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Avoid the term 'sparse' where possible 

This intends to avoid confusing users by the subtle difference between
a native histogram and a sparse bucket.

Signed-off-by: beorn7 <beorn@grafana.com>
This commit is contained in:
beorn7 2022-10-27 22:31:38 +02:00
parent d31f13b599
commit e92a8c7f48
3 changed files with 243 additions and 233 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
examples/random/main.go
View File

@ -68,7 +68,7 @@ func main() {
Name: "rpc_durations_histogram_seconds", Name: "rpc_durations_histogram_seconds",
Help: "RPC latency distributions.", Help: "RPC latency distributions.",
Buckets: prometheus.LinearBuckets(*normMean-5**normDomain, .5**normDomain, 20), Buckets: prometheus.LinearBuckets(*normMean-5**normDomain, .5**normDomain, 20),
SparseBucketsFactor: 1.1, NativeHistogramBucketFactor: 1.1,
}) })
) )

418
prometheus/histogram.go
View File

@ -28,16 +28,16 @@ import (
dto "github.com/prometheus/client_model/go" dto "github.com/prometheus/client_model/go"
) )
// sparseBounds for the frac of observed values. Only relevant for schema > 0. // nativeHistogramBounds for the frac of observed values. Only relevant for
// Position in the slice is the schema. (0 is never used, just here for // schema > 0. The position in the slice is the schema. (0 is never used, just
// convenience of using the schema directly as the index.) // here for convenience of using the schema directly as the index.)
// //
// TODO(beorn7): Currently, we do a binary search into these slices. There are // TODO(beorn7): Currently, we do a binary search into these slices. There are
// ways to turn it into a small number of simple array lookups. It probably only // ways to turn it into a small number of simple array lookups. It probably only
// matters for schema 5 and beyond, but should be investigated. See this comment // matters for schema 5 and beyond, but should be investigated. See this comment
// as a starting point: // as a starting point:
// https://github.com/open-telemetry/opentelemetry-specification/issues/1776#issuecomment-870164310 // https://github.com/open-telemetry/opentelemetry-specification/issues/1776#issuecomment-870164310
var sparseBounds = [][]float64{ var nativeHistogramBounds = [][]float64{
// Schema "0": // Schema "0":
{0.5}, {0.5},
// Schema 1: // Schema 1:
@ -190,35 +190,40 @@ var sparseBounds = [][]float64{
}, },
} }
// The sparseBounds above can be generated with the code below. // The nativeHistogramBounds above can be generated with the code below.
// TODO(beorn7): Actually do it via go generate.
// //
// var sparseBounds [][]float64 = make([][]float64, 9) // TODO(beorn7): It's tempting to actually use `go generate` to generate the
// code above. However, this could lead to slightly different numbers on
// different architectures. We still need to come to terms if we are fine with
// that, or if we might prefer to specify precise numbers in the standard.
//
// var nativeHistogramBounds [][]float64 = make([][]float64, 9)
// //
// func init() { // func init() {
// // Populate sparseBounds. // // Populate nativeHistogramBounds.
// numBuckets := 1 // numBuckets := 1
// for i := range sparseBounds { // for i := range nativeHistogramBounds {
// bounds := []float64{0.5} // bounds := []float64{0.5}
// factor := math.Exp2(math.Exp2(float64(-i))) // factor := math.Exp2(math.Exp2(float64(-i)))
// for j := 0; j < numBuckets-1; j++ { // for j := 0; j < numBuckets-1; j++ {
// var bound float64 // var bound float64
// if (j+1)%2 == 0 { // if (j+1)%2 == 0 {
// // Use previously calculated value for increased precision. // // Use previously calculated value for increased precision.
// bound = sparseBounds[i-1][j/2+1] // bound = nativeHistogramBounds[i-1][j/2+1]
// } else { // } else {
// bound = bounds[j] * factor // bound = bounds[j] * factor
// } // }
// bounds = append(bounds, bound) // bounds = append(bounds, bound)
// } // }
// numBuckets *= 2 // numBuckets *= 2
// sparseBounds[i] = bounds // nativeHistogramBounds[i] = bounds
// } // }
// } // }
// A Histogram counts individual observations from an event or sample stream in // A Histogram counts individual observations from an event or sample stream in
// configurable buckets. Similar to a Summary, it also provides a sum of // configurable static buckets (or in dynamic sparse buckets as part of the
// observations and an observation count. // experimental Native Histograms, see below for more details). Similar to a
// Summary, it also provides a sum of observations and an observation count.
// //
// On the Prometheus server, quantiles can be calculated from a Histogram using // On the Prometheus server, quantiles can be calculated from a Histogram using
// the histogram_quantile PromQL function. // the histogram_quantile PromQL function.
@ -227,7 +232,7 @@ var sparseBounds = [][]float64{
// (see the documentation for detailed procedures). However, Histograms require // (see the documentation for detailed procedures). However, Histograms require
// the user to pre-define suitable buckets, and they are in general less // the user to pre-define suitable buckets, and they are in general less
// accurate. (Both problems are addressed by the experimental Native // accurate. (Both problems are addressed by the experimental Native
// Histograms. To use them, configure so-called sparse buckets in the // Histograms. To use them, configure a NativeHistogramBucketFactor in the
// HistogramOpts. They also require a Prometheus server v2.40+ with the // HistogramOpts. They also require a Prometheus server v2.40+ with the
// corresponding feature flag enabled.) // corresponding feature flag enabled.)
// //
@ -259,17 +264,17 @@ const bucketLabel = "le"
// customized to your use case. // customized to your use case.
var DefBuckets = []float64{.005, .01, .025, .05, .1, .25, .5, 1, 2.5, 5, 10} var DefBuckets = []float64{.005, .01, .025, .05, .1, .25, .5, 1, 2.5, 5, 10}
// DefSparseBucketsZeroThreshold is the default value for // DefNativeHistogramZeroThreshold is the default value for
// SparseBucketsZeroThreshold in the HistogramOpts. // NativeHistogramZeroThreshold in the HistogramOpts.
