*
* B0 B1 B2 B3 B4
* ||_______|_______|_______|_______|_______||___
* 200 400 600 800 1000 1200 timestamp
* ^
* time=888
* startTime of Bucket 3: 800, so it's up-to-date
*
* If current {@code windowStart} is equal to the start timestamp of old bucket,
* that means the time is within the bucket, so directly return the bucket.
*/
return old;
} else if (windowStart > old.windowStart()) {
// 3.获取的时间窗口为老的，进行窗口reset操作复用
/*
* (old)
* B0 B1 B2 NULL B4
* |_______||_______|_______|_______|_______|_______||___
* ... 1200 1400 1600 1800 2000 2200 timestamp
* ^
* time=1676
* startTime of Bucket 2: 400, deprecated, should be reset
*
* If the start timestamp of old bucket is behind provided time, that means
* the bucket is deprecated. We have to reset the bucket to current {@code windowStart}.
* Note that the reset and clean-up operations are hard to be atomic,
* so we need a update lock to guarantee the correctness of bucket update.
*
* The update lock is conditional (tiny scope) and will take effect only when
* bucket is deprecated, so in most cases it won't lead to performance loss.
*/
if (updateLock.tryLock()) {
try {
// Successfully get the update lock, now we reset the bucket.
return resetWindowTo(old, windowStart);
} finally {
updateLock.unlock();
}
} else {
Thread.yield();
}
} else if (windowStart < old.windowStart()) {
// 4.时间回拨了，正常情况下不会走到这里
return new WindowWrap<T>(windowLengthInMs, windowStart, newEmptyBucket(timeMillis));
}
}
}
private int calculateTimeIdx(/*@Valid*/ long timeMillis) {
long timeId = timeMillis / windowLengthInMs;
// Calculate current index so we can map the timestamp to the leap array.
return (int)(timeId % array.length());
}
protected long calculateWindowStart(/*@Valid*/ long timeMillis) {
return timeMillis - timeMillis % windowLengthInMs;
}
}
public class BucketLeapArray extends LeapArray<MetricBucket> {
public BucketLeapArray(int sampleCount, int intervalInMs) {
super(sampleCount, intervalInMs);
}
@Override
public MetricBucket newEmptyBucket(long time) {
return new MetricBucket();
}
@Override
protected WindowWrap<MetricBucket> resetWindowTo(WindowWrap<MetricBucket> w, long startTime) {
// 重置窗口的开始时间和对应的统计值
w.resetTo(startTime);
w.value().reset();
return w;
}
}
- 1.为空表示当前时间窗口为初始化过，创建WindowWrap并cas设置到array中
- 2.获取的时间窗口正好对应当前时间，直接返回
- 3.获取的时间窗口为老的，进行窗口reset操作复用。reset操作负责重置时间窗口的开始时间和窗口统计值。
- 4.时间回拨了正常情况下不会走到这里
指标保存
public class ArrayMetric implements Metric {
private final LeapArray<MetricBucket> data;
public ArrayMetric(int sampleCount, int intervalInMs) {
this.data = new OccupiableBucketLeapArray(sampleCount, intervalInMs);
}
@Override
public void addPass(int count) {
WindowWrap<MetricBucket> wrap = data.currentWindow();
wrap.value().addPass(count);
}
}
public class MetricBucket {
private final LongAdder[] counters;
public void addPass(int n) {
add(MetricEvent.PASS, n);
}
public MetricBucket add(MetricEvent event, long n) {
counters[event.ordinal()].add(n);
return this;
}
}
- currentWindow返回当前时间对应的滑动窗口。
- addPass通过add指定类型的MetricEvent指标到LongAdder当中。
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