From 621e49bb465f500cc46d47e39e828cf76d6381d7 Mon Sep 17 00:00:00 2001 From: Dimitri Sokolyuk Date: Tue, 24 Jul 2018 14:35:44 +0200 Subject: update vendor --- .../x/net/internal/timeseries/timeseries.go | 525 +++++++++++++++++++++ .../x/net/internal/timeseries/timeseries_test.go | 170 +++++++ 2 files changed, 695 insertions(+) create mode 100644 vendor/golang.org/x/net/internal/timeseries/timeseries.go create mode 100644 vendor/golang.org/x/net/internal/timeseries/timeseries_test.go (limited to 'vendor/golang.org/x/net/internal/timeseries') 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 +} diff --git a/vendor/golang.org/x/net/internal/timeseries/timeseries_test.go b/vendor/golang.org/x/net/internal/timeseries/timeseries_test.go new file mode 100644 index 0000000..66325a9 --- /dev/null +++ b/vendor/golang.org/x/net/internal/timeseries/timeseries_test.go @@ -0,0 +1,170 @@ +// 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 + +import ( + "math" + "testing" + "time" +) + +func isNear(x *Float, y float64, tolerance float64) bool { + return math.Abs(x.Value()-y) < tolerance +} + +func isApproximate(x *Float, y float64) bool { + return isNear(x, y, 1e-2) +} + +func checkApproximate(t *testing.T, o Observable, y float64) { + x := o.(*Float) + if !isApproximate(x, y) { + t.Errorf("Wanted %g, got %g", y, x.Value()) + } +} + +func checkNear(t *testing.T, o Observable, y, tolerance float64) { + x := o.(*Float) + if !isNear(x, y, tolerance) { + t.Errorf("Wanted %g +- %g, got %g", y, tolerance, x.Value()) + } +} + +var baseTime = time.Date(2013, 1, 1, 0, 0, 0, 0, time.UTC) + +func tu(s int64) time.Time { + return baseTime.Add(time.Duration(s) * time.Second) +} + +func tu2(s int64, ns int64) time.Time { + return baseTime.Add(time.Duration(s)*time.Second + time.Duration(ns)*time.Nanosecond) +} + +func TestBasicTimeSeries(t *testing.T) { + ts := NewTimeSeries(NewFloat) + fo := new(Float) + *fo = Float(10) + ts.AddWithTime(fo, tu(1)) + ts.AddWithTime(fo, tu(1)) + ts.AddWithTime(fo, tu(1)) + ts.AddWithTime(fo, tu(1)) + checkApproximate(t, ts.Range(tu(0), tu(1)), 40) + checkApproximate(t, ts.Total(), 40) + ts.AddWithTime(fo, tu(3)) + ts.AddWithTime(fo, tu(3)) + ts.AddWithTime(fo, tu(3)) + checkApproximate(t, ts.Range(tu(0), tu(2)), 40) + checkApproximate(t, ts.Range(tu(2), tu(4)), 30) + checkApproximate(t, ts.Total(), 70) + ts.AddWithTime(fo, tu(1)) + ts.AddWithTime(fo, tu(1)) + checkApproximate(t, ts.Range(tu(0), tu(2)), 60) + checkApproximate(t, ts.Range(tu(2), tu(4)), 30) + checkApproximate(t, ts.Total(), 90) + *fo = Float(100) + ts.AddWithTime(fo, tu(100)) + checkApproximate(t, ts.Range(tu(99), tu(100)), 100) + checkApproximate(t, ts.Range(tu(0), tu(4)), 36) + checkApproximate(t, ts.Total(), 190) + *fo = Float(10) + ts.AddWithTime(fo, tu(1)) + ts.AddWithTime(fo, tu(1)) + checkApproximate(t, ts.Range(tu(0), tu(4)), 44) + checkApproximate(t, ts.Range(tu(37), tu2(100, 100e6)), 100) + checkApproximate(t, ts.Range(tu(50), tu2(100, 100e6)), 100) + checkApproximate(t, ts.Range(tu(99), tu2(100, 100e6)), 100) + checkApproximate(t, ts.Total(), 210) + + for i, l := range ts.ComputeRange(tu(36), tu(100), 64) { + if i == 63 { + checkApproximate(t, l, 100) + } else { + checkApproximate(t, l, 0) + } + } + + checkApproximate(t, ts.Range(tu(0), tu(100)), 210) + checkApproximate(t, ts.Range(tu(10), tu(100)), 100) + + for i, l := range ts.ComputeRange(tu(0), tu(100), 100) { + if i < 10 { + checkApproximate(t, l, 11) + } else if i >= 90 { + checkApproximate(t, l, 10) + } else { + checkApproximate(t, l, 0) + } + } +} + +func TestFloat(t *testing.T) { + f := Float(1) + if g, w := f.String(), "1"; g != w { + t.Errorf("Float(1).String = %q; want %q", g, w) + } + f2 := Float(2) + var o Observable = &f2 + f.Add(o) + if g, w := f.Value(), 3.0; g != w { + t.Errorf("Float post-add = %v; want %v", g, w) + } + f.Multiply(2) + if g, w := f.Value(), 6.0; g != w { + t.Errorf("Float post-multiply = %v; want %v", g, w) + } + f.Clear() + if g, w := f.Value(), 0.0; g != w { + t.Errorf("Float post-clear = %v; want %v", g, w) + } + f.CopyFrom(&f2) + if g, w := f.Value(), 2.0; g != w { + t.Errorf("Float post-CopyFrom = %v; want %v", g, w) + } +} + +type mockClock struct { + time time.Time +} + +func (m *mockClock) Time() time.Time { return m.time } +func (m *mockClock) Set(t time.Time) { m.time = t } + +const buckets = 6 + +var testResolutions = []time.Duration{ + 10 * time.Second, // level holds one minute of observations + 100 * time.Second, // level holds ten minutes of observations + 10 * time.Minute, // level holds one hour of observations +} + +// TestTimeSeries uses a small number of buckets to force a higher +// error rate on approximations from the timeseries. +type TestTimeSeries struct { + timeSeries +} + +func TestExpectedErrorRate(t *testing.T) { + ts := new(TestTimeSeries) + fake := new(mockClock) + fake.Set(time.Now()) + ts.timeSeries.init(testResolutions, NewFloat, buckets, fake) + for i := 1; i <= 61*61; i++ { + fake.Set(fake.Time().Add(1 * time.Second)) + ob := Float(1) + ts.AddWithTime(&ob, fake.Time()) + + // The results should be accurate within one missing bucket (1/6) of the observations recorded. + checkNear(t, ts.Latest(0, buckets), min(float64(i), 60), 10) + checkNear(t, ts.Latest(1, buckets), min(float64(i), 600), 100) + checkNear(t, ts.Latest(2, buckets), min(float64(i), 3600), 600) + } +} + +func min(a, b float64) float64 { + if a < b { + return a + } + return b +} -- cgit v1.2.3