/
float_histogram.go
1204 lines (1106 loc) · 41.2 KB
/
float_histogram.go
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// Copyright 2021 The Prometheus 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 histogram
import (
"fmt"
"math"
"strings"
)
// FloatHistogram is similar to Histogram but uses float64 for all
// counts. Additionally, bucket counts are absolute and not deltas.
//
// A FloatHistogram is needed by PromQL to handle operations that might result
// in fractional counts. Since the counts in a histogram are unlikely to be too
// large to be represented precisely by a float64, a FloatHistogram can also be
// used to represent a histogram with integer counts and thus serves as a more
// generalized representation.
type FloatHistogram struct {
// Counter reset information.
CounterResetHint CounterResetHint
// Currently valid schema numbers are -4 <= n <= 8. They are all for
// base-2 bucket schemas, where 1 is a bucket boundary in each case, and
// then each power of two is divided into 2^n logarithmic buckets. Or
// in other words, each bucket boundary is the previous boundary times
// 2^(2^-n).
Schema int32
// Width of the zero bucket.
ZeroThreshold float64
// Observations falling into the zero bucket. Must be zero or positive.
ZeroCount float64
// Total number of observations. Must be zero or positive.
Count float64
// Sum of observations. This is also used as the stale marker.
Sum float64
// Spans for positive and negative buckets (see Span below).
PositiveSpans, NegativeSpans []Span
// Observation counts in buckets. Each represents an absolute count and
// must be zero or positive.
PositiveBuckets, NegativeBuckets []float64
}
// Copy returns a deep copy of the Histogram.
func (h *FloatHistogram) Copy() *FloatHistogram {
c := FloatHistogram{
CounterResetHint: h.CounterResetHint,
Schema: h.Schema,
ZeroThreshold: h.ZeroThreshold,
ZeroCount: h.ZeroCount,
Count: h.Count,
Sum: h.Sum,
}
if len(h.PositiveSpans) != 0 {
c.PositiveSpans = make([]Span, len(h.PositiveSpans))
copy(c.PositiveSpans, h.PositiveSpans)
}
if len(h.NegativeSpans) != 0 {
c.NegativeSpans = make([]Span, len(h.NegativeSpans))
copy(c.NegativeSpans, h.NegativeSpans)
}
if len(h.PositiveBuckets) != 0 {
c.PositiveBuckets = make([]float64, len(h.PositiveBuckets))
copy(c.PositiveBuckets, h.PositiveBuckets)
}
if len(h.NegativeBuckets) != 0 {
c.NegativeBuckets = make([]float64, len(h.NegativeBuckets))
copy(c.NegativeBuckets, h.NegativeBuckets)
}
return &c
}
// CopyTo makes a deep copy into the given FloatHistogram.
// The destination object has to be a non-nil pointer.
func (h *FloatHistogram) CopyTo(to *FloatHistogram) {
to.CounterResetHint = h.CounterResetHint
to.Schema = h.Schema
to.ZeroThreshold = h.ZeroThreshold
to.ZeroCount = h.ZeroCount
to.Count = h.Count
to.Sum = h.Sum
to.PositiveSpans = resize(to.PositiveSpans, len(h.PositiveSpans))
copy(to.PositiveSpans, h.PositiveSpans)
to.NegativeSpans = resize(to.NegativeSpans, len(h.NegativeSpans))
copy(to.NegativeSpans, h.NegativeSpans)
to.PositiveBuckets = resize(to.PositiveBuckets, len(h.PositiveBuckets))
copy(to.PositiveBuckets, h.PositiveBuckets)
to.NegativeBuckets = resize(to.NegativeBuckets, len(h.NegativeBuckets))
copy(to.NegativeBuckets, h.NegativeBuckets)
}
// CopyToSchema works like Copy, but the returned deep copy has the provided
// target schema, which must be ≤ the original schema (i.e. it must have a lower
// resolution).
func (h *FloatHistogram) CopyToSchema(targetSchema int32) *FloatHistogram {
if targetSchema == h.Schema {
// Fast path.
return h.Copy()
}
if targetSchema > h.Schema {
panic(fmt.Errorf("cannot copy from schema %d to %d", h.Schema, targetSchema))
}
c := FloatHistogram{
Schema: targetSchema,
ZeroThreshold: h.ZeroThreshold,
ZeroCount: h.ZeroCount,
Count: h.Count,
Sum: h.Sum,
}
c.PositiveSpans, c.PositiveBuckets = reduceResolution(h.PositiveSpans, h.PositiveBuckets, h.Schema, targetSchema, false, false)
c.NegativeSpans, c.NegativeBuckets = reduceResolution(h.NegativeSpans, h.NegativeBuckets, h.Schema, targetSchema, false, false)
return &c
}
// String returns a string representation of the Histogram.
