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promql: use Kahan summation for Native Histograms (#15687)
As for float samples, Kahan summation is used for the `sum` and `avg` aggregation and for the respective `_over_time` functions. Kahan summation is not perfect. This commit also adds tests that even Kahan summation cannot reliably pass. These tests are commented out. Note that the behavior might be different on other hardware platforms. We have to keep an eye on test failing on other hardware platforms and adjust them accordingly. Signed-off-by: Aleksandr Smirnov <5targazer@mail.ru>
This commit is contained in:
Sasha
GitHub
parent
3155c95c1f
commit
1dcdb07d30
@ -18,6 +18,8 @@ import (
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"fmt"
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"math"
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"strings"
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"github.com/prometheus/prometheus/util/kahansum"
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)
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// FloatHistogram is similar to Histogram but uses float64 for all
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@ -353,7 +355,7 @@ func (h *FloatHistogram) Add(other *FloatHistogram) (res *FloatHistogram, counte
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}
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counterResetCollision = h.adjustCounterReset(other)
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if !h.UsesCustomBuckets() {
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otherZeroCount := h.reconcileZeroBuckets(other)
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otherZeroCount, _ := h.reconcileZeroBuckets(other, nil)
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h.ZeroCount += otherZeroCount
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}
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h.Count += other.Count
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@ -374,11 +376,11 @@ func (h *FloatHistogram) Add(other *FloatHistogram) (res *FloatHistogram, counte
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intersectedBounds := intersectCustomBucketBounds(h.CustomValues, other.CustomValues)
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// Add with mapping - maps both histograms to intersected layout.
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h.PositiveSpans, h.PositiveBuckets = addCustomBucketsWithMismatches(
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h.PositiveSpans, h.PositiveBuckets, _ = addCustomBucketsWithMismatches(
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false,
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hPositiveSpans, hPositiveBuckets, h.CustomValues,
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otherPositiveSpans, otherPositiveBuckets, other.CustomValues,
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intersectedBounds)
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nil, intersectedBounds)
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h.CustomValues = intersectedBounds
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}
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return h, counterResetCollision, nhcbBoundsReconciled, nil
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@ -408,6 +410,121 @@ func (h *FloatHistogram) Add(other *FloatHistogram) (res *FloatHistogram, counte
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return h, counterResetCollision, nhcbBoundsReconciled, nil
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}
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// KahanAdd works like Add but using the Kahan summation algorithm to minimize numerical errors.
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// c is a histogram holding the Kahan compensation term. It is modified in-place if non-nil.
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// If c is nil, a new compensation histogram is created inside the function. In this case,
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// the caller must use the returned updatedC, because the original c variable is not modified.
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func (h *FloatHistogram) KahanAdd(other, c *FloatHistogram) (updatedC *FloatHistogram, counterResetCollision, nhcbBoundsReconciled bool, err error) {
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if err := h.checkSchemaAndBounds(other); err != nil {
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return nil, false, false, err
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}
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counterResetCollision = h.adjustCounterReset(other)
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if c == nil {
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c = h.newCompensationHistogram()
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}
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if !h.UsesCustomBuckets() {
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otherZeroCount, otherCZeroCount := h.reconcileZeroBuckets(other, c)
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h.ZeroCount, c.ZeroCount = kahansum.Inc(otherZeroCount, h.ZeroCount, c.ZeroCount)
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h.ZeroCount, c.ZeroCount = kahansum.Inc(otherCZeroCount, h.ZeroCount, c.ZeroCount)
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}
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h.Count, c.Count = kahansum.Inc(other.Count, h.Count, c.Count)
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h.Sum, c.Sum = kahansum.Inc(other.Sum, h.Sum, c.Sum)
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var (
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hPositiveSpans = h.PositiveSpans
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hPositiveBuckets = h.PositiveBuckets
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otherPositiveSpans = other.PositiveSpans
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otherPositiveBuckets = other.PositiveBuckets
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cPositiveBuckets = c.PositiveBuckets
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)
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if h.UsesCustomBuckets() {
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if CustomBucketBoundsMatch(h.CustomValues, other.CustomValues) {
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h.PositiveSpans, h.PositiveBuckets, c.PositiveBuckets = kahanAddBuckets(
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h.Schema, h.ZeroThreshold, false,
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hPositiveSpans, hPositiveBuckets,
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otherPositiveSpans, otherPositiveBuckets,
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cPositiveBuckets, nil,
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)
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} else {
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nhcbBoundsReconciled = true
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intersectedBounds := intersectCustomBucketBounds(h.CustomValues, other.CustomValues)
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// Add with mapping - maps both histograms to intersected layout.