// //
// The value is 2^-128 (or 0.5*2^-127 in the actual IEEE 754 representation), // The value is 2^-128 (or 0.5*2^-127 in the actual IEEE 754 representation),
// which is a bucket boundary at all possible resolutions. // which is a bucket boundary at all possible resolutions.
const DefSparseBucketsZeroThreshold = 2.938735877055719e-39 const DefNativeHistogramZeroThreshold = 2.938735877055719e-39
// SparseBucketsZeroThresholdZero can be used as SparseBucketsZeroThreshold in // NativeHistogramZeroThresholdZero can be used as NativeHistogramZeroThreshold
// the HistogramOpts to create a zero bucket of width zero, i.e. a zero bucket // in the HistogramOpts to create a zero bucket of width zero, i.e. a zero
// that only receives observations of precisely zero. // bucket that only receives observations of precisely zero.
const SparseBucketsZeroThresholdZero = -1 const NativeHistogramZeroThresholdZero = -1
var errBucketLabelNotAllowed = fmt.Errorf( var errBucketLabelNotAllowed = fmt.Errorf(
"%q is not allowed as label name in histograms", bucketLabel, "%q is not allowed as label name in histograms", bucketLabel,
@ -385,81 +390,83 @@ type HistogramOpts struct {
// to add a highest bucket with +Inf bound, it will be added // to add a highest bucket with +Inf bound, it will be added
// implicitly. If Buckets is left as nil or set to a slice of length // implicitly. If Buckets is left as nil or set to a slice of length
// zero, it is replaced by default buckets. The default buckets are // zero, it is replaced by default buckets. The default buckets are
// DefBuckets if no sparse buckets (see below) are used, otherwise the // DefBuckets if no buckets for a native histogram (see below) are used,
// default is no buckets. (In other words, if you want to use both // otherwise the default is no buckets. (In other words, if you want to
// reguler buckets and sparse buckets, you have to define the regular // use both reguler buckets and buckets for a native histogram, you have
// buckets here explicitly.) // to define the regular buckets here explicitly.)
Buckets []float64 Buckets []float64
// If SparseBucketsFactor is greater than one, sparse buckets are used // If NativeHistogramBucketFactor is greater than one, so-called sparse
// (in addition to the regular buckets, if defined above). A Histogram // buckets are used (in addition to the regular buckets, if defined
// with sparse buckets will be ingested as a Native Histogram by a // above). A Histogram with sparse buckets will be ingested as a Native
// Prometheus server with that feature enabled (requires Prometheus // Histogram by a Prometheus server with that feature enabled (requires
// v2.40+). Sparse buckets are exponential buckets covering the whole // Prometheus v2.40+). Sparse buckets are exponential buckets covering
// float64 range (with the exception of the “zero” bucket, see // the whole float64 range (with the exception of the “zero” bucket, see
// SparseBucketsZeroThreshold below). From any one bucket to the next, // SparseBucketsZeroThreshold below). From any one bucket to the next,
// the width of the bucket grows by a constant // the width of the bucket grows by a constant
// factor. SparseBucketsFactor provides an upper bound for this factor // factor. NativeHistogramBucketFactor provides an upper bound for this
// (exception see below). The smaller SparseBucketsFactor, the more // factor (exception see below). The smaller
// buckets will be used and thus the more costly the histogram will // NativeHistogramBucketFactor, the more buckets will be used and thus
// become. A generally good trade-off between cost and accuracy is a // the more costly the histogram will become. A generally good trade-off
// value of 1.1 (each bucket is at most 10% wider than the previous // between cost and accuracy is a value of 1.1 (each bucket is at most
// one), which will result in each power of two divided into 8 buckets // 10% wider than the previous one), which will result in each power of
// (e.g. there will be 8 buckets between 1 and 2, same as between 2 and // two divided into 8 buckets (e.g. there will be 8 buckets between 1
// 4, and 4 and 8, etc.). // and 2, same as between 2 and 4, and 4 and 8, etc.).
// //
// Details about the actually used factor: The factor is calculated as // Details about the actually used factor: The factor is calculated as
// 2^(2^n), where n is an integer number between (and including) -8 and // 2^(2^n), where n is an integer number between (and including) -8 and
// 4. n is chosen so that the resulting factor is the largest that is // 4. n is chosen so that the resulting factor is the largest that is
// still smaller or equal to SparseBucketsFactor. Note that the smallest // still smaller or equal to NativeHistogramBucketFactor. Note that the
// possible factor is therefore approx. 1.00271 (i.e. 2^(2^-8) ). If // smallest possible factor is therefore approx. 1.00271 (i.e. 2^(2^-8)
// SparseBucketsFactor is greater than 1 but smaller than 2^(2^-8), then // ). If NativeHistogramBucketFactor is greater than 1 but smaller than
// the actually used factor is still 2^(2^-8) even though it is larger // 2^(2^-8), then the actually used factor is still 2^(2^-8) even though
// than the provided SparseBucketsFactor. // it is larger than the provided NativeHistogramBucketFactor.
// //
// NOTE: Native Histograms are still an experimental feature. Their // NOTE: Native Histograms are still an experimental feature. Their
// behavior might still change without a major version // behavior might still change without a major version
// bump. Subsequently, all SparseBucket... options here might still // bump. Subsequently, all NativeHistogram... options here might still
// change their behavior or name (or might completely disappear) without // change their behavior or name (or might completely disappear) without
// a major version bump. // a major version bump.
SparseBucketsFactor float64 NativeHistogramBucketFactor float64
// All observations with an absolute value of less or equal // All observations with an absolute value of less or equal
// SparseBucketsZeroThreshold are accumulated into a “zero” bucket. For // NativeHistogramZeroThreshold are accumulated into a “zero”
// best results, this should be close to a bucket boundary. This is // bucket. For best results, this should be close to a bucket
// usually the case if picking a power of two. If // boundary. This is usually the case if picking a power of two. If
// SparseBucketsZeroThreshold is left at zero, // NativeHistogramZeroThreshold is left at zero,
// DefSparseBucketsZeroThreshold is used as the threshold. To configure // DefSparseBucketsZeroThreshold is used as the threshold. To configure
// a zero bucket with an actual threshold of zero (i.e. only // a zero bucket with an actual threshold of zero (i.e. only
// observations of precisely zero will go into the zero bucket), set // observations of precisely zero will go into the zero bucket), set
// SparseBucketsZeroThreshold to the SparseBucketsZeroThresholdZero // NativeHistogramZeroThreshold to the NativeHistogramZeroThresholdZero
// constant (or any negative float value). // constant (or any negative float value).