func (h *FloatHistogram) String() string {
var sb strings.Builder
fmt.Fprintf(&sb, "{count:%g, sum:%g", h.Count, h.Sum)
var nBuckets []Bucket[float64]
for it := h.NegativeBucketIterator(); it.Next(); {
bucket := it.At()
if bucket.Count != 0 {
nBuckets = append(nBuckets, it.At())
}
}
for i := len(nBuckets) - 1; i >= 0; i-- {
fmt.Fprintf(&sb, ", %s", nBuckets[i].String())
}
if h.ZeroCount != 0 {
fmt.Fprintf(&sb, ", %s", h.ZeroBucket().String())
}
for it := h.PositiveBucketIterator(); it.Next(); {
bucket := it.At()
if bucket.Count != 0 {
fmt.Fprintf(&sb, ", %s", bucket.String())
}
}
sb.WriteRune('}')
return sb.String()
}
// TestExpression returns the string representation of this histogram as it is used in the internal PromQL testing
// framework as well as in promtool rules unit tests.
// The syntax is described in https://prometheus.io/docs/prometheus/latest/configuration/unit_testing_rules/#series
func (h *FloatHistogram) TestExpression() string {
var res []string
m := h.Copy()
m.Compact(math.MaxInt) // Compact to reduce the number of positive and negative spans to 1.
if m.Schema != 0 {
res = append(res, fmt.Sprintf("schema:%d", m.Schema))
}
if m.Count != 0 {
res = append(res, fmt.Sprintf("count:%g", m.Count))
}
if m.Sum != 0 {
res = append(res, fmt.Sprintf("sum:%g", m.Sum))
}
if m.ZeroCount != 0 {
res = append(res, fmt.Sprintf("z_bucket:%g", m.ZeroCount))
}
if m.ZeroThreshold != 0 {
res = append(res, fmt.Sprintf("z_bucket_w:%g", m.ZeroThreshold))
}
addBuckets := func(kind, bucketsKey, offsetKey string, buckets []float64, spans []Span) []string {
if len(spans) > 1 {
panic(fmt.Sprintf("histogram with multiple %s spans not supported", kind))
}
for _, span := range spans {
if span.Offset != 0 {
res = append(res, fmt.Sprintf("%s:%d", offsetKey, span.Offset))
}
}
var bucketStr []string
for _, bucket := range buckets {
bucketStr = append(bucketStr, fmt.Sprintf("%g", bucket))
}
if len(bucketStr) > 0 {
res = append(res, fmt.Sprintf("%s:[%s]", bucketsKey, strings.Join(bucketStr, " ")))
}
return res
}
res = addBuckets("positive", "buckets", "offset", m.PositiveBuckets, m.PositiveSpans)
res = addBuckets("negative", "n_buckets", "n_offset", m.NegativeBuckets, m.NegativeSpans)
return "{{" + strings.Join(res, " ") + "}}"
}
// ZeroBucket returns the zero bucket.
func (h *FloatHistogram) ZeroBucket() Bucket[float64] {
return Bucket[float64]{
Lower: -h.ZeroThreshold,
Upper: h.ZeroThreshold,
LowerInclusive: true,
UpperInclusive: true,
Count: h.ZeroCount,
}
}
// Mul multiplies the FloatHistogram by the provided factor, i.e. it scales all
// bucket counts including the zero bucket and the count and the sum of
// observations. The bucket layout stays the same. This method changes the
// receiving histogram directly (rather than acting on a copy). It returns a
// pointer to the receiving histogram for convenience.
func (h *FloatHistogram) Mul(factor float64) *FloatHistogram {
h.ZeroCount *= factor
h.Count *= factor
h.Sum *= factor
for i := range h.PositiveBuckets {
h.PositiveBuckets[i] *= factor
}
for i := range h.NegativeBuckets {
h.NegativeBuckets[i] *= factor
}
return h
}
// Div works like Mul but divides instead of multiplies.
// When dividing by 0, everything will be set to Inf.
func (h *FloatHistogram) Div(scalar float64) *FloatHistogram {
h.ZeroCount /= scalar
h.Count /= scalar
h.Sum /= scalar
for i := range h.PositiveBuckets {
h.PositiveBuckets[i] /= scalar
}
for i := range h.NegativeBuckets {
h.NegativeBuckets[i] /= scalar
}
return h
}
// Add adds the provided other histogram to the receiving histogram. Count, Sum,
// and buckets from the other histogram are added to the corresponding
// components of the receiving histogram. Buckets in the other histogram that do
// not exist in the receiving histogram are inserted into the latter. The
// resulting histogram might have buckets with a population of zero or directly
// adjacent spans (offset=0). To normalize those, call the Compact method.