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h.PositiveSpans, h.PositiveBuckets, c.PositiveBuckets = addCustomBucketsWithMismatches(
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false,
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hPositiveSpans, hPositiveBuckets, h.CustomValues,
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otherPositiveSpans, otherPositiveBuckets, other.CustomValues,
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cPositiveBuckets, intersectedBounds)
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h.CustomValues = intersectedBounds
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c.CustomValues = intersectedBounds
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}
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c.PositiveSpans = h.PositiveSpans
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return c, counterResetCollision, nhcbBoundsReconciled, nil
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}
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otherC := other.newCompensationHistogram()
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var (
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hNegativeSpans = h.NegativeSpans
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hNegativeBuckets = h.NegativeBuckets
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otherNegativeSpans = other.NegativeSpans
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otherNegativeBuckets = other.NegativeBuckets
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cNegativeBuckets = c.NegativeBuckets
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otherCPositiveBuckets = otherC.PositiveBuckets
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otherCNegativeBuckets = otherC.NegativeBuckets
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)
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switch {
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case other.Schema < h.Schema:
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hPositiveSpans, hPositiveBuckets, cPositiveBuckets = kahanReduceResolution(
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hPositiveSpans, hPositiveBuckets, cPositiveBuckets,
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h.Schema, other.Schema,
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true,
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)
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hNegativeSpans, hNegativeBuckets, cNegativeBuckets = kahanReduceResolution(
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hNegativeSpans, hNegativeBuckets, cNegativeBuckets,
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h.Schema, other.Schema,
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true,
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)
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h.Schema = other.Schema
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case other.Schema > h.Schema:
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otherPositiveSpans, otherPositiveBuckets, otherCPositiveBuckets = kahanReduceResolution(
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otherPositiveSpans, otherPositiveBuckets, otherCPositiveBuckets,
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other.Schema, h.Schema,
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false,
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)
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otherNegativeSpans, otherNegativeBuckets, otherCNegativeBuckets = kahanReduceResolution(
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otherNegativeSpans, otherNegativeBuckets, otherCNegativeBuckets,
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other.Schema, h.Schema,
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false,
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)
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}
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h.PositiveSpans, h.PositiveBuckets, c.PositiveBuckets = kahanAddBuckets(
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h.Schema, h.ZeroThreshold, false,
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hPositiveSpans, hPositiveBuckets,
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otherPositiveSpans, otherPositiveBuckets,
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cPositiveBuckets, otherCPositiveBuckets,
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)
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h.NegativeSpans, h.NegativeBuckets, c.NegativeBuckets = kahanAddBuckets(
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h.Schema, h.ZeroThreshold, false,
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hNegativeSpans, hNegativeBuckets,
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otherNegativeSpans, otherNegativeBuckets,
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cNegativeBuckets, otherCNegativeBuckets,
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)
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c.Schema = h.Schema
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c.ZeroThreshold = h.ZeroThreshold
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c.PositiveSpans = h.PositiveSpans
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c.NegativeSpans = h.NegativeSpans
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return c, counterResetCollision, nhcbBoundsReconciled, nil
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}
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// Sub works like Add but subtracts the other histogram. It uses the same logic
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// to adjust the counter reset hint. This is useful where this method is used
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// for incremental mean calculation. However, if it is used for the actual "-"
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@ -419,7 +536,7 @@ func (h *FloatHistogram) Sub(other *FloatHistogram) (res *FloatHistogram, counte
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}
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counterResetCollision = h.adjustCounterReset(other)
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if !h.UsesCustomBuckets() {
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otherZeroCount := h.reconcileZeroBuckets(other)
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otherZeroCount, _ := h.reconcileZeroBuckets(other, nil)
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h.ZeroCount -= otherZeroCount
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}
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h.Count -= other.Count
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@ -440,11 +557,11 @@ func (h *FloatHistogram) Sub(other *FloatHistogram) (res *FloatHistogram, counte
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intersectedBounds := intersectCustomBucketBounds(h.CustomValues, other.CustomValues)
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// Subtract with mapping - maps both histograms to intersected layout.
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h.PositiveSpans, h.PositiveBuckets = addCustomBucketsWithMismatches(
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h.PositiveSpans, h.PositiveBuckets, _ = addCustomBucketsWithMismatches(
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true,
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hPositiveSpans, hPositiveBuckets, h.CustomValues,
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otherPositiveSpans, otherPositiveBuckets, other.CustomValues,
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intersectedBounds)
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nil, intersectedBounds)
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h.CustomValues = intersectedBounds
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}
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return h, counterResetCollision, nhcbBoundsReconciled, nil
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@ -576,15 +693,28 @@ func (h *FloatHistogram) Size() int {
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// easier to iterate through. Still, the safest bet is to use maxEmptyBuckets==0
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// and only use a larger number if you know what you are doing.
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func (h *FloatHistogram) Compact(maxEmptyBuckets int) *FloatHistogram {
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h.PositiveBuckets, h.PositiveSpans = compactBuckets(
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h.PositiveBuckets, h.PositiveSpans, maxEmptyBuckets, false,
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h.PositiveBuckets, _, h.PositiveSpans = compactBuckets(
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h.PositiveBuckets, nil, h.PositiveSpans, maxEmptyBuckets, false,
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)
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h.NegativeBuckets, h.NegativeSpans = compactBuckets(
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h.NegativeBuckets, h.NegativeSpans, maxEmptyBuckets, false,
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h.NegativeBuckets, _, h.NegativeSpans = compactBuckets(
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h.NegativeBuckets, nil, h.NegativeSpans, maxEmptyBuckets, false,
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)
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return h
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}
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// kahanCompact works like Compact, but it is specialized for FloatHistogram's KahanAdd method.
