victoriaMetrics中有一个fasttime库，用于快速获取当前的Unix时间，实现其实挺简单，就是在后台使用一个goroutine不断以1s为周期刷新表示当前时间的变量currentTimestamp，获取的时候直接原子加载该变量即可。其性能约是time.Now()的8倍。
其核心方式就是将主要任务放到后台运行，通过一个中间变量来传递运算结果，以此来通过异步的方式提升性能，但需要业务能包容一定的精度偏差。
func init() { go func() { ticker := time.NewTicker(time.Second) defer ticker.Stop() for tm := range ticker.C { t := uint64(tm.Unix()) atomic.StoreUint64(¤tTimestamp, t) } }() } var currentTimestamp = uint64(time.Now().Unix()) // UnixTimestamp returns the current unix timestamp in seconds. // // It is faster than time.Now().Unix() func UnixTimestamp() uint64 { return atomic.LoadUint64(¤tTimestamp) } hashUint64函数中使用xxhash.Sum64计算了结构体Key的哈希值。通过unsafe.Pointer将指针转换为*[]byte类型，byte数组的长度为unsafe.Sizeof(*k)，unsafe.Sizeof()返回结构体的字节大小。
如果一个数据为固定的长度，如h的类型为uint64，则可以直接指定长度为8进行转换，如：bp:=([8]byte)(unsafe.Pointer(&h))
需要注意的是
unsafe.Sizeof()返回的是数据结构的大小而不是其指向内容的数据大小，如下返回的slice大小为24，为slice首部数据结构SliceHeader的大小，而不是其引用的数据大小(可以使用len获取slice引用的数据大小)。此外如果结构体中有指针，则转换成的byte中存储的也是指针存储的地址。slice := []int{1,2,3,4,5,6,7,8,9,10} fmt.Println(unsafe.Sizeof(slice)) //24
type Key struct { Part interface{} Offset uint64 } func (k *Key) hashUint64() uint64 { buf := (*[unsafe.Sizeof(*k)]byte)(unsafe.Pointer(k)) return xxhash.Sum64(buf[:]) } 使用如下方式即可：
str := "1231445" arr := []byte{1, 2, 3} arr = append(arr, str...) 直接操作了底层的SliceHeader
func int64ToByteSlice(a []int64) (b []byte) { sh := (*reflect.SliceHeader)(unsafe.Pointer(&b)) sh.Data = uintptr(unsafe.Pointer(&a[0])) sh.Len = len(a) * int(unsafe.Sizeof(a[0])) sh.Cap = sh.Len return } 并发访问的sync.WaitGroup的目的是为了在运行时添加需要等待的goroutine
// WaitGroup wraps sync.WaitGroup and makes safe to call Add/Wait // from concurrent goroutines. // // An additional limitation is that call to Wait prohibits further calls to Add // until return. type WaitGroup struct { sync.WaitGroup mu sync.Mutex } // Add registers n additional workers. Add may be called from concurrent goroutines. func (wg *WaitGroup) Add(n int) { wg.mu.Lock() wg.WaitGroup.Add(n) wg.mu.Unlock() } // Wait waits until all the goroutines call Done. // // Wait may be called from concurrent goroutines. // // Further calls to Add are blocked until return from Wait. func (wg *WaitGroup) Wait() { wg.mu.Lock() wg.WaitGroup.Wait() wg.mu.Unlock() } // WaitAndBlock waits until all the goroutines call Done and then prevents // from new goroutines calling Add. // // Further calls to Add are always blocked. This is useful for graceful shutdown // when other goroutines calling Add must be stopped. // // wg cannot be used after this call. func (wg *WaitGroup) WaitAndBlock() { wg.mu.Lock() wg.WaitGroup.Wait() // Do not unlock wg.mu, so other goroutines calling Add are blocked. } // There is no need in wrapping WaitGroup.Done, since it is already goroutine-safe. 高频次创建timer会消耗一定的性能，为了减少某些情况下的性能损耗，可以使用sync.Pool来回收利用创建的timer
// Get returns a timer for the given duration d from the pool. // // Return back the timer to the pool with Put. func Get(d time.Duration) *time.Timer { if v := timerPool.Get(); v != nil { t := v.(*time.Timer) if t.Reset(d) { logger.Panicf("BUG: active timer trapped to the pool!") } return t } return time.NewTimer(d) } // Put returns t to the pool. // // t cannot be accessed after returning to the pool. func Put(t *time.Timer) { if !t.Stop() { // Drain t.C if it wasn't obtained by the caller yet. select { case <-t.C: default: } } timerPool.Put(t) } var timerPool sync.Pool victoriaMetrics的vminsert作为vmagent和vmstorage之间的组件，接收vmagent的流量并将其转发到vmstorage。在vmstorage卡死、处理过慢或下线的情况下，有可能会导致无法转发流量，进而造成vminsert CPU和内存飙升，造成组件故障。为了防止这种情况，vminsert使用了限速器，当接收到的流量激增时，可以在牺牲一部分数据的情况下保证系统的稳定性。
victoriaMetrics的源码中对限速器有如下描述：
Limit the number of conurrent f calls in order to prevent from excess memory usage and CPU thrashing
限速器使用了两个参数：maxConcurrentInserts和maxQueueDuration，前者给出了突发情况下可以处理的最大请求数，后者给出了某个请求的最大超时时间。需要注意的是Do(f func() error)是异步执行的，而ch又是全局的，因此会异步等待其他请求释放资源(struct{})。
可以看到限速器使用了指标来指示当前的限速状态。同时使用cgroup.AvailableCPUs()*4 (即runtime.GOMAXPROCS(-1)*4)来设置默认的maxConcurrentInserts长度。
当该限速器用在处理如http请求时，该限速器并不能限制底层上送的请求，其限制的是对请求的处理。在高流量业务处理中，这也是最消耗内存的地方，通常包含数据读取、内存申请拷贝等。底层的数据受
