VOL-2017 voltha-lib moved from voltha-go;
release version 2.2.1
Based on voltha-go commit 5259f8e52b3e3f5c7ad422a4b0e506e1d07f6b36
Change-Id: I8bbecdf456e420714a4016120eafc0d237c80565
diff --git a/vendor/golang.org/x/net/internal/timeseries/timeseries.go b/vendor/golang.org/x/net/internal/timeseries/timeseries.go
new file mode 100644
index 0000000..685f0e7
--- /dev/null
+++ b/vendor/golang.org/x/net/internal/timeseries/timeseries.go
@@ -0,0 +1,525 @@
+// Copyright 2015 The Go Authors. All rights reserved.
+// Use of this source code is governed by a BSD-style
+// license that can be found in the LICENSE file.
+
+// Package timeseries implements a time series structure for stats collection.
+package timeseries // import "golang.org/x/net/internal/timeseries"
+
+import (
+ "fmt"
+ "log"
+ "time"
+)
+
+const (
+ timeSeriesNumBuckets = 64
+ minuteHourSeriesNumBuckets = 60
+)
+
+var timeSeriesResolutions = []time.Duration{
+ 1 * time.Second,
+ 10 * time.Second,
+ 1 * time.Minute,
+ 10 * time.Minute,
+ 1 * time.Hour,
+ 6 * time.Hour,
+ 24 * time.Hour, // 1 day
+ 7 * 24 * time.Hour, // 1 week
+ 4 * 7 * 24 * time.Hour, // 4 weeks
+ 16 * 7 * 24 * time.Hour, // 16 weeks
+}
+
+var minuteHourSeriesResolutions = []time.Duration{
+ 1 * time.Second,
+ 1 * time.Minute,
+}
+
+// An Observable is a kind of data that can be aggregated in a time series.
+type Observable interface {
+ Multiply(ratio float64) // Multiplies the data in self by a given ratio
+ Add(other Observable) // Adds the data from a different observation to self
+ Clear() // Clears the observation so it can be reused.
+ CopyFrom(other Observable) // Copies the contents of a given observation to self
+}
+
+// Float attaches the methods of Observable to a float64.
+type Float float64
+
+// NewFloat returns a Float.
+func NewFloat() Observable {
+ f := Float(0)
+ return &f
+}
+
+// String returns the float as a string.
+func (f *Float) String() string { return fmt.Sprintf("%g", f.Value()) }
+
+// Value returns the float's value.
+func (f *Float) Value() float64 { return float64(*f) }
+
+func (f *Float) Multiply(ratio float64) { *f *= Float(ratio) }
+
+func (f *Float) Add(other Observable) {
+ o := other.(*Float)
+ *f += *o
+}
+
+func (f *Float) Clear() { *f = 0 }
+
+func (f *Float) CopyFrom(other Observable) {
+ o := other.(*Float)
+ *f = *o
+}
+
+// A Clock tells the current time.
+type Clock interface {
+ Time() time.Time
+}
+
+type defaultClock int
+
+var defaultClockInstance defaultClock
+
+func (defaultClock) Time() time.Time { return time.Now() }
+
+// Information kept per level. Each level consists of a circular list of
+// observations. The start of the level may be derived from end and the
+// len(buckets) * sizeInMillis.
+type tsLevel struct {
+ oldest int // index to oldest bucketed Observable
+ newest int // index to newest bucketed Observable
+ end time.Time // end timestamp for this level
+ size time.Duration // duration of the bucketed Observable
+ buckets []Observable // collections of observations
+ provider func() Observable // used for creating new Observable
+}
+
+func (l *tsLevel) Clear() {
+ l.oldest = 0
+ l.newest = len(l.buckets) - 1
+ l.end = time.Time{}
+ for i := range l.buckets {
+ if l.buckets[i] != nil {
+ l.buckets[i].Clear()
+ l.buckets[i] = nil
+ }
+ }
+}
+
+func (l *tsLevel) InitLevel(size time.Duration, numBuckets int, f func() Observable) {
+ l.size = size
+ l.provider = f
+ l.buckets = make([]Observable, numBuckets)
+}
+
+// Keeps a sequence of levels. Each level is responsible for storing data at
+// a given resolution. For example, the first level stores data at a one
+// minute resolution while the second level stores data at a one hour
+// resolution.
+
+// Each level is represented by a sequence of buckets. Each bucket spans an
+// interval equal to the resolution of the level. New observations are added
+// to the last bucket.