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// c is a histogram holding the Kahan compensation term.
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func (h *FloatHistogram) kahanCompact(maxEmptyBuckets int, c *FloatHistogram,
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) (updatedH, updatedC *FloatHistogram) {
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h.PositiveBuckets, c.PositiveBuckets, h.PositiveSpans = compactBuckets(
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h.PositiveBuckets, c.PositiveBuckets, h.PositiveSpans, maxEmptyBuckets, false,
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)
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h.NegativeBuckets, c.NegativeBuckets, h.NegativeSpans = compactBuckets(
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h.NegativeBuckets, c.NegativeBuckets, h.NegativeSpans, maxEmptyBuckets, false,
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)
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return h, c
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}
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// DetectReset returns true if the receiving histogram is missing any buckets
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// that have a non-zero population in the provided previous histogram. It also
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// returns true if any count (in any bucket, in the zero count, or in the count
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@ -652,7 +782,7 @@ func (h *FloatHistogram) DetectReset(previous *FloatHistogram) bool {
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// ZeroThreshold decreased.
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return true
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}
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previousZeroCount, newThreshold := previous.zeroCountForLargerThreshold(h.ZeroThreshold)
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previousZeroCount, newThreshold, _ := previous.zeroCountForLargerThreshold(h.ZeroThreshold, nil)
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if newThreshold != h.ZeroThreshold {
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// ZeroThreshold is within a populated bucket in previous
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// histogram.
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@ -847,30 +977,42 @@ func (h *FloatHistogram) Validate() error {
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}
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// zeroCountForLargerThreshold returns what the histogram's zero count would be
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// if the ZeroThreshold had the provided larger (or equal) value. If the
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// provided value is less than the histogram's ZeroThreshold, the method panics.
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// if the ZeroThreshold had the provided larger (or equal) value. It also returns the
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// zero count of the compensation histogram `c` if provided (used for Kahan summation).
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//
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// If the provided ZeroThreshold is less than the histogram's ZeroThreshold, the method panics.
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// If the largerThreshold ends up within a populated bucket of the histogram, it
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// is adjusted upwards to the lower limit of that bucket (all in terms of
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// absolute values) and that bucket's count is included in the returned
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// count. The adjusted threshold is returned, too.
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func (h *FloatHistogram) zeroCountForLargerThreshold(largerThreshold float64) (count, threshold float64) {
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func (h *FloatHistogram) zeroCountForLargerThreshold(
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largerThreshold float64, c *FloatHistogram) (hZeroCount, threshold, cZeroCount float64,
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) {
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if c != nil {
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cZeroCount = c.ZeroCount
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}
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// Fast path.
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if largerThreshold == h.ZeroThreshold {
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return h.ZeroCount, largerThreshold
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return h.ZeroCount, largerThreshold, cZeroCount
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}
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if largerThreshold < h.ZeroThreshold {
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panic(fmt.Errorf("new threshold %f is less than old threshold %f", largerThreshold, h.ZeroThreshold))
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}
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outer:
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for {
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count = h.ZeroCount
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hZeroCount = h.ZeroCount
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i := h.PositiveBucketIterator()
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bucketsIdx := 0
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for i.Next() {
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b := i.At()
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if b.Lower >= largerThreshold {
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break
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}
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count += b.Count // Bucket to be merged into zero bucket.
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// Bucket to be merged into zero bucket.
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hZeroCount, cZeroCount = kahansum.Inc(b.Count, hZeroCount, cZeroCount)
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if c != nil {
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hZeroCount, cZeroCount = kahansum.Inc(c.PositiveBuckets[bucketsIdx], hZeroCount, cZeroCount)
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}
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if b.Upper > largerThreshold {
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// New threshold ended up within a bucket. if it's
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// populated, we need to adjust largerThreshold before
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@ -880,14 +1022,20 @@ outer:
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}
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break
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}
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bucketsIdx++
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}
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i = h.NegativeBucketIterator()
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bucketsIdx = 0
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for i.Next() {
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b := i.At()
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if b.Upper <= -largerThreshold {
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break
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}
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count += b.Count // Bucket to be merged into zero bucket.
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// Bucket to be merged into zero bucket.
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hZeroCount, cZeroCount = kahansum.Inc(b.Count, hZeroCount, cZeroCount)
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if c != nil {
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hZeroCount, cZeroCount = kahansum.Inc(c.NegativeBuckets[bucketsIdx], hZeroCount, cZeroCount)
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}
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if b.Lower < -largerThreshold {
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// New threshold ended up within a bucket. If
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// it's populated, we need to adjust
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@ -900,15 +1048,17 @@ outer:
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}
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break
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}
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bucketsIdx++
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}
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return count, largerThreshold
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return hZeroCount, largerThreshold, cZeroCount
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}
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}
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// trimBucketsInZeroBucket removes all buckets that are within the zero
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// bucket. It assumes that the zero threshold is at a bucket boundary and that
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// the counts in the buckets to remove are already part of the zero count.
