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// 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 := int(dstStart.Sub(srcStart) / srcInterval)
srcIndex += advance
srcStart = srcStart.Add(time.Duration(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
}
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