+type timeSeries struct {
+ provider func() Observable // make more Observable
+ numBuckets int // number of buckets in each level
+ levels []*tsLevel // levels of bucketed Observable
+ lastAdd time.Time // time of last Observable tracked
+ total Observable // convenient aggregation of all Observable
+ clock Clock // Clock for getting current time
+ pending Observable // observations not yet bucketed
+ pendingTime time.Time // what time are we keeping in pending
+ dirty bool // if there are pending observations
+}
+
+// init initializes a level according to the supplied criteria.
+func (ts *timeSeries) init(resolutions []time.Duration, f func() Observable, numBuckets int, clock Clock) {
+ ts.provider = f
+ ts.numBuckets = numBuckets
+ ts.clock = clock
+ ts.levels = make([]*tsLevel, len(resolutions))
+
+ for i := range resolutions {
+ if i > 0 && resolutions[i-1] >= resolutions[i] {
+ log.Print("timeseries: resolutions must be monotonically increasing")
+ break
+ }
+ newLevel := new(tsLevel)
+ newLevel.InitLevel(resolutions[i], ts.numBuckets, ts.provider)
+ ts.levels[i] = newLevel
+ }
+
+ ts.Clear()
+}
+
+// Clear removes all observations from the time series.
+func (ts *timeSeries) Clear() {
+ ts.lastAdd = time.Time{}
+ ts.total = ts.resetObservation(ts.total)
+ ts.pending = ts.resetObservation(ts.pending)
+ ts.pendingTime = time.Time{}
+ ts.dirty = false
+
+ for i := range ts.levels {
+ ts.levels[i].Clear()
+ }
+}
+
+// Add records an observation at the current time.
+func (ts *timeSeries) Add(observation Observable) {
+ ts.AddWithTime(observation, ts.clock.Time())
+}
+
+// AddWithTime records an observation at the specified time.
+func (ts *timeSeries) AddWithTime(observation Observable, t time.Time) {
+
+ smallBucketDuration := ts.levels[0].size
+
+ if t.After(ts.lastAdd) {
+ ts.lastAdd = t
+ }
+
+ if t.After(ts.pendingTime) {
+ ts.advance(t)
+ ts.mergePendingUpdates()
+ ts.pendingTime = ts.levels[0].end
+ ts.pending.CopyFrom(observation)
+ ts.dirty = true
+ } else if t.After(ts.pendingTime.Add(-1 * smallBucketDuration)) {
+ // The observation is close enough to go into the pending bucket.
+ // This compensates for clock skewing and small scheduling delays
+ // by letting the update stay in the fast path.
+ ts.pending.Add(observation)
+ ts.dirty = true
+ } else {
+ ts.mergeValue(observation, t)
+ }
+}
+
+// mergeValue inserts the observation at the specified time in the past into all levels.
+func (ts *timeSeries) mergeValue(observation Observable, t time.Time) {
+ for _, level := range ts.levels {
+ index := (ts.numBuckets - 1) - int(level.end.Sub(t)/level.size)
+ if 0 <= index && index < ts.numBuckets {
+ bucketNumber := (level.oldest + index) % ts.numBuckets
+ if level.buckets[bucketNumber] == nil {
+ level.buckets[bucketNumber] = level.provider()
+ }
+ level.buckets[bucketNumber].Add(observation)
+ }
+ }
+ ts.total.Add(observation)
+}
+
+// mergePendingUpdates applies the pending updates into all levels.
+func (ts *timeSeries) mergePendingUpdates() {
+ if ts.dirty {
+ ts.mergeValue(ts.pending, ts.pendingTime)
+ ts.pending = ts.resetObservation(ts.pending)
+ ts.dirty = false
+ }
+}
+
+// advance cycles the buckets at each level until the latest bucket in
+// each level can hold the time specified.
+func (ts *timeSeries) advance(t time.Time) {
+ if !t.After(ts.levels[0].end) {
+ return
+ }
+ for i := 0; i < len(ts.levels); i++ {
+ level := ts.levels[i]
+ if !level.end.Before(t) {
+ break
+ }
+
+ // If the time is sufficiently far, just clear the level and advance
+ // directly.
+ if !t.Before(level.end.Add(level.size * time.Duration(ts.numBuckets))) {
+ for _, b := range level.buckets {
+ ts.resetObservation(b)
+ }
+ level.end = time.Unix(0, (t.UnixNano()/level.size.Nanoseconds())*level.size.Nanoseconds())
+ }
+
+ for t.After(level.end) {
+ level.end = level.end.Add(level.size)
+ level.newest = level.oldest
+ level.oldest = (level.oldest + 1) % ts.numBuckets
+ ts.resetObservation(level.buckets[level.newest])
+ }
+
+ t = level.end
+ }
+}
+
+// Latest returns the sum of the num latest buckets from the level.