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func (h *FloatHistogram) trimBucketsInZeroBucket() {
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// c is a histogram holding the Kahan compensation term.
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func (h *FloatHistogram) trimBucketsInZeroBucket(c *FloatHistogram) {
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i := h.PositiveBucketIterator()
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bucketsIdx := 0
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for i.Next() {
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@ -917,6 +1067,9 @@ func (h *FloatHistogram) trimBucketsInZeroBucket() {
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break
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}
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h.PositiveBuckets[bucketsIdx] = 0
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if c != nil {
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c.PositiveBuckets[bucketsIdx] = 0
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}
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bucketsIdx++
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}
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i = h.NegativeBucketIterator()
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@ -927,34 +1080,46 @@ func (h *FloatHistogram) trimBucketsInZeroBucket() {
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break
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}
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h.NegativeBuckets[bucketsIdx] = 0
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if c != nil {
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c.NegativeBuckets[bucketsIdx] = 0
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}
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bucketsIdx++
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}
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// We are abusing Compact to trim the buckets set to zero
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// above. Premature compacting could cause additional cost, but this
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// code path is probably rarely used anyway.
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h.Compact(0)
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if c != nil {
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h.kahanCompact(0, c)
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} else {
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h.Compact(0)
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}
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}
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// reconcileZeroBuckets finds a zero bucket large enough to include the zero
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// buckets of both histograms (the receiving histogram and the other histogram)
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// with a zero threshold that is not within a populated bucket in either
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// histogram. This method modifies the receiving histogram accordingly, but
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// leaves the other histogram as is. Instead, it returns the zero count the
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// other histogram would have if it were modified.
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func (h *FloatHistogram) reconcileZeroBuckets(other *FloatHistogram) float64 {
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otherZeroCount := other.ZeroCount
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// histogram. This method modifies the receiving histogram accordingly, and
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// also modifies the compensation histogram `c` (used for Kahan summation) if provided,
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// but leaves the other histogram as is. Instead, it returns the zero count the
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// other histogram would have if it were modified, as well as its Kahan compensation term.
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func (h *FloatHistogram) reconcileZeroBuckets(other, c *FloatHistogram) (otherZeroCount, otherCZeroCount float64) {
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otherZeroCount = other.ZeroCount
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otherZeroThreshold := other.ZeroThreshold
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for otherZeroThreshold != h.ZeroThreshold {
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if h.ZeroThreshold > otherZeroThreshold {
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otherZeroCount, otherZeroThreshold = other.zeroCountForLargerThreshold(h.ZeroThreshold)
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otherZeroCount, otherZeroThreshold, otherCZeroCount = other.zeroCountForLargerThreshold(h.ZeroThreshold, nil)
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}
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if otherZeroThreshold > h.ZeroThreshold {
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h.ZeroCount, h.ZeroThreshold = h.zeroCountForLargerThreshold(otherZeroThreshold)
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h.trimBucketsInZeroBucket()
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var cZeroCount float64
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h.ZeroCount, h.ZeroThreshold, cZeroCount = h.zeroCountForLargerThreshold(otherZeroThreshold, c)
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if c != nil {
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c.ZeroCount = cZeroCount
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}
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h.trimBucketsInZeroBucket(c)
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}
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}
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return otherZeroCount
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return otherZeroCount, otherCZeroCount
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}
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// floatBucketIterator is a low-level constructor for bucket iterators.
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@ -1369,6 +1534,145 @@ func addBuckets(
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return spansA, bucketsA
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}
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// kahanAddBuckets works like addBuckets but it is used in FloatHistogram's KahanAdd method
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// and takes additional arguments, compensationBucketsA and compensationBucketsB,
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// which hold the Kahan compensation values associated with histograms A and B.
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// It returns the resulting spans/buckets and compensation buckets.
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func kahanAddBuckets(
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schema int32, threshold float64, negative bool,
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spansA []Span, bucketsA []float64,
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spansB []Span, bucketsB []float64,
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compensationBucketsA, compensationBucketsB []float64,
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) (newSpans []Span, newBucketsA, newBucketsC []float64) {
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var (
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iSpan = -1
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iBucket = -1
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iInSpan int32
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indexA int32
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indexB int32
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bIdxB int
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bucketB float64
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compensationBucketB float64
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deltaIndex int32
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lowerThanThreshold = true
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)
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for _, spanB := range spansB {
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indexB += spanB.Offset
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for j := 0; j < int(spanB.Length); j++ {
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if lowerThanThreshold && IsExponentialSchema(schema) && getBoundExponential(indexB, schema) <= threshold {
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goto nextLoop
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}
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lowerThanThreshold = false
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bucketB = bucketsB[bIdxB]
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if compensationBucketsB != nil {
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compensationBucketB = compensationBucketsB[bIdxB]
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}
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if negative {
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bucketB *= -1
|
||||
compensationBucketB *= -1
|
||||
}
|
||||
|
||||
if iSpan == -1 {
|
||||
if len(spansA) == 0 || spansA[0].Offset > indexB {
|
||||
// Add bucket before all others.