/proc/sys/net/core/somaxconn和socket缓存区的限制。
var ( maxConcurrentInserts = flag.Int("maxConcurrentInserts", cgroup.AvailableCPUs()*4, "The maximum number of concurrent inserts. Default value should work for most cases, "+ "since it minimizes the overhead for concurrent inserts. This option is tigthly coupled with -insert.maxQueueDuration") maxQueueDuration = flag.Duration("insert.maxQueueDuration", time.Minute, "The maximum duration for waiting in the queue for insert requests due to -maxConcurrentInserts") ) // ch is the channel for limiting concurrent calls to Do. var ch chan struct{} // Init initializes concurrencylimiter. // // Init must be called after flag.Parse call. func Init() { ch = make(chan struct{}, *maxConcurrentInserts) //初始化limiter，最大突发并行请求量为maxConcurrentInserts } // Do calls f with the limited concurrency. func Do(f func() error) error { // Limit the number of conurrent f calls in order to prevent from excess // memory usage and CPU thrashing. select { case ch <- struct{}{}: //在channel中添加一个元素，表示开始处理一个请求 err := f() //阻塞等大请求处理结束 <-ch //请求处理完之后释放channel中的一个元素，释放出的空间可以用于处理下一个请求 return err default: } //如果当前达到处理上限maxConcurrentInserts，则需要等到其他Do(f func() error)释放资源。 // All the workers are busy. // Sleep for up to *maxQueueDuration. concurrencyLimitReached.Inc() t := timerpool.Get(*maxQueueDuration) //获取一个timer，设置等待超时时间为 maxQueueDuration select { case ch <- struct{}{}: //在maxQueueDuration时间内等待其他请求释放资源，如果获取到资源，则回收timer，继续处理 timerpool.Put(t) err := f() <- return err case <-t.C: //在maxQueueDuration时间内没有获取到资源，定时器超时后回收timer，丢弃请求并返回错误信息 timerpool.Put(t) concurrencyLimitTimeout.Inc() return &httpserver.ErrorWithStatusCode{ Err: fmt.Errorf("cannot handle more than %d concurrent inserts during %s; possible solutions: "+ "increase `-insert.maxQueueDuration`, increase `-maxConcurrentInserts`, increase server capacity", *maxConcurrentInserts, *maxQueueDuration), StatusCode: http.StatusServiceUnavailable, } } } var ( concurrencyLimitReached = metrics.NewCounter(`vm_concurrent_insert_limit_reached_total`) concurrencyLimitTimeout = metrics.NewCounter(`vm_concurrent_insert_limit_timeout_total`) _ = metrics.NewGauge(`vm_concurrent_insert_capacity`, func() float64 { return float64(cap(ch)) }) _ = metrics.NewGauge(`vm_concurrent_insert_current`, func() float64 { return float64(len(ch)) }) ) victoriaMetrics的pacelimiter库实现了优先级控制。主要方法由Inc、Dec和WaitIfNeeded。低优先级任务需要调用WaitIfNeeded方法，如果此时有高优先级任务(调用Inc方法)，则低优先级任务需要等待高优先级任务结束(调用Dec方法)之后才能继续执行。
// PaceLimiter throttles WaitIfNeeded callers while the number of Inc calls is bigger than the number of Dec calls. // // It is expected that Inc is called before performing high-priority work, // while Dec is called when the work is done. // WaitIfNeeded must be called inside the work which must be throttled (i.e. lower-priority work). // It may be called in the loop before performing a part of low-priority work. type PaceLimiter struct { mu sync.Mutex cond *sync.Cond delaysTotal uint64 n int32 } // New returns pace limiter that throttles WaitIfNeeded callers while the number of Inc calls is bigger than the number of Dec calls. func New() *PaceLimiter { var pl PaceLimiter pl.cond = sync.NewCond(&pl.mu) return &pl } // Inc increments pl. func (pl *PaceLimiter) Inc() { atomic.AddInt32(&pl.n, 1) } // Dec decrements pl. func (pl *PaceLimiter) Dec() { if atomic.AddInt32(&pl.n, -1) == 0 { // Wake up all the goroutines blocked in WaitIfNeeded, // since the number of Dec calls equals the number of Inc calls. pl.cond.Broadcast() } } // WaitIfNeeded blocks while the number of Inc calls is bigger than the number of Dec calls. func (pl *PaceLimiter) WaitIfNeeded() { if atomic.LoadInt32(&pl.n) <= 0 { // Fast path - there is no need in lock. return } // Slow path - wait until Dec is called. pl.mu.Lock() for atomic.LoadInt32(&pl.n) > 0 { pl.delaysTotal++ pl.cond.Wait() } pl.mu.Unlock() } // DelaysTotal returns the number of delays inside WaitIfNeeded. func (pl *PaceLimiter) DelaysTotal() uint64 { pl.mu.Lock() n := pl.delaysTotal pl.mu.Unlock() return n } 
