golang 实现时间滑动窗口_golang 滑动窗口

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：

1. 计算已经过期的bucket(样本窗口)，将已经过期的bucket重置。
2. 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
	})