+func (ts *timeSeries) Latest(level, num int) Observable {
+ now := ts.clock.Time()
+ if ts.levels[0].end.Before(now) {
+ ts.advance(now)
+ }
+
+ ts.mergePendingUpdates()
+
+ result := ts.provider()
+ l := ts.levels[level]
+ index := l.newest
+
+ for i := 0; i < num; i++ {
+ if l.buckets[index] != nil {
+ result.Add(l.buckets[index])
+ }
+ if index == 0 {
+ index = ts.numBuckets
+ }
+ index--
+ }
+
+ return result
+}
+
+// LatestBuckets returns a copy of the num latest buckets from level.
+func (ts *timeSeries) LatestBuckets(level, num int) []Observable {
+ if level < 0 || level > len(ts.levels) {
+ log.Print("timeseries: bad level argument: ", level)
+ return nil
+ }
+ if num < 0 || num >= ts.numBuckets {
+ log.Print("timeseries: bad num argument: ", num)
+ return nil
+ }
+
+ results := make([]Observable, num)
+ now := ts.clock.Time()
+ if ts.levels[0].end.Before(now) {
+ ts.advance(now)
+ }
+
+ ts.mergePendingUpdates()
+
+ l := ts.levels[level]
+ index := l.newest
+
+ for i := 0; i < num; i++ {
+ result := ts.provider()
+ results[i] = result
+ if l.buckets[index] != nil {
+ result.CopyFrom(l.buckets[index])
+ }
+
+ if index == 0 {
+ index = ts.numBuckets
+ }
+ index -= 1
+ }
+ return results
+}
+
+// ScaleBy updates observations by scaling by factor.
+func (ts *timeSeries) ScaleBy(factor float64) {
+ for _, l := range ts.levels {
+ for i := 0; i < ts.numBuckets; i++ {
+ l.buckets[i].Multiply(factor)
+ }
+ }
+
+ ts.total.Multiply(factor)
+ ts.pending.Multiply(factor)
+}
+
+// Range returns the sum of observations added over the specified time range.
+// If start or finish times don't fall on bucket boundaries of the same
+// level, then return values are approximate answers.
+func (ts *timeSeries) Range(start, finish time.Time) Observable {
+ return ts.ComputeRange(start, finish, 1)[0]
+}
+
+// Recent returns the sum of observations from the last delta.
+func (ts *timeSeries) Recent(delta time.Duration) Observable {
+ now := ts.clock.Time()
+ return ts.Range(now.Add(-delta), now)
+}
+
+// Total returns the total of all observations.
+func (ts *timeSeries) Total() Observable {
+ ts.mergePendingUpdates()
+ return ts.total
+}
+
+// ComputeRange computes a specified number of values into a slice using
+// the observations recorded over the specified time period. The return
+// values are approximate if the start or finish times don't fall on the
+// bucket boundaries at the same level or if the number of buckets spanning
+// the range is not an integral multiple of num.
+func (ts *timeSeries) ComputeRange(start, finish time.Time, num int) []Observable {
+ if start.After(finish) {
+ log.Printf("timeseries: start > finish, %v>%v", start, finish)
+ return nil
+ }
+
+ if num < 0 {
+ log.Printf("timeseries: num < 0, %v", num)
+ return nil
+ }
+
+ results := make([]Observable, num)
+
+ for _, l := range ts.levels {
+ if !start.Before(l.end.Add(-l.size * time.Duration(ts.numBuckets))) {
+ ts.extract(l, start, finish, num, results)
+ return results
+ }
+ }
+
+ // Failed to find a level that covers the desired range. So just
+ // extract from the last level, even if it doesn't cover the entire
+ // desired range.
+ ts.extract(ts.levels[len(ts.levels)-1], start, finish, num, results)
+
+ return results
+}
+
+// RecentList returns the specified number of values in slice over the most
+// recent time period of the specified range.
+func (ts *timeSeries) RecentList(delta time.Duration, num int) []Observable {
+ if delta < 0 {
+ return nil
+ }
+ now := ts.clock.Time()
+ return ts.ComputeRange(now.Add(-delta), now, num)
+}
+
+// extract returns a slice of specified number of observations from a given
+// level over a given range.
+func (ts *timeSeries) extract(l *tsLevel, start, finish time.Time, num int, results []Observable) {
+ ts.mergePendingUpdates()
+
+ srcInterval := l.size
+ dstInterval := finish.Sub(start) / time.Duration(num)
+ dstStart := start
+ srcStart := l.end.Add(-srcInterval * time.Duration(ts.numBuckets))
+
+ srcIndex := 0
+
+ // Where should scanning start?
