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exponential_histogram.go
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exponential_histogram.go
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// Copyright The OpenTelemetry Authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
import (
"context"
"errors"
"math"
"sync"
"time"
"go.opentelemetry.io/otel"
"go.opentelemetry.io/otel/attribute"
"go.opentelemetry.io/otel/sdk/metric/metricdata"
)
const (
expoMaxScale = 20
expoMinScale = -10
smallestNonZeroNormalFloat64 = 0x1p-1022
// These redefine the Math constants with a type, so the compiler won't coerce
// them into an int on 32 bit platforms.
maxInt64 int64 = math.MaxInt64
minInt64 int64 = math.MinInt64
)
// expoHistogramDataPoint is a single data point in an exponential histogram.
type expoHistogramDataPoint[N int64 | float64] struct {
count uint64
min N
max N
sum N
maxSize int
noMinMax bool
noSum bool
scale int
posBuckets expoBuckets
negBuckets expoBuckets
zeroCount uint64
}
func newExpoHistogramDataPoint[N int64 | float64](maxSize, maxScale int, noMinMax, noSum bool) *expoHistogramDataPoint[N] {
f := math.MaxFloat64
max := N(f) // if N is int64, max will overflow to -9223372036854775808
min := N(-f)
if N(maxInt64) > N(f) {
max = N(maxInt64)
min = N(minInt64)
}
return &expoHistogramDataPoint[N]{
min: max,
max: min,
maxSize: maxSize,
noMinMax: noMinMax,
noSum: noSum,
scale: maxScale,
}
}
// record adds a new measurement to the histogram. It will rescale the buckets if needed.
func (p *expoHistogramDataPoint[N]) record(v N) {
// Ignore NaN and infinity.
if math.IsInf(float64(v), 0) || math.IsNaN(float64(v)) {
return
}
p.count++
if !p.noMinMax {
if v < p.min {
p.min = v
}
if v > p.max {
p.max = v
}
}
if !p.noSum {
p.sum += v
}
absV := math.Abs(float64(v))
if float64(absV) == 0.0 {
p.zeroCount++
return
}
bin := p.getBin(absV)
bucket := &p.posBuckets
if v < 0 {
bucket = &p.negBuckets
}
// If the new bin would make the counts larger than maxScale, we need to
// downscale current measurements.
if scaleDelta := p.scaleChange(bin, bucket.startBin, len(bucket.counts)); scaleDelta > 0 {
if p.scale-scaleDelta < expoMinScale {
// With a scale of -10 there is only two buckets for the whole range of float64 values.
// This can only happen if there is a max size of 1.
otel.Handle(errors.New("exponential histogram scale underflow"))
return
}
//Downscale
p.scale -= scaleDelta
p.posBuckets.downscale(scaleDelta)
p.negBuckets.downscale(scaleDelta)
bin = p.getBin(absV)
}
bucket.record(bin)
}
// getBin returns the bin v should be recorded into.
func (p *expoHistogramDataPoint[N]) getBin(v float64) int {
frac, exp := math.Frexp(v)
if p.scale <= 0 {
// Because of the choice of fraction is always 1 power of two higher than we want.
correction := 1
if frac == .5 {
// If v is an exact power of two the frac will be .5 and the exp
// will be one higher than we want.
correction = 2
}
return (exp - correction) >> (-p.scale)
}
return exp<<p.scale + int(math.Log(frac)*scaleFactors[p.scale]) - 1
}
// scaleFactors are constants used in calculating the logarithm index. They are
// equivalent to 2^index/log(2).
var scaleFactors = [21]float64{
math.Ldexp(math.Log2E, 0),
math.Ldexp(math.Log2E, 1),
math.Ldexp(math.Log2E, 2),
math.Ldexp(math.Log2E, 3),
math.Ldexp(math.Log2E, 4),
math.Ldexp(math.Log2E, 5),
math.Ldexp(math.Log2E, 6),
math.Ldexp(math.Log2E, 7),
math.Ldexp(math.Log2E, 8),
math.Ldexp(math.Log2E, 9),
math.Ldexp(math.Log2E, 10),
math.Ldexp(math.Log2E, 11),
math.Ldexp(math.Log2E, 12),
math.Ldexp(math.Log2E, 13),
math.Ldexp(math.Log2E, 14),
math.Ldexp(math.Log2E, 15),
math.Ldexp(math.Log2E, 16),
math.Ldexp(math.Log2E, 17),
math.Ldexp(math.Log2E, 18),
math.Ldexp(math.Log2E, 19),
math.Ldexp(math.Log2E, 20),
}
// scaleChange returns the magnitude of the scale change needed to fit bin in
// the bucket. If no scale change is needed 0 is returned.