|
||||
bucketsA = append(bucketsA, 0)
|
||||
copy(bucketsA[1:], bucketsA)
|
||||
bucketsA[0] = bucketB
|
||||
compensationBucketsA = append(compensationBucketsA, 0)
|
||||
copy(compensationBucketsA[1:], compensationBucketsA)
|
||||
compensationBucketsA[0] = compensationBucketB
|
||||
if len(spansA) > 0 && spansA[0].Offset == indexB+1 {
|
||||
spansA[0].Length++
|
||||
spansA[0].Offset--
|
||||
goto nextLoop
|
||||
}
|
||||
spansA = append(spansA, Span{})
|
||||
copy(spansA[1:], spansA)
|
||||
spansA[0] = Span{Offset: indexB, Length: 1}
|
||||
if len(spansA) > 1 {
|
||||
// Convert the absolute offset in the formerly
|
||||
// first span to a relative offset.
|
||||
spansA[1].Offset -= indexB + 1
|
||||
}
|
||||
goto nextLoop
|
||||
} else if spansA[0].Offset == indexB {
|
||||
// Just add to first bucket.
|
||||
bucketsA[0], compensationBucketsA[0] = kahansum.Inc(bucketB, bucketsA[0], compensationBucketsA[0])
|
||||
bucketsA[0], compensationBucketsA[0] = kahansum.Inc(compensationBucketB, bucketsA[0], compensationBucketsA[0])
|
||||
goto nextLoop
|
||||
}
|
||||
iSpan, iBucket, iInSpan = 0, 0, 0
|
||||
indexA = spansA[0].Offset
|
||||
}
|
||||
deltaIndex = indexB - indexA
|
||||
for {
|
||||
remainingInSpan := int32(spansA[iSpan].Length) - iInSpan
|
||||
if deltaIndex < remainingInSpan {
|
||||
// Bucket is in current span.
|
||||
iBucket += int(deltaIndex)
|
||||
iInSpan += deltaIndex
|
||||
bucketsA[iBucket], compensationBucketsA[iBucket] = kahansum.Inc(bucketB, bucketsA[iBucket], compensationBucketsA[iBucket])
|
||||
bucketsA[iBucket], compensationBucketsA[iBucket] = kahansum.Inc(compensationBucketB, bucketsA[iBucket], compensationBucketsA[iBucket])
|
||||
break
|
||||
}
|
||||
deltaIndex -= remainingInSpan
|
||||
iBucket += int(remainingInSpan)
|
||||
iSpan++
|
||||
if iSpan == len(spansA) || deltaIndex < spansA[iSpan].Offset {
|
||||
// Bucket is in gap behind previous span (or there are no further spans).
|
||||
bucketsA = append(bucketsA, 0)
|
||||
copy(bucketsA[iBucket+1:], bucketsA[iBucket:])
|
||||
bucketsA[iBucket] = bucketB
|
||||
compensationBucketsA = append(compensationBucketsA, 0)
|
||||
copy(compensationBucketsA[iBucket+1:], compensationBucketsA[iBucket:])
|
||||
compensationBucketsA[iBucket] = compensationBucketB
|
||||
switch {
|
||||
case deltaIndex == 0:
|
||||
// Directly after previous span, extend previous span.
|
||||
if iSpan < len(spansA) {
|
||||
spansA[iSpan].Offset--
|
||||
}
|
||||
iSpan--
|
||||
iInSpan = int32(spansA[iSpan].Length)
|
||||
spansA[iSpan].Length++
|
||||
goto nextLoop
|
||||
case iSpan < len(spansA) && deltaIndex == spansA[iSpan].Offset-1:
|
||||
// Directly before next span, extend next span.
|
||||
iInSpan = 0
|
||||
spansA[iSpan].Offset--
|
||||
spansA[iSpan].Length++
|
||||
goto nextLoop
|
||||
default:
|
||||
// No next span, or next span is not directly adjacent to new bucket.
|
||||
// Add new span.
|
||||
iInSpan = 0
|
||||
if iSpan < len(spansA) {
|
||||
spansA[iSpan].Offset -= deltaIndex + 1
|
||||
}
|
||||
spansA = append(spansA, Span{})
|
||||
copy(spansA[iSpan+1:], spansA[iSpan:])
|
||||
spansA[iSpan] = Span{Length: 1, Offset: deltaIndex}
|
||||
goto nextLoop
|
||||
}
|
||||
} else {
|
||||
// Try start of next span.
|
||||
deltaIndex -= spansA[iSpan].Offset
|
||||
iInSpan = 0
|
||||
}
|
||||
}
|
||||
|
||||
nextLoop:
|
||||
indexA = indexB
|
||||
indexB++
|
||||
bIdxB++
|
||||
}
|
||||
}
|
||||
|
||||
return spansA, bucketsA, compensationBucketsA
|
||||
}
|
||||
|
||||
// floatBucketsMatch compares bucket values of two float histograms using binary float comparison
|
||||
// and returns true if all values match.