SparseBucketsZeroThreshold float64 NativeHistogramZeroThreshold float64
// The remaining fields define a strategy to limit the number of // The remaining fields define a strategy to limit the number of
// populated sparse buckets. If SparseBucketsMaxNumber is left at zero, // populated sparse buckets. If NativeHistogramMaxBucketNumber is left
// the number of buckets is not limited. (Note that this might lead to // at zero, the number of buckets is not limited. (Note that this might
// unbounded memory consumption if the values observed by the Histogram // lead to unbounded memory consumption if the values observed by the
// are sufficiently wide-spread. In particular, this could be used as a // Histogram are sufficiently wide-spread. In particular, this could be
// DoS attack vector. Where the observed values depend on external // used as a DoS attack vector. Where the observed values depend on
// inputs, it is highly recommended to set a SparseBucketsMaxNumber.) // external inputs, it is highly recommended to set a
// Once the set SparseBucketsMaxNumber is exceeded, the following // NativeHistogramMaxBucketNumber.) Once the set
// strategy is enacted: First, if the last reset (or the creation) of // NativeHistogramMaxBucketNumber is exceeded, the following strategy is
// the histogram is at least SparseBucketsMinResetDuration ago, then the // enacted: First, if the last reset (or the creation) of the histogram
// whole histogram is reset to its initial state (including regular // is at least NativeHistogramMinResetDuration ago, then the whole
// histogram is reset to its initial state (including regular
// buckets). If less time has passed, or if // buckets). If less time has passed, or if
// SparseBucketsMinResetDuration is zero, no reset is // NativeHistogramMinResetDuration is zero, no reset is
// performed. Instead, the zero threshold is increased sufficiently to // performed. Instead, the zero threshold is increased sufficiently to
// reduce the number of buckets to or below SparseBucketsMaxNumber, but // reduce the number of buckets to or below
// not to more than SparseBucketsMaxZeroThreshold. Thus, if // NativeHistogramMaxBucketNumber, but not to more than
// SparseBucketsMaxZeroThreshold is already at or below the current zero // NativeHistogramMaxZeroThreshold. Thus, if
// threshold, nothing happens at this step. After that, if the number of // NativeHistogramMaxZeroThreshold is already at or below the current
// buckets still exceeds SparseBucketsMaxNumber, the resolution of the // zero threshold, nothing happens at this step. After that, if the
// histogram is reduced by doubling the width of the sparse buckets (up // number of buckets still exceeds NativeHistogramMaxBucketNumber, the
// to a growth factor between one bucket to the next of 2^(2^4) = 65536, // resolution of the histogram is reduced by doubling the width of the
// see above). // sparse buckets (up to a growth factor between one bucket to the next
SparseBucketsMaxNumber uint32 // of 2^(2^4) = 65536, see above).
SparseBucketsMinResetDuration time.Duration NativeHistogramMaxBucketNumber uint32
SparseBucketsMaxZeroThreshold float64 NativeHistogramMinResetDuration time.Duration
NativeHistogramMaxZeroThreshold float64
} }
// NewHistogram creates a new Histogram based on the provided HistogramOpts. It // NewHistogram creates a new Histogram based on the provided HistogramOpts. It
@ -500,25 +507,25 @@ func newHistogram(desc *Desc, opts HistogramOpts, labelValues ...string) Histogr
desc: desc, desc: desc,
upperBounds: opts.Buckets, upperBounds: opts.Buckets,
labelPairs: MakeLabelPairs(desc, labelValues), labelPairs: MakeLabelPairs(desc, labelValues),
sparseMaxBuckets: opts.SparseBucketsMaxNumber, nativeHistogramMaxBuckets: opts.NativeHistogramMaxBucketNumber,
sparseMaxZeroThreshold: opts.SparseBucketsMaxZeroThreshold, nativeHistogramMaxZeroThreshold: opts.NativeHistogramMaxZeroThreshold,
sparseMinResetDuration: opts.SparseBucketsMinResetDuration, nativeHistogramMinResetDuration: opts.NativeHistogramMinResetDuration,
lastResetTime: time.Now(), lastResetTime: time.Now(),
now: time.Now, now: time.Now,
} }
if len(h.upperBounds) == 0 && opts.SparseBucketsFactor <= 1 { if len(h.upperBounds) == 0 && opts.NativeHistogramBucketFactor <= 1 {
h.upperBounds = DefBuckets h.upperBounds = DefBuckets
} }
if opts.SparseBucketsFactor <= 1 { if opts.NativeHistogramBucketFactor <= 1 {
h.sparseSchema = math.MinInt32 // To mark that there are no sparse buckets. h.nativeHistogramSchema = math.MinInt32 // To mark that there are no sparse buckets.
} else { } else {
switch { switch {
case opts.SparseBucketsZeroThreshold > 0: case opts.NativeHistogramZeroThreshold > 0:
h.sparseZeroThreshold = opts.SparseBucketsZeroThreshold h.nativeHistogramZeroThreshold = opts.NativeHistogramZeroThreshold
case opts.SparseBucketsZeroThreshold == 0: case opts.NativeHistogramZeroThreshold == 0:
h.sparseZeroThreshold = DefSparseBucketsZeroThreshold h.nativeHistogramZeroThreshold = DefNativeHistogramZeroThreshold
} // Leave h.sparseThreshold at 0 otherwise. } // Leave h.nativeHistogramZeroThreshold at 0 otherwise.
h.sparseSchema = pickSparseSchema(opts.SparseBucketsFactor) h.nativeHistogramSchema = pickSchema(opts.NativeHistogramBucketFactor)
} }
for i, upperBound := range h.upperBounds { for i, upperBound := range h.upperBounds {
if i < len(h.upperBounds)-1 { if i < len(h.upperBounds)-1 {
@ -539,13 +546,13 @@ func newHistogram(desc *Desc, opts HistogramOpts, labelValues ...string) Histogr
// for both counts as well as exemplars: // for both counts as well as exemplars:
h.counts[0] = &histogramCounts{ h.counts[0] = &histogramCounts{
buckets: make([]uint64, len(h.upperBounds)), buckets: make([]uint64, len(h.upperBounds)),
sparseZeroThresholdBits: math.Float64bits(h.sparseZeroThreshold), nativeHistogramZeroThresholdBits: math.Float64bits(h.nativeHistogramZeroThreshold),
sparseSchema: h.sparseSchema, nativeHistogramSchema: h.nativeHistogramSchema,
} }
h.counts[1] = &histogramCounts{ h.counts[1] = &histogramCounts{
buckets: make([]uint64, len(h.upperBounds)), buckets: make([]uint64, len(h.upperBounds)),
sparseZeroThresholdBits: math.Float64bits(h.sparseZeroThreshold), nativeHistogramZeroThresholdBits: math.Float64bits(h.nativeHistogramZeroThreshold),
sparseSchema: h.sparseSchema, nativeHistogramSchema: h.nativeHistogramSchema,
} }
h.exemplars = make([]atomic.Value, len(h.upperBounds)+1) h.exemplars = make([]atomic.Value, len(h.upperBounds)+1)
@ -562,36 +569,38 @@ type histogramCounts struct {
sumBits uint64 sumBits uint64
count uint64 count uint64
// sparseZeroBucket counts all (positive and negative) observations in // nativeHistogramZeroBucket counts all (positive and negative)
// the zero bucket (with an absolute value less or equal the current // observations in the zero bucket (with an absolute value less or equal
// threshold, see next field. // the current threshold, see next field.
sparseZeroBucket uint64 nativeHistogramZeroBucket uint64
// sparseZeroThresholdBits is the bit pattern of the current threshold // nativeHistogramZeroThresholdBits is the bit pattern of the current
// for the zero bucket. It's initially equal to sparseZeroThreshold but // threshold for the zero bucket. It's initially equal to
// may change according to the bucket count limitation strategy. // nativeHistogramZeroThreshold but may change according to the bucket
sparseZeroThresholdBits uint64 // count limitation strategy.
