/
float_histogram.go
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/
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"
"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 {
// 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 := *h
if h.PositiveSpans != nil {
c.PositiveSpans = make([]Span, len(h.PositiveSpans))
copy(c.PositiveSpans, h.PositiveSpans)
}
if h.NegativeSpans != nil {
c.NegativeSpans = make([]Span, len(h.NegativeSpans))
copy(c.NegativeSpans, h.NegativeSpans)
}
if h.PositiveBuckets != nil {
c.PositiveBuckets = make([]float64, len(h.PositiveBuckets))
copy(c.PositiveBuckets, h.PositiveBuckets)
}
if h.NegativeBuckets != nil {
c.NegativeBuckets = make([]float64, len(h.NegativeBuckets))
copy(c.NegativeBuckets, h.NegativeBuckets)
}
return &c
}
// 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,
}
// TODO(beorn7): This is a straight-forward implementation using merging
// iterators for the original buckets and then adding one merged bucket
// after another to the newly created FloatHistogram. It's well possible
// that a more involved implementation performs much better, which we
// could do if this code path turns out to be performance-critical.
var iInSpan, index int32
for iSpan, iBucket, it := -1, -1, h.floatBucketIterator(true, 0, targetSchema); it.Next(); {
b := it.At()
c.PositiveSpans, c.PositiveBuckets, iSpan, iBucket, iInSpan = addBucket(
b, c.PositiveSpans, c.PositiveBuckets, iSpan, iBucket, iInSpan, index,
)
index = b.Index
}
for iSpan, iBucket, it := -1, -1, h.floatBucketIterator(false, 0, targetSchema); it.Next(); {
b := it.At()
c.NegativeSpans, c.NegativeBuckets, iSpan, iBucket, iInSpan = addBucket(
b, c.NegativeSpans, c.NegativeBuckets, iSpan, iBucket, iInSpan, index,
)
index = b.Index
}
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()
}
// 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,
}
}
// Scale scales 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) Scale(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
}
// 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,
// but the schema of the other histogram must be ≥ the schema of the receiving
// histogram (i.e. must have an equal or higher resolution). This means that the
// schema of the receiving histogram won't change. Its zero threshold, however,
// will change 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 {
otherZeroCount := h.reconcileZeroBuckets(other)
h.ZeroCount += otherZeroCount
h.Count += other.Count
h.Sum += other.Sum
// TODO(beorn7): If needed, this can be optimized by inspecting the
// spans in other and create missing buckets in h in batches.
var iInSpan, index int32
for iSpan, iBucket, it := -1, -1, other.floatBucketIterator(true, h.ZeroThreshold, h.Schema); it.Next(); {
b := it.At()
h.PositiveSpans, h.PositiveBuckets, iSpan, iBucket, iInSpan = addBucket(
b, h.PositiveSpans, h.PositiveBuckets, iSpan, iBucket, iInSpan, index,
)
index = b.Index
}
for iSpan, iBucket, it := -1, -1, other.floatBucketIterator(false, h.ZeroThreshold, h.Schema); it.Next(); {
b := it.At()
h.NegativeSpans, h.NegativeBuckets, iSpan, iBucket, iInSpan = addBucket(
b, h.NegativeSpans, h.NegativeBuckets, iSpan, iBucket, iInSpan, index,
)
index = b.Index
}
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
// TODO(beorn7): If needed, this can be optimized by inspecting the
// spans in other and create missing buckets in h in batches.
var iInSpan, index int32
for iSpan, iBucket, it := -1, -1, other.floatBucketIterator(true, h.ZeroThreshold, h.Schema); it.Next(); {
b := it.At()
b.Count *= -1
h.PositiveSpans, h.PositiveBuckets, iSpan, iBucket, iInSpan = addBucket(
b, h.PositiveSpans, h.PositiveBuckets, iSpan, iBucket, iInSpan, index,
)
index = b.Index
}
for iSpan, iBucket, it := -1, -1, other.floatBucketIterator(false, h.ZeroThreshold, h.Schema); it.Next(); {
b := it.At()
b.Count *= -1
h.NegativeSpans, h.NegativeBuckets, iSpan, iBucket, iInSpan = addBucket(
b, h.NegativeSpans, h.NegativeBuckets, iSpan, iBucket, iInSpan, index,
)
index = b.Index
}
return h
}
// addBucket takes the "coordinates" of the last bucket that was handled and
// adds the provided bucket after it. If a corresponding bucket exists, the
// count is added. If not, the bucket is inserted. The updated slices and the
// coordinates of the inserted or added-to bucket are returned.
func addBucket(
b Bucket[float64],
spans []Span, buckets []float64,
iSpan, iBucket int,
iInSpan, index int32,
) (
newSpans []Span, newBuckets []float64,
newISpan, newIBucket int, newIInSpan int32,
) {
if iSpan == -1 {
// First add, check if it is before all spans.
if len(spans) == 0 || spans[0].Offset > b.Index {
// Add bucket before all others.
buckets = append(buckets, 0)
copy(buckets[1:], buckets)
buckets[0] = b.Count
if len(spans) > 0 && spans[0].Offset == b.Index+1 {
spans[0].Length++
spans[0].Offset--
return spans, buckets, 0, 0, 0
}
spans = append(spans, Span{})
copy(spans[1:], spans)
spans[0] = Span{Offset: b.Index, Length: 1}
if len(spans) > 1 {
// Convert the absolute offset in the formerly
// first span to a relative offset.