//
// The method reconciles differences in the zero threshold and in the schema, and
// changes them if needed. The other histogram will not be modified in any case.
//
// This method returns a pointer to the receiving histogram for convenience.
func (h *FloatHistogram) Add(other *FloatHistogram) *FloatHistogram {
switch {
case other.CounterResetHint == h.CounterResetHint:
// Adding apples to apples, all good. No need to change anything.
case h.CounterResetHint == GaugeType:
// Adding something else to a gauge. That's probably OK. Outcome is a gauge.
// Nothing to do since the receiver is already marked as gauge.
case other.CounterResetHint == GaugeType:
// Similar to before, but this time the receiver is "something else" and we have to change it to gauge.
h.CounterResetHint = GaugeType
case h.CounterResetHint == UnknownCounterReset:
// With the receiver's CounterResetHint being "unknown", this could still be legitimate
// if the caller knows what they are doing. Outcome is then again "unknown".
// No need to do anything since the receiver's CounterResetHint is already "unknown".
case other.CounterResetHint == UnknownCounterReset:
// Similar to before, but now we have to set the receiver's CounterResetHint to "unknown".
h.CounterResetHint = UnknownCounterReset
default:
// All other cases shouldn't actually happen.
// They are a direct collision of CounterReset and NotCounterReset.
// Conservatively set the CounterResetHint to "unknown" and isse a warning.
h.CounterResetHint = UnknownCounterReset
// TODO(trevorwhitney): Actually issue the warning as soon as the plumbing for it is in place
}
otherZeroCount := h.reconcileZeroBuckets(other)
h.ZeroCount += otherZeroCount
h.Count += other.Count
h.Sum += other.Sum
var (
hPositiveSpans = h.PositiveSpans
hPositiveBuckets = h.PositiveBuckets
hNegativeSpans = h.NegativeSpans
hNegativeBuckets = h.NegativeBuckets
otherPositiveSpans = other.PositiveSpans
otherPositiveBuckets = other.PositiveBuckets
otherNegativeSpans = other.NegativeSpans
otherNegativeBuckets = other.NegativeBuckets
)
switch {
case other.Schema < h.Schema:
hPositiveSpans, hPositiveBuckets = reduceResolution(hPositiveSpans, hPositiveBuckets, h.Schema, other.Schema, false, true)
hNegativeSpans, hNegativeBuckets = reduceResolution(hNegativeSpans, hNegativeBuckets, h.Schema, other.Schema, false, true)
h.Schema = other.Schema
case other.Schema > h.Schema:
otherPositiveSpans, otherPositiveBuckets = reduceResolution(otherPositiveSpans, otherPositiveBuckets, other.Schema, h.Schema, false, false)
otherNegativeSpans, otherNegativeBuckets = reduceResolution(otherNegativeSpans, otherNegativeBuckets, other.Schema, h.Schema, false, false)
}
h.PositiveSpans, h.PositiveBuckets = addBuckets(h.Schema, h.ZeroThreshold, false, hPositiveSpans, hPositiveBuckets, otherPositiveSpans, otherPositiveBuckets)
h.NegativeSpans, h.NegativeBuckets = addBuckets(h.Schema, h.ZeroThreshold, false, hNegativeSpans, hNegativeBuckets, otherNegativeSpans, otherNegativeBuckets)
return h
}
// Sub works like Add but subtracts the other histogram.
func (h *FloatHistogram) Sub(other *FloatHistogram) *FloatHistogram {
otherZeroCount := h.reconcileZeroBuckets(other)
h.ZeroCount -= otherZeroCount
h.Count -= other.Count
h.Sum -= other.Sum
var (
hPositiveSpans = h.PositiveSpans
hPositiveBuckets = h.PositiveBuckets
hNegativeSpans = h.NegativeSpans
hNegativeBuckets = h.NegativeBuckets
otherPositiveSpans = other.PositiveSpans
otherPositiveBuckets = other.PositiveBuckets
otherNegativeSpans = other.NegativeSpans
otherNegativeBuckets = other.NegativeBuckets
)
switch {
case other.Schema < h.Schema:
hPositiveSpans, hPositiveBuckets = reduceResolution(hPositiveSpans, hPositiveBuckets, h.Schema, other.Schema, false, true)
hNegativeSpans, hNegativeBuckets = reduceResolution(hNegativeSpans, hNegativeBuckets, h.Schema, other.Schema, false, true)
h.Schema = other.Schema
case other.Schema > h.Schema:
otherPositiveSpans, otherPositiveBuckets = reduceResolution(otherPositiveSpans, otherPositiveBuckets, other.Schema, h.Schema, false, false)
otherNegativeSpans, otherNegativeBuckets = reduceResolution(otherNegativeSpans, otherNegativeBuckets, other.Schema, h.Schema, false, false)
}
h.PositiveSpans, h.PositiveBuckets = addBuckets(h.Schema, h.ZeroThreshold, true, hPositiveSpans, hPositiveBuckets, otherPositiveSpans, otherPositiveBuckets)
h.NegativeSpans, h.NegativeBuckets = addBuckets(h.Schema, h.ZeroThreshold, true, hNegativeSpans, hNegativeBuckets, otherNegativeSpans, otherNegativeBuckets)
return h
}
// Equals returns true if the given float histogram matches exactly.