// sparseSchema may change over time according to the bucket count nativeHistogramZeroThresholdBits uint64
// limitation strategy and therefore has to be saved here. // nativeHistogramSchema may change over time according to the bucket
sparseSchema int32 // count limitation strategy and therefore has to be saved here.
nativeHistogramSchema int32
// Number of (positive and negative) sparse buckets. // Number of (positive and negative) sparse buckets.
sparseBucketsNumber uint32 nativeHistogramBucketsNumber uint32
// Regular buckets. // Regular buckets.
buckets []uint64 buckets []uint64
// Sparse buckets are implemented with a sync.Map for now. A dedicated // The sparse buckets for native histograms are implemented with a
// data structure will likely be more efficient. There are separate maps // sync.Map for now. A dedicated data structure will likely be more
// for negative and positive observations. The map's value is an *int64, // efficient. There are separate maps for negative and positive
// counting observations in that bucket. (Note that we don't use uint64 // observations. The map's value is an *int64, counting observations in
// as an int64 won't overflow in practice, and working with signed // that bucket. (Note that we don't use uint64 as an int64 won't
// numbers from the beginning simplifies the handling of deltas.) The // overflow in practice, and working with signed numbers from the
// map's key is the index of the bucket according to the used // beginning simplifies the handling of deltas.) The map's key is the
// sparseSchema. Index 0 is for an upper bound of 1. // index of the bucket according to the used
sparseBucketsPositive, sparseBucketsNegative sync.Map // nativeHistogramSchema. Index 0 is for an upper bound of 1.
nativeHistogramBucketsPositive, nativeHistogramBucketsNegative sync.Map
} }
// observe manages the parts of observe that only affects // observe manages the parts of observe that only affects
// histogramCounts. doSparse is true if spare buckets should be done, // histogramCounts. doSparse is true if sparse buckets should be done,
// too. // too.
func (hc *histogramCounts) observe(v float64, bucket int, doSparse bool) { func (hc *histogramCounts) observe(v float64, bucket int, doSparse bool) {
if bucket < len(hc.buckets) { if bucket < len(hc.buckets) {
@ -600,13 +609,13 @@ func (hc *histogramCounts) observe(v float64, bucket int, doSparse bool) {
atomicAddFloat(&hc.sumBits, v) atomicAddFloat(&hc.sumBits, v)
if doSparse && !math.IsNaN(v) { if doSparse && !math.IsNaN(v) {
var ( var (
sparseKey int key int
sparseSchema = atomic.LoadInt32(&hc.sparseSchema) schema = atomic.LoadInt32(&hc.nativeHistogramSchema)
sparseZeroThreshold = math.Float64frombits(atomic.LoadUint64(&hc.sparseZeroThresholdBits)) zeroThreshold = math.Float64frombits(atomic.LoadUint64(&hc.nativeHistogramZeroThresholdBits))
bucketCreated, isInf bool bucketCreated, isInf bool
) )
if math.IsInf(v, 0) { if math.IsInf(v, 0) {
// Pretend v is MaxFloat64 but later increment sparseKey by one. // Pretend v is MaxFloat64 but later increment key by one.
if math.IsInf(v, +1) { if math.IsInf(v, +1) {
v = math.MaxFloat64 v = math.MaxFloat64
} else { } else {
@ -615,30 +624,30 @@ func (hc *histogramCounts) observe(v float64, bucket int, doSparse bool) {
isInf = true isInf = true
} }
frac, exp := math.Frexp(math.Abs(v)) frac, exp := math.Frexp(math.Abs(v))
if sparseSchema > 0 { if schema > 0 {
bounds := sparseBounds[sparseSchema] bounds := nativeHistogramBounds[schema]
sparseKey = sort.SearchFloat64s(bounds, frac) + (exp-1)*len(bounds) key = sort.SearchFloat64s(bounds, frac) + (exp-1)*len(bounds)
} else { } else {
sparseKey = exp key = exp
if frac == 0.5 { if frac == 0.5 {
sparseKey-- key--
} }
div := 1 << -sparseSchema div := 1 << -schema
sparseKey = (sparseKey + div - 1) / div key = (key + div - 1) / div
} }
if isInf { if isInf {
sparseKey++ key++
} }
switch { switch {
case v > sparseZeroThreshold: case v > zeroThreshold:
bucketCreated = addToSparseBucket(&hc.sparseBucketsPositive, sparseKey, 1) bucketCreated = addToBucket(&hc.nativeHistogramBucketsPositive, key, 1)
case v < -sparseZeroThreshold: case v < -zeroThreshold:
bucketCreated = addToSparseBucket(&hc.sparseBucketsNegative, sparseKey, 1) bucketCreated = addToBucket(&hc.nativeHistogramBucketsNegative, key, 1)
default: default:
atomic.AddUint64(&hc.sparseZeroBucket, 1) atomic.AddUint64(&hc.nativeHistogramZeroBucket, 1)
} }
if bucketCreated { if bucketCreated {
atomic.AddUint32(&hc.sparseBucketsNumber, 1) atomic.AddUint32(&hc.nativeHistogramBucketsNumber, 1)
} }
} }
// Increment count last as we take it as a signal that the observation // Increment count last as we take it as a signal that the observation
@ -680,11 +689,11 @@ type histogram struct {
upperBounds []float64 upperBounds []float64
labelPairs []*dto.LabelPair labelPairs []*dto.LabelPair
exemplars []atomic.Value // One more than buckets (to include +Inf), each a *dto.Exemplar. exemplars []atomic.Value // One more than buckets (to include +Inf), each a *dto.Exemplar.
sparseSchema int32 // The initial schema. Set to math.MinInt32 if no sparse buckets are used. nativeHistogramSchema int32 // The initial schema. Set to math.MinInt32 if no sparse buckets are used.
sparseZeroThreshold float64 // The initial zero threshold. nativeHistogramZeroThreshold float64 // The initial zero threshold.
sparseMaxZeroThreshold float64 nativeHistogramMaxZeroThreshold float64
sparseMaxBuckets uint32 nativeHistogramMaxBuckets uint32
sparseMinResetDuration time.Duration nativeHistogramMinResetDuration time.Duration
lastResetTime time.Time // Protected by mtx. lastResetTime time.Time // Protected by mtx.
now func() time.Time // To mock out time.Now() for testing. now func() time.Time // To mock out time.Now() for testing.