func (p *expoHistogramDataPoint[N]) scaleChange(bin, startBin, length int) int {
if length == 0 {
// No need to rescale if there are no buckets.
return 0
}
low := startBin
high := bin
if startBin >= bin {
low = bin
high = startBin + length - 1
}
count := 0
for high-low >= p.maxSize {
low = low >> 1
high = high >> 1
count++
if count > expoMaxScale-expoMinScale {
return count
}
}
return count
}
// expoBuckets is a set of buckets in an exponential histogram.
type expoBuckets struct {
startBin int
counts []uint64
}
// record increments the count for the given bin, and expands the buckets if needed.
// Size changes must be done before calling this function.
func (b *expoBuckets) record(bin int) {
if len(b.counts) == 0 {
b.counts = []uint64{1}
b.startBin = bin
return
}
endBin := b.startBin + len(b.counts) - 1
// if the new bin is inside the current range
if bin >= b.startBin && bin <= endBin {
b.counts[bin-b.startBin]++
return
}
// if the new bin is before the current start add spaces to the counts
if bin < b.startBin {
origLen := len(b.counts)
newLength := endBin - bin + 1
shift := b.startBin - bin
if newLength > cap(b.counts) {
b.counts = append(b.counts, make([]uint64, newLength-len(b.counts))...)
}
copy(b.counts[shift:origLen+shift], b.counts[:])
b.counts = b.counts[:newLength]
for i := 1; i < shift; i++ {
b.counts[i] = 0
}
b.startBin = bin
b.counts[0] = 1
return
}
// if the new is after the end add spaces to the end
if bin > endBin {
if bin-b.startBin < cap(b.counts) {
b.counts = b.counts[:bin-b.startBin+1]
for i := endBin + 1 - b.startBin; i < len(b.counts); i++ {
b.counts[i] = 0
}
b.counts[bin-b.startBin] = 1
return
}
end := make([]uint64, bin-b.startBin-len(b.counts)+1)
b.counts = append(b.counts, end...)
b.counts[bin-b.startBin] = 1
}
}
// downscale shrinks a bucket by a factor of 2*s. It will sum counts into the
// correct lower resolution bucket.
func (b *expoBuckets) downscale(delta int) {
// Example
// delta = 2
// Original offset: -6
// Counts: [ 3, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
// bins: -6 -5, -4, -3, -2, -1, 0, 1, 2, 3, 4
// new bins:-2, -2, -1, -1, -1, -1, 0, 0, 0, 0, 1
// new Offset: -2
// new Counts: [4, 14, 30, 10]
if len(b.counts) <= 1 || delta < 1 {
b.startBin = b.startBin >> delta
return
}
steps := 1 << delta
offset := b.startBin % steps
offset = (offset + steps) % steps // to make offset positive
for i := 1; i < len(b.counts); i++ {
idx := i + offset
if idx%steps == 0 {
b.counts[idx/steps] = b.counts[i]
continue
}
b.counts[idx/steps] += b.counts[i]
}
lastIdx := (len(b.counts) - 1 + offset) / steps
b.counts = b.counts[:lastIdx+1]
b.startBin = b.startBin >> delta
}
// newExponentialHistogram returns an Aggregator that summarizes a set of
// measurements as an exponential histogram. Each histogram is scoped by attributes
// and the aggregation cycle the measurements were made in.
func newExponentialHistogram[N int64 | float64](maxSize, maxScale int32, noMinMax, noSum bool) *expoHistogram[N] {
return &expoHistogram[N]{
noSum: noSum,
noMinMax: noMinMax,
maxSize: int(maxSize),
maxScale: int(maxScale),
values: make(map[attribute.Set]*expoHistogramDataPoint[N]),
start: now(),
}
}
// expoHistogram summarizes a set of measurements as an histogram with exponentially
// defined buckets.