|
||||
func floatBucketsMatch(b1, b2 []float64) bool {
|
||||
@ -1496,15 +1800,18 @@ func intersectCustomBucketBounds(boundsA, boundsB []float64) []float64 {
|
||||
|
||||
// addCustomBucketsWithMismatches handles adding/subtracting custom bucket histograms
|
||||
// with mismatched bucket layouts by mapping both to an intersected layout.
|
||||
// It also processes the Kahan compensation term if provided.
|
||||
func addCustomBucketsWithMismatches(
|
||||
negative bool,
|
||||
spansA []Span, bucketsA, boundsA []float64,
|
||||
spansB []Span, bucketsB, boundsB []float64,
|
||||
bucketsC []float64,
|
||||
intersectedBounds []float64,
|
||||
) ([]Span, []float64) {
|
||||
) ([]Span, []float64, []float64) {
|
||||
targetBuckets := make([]float64, len(intersectedBounds)+1)
|
||||
cTargetBuckets := make([]float64, len(intersectedBounds)+1)
|
||||
|
||||
mapBuckets := func(spans []Span, buckets, bounds []float64, negative bool) {
|
||||
mapBuckets := func(spans []Span, buckets, bounds []float64, negative, withCompensation bool) {
|
||||
srcIdx := 0
|
||||
bucketIdx := 0
|
||||
intersectIdx := 0
|
||||
@ -1530,9 +1837,12 @@ func addCustomBucketsWithMismatches(
|
||||
}
|
||||
|
||||
if negative {
|
||||
targetBuckets[targetIdx] -= value
|
||||
targetBuckets[targetIdx], cTargetBuckets[targetIdx] = kahansum.Dec(value, targetBuckets[targetIdx], cTargetBuckets[targetIdx])
|
||||
} else {
|
||||
targetBuckets[targetIdx] += value
|
||||
targetBuckets[targetIdx], cTargetBuckets[targetIdx] = kahansum.Inc(value, targetBuckets[targetIdx], cTargetBuckets[targetIdx])
|
||||
if withCompensation && bucketsC != nil {
|
||||
targetBuckets[targetIdx], cTargetBuckets[targetIdx] = kahansum.Inc(bucketsC[bucketIdx], targetBuckets[targetIdx], cTargetBuckets[targetIdx])
|
||||
}
|
||||
}
|
||||
}
|
||||
srcIdx++
|
||||
@ -1541,21 +1851,23 @@ func addCustomBucketsWithMismatches(
|
||||
}
|
||||
}
|
||||
|
||||
// Map both histograms to the intersected layout.
|
||||
mapBuckets(spansA, bucketsA, boundsA, false)
|
||||
mapBuckets(spansB, bucketsB, boundsB, negative)
|
||||
// Map histograms to the intersected layout.
|
||||
mapBuckets(spansA, bucketsA, boundsA, false, true)
|
||||
mapBuckets(spansB, bucketsB, boundsB, negative, false)
|
||||
|
||||
// Build spans and buckets, excluding zero-valued buckets from the final result.
|
||||
destSpans := spansA[:0] // Reuse spansA capacity for destSpans since we don't need it anymore.
|
||||
destBuckets := targetBuckets[:0] // Reuse targetBuckets capacity for destBuckets since it's guaranteed to be large enough.
|
||||
destSpans := spansA[:0] // Reuse spansA capacity for destSpans since we don't need it anymore.
|
||||
destBuckets := targetBuckets[:0] // Reuse targetBuckets capacity for destBuckets since it's guaranteed to be large enough.
|
||||
cDestBuckets := cTargetBuckets[:0] // Reuse cTargetBuckets capacity for cDestBuckets since it's guaranteed to be large enough.
|
||||
lastIdx := int32(-1)
|
||||
|
||||
for i, count := range targetBuckets {
|
||||
if count == 0 {
|
||||
for i := range targetBuckets {
|
||||
if targetBuckets[i] == 0 && cTargetBuckets[i] == 0 {
|
||||
continue
|
||||
}
|
||||
|
||||
destBuckets = append(destBuckets, count)
|
||||
destBuckets = append(destBuckets, targetBuckets[i])
|
||||
cDestBuckets = append(cDestBuckets, cTargetBuckets[i])
|
||||
idx := int32(i)
|
||||
|
||||
if len(destSpans) > 0 && idx == lastIdx+1 {
|
||||
@ -1578,7 +1890,7 @@ func addCustomBucketsWithMismatches(
|
||||
lastIdx = idx
|
||||
}
|
||||
|
||||
return destSpans, destBuckets
|
||||
return destSpans, destBuckets, cDestBuckets
|
||||
}
|
||||
|
||||
// ReduceResolution reduces the float histogram's spans, buckets into target schema.
|
||||
@ -1618,6 +1930,121 @@ func (h *FloatHistogram) ReduceResolution(targetSchema int32) error {
|
||||
return nil
|
||||
}
|
||||
|
||||
// kahanReduceResolution works like reduceResolution, but it is specialized for FloatHistogram's KahanAdd method.