@ -753,19 +762,19 @@ func (h *histogram) Write(out *dto.Metric) error {
} }
his.Bucket = append(his.Bucket, b) his.Bucket = append(his.Bucket, b)
} }
if h.sparseSchema > math.MinInt32 { if h.nativeHistogramSchema > math.MinInt32 {
his.ZeroThreshold = proto.Float64(math.Float64frombits(atomic.LoadUint64(&coldCounts.sparseZeroThresholdBits))) his.ZeroThreshold = proto.Float64(math.Float64frombits(atomic.LoadUint64(&coldCounts.nativeHistogramZeroThresholdBits)))
his.Schema = proto.Int32(atomic.LoadInt32(&coldCounts.sparseSchema)) his.Schema = proto.Int32(atomic.LoadInt32(&coldCounts.nativeHistogramSchema))
zeroBucket := atomic.LoadUint64(&coldCounts.sparseZeroBucket) zeroBucket := atomic.LoadUint64(&coldCounts.nativeHistogramZeroBucket)
defer func() { defer func() {
coldCounts.sparseBucketsPositive.Range(addAndReset(&hotCounts.sparseBucketsPositive, &hotCounts.sparseBucketsNumber)) coldCounts.nativeHistogramBucketsPositive.Range(addAndReset(&hotCounts.nativeHistogramBucketsPositive, &hotCounts.nativeHistogramBucketsNumber))
coldCounts.sparseBucketsNegative.Range(addAndReset(&hotCounts.sparseBucketsNegative, &hotCounts.sparseBucketsNumber)) coldCounts.nativeHistogramBucketsNegative.Range(addAndReset(&hotCounts.nativeHistogramBucketsNegative, &hotCounts.nativeHistogramBucketsNumber))
}() }()
his.ZeroCount = proto.Uint64(zeroBucket) his.ZeroCount = proto.Uint64(zeroBucket)
his.NegativeSpan, his.NegativeDelta = makeSparseBuckets(&coldCounts.sparseBucketsNegative) his.NegativeSpan, his.NegativeDelta = makeBuckets(&coldCounts.nativeHistogramBucketsNegative)
his.PositiveSpan, his.PositiveDelta = makeSparseBuckets(&coldCounts.sparseBucketsPositive) his.PositiveSpan, his.PositiveDelta = makeBuckets(&coldCounts.nativeHistogramBucketsPositive)
} }
addAndResetCounts(hotCounts, coldCounts) addAndResetCounts(hotCounts, coldCounts)
return nil return nil
@ -789,7 +798,7 @@ func (h *histogram) findBucket(v float64) int {
// observe is the implementation for Observe without the findBucket part. // observe is the implementation for Observe without the findBucket part.
func (h *histogram) observe(v float64, bucket int) { func (h *histogram) observe(v float64, bucket int) {
// Do not add to sparse buckets for NaN observations. // Do not add to sparse buckets for NaN observations.
doSparse := h.sparseSchema > math.MinInt32 && !math.IsNaN(v) doSparse := h.nativeHistogramSchema > math.MinInt32 && !math.IsNaN(v)
// We increment h.countAndHotIdx so that the counter in the lower // We increment h.countAndHotIdx so that the counter in the lower
// 63 bits gets incremented. At the same time, we get the new value // 63 bits gets incremented. At the same time, we get the new value
// back, which we can use to find the currently-hot counts. // back, which we can use to find the currently-hot counts.
@ -797,7 +806,7 @@ func (h *histogram) observe(v float64, bucket int) {
hotCounts := h.counts[n>>63] hotCounts := h.counts[n>>63]
hotCounts.observe(v, bucket, doSparse) hotCounts.observe(v, bucket, doSparse)
if doSparse { if doSparse {
h.limitSparseBuckets(hotCounts, v, bucket) h.limitBuckets(hotCounts, v, bucket)
} }
} }
@ -806,11 +815,11 @@ func (h *histogram) observe(v float64, bucket int) {
// number can go higher (if even the lowest resolution isn't enough to reduce // number can go higher (if even the lowest resolution isn't enough to reduce
// the number sufficiently, or if the provided counts aren't fully updated yet // the number sufficiently, or if the provided counts aren't fully updated yet
// by a concurrently happening Write call). // by a concurrently happening Write call).
func (h *histogram) limitSparseBuckets(counts *histogramCounts, value float64, bucket int) { func (h *histogram) limitBuckets(counts *histogramCounts, value float64, bucket int) {
if h.sparseMaxBuckets == 0 { if h.nativeHistogramMaxBuckets == 0 {
return // No limit configured. return // No limit configured.
} }
if h.sparseMaxBuckets >= atomic.LoadUint32(&counts.sparseBucketsNumber) { if h.nativeHistogramMaxBuckets >= atomic.LoadUint32(&counts.nativeHistogramBucketsNumber) {
return // Bucket limit not exceeded yet. return // Bucket limit not exceeded yet.
} }
@ -825,7 +834,7 @@ func (h *histogram) limitSparseBuckets(counts *histogramCounts, value float64, b
hotCounts := h.counts[hotIdx] hotCounts := h.counts[hotIdx]
coldCounts := h.counts[coldIdx] coldCounts := h.counts[coldIdx]
// ...and then check again if we really have to reduce the bucket count. // ...and then check again if we really have to reduce the bucket count.
if h.sparseMaxBuckets >= atomic.LoadUint32(&hotCounts.sparseBucketsNumber) { if h.nativeHistogramMaxBuckets >= atomic.LoadUint32(&hotCounts.nativeHistogramBucketsNumber) {
return // Bucket limit not exceeded after all. return // Bucket limit not exceeded after all.
} }
// Try the various strategies in order. // Try the various strategies in order.
@ -838,13 +847,13 @@ func (h *histogram) limitSparseBuckets(counts *histogramCounts, value float64, b
h.doubleBucketWidth(hotCounts, coldCounts) h.doubleBucketWidth(hotCounts, coldCounts)
} }
// maybeReset resests the whole histogram if at least h.sparseMinResetDuration // maybeReset resests the whole histogram if at least h.nativeHistogramMinResetDuration
// has been passed. It returns true if the histogram has been reset. The caller // has been passed. It returns true if the histogram has been reset. The caller
// must have locked h.mtx. // must have locked h.mtx.
func (h *histogram) maybeReset(hot, cold *histogramCounts, coldIdx uint64, value float64, bucket int) bool { func (h *histogram) maybeReset(hot, cold *histogramCounts, coldIdx uint64, value float64, bucket int) bool {
// We are using the possibly mocked h.now() rather than // We are using the possibly mocked h.now() rather than
// time.Since(h.lastResetTime) to enable testing. // time.Since(h.lastResetTime) to enable testing.
if h.sparseMinResetDuration == 0 || h.now().Sub(h.lastResetTime) < h.sparseMinResetDuration { if h.nativeHistogramMinResetDuration == 0 || h.now().Sub(h.lastResetTime) < h.nativeHistogramMinResetDuration {
return false return false
} }
// Completely reset coldCounts. // Completely reset coldCounts.