// Exact match is when there are no new buckets (even empty) and no missing buckets,
// and all the bucket values match. Spans can have different empty length spans in between,
// but they must represent the same bucket layout to match.
// Sum, Count, ZeroCount and bucket values are compared based on their bit patterns
// because this method is about data equality rather than mathematical equality.
func (h *FloatHistogram) Equals(h2 *FloatHistogram) bool {
if h2 == nil {
return false
}
if h.Schema != h2.Schema || h.ZeroThreshold != h2.ZeroThreshold ||
math.Float64bits(h.ZeroCount) != math.Float64bits(h2.ZeroCount) ||
math.Float64bits(h.Count) != math.Float64bits(h2.Count) ||
math.Float64bits(h.Sum) != math.Float64bits(h2.Sum) {
return false
}
if !spansMatch(h.PositiveSpans, h2.PositiveSpans) {
return false
}
if !spansMatch(h.NegativeSpans, h2.NegativeSpans) {
return false
}
if !floatBucketsMatch(h.PositiveBuckets, h2.PositiveBuckets) {
return false
}
if !floatBucketsMatch(h.NegativeBuckets, h2.NegativeBuckets) {
return false
}
return true
}
// Size returns the total size of the FloatHistogram, which includes the size of the pointer
// to FloatHistogram, all its fields, and all elements contained in slices.
// NOTE: this is only valid for 64 bit architectures.
func (h *FloatHistogram) Size() int {
// Size of each slice separately.
posSpanSize := len(h.PositiveSpans) * 8 // 8 bytes (int32 + uint32).
negSpanSize := len(h.NegativeSpans) * 8 // 8 bytes (int32 + uint32).
posBucketSize := len(h.PositiveBuckets) * 8 // 8 bytes (float64).
negBucketSize := len(h.NegativeBuckets) * 8 // 8 bytes (float64).
// Total size of the struct.
// fh is 8 bytes.
// fh.CounterResetHint is 4 bytes (1 byte bool + 3 bytes padding).
// fh.Schema is 4 bytes.
// fh.ZeroThreshold is 8 bytes.
// fh.ZeroCount is 8 bytes.
// fh.Count is 8 bytes.
// fh.Sum is 8 bytes.
// fh.PositiveSpans is 24 bytes.
// fh.NegativeSpans is 24 bytes.
// fh.PositiveBuckets is 24 bytes.
// fh.NegativeBuckets is 24 bytes.
structSize := 144
return structSize + posSpanSize + negSpanSize + posBucketSize + negBucketSize
}
// Compact eliminates empty buckets at the beginning and end of each span, then
// merges spans that are consecutive or at most maxEmptyBuckets apart, and
// finally splits spans that contain more consecutive empty buckets than
// maxEmptyBuckets. (The actual implementation might do something more efficient
// but with the same result.) The compaction happens "in place" in the
// receiving histogram, but a pointer to it is returned for convenience.
//
// The ideal value for maxEmptyBuckets depends on circumstances. The motivation
// to set maxEmptyBuckets > 0 is the assumption that is less overhead to
// represent very few empty buckets explicitly within one span than cutting the
// one span into two to treat the empty buckets as a gap between the two spans,
// both in terms of storage requirement as well as in terms of encoding and
// decoding effort. However, the tradeoffs are subtle. For one, they are
// different in the exposition format vs. in a TSDB chunk vs. for the in-memory
// representation as Go types. In the TSDB, as an additional aspects, the span
// layout is only stored once per chunk, while many histograms with that same
// chunk layout are then only stored with their buckets (so that even a single
// empty bucket will be stored many times).
