golang 时间滑动窗口
wangxiaoangg 人气:0一 概念
固定窗口就像是滑动窗口的一个特例,固定窗口是大小固定且不能随着时间而变化的。
滑动时间窗口就是把一段时间片分为多个样本窗口,可以通过更细粒度对数据进行统计。然后计算对应的时间落在那个窗口上,来对数据统计;滑动时间窗口,随着时间流失,最开始的样本窗口将会失效,同时会生成新的样本窗口。
例如 我们将1s划分为4个样本窗口,每个样本窗口对应250ms。
二 go-zero中的滑动窗口实现
1.Bucket 样本窗口
Bucket用于记录每个样本窗口的值
// Bucket defines the bucket that holds sum and num of additions. type Bucket struct { Sum float64 //样本窗口的值 Count int64 //样本窗口被add的次数 } func (b *Bucket) add(v float64) { b.Sum += v b.Count++ } //重置样本窗口,样本窗口过期时 func (b *Bucket) reset() { b.Sum = 0 b.Count = 0 }
2. window 滑动窗口
type window struct { buckets []*Bucket //样本窗口 size int //样本窗口个数 } func newWindow(size int) *window { buckets := make([]*Bucket, size) for i := 0; i < size; i++ { buckets[i] = new(Bucket) } return &window{ buckets: buckets, size: size, } } func (w *window) add(offset int, v float64) { w.buckets[offset%w.size].add(v) } func (w *window) reduce(start, count int, fn func(b *Bucket)) { for i := 0; i < count; i++ { fn(w.buckets[(start+i)%w.size]) } } func (w *window) resetBucket(offset int) { w.buckets[offset%w.size].reset() }
3. RollingWindow窗口
bucket和window的实现都很简单,逻辑很好理解。
RollingWindow相对复杂一些。
当add值时需要如下操作:
- 计算已经过期的bucket(样本窗口),将已经过期的bucket重置。
- 计算offset,当前add操作应当记录到哪个bucket中。
type ( // RollingWindowOption let callers customize the RollingWindow. RollingWindowOption func(rollingWindow *RollingWindow) // RollingWindow defines a rolling window to calculate the events in buckets with time interval. RollingWindow struct { lock sync.RWMutex size int win *window interval time.Duration offset int ignoreCurrent bool lastTime time.Duration // start time of the last bucket } ) // NewRollingWindow returns a RollingWindow that with size buckets and time interval, // use opts to customize the RollingWindow. func NewRollingWindow(size int, interval time.Duration, opts ...RollingWindowOption) *RollingWindow { if size < 1 { panic("size must be greater than 0") } w := &RollingWindow{ size: size, win: newWindow(size), interval: interval, lastTime: timex.Now(), } for _, opt := range opts { opt(w) } return w } // Add adds value to current bucket. func (rw *RollingWindow) Add(v float64) { rw.lock.Lock() defer rw.lock.Unlock() rw.updateOffset() rw.win.add(rw.offset, v) } // Reduce runs fn on all buckets, ignore current bucket if ignoreCurrent was set. func (rw *RollingWindow) Reduce(fn func(b *Bucket)) { rw.lock.RLock() defer rw.lock.RUnlock() var diff int //获取跨度,并计算还有几个bucket还在窗口期内 span := rw.span() // ignore current bucket, because of partial data if span == 0 && rw.ignoreCurrent { diff = rw.size - 1 } else { diff = rw.size - span } if diff > 0 { offset := (rw.offset + span + 1) % rw.size rw.win.reduce(offset, diff, fn) } } //距离上次add操作跨度, //例如 lastTime = 1s, 当前时间1777ms。样本窗口时间250ms,那么跨度为3个样本窗口 func (rw *RollingWindow) span() int { offset := int(timex.Since(rw.lastTime) / rw.interval) if 0 <= offset && offset < rw.size { return offset } return rw.size } //g func (rw *RollingWindow) updateOffset() { span := rw.span() if span <= 0 { return } offset := rw.offset // reset expired buckets ,重置已经超时的bucket for i := 0; i < span; i++ { rw.win.resetBucket((offset + i + 1) % rw.size) } rw.offset = (offset + span) % rw.size now := timex.Now() //和样本窗口时间对齐 rw.lastTime = now - (now-rw.lastTime)%rw.interval }
三 使用
//1.新建一个4样本窗口,每个样本窗口250ms rollingWindow:= NewRollingWindow(4, time.Millisecond*250,IgnoreCurrentBucket()) //2.add rollingWindow.Add(1) rollingWindow.Add(2) time.Sleep(time.Millisecond*250) rollingWindow.Add(3) rollingWindow.Add(4) //3.获取滑动窗口的值 var Sum float64 var total int64 rollingWindow.Reduce(func(b *collection.Bucket) { Sum += int64(b.Sum) total += b.Count })
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