@ -864,34 +873,35 @@ func (h *histogram) maybeReset(hot, cold *histogramCounts, coldIdx uint64, value
// maybeWidenZeroBucket widens the zero bucket until it includes the existing // maybeWidenZeroBucket widens the zero bucket until it includes the existing
// buckets closest to the zero bucket (which could be two, if an equidistant // buckets closest to the zero bucket (which could be two, if an equidistant
// negative and a positive bucket exists, but usually it's only one bucket to be // negative and a positive bucket exists, but usually it's only one bucket to be
// merged into the new wider zero bucket). h.sparseMaxZeroThreshold limits how // merged into the new wider zero bucket). h.nativeHistogramMaxZeroThreshold
// far the zero bucket can be extended, and if that's not enough to include an // limits how far the zero bucket can be extended, and if that's not enough to
// existing bucket, the method returns false. The caller must have locked h.mtx. // include an existing bucket, the method returns false. The caller must have
// locked h.mtx.
func (h *histogram) maybeWidenZeroBucket(hot, cold *histogramCounts) bool { func (h *histogram) maybeWidenZeroBucket(hot, cold *histogramCounts) bool {
currentZeroThreshold := math.Float64frombits(atomic.LoadUint64(&hot.sparseZeroThresholdBits)) currentZeroThreshold := math.Float64frombits(atomic.LoadUint64(&hot.nativeHistogramZeroThresholdBits))
if currentZeroThreshold >= h.sparseMaxZeroThreshold { if currentZeroThreshold >= h.nativeHistogramMaxZeroThreshold {
return false return false
} }
// Find the key of the bucket closest to zero. // Find the key of the bucket closest to zero.
smallestKey := findSmallestKey(&hot.sparseBucketsPositive) smallestKey := findSmallestKey(&hot.nativeHistogramBucketsPositive)
smallestNegativeKey := findSmallestKey(&hot.sparseBucketsNegative) smallestNegativeKey := findSmallestKey(&hot.nativeHistogramBucketsNegative)
if smallestNegativeKey < smallestKey { if smallestNegativeKey < smallestKey {
smallestKey = smallestNegativeKey smallestKey = smallestNegativeKey
} }
if smallestKey == math.MaxInt32 { if smallestKey == math.MaxInt32 {
return false return false
} }
newZeroThreshold := getLe(smallestKey, atomic.LoadInt32(&hot.sparseSchema)) newZeroThreshold := getLe(smallestKey, atomic.LoadInt32(&hot.nativeHistogramSchema))
if newZeroThreshold > h.sparseMaxZeroThreshold { if newZeroThreshold > h.nativeHistogramMaxZeroThreshold {
return false // New threshold would exceed the max threshold. return false // New threshold would exceed the max threshold.
} }
atomic.StoreUint64(&cold.sparseZeroThresholdBits, math.Float64bits(newZeroThreshold)) atomic.StoreUint64(&cold.nativeHistogramZeroThresholdBits, math.Float64bits(newZeroThreshold))
// Remove applicable buckets. // Remove applicable buckets.
if _, loaded := cold.sparseBucketsNegative.LoadAndDelete(smallestKey); loaded { if _, loaded := cold.nativeHistogramBucketsNegative.LoadAndDelete(smallestKey); loaded {
atomicDecUint32(&cold.sparseBucketsNumber) atomicDecUint32(&cold.nativeHistogramBucketsNumber)
} }
if _, loaded := cold.sparseBucketsPositive.LoadAndDelete(smallestKey); loaded { if _, loaded := cold.nativeHistogramBucketsPositive.LoadAndDelete(smallestKey); loaded {
atomicDecUint32(&cold.sparseBucketsNumber) atomicDecUint32(&cold.nativeHistogramBucketsNumber)
} }
// Make cold counts the new hot counts. // Make cold counts the new hot counts.
n := atomic.AddUint64(&h.countAndHotIdx, 1<<63) n := atomic.AddUint64(&h.countAndHotIdx, 1<<63)
@ -903,7 +913,7 @@ func (h *histogram) maybeWidenZeroBucket(hot, cold *histogramCounts) bool {
// Add all the now cold counts to the new hot counts... // Add all the now cold counts to the new hot counts...
addAndResetCounts(hot, cold) addAndResetCounts(hot, cold)
// ...adjust the new zero threshold in the cold counts, too... // ...adjust the new zero threshold in the cold counts, too...
atomic.StoreUint64(&cold.sparseZeroThresholdBits, math.Float64bits(newZeroThreshold)) atomic.StoreUint64(&cold.nativeHistogramZeroThresholdBits, math.Float64bits(newZeroThreshold))
// ...and then merge the newly deleted buckets into the wider zero // ...and then merge the newly deleted buckets into the wider zero
// bucket. // bucket.
mergeAndDeleteOrAddAndReset := func(hotBuckets, coldBuckets *sync.Map) func(k, v interface{}) bool { mergeAndDeleteOrAddAndReset := func(hotBuckets, coldBuckets *sync.Map) func(k, v interface{}) bool {
@ -912,14 +922,14 @@ func (h *histogram) maybeWidenZeroBucket(hot, cold *histogramCounts) bool {
bucket := v.(*int64) bucket := v.(*int64)
if key == smallestKey { if key == smallestKey {
// Merge into hot zero bucket... // Merge into hot zero bucket...
atomic.AddUint64(&hot.sparseZeroBucket, uint64(atomic.LoadInt64(bucket))) atomic.AddUint64(&hot.nativeHistogramZeroBucket, uint64(atomic.LoadInt64(bucket)))
// ...and delete from cold counts. // ...and delete from cold counts.
coldBuckets.Delete(key) coldBuckets.Delete(key)
atomicDecUint32(&cold.sparseBucketsNumber) atomicDecUint32(&cold.nativeHistogramBucketsNumber)
} else { } else {
// Add to corresponding hot bucket... // Add to corresponding hot bucket...
if addToSparseBucket(hotBuckets, key, atomic.LoadInt64(bucket)) { if addToBucket(hotBuckets, key, atomic.LoadInt64(bucket)) {
atomic.AddUint32(&hot.sparseBucketsNumber, 1) atomic.AddUint32(&hot.nativeHistogramBucketsNumber, 1)
} }
// ...and reset cold bucket. // ...and reset cold bucket.
atomic.StoreInt64(bucket, 0) atomic.StoreInt64(bucket, 0)
@ -928,8 +938,8 @@ func (h *histogram) maybeWidenZeroBucket(hot, cold *histogramCounts) bool {
} }
} }
cold.sparseBucketsPositive.Range(mergeAndDeleteOrAddAndReset(&hot.sparseBucketsPositive, &cold.sparseBucketsPositive)) cold.nativeHistogramBucketsPositive.Range(mergeAndDeleteOrAddAndReset(&hot.nativeHistogramBucketsPositive, &cold.nativeHistogramBucketsPositive))
cold.sparseBucketsNegative.Range(mergeAndDeleteOrAddAndReset(&hot.sparseBucketsNegative, &cold.sparseBucketsNegative)) cold.nativeHistogramBucketsNegative.Range(mergeAndDeleteOrAddAndReset(&hot.nativeHistogramBucketsNegative, &cold.nativeHistogramBucketsNegative))
return true return true
} }
@ -938,16 +948,16 @@ func (h *histogram) maybeWidenZeroBucket(hot, cold *histogramCounts) bool {
// bucket count (or even no reduction at all). The method does nothing if the // bucket count (or even no reduction at all). The method does nothing if the
// schema is already -4. // schema is already -4.
func (h *histogram) doubleBucketWidth(hot, cold *histogramCounts) { func (h *histogram) doubleBucketWidth(hot, cold *histogramCounts) {
coldSchema := atomic.LoadInt32(&cold.sparseSchema) coldSchema := atomic.LoadInt32(&cold.nativeHistogramSchema)
if coldSchema == -4 { if coldSchema == -4 {
return // Already at lowest resolution. return // Already at lowest resolution.