//
// For the Go types, an additional Span takes 8 bytes. Similarly, an additional
// bucket takes 8 bytes. Therefore, with a single separating empty bucket, both
// options have the same storage requirement, but the single-span solution is
// easier to iterate through. Still, the safest bet is to use maxEmptyBuckets==0
// and only use a larger number if you know what you are doing.
func (h *FloatHistogram) Compact(maxEmptyBuckets int) *FloatHistogram {
h.PositiveBuckets, h.PositiveSpans = compactBuckets(
h.PositiveBuckets, h.PositiveSpans, maxEmptyBuckets, false,
)
h.NegativeBuckets, h.NegativeSpans = compactBuckets(
h.NegativeBuckets, h.NegativeSpans, maxEmptyBuckets, false,
)
return h
}
// DetectReset returns true if the receiving histogram is missing any buckets
// that have a non-zero population in the provided previous histogram. It also
// returns true if any count (in any bucket, in the zero count, or in the count
// of observations, but NOT the sum of observations) is smaller in the receiving
// histogram compared to the previous histogram. Otherwise, it returns false.
//
// This method will shortcut to true if a CounterReset is detected, and shortcut
// to false if NotCounterReset is detected. Otherwise it will do the work to detect
// a reset.
//
// Special behavior in case the Schema or the ZeroThreshold are not the same in
// both histograms:
//
// - A decrease of the ZeroThreshold or an increase of the Schema (i.e. an
// increase of resolution) can only happen together with a reset. Thus, the
// method returns true in either case.
//
// - Upon an increase of the ZeroThreshold, the buckets in the previous
// histogram that fall within the new ZeroThreshold are added to the ZeroCount
// of the previous histogram (without mutating the provided previous
// histogram). The scenario that a populated bucket of the previous histogram
// is partially within, partially outside of the new ZeroThreshold, can only
// happen together with a counter reset and therefore shortcuts to returning
// true.
//
// - Upon a decrease of the Schema, the buckets of the previous histogram are
// merged so that they match the new, lower-resolution schema (again without
// mutating the provided previous histogram).
func (h *FloatHistogram) DetectReset(previous *FloatHistogram) bool {
if h.CounterResetHint == CounterReset {
return true
}
if h.CounterResetHint == NotCounterReset {
return false
}
// In all other cases of CounterResetHint (UnknownCounterReset and GaugeType),
// we go on as we would otherwise, for reasons explained below.
//
// If the CounterResetHint is UnknownCounterReset, we do not know yet if this histogram comes
// with a counter reset. Therefore, we have to do all the detailed work to find out if there
// is a counter reset or not.
// We do the same if the CounterResetHint is GaugeType, which should not happen, but PromQL still
// allows the user to apply functions to gauge histograms that are only meant for counter histograms.
// In this case, we treat the gauge histograms as counter histograms. A warning should be returned
// to the user in this case.
if h.Count < previous.Count {
return true
}
if h.Schema > previous.Schema {
return true
}
if h.ZeroThreshold < previous.ZeroThreshold {
// ZeroThreshold decreased.
return true
}
previousZeroCount, newThreshold := previous.zeroCountForLargerThreshold(h.ZeroThreshold)
if newThreshold != h.ZeroThreshold {
// ZeroThreshold is within a populated bucket in previous
// histogram.
return true
}
if h.ZeroCount < previousZeroCount {
return true
}
currIt := h.floatBucketIterator(true, h.ZeroThreshold, h.Schema)
prevIt := previous.floatBucketIterator(true, h.ZeroThreshold, h.Schema)
if detectReset(&currIt, &prevIt) {
return true
}
currIt = h.floatBucketIterator(false, h.ZeroThreshold, h.Schema)
prevIt = previous.floatBucketIterator(false, h.ZeroThreshold, h.Schema)
return detectReset(&currIt, &prevIt)
}
func detectReset(currIt, prevIt *floatBucketIterator) bool {
if !prevIt.Next() {
return false // If no buckets in previous histogram, nothing can be reset.
}
prevBucket := prevIt.strippedAt()
if !currIt.Next() {
// No bucket in current, but at least one in previous
// histogram. Check if any of those are non-zero, in which case
// this is a reset.
for {
if prevBucket.count != 0 {
return true
}
if !prevIt.Next() {
return false
}
}
}
currBucket := currIt.strippedAt()
for {
// Forward currIt until we find the bucket corresponding to prevBucket.
for currBucket.index < prevBucket.index {
if !currIt.Next() {
// Reached end of currIt early, therefore
// previous histogram has a bucket that the
// current one does not have. Unlass all
// remaining buckets in the previous histogram
// are unpopulated, this is a reset.
for {
if prevBucket.count != 0 {
return true
}
if !prevIt.Next() {
return false
}
}
}
currBucket = currIt.strippedAt()
}
if currBucket.index > prevBucket.index {
// Previous histogram has a bucket the current one does
// not have. If it's populated, it's a reset.
if prevBucket.count != 0 {
return true
}
} else {
// We have reached corresponding buckets in both iterators.