spans[1].Offset -= b.Index + 1
}
return spans, buckets, 0, 0, 0
}
if spans[0].Offset == b.Index {
// Just add to first bucket.
buckets[0] += b.Count
return spans, buckets, 0, 0, 0
}
// We are behind the first bucket, so set everything to the
// first bucket and continue normally.
iSpan, iBucket, iInSpan = 0, 0, 0
index = spans[0].Offset
}
deltaIndex := b.Index - index
for {
remainingInSpan := int32(spans[iSpan].Length) - iInSpan
if deltaIndex < remainingInSpan {
// Bucket is in current span.
iBucket += int(deltaIndex)
iInSpan += deltaIndex
buckets[iBucket] += b.Count
return spans, buckets, iSpan, iBucket, iInSpan
}
deltaIndex -= remainingInSpan
iBucket += int(remainingInSpan)
iSpan++
if iSpan == len(spans) || deltaIndex < spans[iSpan].Offset {
// Bucket is in gap behind previous span (or there are no further spans).
buckets = append(buckets, 0)
copy(buckets[iBucket+1:], buckets[iBucket:])
buckets[iBucket] = b.Count
if deltaIndex == 0 {
// Directly after previous span, extend previous span.
if iSpan < len(spans) {
spans[iSpan].Offset--
}
iSpan--
iInSpan = int32(spans[iSpan].Length)
spans[iSpan].Length++
return spans, buckets, iSpan, iBucket, iInSpan
}
if iSpan < len(spans) && deltaIndex == spans[iSpan].Offset-1 {
// Directly before next span, extend next span.
iInSpan = 0
spans[iSpan].Offset--
spans[iSpan].Length++
return spans, buckets, iSpan, iBucket, iInSpan
}
// No next span, or next span is not directly adjacent to new bucket.
// Add new span.
iInSpan = 0
if iSpan < len(spans) {
spans[iSpan].Offset -= deltaIndex + 1
}
spans = append(spans, Span{})
copy(spans[iSpan+1:], spans[iSpan:])
spans[iSpan] = Span{Length: 1, Offset: deltaIndex}
return spans, buckets, iSpan, iBucket, iInSpan
}
// Try start of next span.
deltaIndex -= spans[iSpan].Offset
iInSpan = 0
}
}
// 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 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.
//
// 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).
//
// Note that this kind of reset detection is quite expensive. Ideally, resets
// are detected at ingest time and stored in the TSDB, so that the reset
// information can be read directly from there rather than be detected each time
// again.
func (h *FloatHistogram) DetectReset(previous *FloatHistogram) bool {
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 BucketIterator[float64]) bool {
if !prevIt.Next() {
return false // If no buckets in previous histogram, nothing can be reset.
}
prevBucket := prevIt.At()
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.At()
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.At()
}
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.At()
}
}
// 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] {
return h.floatBucketIterator(true, 0, h.Schema)
}
// 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] {
return h.floatBucketIterator(false, 0, h.Schema)
}
// 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] {
return newReverseFloatBucketIterator(h.PositiveSpans, h.PositiveBuckets, h.Schema, true)
}
// 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] {
return newReverseFloatBucketIterator(h.NegativeSpans, h.NegativeBuckets, h.Schema, false)
}
// 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,
negIter: h.NegativeReverseBucketIterator(),
posIter: h.PositiveBucketIterator(),
state: -1,
}
}
// 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,
}
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.
}
func (i *floatBucketIterator) Next() bool {
if i.spansIdx >= len(i.spans) {
return false
}
// 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 := i.targetIdx(origIdx)
if firstPass {
i.currIdx = currIdx
firstPass = false
} else if 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 getBound(i.currIdx, i.targetSchema) <= i.absoluteStartValue {
return i.Next()
}
return true
}
// targetIdx returns the bucket index within i.targetSchema for the given bucket
// index within i.schema.
func (i *floatBucketIterator) targetIdx(idx int32) int32 {
if i.schema == i.targetSchema {
// Fast path for the common case. The below would yield the same
// result, just with more effort.
return idx
}
return ((idx - 1) >> (i.schema - i.targetSchema)) + 1
}
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
negIter, posIter BucketIterator[float64]
// -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.negIter.Next() {
i.currBucket = i.negIter.At()
if i.currBucket.Upper > -i.h.ZeroThreshold {
i.currBucket.Upper = -i.h.ZeroThreshold
}
return true
}
i.state = 0
return i.Next()
case 0:
i.state = 1
if i.h.ZeroCount > 0 {
i.currBucket = Bucket[float64]{
Lower: -i.h.ZeroThreshold,
Upper: i.h.ZeroThreshold,
LowerInclusive: true,
UpperInclusive: true,
Count: i.h.ZeroCount,
// Index is irrelevant for the zero bucket.
}
return true
}
return i.Next()
case 1:
if i.posIter.Next() {
i.currBucket = i.posIter.At()
if i.currBucket.Lower < i.h.ZeroThreshold {
i.currBucket.Lower = i.h.ZeroThreshold
}
return true
}
i.state = 42
return false
}
return false
}
func (i *allFloatBucketIterator) At() Bucket[float64] {
return i.currBucket
}