} }
coldSchema-- coldSchema--
atomic.StoreInt32(&cold.sparseSchema, coldSchema) atomic.StoreInt32(&cold.nativeHistogramSchema, coldSchema)
// Play it simple and just delete all cold buckets. // Play it simple and just delete all cold buckets.
atomic.StoreUint32(&cold.sparseBucketsNumber, 0) atomic.StoreUint32(&cold.nativeHistogramBucketsNumber, 0)
deleteSyncMap(&cold.sparseBucketsNegative) deleteSyncMap(&cold.nativeHistogramBucketsNegative)
deleteSyncMap(&cold.sparseBucketsPositive) deleteSyncMap(&cold.nativeHistogramBucketsPositive)
// Make coldCounts the new hot counts. // Make coldCounts the new hot counts.
n := atomic.AddUint64(&h.countAndHotIdx, 1<<63) n := atomic.AddUint64(&h.countAndHotIdx, 1<<63)
count := n & ((1 << 63) - 1) count := n & ((1 << 63) - 1)
@ -958,7 +968,7 @@ func (h *histogram) doubleBucketWidth(hot, cold *histogramCounts) {
// Add all the now cold counts to the new hot counts... // Add all the now cold counts to the new hot counts...
addAndResetCounts(hot, cold) addAndResetCounts(hot, cold)
// ...adjust the schema in the cold counts, too... // ...adjust the schema in the cold counts, too...
atomic.StoreInt32(&cold.sparseSchema, coldSchema) atomic.StoreInt32(&cold.nativeHistogramSchema, coldSchema)
// ...and then merge the cold buckets into the wider hot buckets. // ...and then merge the cold buckets into the wider hot buckets.
merge := func(hotBuckets *sync.Map) func(k, v interface{}) bool { merge := func(hotBuckets *sync.Map) func(k, v interface{}) bool {
return func(k, v interface{}) bool { return func(k, v interface{}) bool {
@ -970,33 +980,33 @@ func (h *histogram) doubleBucketWidth(hot, cold *histogramCounts) {
} }
key /= 2 key /= 2
// Add to corresponding hot bucket. // Add to corresponding hot bucket.
if addToSparseBucket(hotBuckets, key, atomic.LoadInt64(bucket)) { if addToBucket(hotBuckets, key, atomic.LoadInt64(bucket)) {
atomic.AddUint32(&hot.sparseBucketsNumber, 1) atomic.AddUint32(&hot.nativeHistogramBucketsNumber, 1)
} }
return true return true
} }
} }
cold.sparseBucketsPositive.Range(merge(&hot.sparseBucketsPositive)) cold.nativeHistogramBucketsPositive.Range(merge(&hot.nativeHistogramBucketsPositive))
cold.sparseBucketsNegative.Range(merge(&hot.sparseBucketsNegative)) cold.nativeHistogramBucketsNegative.Range(merge(&hot.nativeHistogramBucketsNegative))
// Play it simple again and just delete all cold buckets. // Play it simple again and just delete all cold buckets.
atomic.StoreUint32(&cold.sparseBucketsNumber, 0) atomic.StoreUint32(&cold.nativeHistogramBucketsNumber, 0)
deleteSyncMap(&cold.sparseBucketsNegative) deleteSyncMap(&cold.nativeHistogramBucketsNegative)
deleteSyncMap(&cold.sparseBucketsPositive) deleteSyncMap(&cold.nativeHistogramBucketsPositive)
} }
func (h *histogram) resetCounts(counts *histogramCounts) { func (h *histogram) resetCounts(counts *histogramCounts) {
atomic.StoreUint64(&counts.sumBits, 0) atomic.StoreUint64(&counts.sumBits, 0)
atomic.StoreUint64(&counts.count, 0) atomic.StoreUint64(&counts.count, 0)
atomic.StoreUint64(&counts.sparseZeroBucket, 0) atomic.StoreUint64(&counts.nativeHistogramZeroBucket, 0)
atomic.StoreUint64(&counts.sparseZeroThresholdBits, math.Float64bits(h.sparseZeroThreshold)) atomic.StoreUint64(&counts.nativeHistogramZeroThresholdBits, math.Float64bits(h.nativeHistogramZeroThreshold))
atomic.StoreInt32(&counts.sparseSchema, h.sparseSchema) atomic.StoreInt32(&counts.nativeHistogramSchema, h.nativeHistogramSchema)
atomic.StoreUint32(&counts.sparseBucketsNumber, 0) atomic.StoreUint32(&counts.nativeHistogramBucketsNumber, 0)
for i := range h.upperBounds { for i := range h.upperBounds {
atomic.StoreUint64(&counts.buckets[i], 0) atomic.StoreUint64(&counts.buckets[i], 0)
} }
deleteSyncMap(&counts.sparseBucketsNegative) deleteSyncMap(&counts.nativeHistogramBucketsNegative)
deleteSyncMap(&counts.sparseBucketsPositive) deleteSyncMap(&counts.nativeHistogramBucketsPositive)
} }
// updateExemplar replaces the exemplar for the provided bucket. With empty // updateExemplar replaces the exemplar for the provided bucket. With empty
@ -1247,13 +1257,13 @@ func (s buckSort) Less(i, j int) bool {
return s[i].GetUpperBound() < s[j].GetUpperBound() return s[i].GetUpperBound() < s[j].GetUpperBound()
} }
// pickSparseschema returns the largest number n between -4 and 8 such that // pickSchema returns the largest number n between -4 and 8 such that
// 2^(2^-n) is less or equal the provided bucketFactor. // 2^(2^-n) is less or equal the provided bucketFactor.
// //
// Special cases: // Special cases:
// - bucketFactor <= 1: panics. // - bucketFactor <= 1: panics.