|
||||
// Unlike reduceResolution, which supports both float and integer buckets, this function only operates on float buckets.
|
||||
// It also takes an additional argument, originCompensationBuckets, representing the compensation buckets for the origin histogram.
|
||||
// Modifies both the origin histogram buckets and their associated compensation buckets.
|
||||
func kahanReduceResolution(
|
||||
originSpans []Span,
|
||||
originReceivingBuckets []float64,
|
||||
originCompensationBuckets []float64,
|
||||
originSchema,
|
||||
targetSchema int32,
|
||||
inplace bool,
|
||||
) (newSpans []Span, newReceivingBuckets, newCompensationBuckets []float64) {
|
||||
var (
|
||||
targetSpans []Span // The spans in the target schema.
|
||||
targetReceivingBuckets []float64 // The receiving bucket counts in the target schema.
|
||||
targetCompensationBuckets []float64 // The compensation bucket counts in the target schema.
|
||||
bucketIdx int32 // The index of bucket in the origin schema.
|
||||
bucketCountIdx int // The position of a bucket in origin bucket count slice `originBuckets`.
|
||||
targetBucketIdx int32 // The index of bucket in the target schema.
|
||||
lastTargetBucketIdx int32 // The index of the last added target bucket.
|
||||
)
|
||||
|
||||
if inplace {
|
||||
// Slice reuse is safe because when reducing the resolution,
|
||||
// target slices don't grow faster than origin slices are being read.
|
||||
targetSpans = originSpans[:0]
|
||||
targetReceivingBuckets = originReceivingBuckets[:0]
|
||||
targetCompensationBuckets = originCompensationBuckets[:0]
|
||||
}
|
||||
|
||||
for _, span := range originSpans {
|
||||
// Determine the index of the first bucket in this span.
|
||||
bucketIdx += span.Offset
|
||||
for j := 0; j < int(span.Length); j++ {
|
||||
// Determine the index of the bucket in the target schema from the index in the original schema.
|
||||
targetBucketIdx = targetIdx(bucketIdx, originSchema, targetSchema)
|
||||
|
||||
switch {
|
||||
case len(targetSpans) == 0:
|
||||
// This is the first span in the targetSpans.
|
||||
span := Span{
|
||||
Offset: targetBucketIdx,
|
||||
Length: 1,
|
||||
}
|
||||
targetSpans = append(targetSpans, span)
|
||||
targetReceivingBuckets = append(targetReceivingBuckets, originReceivingBuckets[bucketCountIdx])
|
||||
lastTargetBucketIdx = targetBucketIdx
|
||||
targetCompensationBuckets = append(targetCompensationBuckets, originCompensationBuckets[bucketCountIdx])
|
||||
|
||||
case lastTargetBucketIdx == targetBucketIdx:
|
||||
// The current bucket has to be merged into the same target bucket as the previous bucket.
|
||||
lastBucketIdx := len(targetReceivingBuckets) - 1
|
||||
targetReceivingBuckets[lastBucketIdx], targetCompensationBuckets[lastBucketIdx] = kahansum.Inc(
|
||||
originReceivingBuckets[bucketCountIdx],
|
||||
targetReceivingBuckets[lastBucketIdx],
|
||||
targetCompensationBuckets[lastBucketIdx],
|
||||
)
|
||||
targetReceivingBuckets[lastBucketIdx], targetCompensationBuckets[lastBucketIdx] = kahansum.Inc(
|
||||
originCompensationBuckets[bucketCountIdx],
|
||||
targetReceivingBuckets[lastBucketIdx],
|
||||
targetCompensationBuckets[lastBucketIdx],
|
||||
)
|
||||
|
||||
case (lastTargetBucketIdx + 1) == targetBucketIdx:
|
||||
// The current bucket has to go into a new target bucket,
|
||||
// and that bucket is next to the previous target bucket,
|
||||
// so we add it to the current target span.
|
||||
targetSpans[len(targetSpans)-1].Length++
|
||||
lastTargetBucketIdx++
|
||||
targetReceivingBuckets = append(targetReceivingBuckets, originReceivingBuckets[bucketCountIdx])
|
||||
targetCompensationBuckets = append(targetCompensationBuckets, originCompensationBuckets[bucketCountIdx])
|
||||
|
||||
case (lastTargetBucketIdx + 1) < targetBucketIdx:
|
||||
// The current bucket has to go into a new target bucket,
|
||||
// and that bucket is separated by a gap from the previous target bucket,
|
||||
// so we need to add a new target span.
|
||||
span := Span{
|
||||
Offset: targetBucketIdx - lastTargetBucketIdx - 1,
|
||||
Length: 1,
|
||||
}
|
||||
targetSpans = append(targetSpans, span)
|
||||
lastTargetBucketIdx = targetBucketIdx
|
||||
targetReceivingBuckets = append(targetReceivingBuckets, originReceivingBuckets[bucketCountIdx])
|
||||
targetCompensationBuckets = append(targetCompensationBuckets, originCompensationBuckets[bucketCountIdx])
|
||||
}
|
||||
|
||||
bucketIdx++
|
||||
bucketCountIdx++
|
||||
}
|
||||
}
|
||||
|
||||
return targetSpans, targetReceivingBuckets, targetCompensationBuckets
|
||||
}
|
||||
|
||||
// newCompensationHistogram initializes a new compensation histogram that can be used
|
||||
// alongside the current FloatHistogram in Kahan summation.