type expoHistogram[N int64 | float64] struct {
noSum bool
noMinMax bool
maxSize int
maxScale int
values map[attribute.Set]*expoHistogramDataPoint[N]
valuesMu sync.Mutex
start time.Time
}
func (e *expoHistogram[N]) measure(_ context.Context, value N, attr attribute.Set) {
e.valuesMu.Lock()
defer e.valuesMu.Unlock()
v, ok := e.values[attr]
if !ok {
v = newExpoHistogramDataPoint[N](e.maxSize, e.maxScale, e.noMinMax, e.noSum)
e.values[attr] = v
}
v.record(value)
}
func (e *expoHistogram[N]) delta(dest *metricdata.Aggregation) int {
t := now()
// If *dest is not a metricdata.ExponentialHistogram, memory reuse is missed.
// In that case, use the zero-value h and hope for better alignment next cycle.
h, _ := (*dest).(metricdata.ExponentialHistogram[N])
h.Temporality = metricdata.DeltaTemporality
e.valuesMu.Lock()
defer e.valuesMu.Unlock()
n := len(e.values)
hDPts := reset(h.DataPoints, n, n)
var i int
for a, b := range e.values {
hDPts[i].Attributes = a
hDPts[i].StartTime = e.start
hDPts[i].Time = t
hDPts[i].Count = b.count
hDPts[i].Scale = int32(b.scale)
hDPts[i].ZeroCount = b.zeroCount
hDPts[i].ZeroThreshold = 0.0
hDPts[i].PositiveBucket.Offset = int32(b.posBuckets.startBin)
hDPts[i].PositiveBucket.Counts = reset(hDPts[i].PositiveBucket.Counts, len(b.posBuckets.counts), len(b.posBuckets.counts))
copy(hDPts[i].PositiveBucket.Counts, b.posBuckets.counts)
hDPts[i].NegativeBucket.Offset = int32(b.negBuckets.startBin)
hDPts[i].NegativeBucket.Counts = reset(hDPts[i].NegativeBucket.Counts, len(b.negBuckets.counts), len(b.negBuckets.counts))
if !e.noSum {
hDPts[i].Sum = b.sum
}
if !e.noMinMax {
hDPts[i].Min = metricdata.NewExtrema(b.min)
hDPts[i].Max = metricdata.NewExtrema(b.max)
}
delete(e.values, a)
i++
}
e.start = t
h.DataPoints = hDPts
*dest = h
return n
}
func (e *expoHistogram[N]) cumulative(dest *metricdata.Aggregation) int {
t := now()
// If *dest is not a metricdata.ExponentialHistogram, memory reuse is missed.
// In that case, use the zero-value h and hope for better alignment next cycle.
h, _ := (*dest).(metricdata.ExponentialHistogram[N])
h.Temporality = metricdata.CumulativeTemporality
e.valuesMu.Lock()
defer e.valuesMu.Unlock()
n := len(e.values)
hDPts := reset(h.DataPoints, n, n)
var i int
for a, b := range e.values {
hDPts[i].Attributes = a
hDPts[i].StartTime = e.start
hDPts[i].Time = t
hDPts[i].Count = b.count
hDPts[i].Scale = int32(b.scale)
hDPts[i].ZeroCount = b.zeroCount
hDPts[i].ZeroThreshold = 0.0
hDPts[i].PositiveBucket.Offset = int32(b.posBuckets.startBin)
hDPts[i].PositiveBucket.Counts = reset(hDPts[i].PositiveBucket.Counts, len(b.posBuckets.counts), len(b.posBuckets.counts))
copy(hDPts[i].PositiveBucket.Counts, b.posBuckets.counts)
hDPts[i].NegativeBucket.Offset = int32(b.negBuckets.startBin)
hDPts[i].NegativeBucket.Counts = reset(hDPts[i].NegativeBucket.Counts, len(b.negBuckets.counts), len(b.negBuckets.counts))
if !e.noSum {
hDPts[i].Sum = b.sum
}
if !e.noMinMax {
hDPts[i].Min = metricdata.NewExtrema(b.min)
hDPts[i].Max = metricdata.NewExtrema(b.max)
}
i++
// TODO (#3006): This will use an unbounded amount of memory if there
// are unbounded number of attribute sets being aggregated. Attribute
// sets that become "stale" need to be forgotten so this will not
// overload the system.
}
h.DataPoints = hDPts
*dest = h
return n
}