// We can finally compare the counts.
if currBucket.count < prevBucket.count {
return true
}
}
if !prevIt.Next() {
// Reached end of prevIt without finding offending buckets.
return false
}
prevBucket = prevIt.strippedAt()
}
}
// PositiveBucketIterator returns a BucketIterator to iterate over all positive
// buckets in ascending order (starting next to the zero bucket and going up).
func (h *FloatHistogram) PositiveBucketIterator() BucketIterator[float64] {
it := h.floatBucketIterator(true, 0, h.Schema)
return &it
}
// NegativeBucketIterator returns a BucketIterator to iterate over all negative
// buckets in descending order (starting next to the zero bucket and going
// down).
func (h *FloatHistogram) NegativeBucketIterator() BucketIterator[float64] {
it := h.floatBucketIterator(false, 0, h.Schema)
return &it
}
// PositiveReverseBucketIterator returns a BucketIterator to iterate over all
// positive buckets in descending order (starting at the highest bucket and
// going down towards the zero bucket).
func (h *FloatHistogram) PositiveReverseBucketIterator() BucketIterator[float64] {
it := newReverseFloatBucketIterator(h.PositiveSpans, h.PositiveBuckets, h.Schema, true)
return &it
}
// NegativeReverseBucketIterator returns a BucketIterator to iterate over all
// negative buckets in ascending order (starting at the lowest bucket and going
// up towards the zero bucket).
func (h *FloatHistogram) NegativeReverseBucketIterator() BucketIterator[float64] {
it := newReverseFloatBucketIterator(h.NegativeSpans, h.NegativeBuckets, h.Schema, false)
return &it
}
// AllBucketIterator returns a BucketIterator to iterate over all negative,
// zero, and positive buckets in ascending order (starting at the lowest bucket
// and going up). If the highest negative bucket or the lowest positive bucket
// overlap with the zero bucket, their upper or lower boundary, respectively, is
// set to the zero threshold.
func (h *FloatHistogram) AllBucketIterator() BucketIterator[float64] {
return &allFloatBucketIterator{
h: h,
leftIter: newReverseFloatBucketIterator(h.NegativeSpans, h.NegativeBuckets, h.Schema, false),
rightIter: h.floatBucketIterator(true, 0, h.Schema),
state: -1,
}
}
// AllReverseBucketIterator returns a BucketIterator to iterate over all negative,
// zero, and positive buckets in descending order (starting at the lowest bucket
// and going up). If the highest negative bucket or the lowest positive bucket
// overlap with the zero bucket, their upper or lower boundary, respectively, is
// set to the zero threshold.
func (h *FloatHistogram) AllReverseBucketIterator() BucketIterator[float64] {
return &allFloatBucketIterator{
h: h,
leftIter: newReverseFloatBucketIterator(h.PositiveSpans, h.PositiveBuckets, h.Schema, true),
rightIter: h.floatBucketIterator(false, 0, h.Schema),
state: -1,
}
}
// Validate validates consistency between span and bucket slices. Also, buckets are checked
// against negative values.
// We do not check for h.Count being at least as large as the sum of the
// counts in the buckets because floating point precision issues can
// create false positives here.
func (h *FloatHistogram) Validate() error {
if err := checkHistogramSpans(h.NegativeSpans, len(h.NegativeBuckets)); err != nil {
return fmt.Errorf("negative side: %w", err)
}
if err := checkHistogramSpans(h.PositiveSpans, len(h.PositiveBuckets)); err != nil {
return fmt.Errorf("positive side: %w", err)
}
var nCount, pCount float64
err := checkHistogramBuckets(h.NegativeBuckets, &nCount, false)
if err != nil {
return fmt.Errorf("negative side: %w", err)
}
err = checkHistogramBuckets(h.PositiveBuckets, &pCount, false)
if err != nil {
return fmt.Errorf("positive side: %w", err)
}
return nil
}
// zeroCountForLargerThreshold returns what the histogram's zero count would be
// if the ZeroThreshold had the provided larger (or equal) value. If the
// provided value is less than the histogram's ZeroThreshold, the method panics.
// If the largerThreshold ends up within a populated bucket of the histogram, it
// is adjusted upwards to the lower limit of that bucket (all in terms of
// absolute values) and that bucket's count is included in the returned
// count. The adjusted threshold is returned, too.
func (h *FloatHistogram) zeroCountForLargerThreshold(largerThreshold float64) (count, threshold float64) {
// Fast path.
if largerThreshold == h.ZeroThreshold {
return h.ZeroCount, largerThreshold
}
if largerThreshold < h.ZeroThreshold {
panic(fmt.Errorf("new threshold %f is less than old threshold %f", largerThreshold, h.ZeroThreshold))
}
outer:
for {
count = h.ZeroCount
i := h.PositiveBucketIterator()
for i.Next() {
b := i.At()
if b.Lower >= largerThreshold {
break
}
count += b.Count // Bucket to be merged into zero bucket.