// - bucketFactor < 2^(2^-8) (but > 1): still returns 8. // - bucketFactor < 2^(2^-8) (but > 1): still returns 8.
func pickSparseSchema(bucketFactor float64) int32 { func pickSchema(bucketFactor float64) int32 {
if bucketFactor <= 1 { if bucketFactor <= 1 {
panic(fmt.Errorf("bucketFactor %f is <=1", bucketFactor)) panic(fmt.Errorf("bucketFactor %f is <=1", bucketFactor))
} }
@ -1268,7 +1278,7 @@ func pickSparseSchema(bucketFactor float64) int32 {
} }
} }
func makeSparseBuckets(buckets *sync.Map) ([]*dto.BucketSpan, []int64) { func makeBuckets(buckets *sync.Map) ([]*dto.BucketSpan, []int64) {
var ii []int var ii []int
buckets.Range(func(k, v interface{}) bool { buckets.Range(func(k, v interface{}) bool {
ii = append(ii, k.(int)) ii = append(ii, k.(int))
@ -1323,9 +1333,9 @@ func makeSparseBuckets(buckets *sync.Map) ([]*dto.BucketSpan, []int64) {
return spans, deltas return spans, deltas
} }
// addToSparseBucket increments the sparse bucket at key by the provided // addToBucket increments the sparse bucket at key by the provided amount. It
// amount. It returns true if a new sparse bucket had to be created for that. // returns true if a new sparse bucket had to be created for that.
func addToSparseBucket(buckets *sync.Map, key int, increment int64) bool { func addToBucket(buckets *sync.Map, key int, increment int64) bool {
if existingBucket, ok := buckets.Load(key); ok { if existingBucket, ok := buckets.Load(key); ok {
// Fast path without allocation. // Fast path without allocation.
atomic.AddInt64(existingBucket.(*int64), increment) atomic.AddInt64(existingBucket.(*int64), increment)
@ -1350,7 +1360,7 @@ func addToSparseBucket(buckets *sync.Map, key int, increment int64) bool {
func addAndReset(hotBuckets *sync.Map, bucketNumber *uint32) func(k, v interface{}) bool { func addAndReset(hotBuckets *sync.Map, bucketNumber *uint32) func(k, v interface{}) bool {
return func(k, v interface{}) bool { return func(k, v interface{}) bool {
bucket := v.(*int64) bucket := v.(*int64)
if addToSparseBucket(hotBuckets, k.(int), atomic.LoadInt64(bucket)) { if addToBucket(hotBuckets, k.(int), atomic.LoadInt64(bucket)) {
atomic.AddUint32(bucketNumber, 1) atomic.AddUint32(bucketNumber, 1)
} }
atomic.StoreInt64(bucket, 0) atomic.StoreInt64(bucket, 0)
@ -1420,7 +1430,7 @@ func getLe(key int, schema int32) float64 {
} }
fracIdx := key & ((1 << schema) - 1) fracIdx := key & ((1 << schema) - 1)
frac := sparseBounds[schema][fracIdx] frac := nativeHistogramBounds[schema][fracIdx]
exp := (key >> schema) + 1 exp := (key >> schema) + 1
if frac == 0.5 && exp == 1025 { if frac == 0.5 && exp == 1025 {
// This is the last bucket before the overflow bucket (for ±Inf // This is the last bucket before the overflow bucket (for ±Inf
@ -1456,9 +1466,9 @@ func atomicDecUint32(p *uint32) {
atomic.AddUint32(p, ^uint32(0)) atomic.AddUint32(p, ^uint32(0))
} }
// addAndResetCounts adds certain fields (count, sum, conventional buckets, // addAndResetCounts adds certain fields (count, sum, conventional buckets, zero
// sparse zero bucket) from the cold counts to the corresponding fields in the // bucket) from the cold counts to the corresponding fields in the hot
// hot counts. Those fields are then reset to 0 in the cold counts. // counts. Those fields are then reset to 0 in the cold counts.
func addAndResetCounts(hot, cold *histogramCounts) { func addAndResetCounts(hot, cold *histogramCounts) {
atomic.AddUint64(&hot.count, atomic.LoadUint64(&cold.count)) atomic.AddUint64(&hot.count, atomic.LoadUint64(&cold.count))
atomic.StoreUint64(&cold.count, 0) atomic.StoreUint64(&cold.count, 0)
@ -1469,6 +1479,6 @@ func addAndResetCounts(hot, cold *histogramCounts) {
atomic.AddUint64(&hot.buckets[i], atomic.LoadUint64(&cold.buckets[i])) atomic.AddUint64(&hot.buckets[i], atomic.LoadUint64(&cold.buckets[i]))
atomic.StoreUint64(&cold.buckets[i], 0) atomic.StoreUint64(&cold.buckets[i], 0)
} }
atomic.AddUint64(&hot.sparseZeroBucket, atomic.LoadUint64(&cold.sparseZeroBucket)) atomic.AddUint64(&hot.nativeHistogramZeroBucket, atomic.LoadUint64(&cold.nativeHistogramZeroBucket))
atomic.StoreUint64(&cold.sparseZeroBucket, 0) atomic.StoreUint64(&cold.nativeHistogramZeroBucket, 0)
} }

20
prometheus/histogram_test.go
View File

@ -658,11 +658,11 @@ func TestSparseHistogram(t *testing.T) {
his := NewHistogram(HistogramOpts{ his := NewHistogram(HistogramOpts{
Name: "name", Name: "name",
Help: "help", Help: "help",
SparseBucketsFactor: s.factor, NativeHistogramBucketFactor: s.factor,
SparseBucketsZeroThreshold: s.zeroThreshold, NativeHistogramZeroThreshold: s.zeroThreshold,
SparseBucketsMaxNumber: s.maxBuckets, NativeHistogramMaxBucketNumber: s.maxBuckets,
SparseBucketsMinResetDuration: s.minResetDuration, NativeHistogramMinResetDuration: s.minResetDuration,
SparseBucketsMaxZeroThreshold: s.maxZeroThreshold, NativeHistogramMaxZeroThreshold: s.maxZeroThreshold,
}) })
ts := time.Now().Add(30 * time.Second) ts := time.Now().Add(30 * time.Second)
now := func() time.Time { now := func() time.Time {
@ -704,11 +704,11 @@ func TestSparseHistogramConcurrency(t *testing.T) {
his := NewHistogram(HistogramOpts{ his := NewHistogram(HistogramOpts{
Name: "test_sparse_histogram", Name: "test_sparse_histogram",
Help: "This help is sparse.", Help: "This help is sparse.",
SparseBucketsFactor: 1.05, NativeHistogramBucketFactor: 1.05,
SparseBucketsZeroThreshold: 0.0000001, NativeHistogramZeroThreshold: 0.0000001,
SparseBucketsMaxNumber: 50, NativeHistogramMaxBucketNumber: 50,
SparseBucketsMinResetDuration: time.Hour, // Comment out to test for totals below. NativeHistogramMinResetDuration: time.Hour, // Comment out to test for totals below.
SparseBucketsMaxZeroThreshold: 0.001, NativeHistogramMaxZeroThreshold: 0.001,
}) })
ts := time.Now().Add(30 * time.Second).Unix() ts := time.Now().Add(30 * time.Second).Unix()