+ if dstStart.After(srcStart) {
+ advance := dstStart.Sub(srcStart) / srcInterval
+ srcIndex += int(advance)
+ srcStart = srcStart.Add(advance * srcInterval)
+ }
+
+ // The i'th value is computed as show below.
+ // interval = (finish/start)/num
+ // i'th value = sum of observation in range
+ // [ start + i * interval,
+ // start + (i + 1) * interval )
+ for i := 0; i < num; i++ {
+ results[i] = ts.resetObservation(results[i])
+ dstEnd := dstStart.Add(dstInterval)
+ for srcIndex < ts.numBuckets && srcStart.Before(dstEnd) {
+ srcEnd := srcStart.Add(srcInterval)
+ if srcEnd.After(ts.lastAdd) {
+ srcEnd = ts.lastAdd
+ }
+
+ if !srcEnd.Before(dstStart) {
+ srcValue := l.buckets[(srcIndex+l.oldest)%ts.numBuckets]
+ if !srcStart.Before(dstStart) && !srcEnd.After(dstEnd) {
+ // dst completely contains src.
+ if srcValue != nil {
+ results[i].Add(srcValue)
+ }
+ } else {
+ // dst partially overlaps src.
+ overlapStart := maxTime(srcStart, dstStart)
+ overlapEnd := minTime(srcEnd, dstEnd)
+ base := srcEnd.Sub(srcStart)
+ fraction := overlapEnd.Sub(overlapStart).Seconds() / base.Seconds()
+
+ used := ts.provider()
+ if srcValue != nil {
+ used.CopyFrom(srcValue)
+ }
+ used.Multiply(fraction)
+ results[i].Add(used)
+ }
+
+ if srcEnd.After(dstEnd) {
+ break
+ }
+ }
+ srcIndex++
+ srcStart = srcStart.Add(srcInterval)
+ }
+ dstStart = dstStart.Add(dstInterval)
+ }
+}
+
+// resetObservation clears the content so the struct may be reused.
+func (ts *timeSeries) resetObservation(observation Observable) Observable {
+ if observation == nil {
+ observation = ts.provider()
+ } else {
+ observation.Clear()
+ }
+ return observation
+}
+
+// TimeSeries tracks data at granularities from 1 second to 16 weeks.
+type TimeSeries struct {
+ timeSeries
+}
+
+// NewTimeSeries creates a new TimeSeries using the function provided for creating new Observable.
+func NewTimeSeries(f func() Observable) *TimeSeries {
+ return NewTimeSeriesWithClock(f, defaultClockInstance)
+}
+
+// NewTimeSeriesWithClock creates a new TimeSeries using the function provided for creating new Observable and the clock for
+// assigning timestamps.
+func NewTimeSeriesWithClock(f func() Observable, clock Clock) *TimeSeries {
+ ts := new(TimeSeries)
+ ts.timeSeries.init(timeSeriesResolutions, f, timeSeriesNumBuckets, clock)
+ return ts
+}
+
+// MinuteHourSeries tracks data at granularities of 1 minute and 1 hour.
+type MinuteHourSeries struct {
+ timeSeries
+}
+
+// NewMinuteHourSeries creates a new MinuteHourSeries using the function provided for creating new Observable.
+func NewMinuteHourSeries(f func() Observable) *MinuteHourSeries {
+ return NewMinuteHourSeriesWithClock(f, defaultClockInstance)
+}
+
+// NewMinuteHourSeriesWithClock creates a new MinuteHourSeries using the function provided for creating new Observable and the clock for
+// assigning timestamps.
+func NewMinuteHourSeriesWithClock(f func() Observable, clock Clock) *MinuteHourSeries {
+ ts := new(MinuteHourSeries)
+ ts.timeSeries.init(minuteHourSeriesResolutions, f,
+ minuteHourSeriesNumBuckets, clock)
+ return ts
+}
+
+func (ts *MinuteHourSeries) Minute() Observable {
+ return ts.timeSeries.Latest(0, 60)
+}
+
+func (ts *MinuteHourSeries) Hour() Observable {
+ return ts.timeSeries.Latest(1, 60)
+}
+
+func minTime(a, b time.Time) time.Time {
+ if a.Before(b) {
+ return a
+ }
+ return b
+}
+
+func maxTime(a, b time.Time) time.Time {
+ if a.After(b) {
+ return a
+ }
+ return b
+}