if b.Upper > largerThreshold {
// New threshold ended up within a bucket. if it's
// populated, we need to adjust largerThreshold before
// we are done here.
if b.Count != 0 {
largerThreshold = b.Upper
}
break
}
}
i = h.NegativeBucketIterator()
for i.Next() {
b := i.At()
if b.Upper <= -largerThreshold {
break
}
count += b.Count // Bucket to be merged into zero bucket.
if b.Lower < -largerThreshold {
// New threshold ended up within a bucket. If
// it's populated, we need to adjust
// largerThreshold and have to redo the whole
// thing because the treatment of the positive
// buckets is invalid now.
if b.Count != 0 {
largerThreshold = -b.Lower
continue outer
}
break
}
}
return count, largerThreshold
}
}
// trimBucketsInZeroBucket removes all buckets that are within the zero
// bucket. It assumes that the zero threshold is at a bucket boundary and that
// the counts in the buckets to remove are already part of the zero count.
func (h *FloatHistogram) trimBucketsInZeroBucket() {
i := h.PositiveBucketIterator()
bucketsIdx := 0
for i.Next() {
b := i.At()
if b.Lower >= h.ZeroThreshold {
break
}
h.PositiveBuckets[bucketsIdx] = 0
bucketsIdx++
}
i = h.NegativeBucketIterator()
bucketsIdx = 0
for i.Next() {
b := i.At()
if b.Upper <= -h.ZeroThreshold {
break
}
h.NegativeBuckets[bucketsIdx] = 0
bucketsIdx++
}
// We are abusing Compact to trim the buckets set to zero
// above. Premature compacting could cause additional cost, but this
// code path is probably rarely used anyway.
h.Compact(0)
}
// reconcileZeroBuckets finds a zero bucket large enough to include the zero
// buckets of both histograms (the receiving histogram and the other histogram)
// with a zero threshold that is not within a populated bucket in either
// histogram. This method modifies the receiving histogram accourdingly, but
// leaves the other histogram as is. Instead, it returns the zero count the
// other histogram would have if it were modified.
func (h *FloatHistogram) reconcileZeroBuckets(other *FloatHistogram) float64 {
otherZeroCount := other.ZeroCount
otherZeroThreshold := other.ZeroThreshold
for otherZeroThreshold != h.ZeroThreshold {
if h.ZeroThreshold > otherZeroThreshold {
otherZeroCount, otherZeroThreshold = other.zeroCountForLargerThreshold(h.ZeroThreshold)
}
if otherZeroThreshold > h.ZeroThreshold {
h.ZeroCount, h.ZeroThreshold = h.zeroCountForLargerThreshold(otherZeroThreshold)
h.trimBucketsInZeroBucket()
}
}
return otherZeroCount
}
// floatBucketIterator is a low-level constructor for bucket iterators.
//
// If positive is true, the returned iterator iterates through the positive
// buckets, otherwise through the negative buckets.
//
// If absoluteStartValue is < the lowest absolute value of any upper bucket
// boundary, the iterator starts with the first bucket. Otherwise, it will skip
// all buckets with an absolute value of their upper boundary ≤
// absoluteStartValue.
//
// targetSchema must be ≤ the schema of FloatHistogram (and of course within the
// legal values for schemas in general). The buckets are merged to match the
// targetSchema prior to iterating (without mutating FloatHistogram).
func (h *FloatHistogram) floatBucketIterator(
positive bool, absoluteStartValue float64, targetSchema int32,
) floatBucketIterator {
if targetSchema > h.Schema {
panic(fmt.Errorf("cannot merge from schema %d to %d", h.Schema, targetSchema))
}
i := floatBucketIterator{
baseBucketIterator: baseBucketIterator[float64, float64]{
schema: h.Schema,
positive: positive,
},
targetSchema: targetSchema,
absoluteStartValue: absoluteStartValue,
boundReachedStartValue: absoluteStartValue == 0,
}
if positive {
i.spans = h.PositiveSpans
i.buckets = h.PositiveBuckets
} else {
i.spans = h.NegativeSpans
i.buckets = h.NegativeBuckets
}
return i
}
// reverseFloatBucketIterator is a low-level constructor for reverse bucket iterators.
func newReverseFloatBucketIterator(
spans []Span, buckets []float64, schema int32, positive bool,
) reverseFloatBucketIterator {
r := reverseFloatBucketIterator{
baseBucketIterator: baseBucketIterator[float64, float64]{
schema: schema,
spans: spans,
buckets: buckets,
positive: positive,
},
}
r.spansIdx = len(r.spans) - 1
r.bucketsIdx = len(r.buckets) - 1
if r.spansIdx >= 0 {
r.idxInSpan = int32(r.spans[r.spansIdx].Length) - 1
}
r.currIdx = 0
for _, s := range r.spans {
r.currIdx += s.Offset + int32(s.Length)
}
return r
}
type floatBucketIterator struct {
baseBucketIterator[float64, float64]
targetSchema int32 // targetSchema is the schema to merge to and must be ≤ schema.
origIdx int32 // The bucket index within the original schema.
absoluteStartValue float64 // Never return buckets with an upper bound ≤ this value.
boundReachedStartValue bool // Has getBound reached absoluteStartValue already?
}
func (i *floatBucketIterator) At() Bucket[float64] {
// Need to use i.targetSchema rather than i.baseBucketIterator.schema.
return i.baseBucketIterator.at(i.targetSchema)
}
func (i *floatBucketIterator) Next() bool {
if i.spansIdx >= len(i.spans) {
return false
}
if i.schema == i.targetSchema {
// Fast path for the common case.
span := i.spans[i.spansIdx]
if i.bucketsIdx == 0 {
// Seed origIdx for the first bucket.
i.currIdx = span.Offset
} else {
i.currIdx++
}
for i.idxInSpan >= span.Length {
// We have exhausted the current span and have to find a new
// one. We even handle pathologic spans of length 0 here.
i.idxInSpan = 0
i.spansIdx++
if i.spansIdx >= len(i.spans) {
return false
}
span = i.spans[i.spansIdx]
i.currIdx += span.Offset
}
i.currCount = i.buckets[i.bucketsIdx]
i.idxInSpan++
i.bucketsIdx++
} else {
// Copy all of these into local variables so that we can forward to the
// next bucket and then roll back if needed.
origIdx, spansIdx, idxInSpan := i.origIdx, i.spansIdx, i.idxInSpan
span := i.spans[spansIdx]
firstPass := true
i.currCount = 0
mergeLoop: // Merge together all buckets from the original schema that fall into one bucket in the targetSchema.
for {
if i.bucketsIdx == 0 {
// Seed origIdx for the first bucket.
origIdx = span.Offset
} else {
origIdx++
}
for idxInSpan >= span.Length {
// We have exhausted the current span and have to find a new
// one. We even handle pathologic spans of length 0 here.
idxInSpan = 0
spansIdx++
if spansIdx >= len(i.spans) {
if firstPass {
return false
}
break mergeLoop
}
span = i.spans[spansIdx]
origIdx += span.Offset
}
currIdx := targetIdx(origIdx, i.schema, i.targetSchema)
switch {
case firstPass:
i.currIdx = currIdx
firstPass = false
case currIdx != i.currIdx:
// Reached next bucket in targetSchema.
// Do not actually forward to the next bucket, but break out.
break mergeLoop
}
i.currCount += i.buckets[i.bucketsIdx]
idxInSpan++
i.bucketsIdx++
i.origIdx, i.spansIdx, i.idxInSpan = origIdx, spansIdx, idxInSpan
if i.schema == i.targetSchema {
// Don't need to test the next bucket for mergeability
// if we have no schema change anyway.
break mergeLoop
}
}
}
// Skip buckets before absoluteStartValue.
// TODO(beorn7): Maybe do something more efficient than this recursive call.
if !i.boundReachedStartValue && getBound(i.currIdx, i.targetSchema) <= i.absoluteStartValue {
return i.Next()
}
i.boundReachedStartValue = true
return true
}
type reverseFloatBucketIterator struct {
baseBucketIterator[float64, float64]
idxInSpan int32 // Changed from uint32 to allow negative values for exhaustion detection.
}
func (i *reverseFloatBucketIterator) Next() bool {
i.currIdx--
if i.bucketsIdx < 0 {
return false
}
for i.idxInSpan < 0 {
// We have exhausted the current span and have to find a new
// one. We'll even handle pathologic spans of length 0.
i.spansIdx--
i.idxInSpan = int32(i.spans[i.spansIdx].Length) - 1
i.currIdx -= i.spans[i.spansIdx+1].Offset
}
i.currCount = i.buckets[i.bucketsIdx]
i.bucketsIdx--
i.idxInSpan--
return true
}
type allFloatBucketIterator struct {
h *FloatHistogram
leftIter reverseFloatBucketIterator
rightIter floatBucketIterator
// -1 means we are iterating negative buckets.
// 0 means it is time for the zero bucket.
// 1 means we are iterating positive buckets.
// Anything else means iteration is over.
state int8
currBucket Bucket[float64]
}
func (i *allFloatBucketIterator) Next() bool {
switch i.state {
case -1:
if i.leftIter.Next() {
i.currBucket = i.leftIter.At()
switch {