|
||||
// The compensation histogram is structured to match the receiving histogram's bucket layout
|
||||
// including its schema, zero threshold and custom values, and it shares spans with the receiving
|
||||
// histogram. However, the bucket values in the compensation histogram are initialized to zero.
|
||||
func (h *FloatHistogram) newCompensationHistogram() *FloatHistogram {
|
||||
c := &FloatHistogram{
|
||||
CounterResetHint: h.CounterResetHint,
|
||||
Schema: h.Schema,
|
||||
ZeroThreshold: h.ZeroThreshold,
|
||||
CustomValues: h.CustomValues,
|
||||
PositiveBuckets: make([]float64, len(h.PositiveBuckets)),
|
||||
PositiveSpans: h.PositiveSpans,
|
||||
NegativeSpans: h.NegativeSpans,
|
||||
}
|
||||
if !h.UsesCustomBuckets() {
|
||||
c.NegativeBuckets = make([]float64, len(h.NegativeBuckets))
|
||||
}
|
||||
return c
|
||||
}
|
||||
|
||||
// checkSchemaAndBounds checks if two histograms are compatible because they
|
||||
// both use a standard exponential schema or because they both are NHCBs.
|
||||
func (h *FloatHistogram) checkSchemaAndBounds(other *FloatHistogram) error {
|
||||
@ -1659,3 +2086,27 @@ func (h *FloatHistogram) adjustCounterReset(other *FloatHistogram) (counterReset
|
||||
}
|
||||
return false
|
||||
}
|
||||
|
||||
// HasOverflow reports whether any of the FloatHistogram's fields contain an infinite value.
|
||||
// This can happen when aggregating multiple histograms and exceeding float64 capacity.
|
||||
func (h *FloatHistogram) HasOverflow() bool {
|
||||
if math.IsInf(h.ZeroCount, 0) || math.IsInf(h.Count, 0) || math.IsInf(h.Sum, 0) {
|
||||
return true
|
||||
}
|
||||
for _, v := range h.PositiveBuckets {
|
||||
if math.IsInf(v, 0) {
|
||||
return true
|
||||
}
|
||||
}
|
||||
for _, v := range h.NegativeBuckets {
|
||||
if math.IsInf(v, 0) {
|
||||
return true
|
||||
}
|
||||
}
|
||||
for _, v := range h.CustomValues {
|
||||
if math.IsInf(v, 0) {
|
||||
return true
|
||||
}
|
||||
}
|
||||
return false
|
||||
}
|
||||
|
||||
@ -2514,6 +2514,243 @@ func TestFloatHistogramAdd(t *testing.T) {
|
||||
t.Run(c.name, func(t *testing.T) {
|
||||
testHistogramAdd(t, c.in1, c.in2, c.expected, c.expErrMsg, c.expCounterResetCollision, c.expNHCBBoundsReconciled)
|
||||
testHistogramAdd(t, c.in2, c.in1, c.expected, c.expErrMsg, c.expCounterResetCollision, c.expNHCBBoundsReconciled)
|
||||
testHistogramKahanAdd(t, c.in1, nil, c.in2, c.expected, c.expErrMsg, c.expCounterResetCollision, c.expNHCBBoundsReconciled)
|
||||
testHistogramKahanAdd(t, c.in2, nil, c.in1, c.expected, c.expErrMsg, c.expCounterResetCollision, c.expNHCBBoundsReconciled)
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
// TestKahanAddWithCompHistogram tests KahanAdd.
|
||||
// Test cases provide two float histograms and a compensation histogram with predefined values.
|
||||
func TestKahanAddWithCompHistogram(t *testing.T) {
|
||||
cases := []struct {
|
||||
name string
|
||||
in1, comp, in2, expectedSum *FloatHistogram
|
||||
expErrMsg string
|
||||
expCounterResetCollision bool
|
||||
expNHCBBoundsReconciled bool
|
||||
}{
|
||||
{
|
||||
name: "larger zero bucket in first histogram",
|
||||
in1: &FloatHistogram{
|
||||
ZeroThreshold: 1,
|
||||
ZeroCount: 17,
|
||||
Count: 21,
|
||||
Sum: 1.234,
|
||||
PositiveSpans: []Span{{1, 2}, {0, 3}},
|
||||
PositiveBuckets: []float64{2, 3, 6, 2, 5},
|
||||
NegativeSpans: []Span{{4, 2}, {1, 2}},
|
||||
NegativeBuckets: []float64{1, 1, 4, 4},
|
||||
},
|
||||
comp: &FloatHistogram{
|
||||
ZeroThreshold: 1,
|
||||
PositiveSpans: []Span{{1, 2}, {0, 3}},
|
||||
PositiveBuckets: []float64{0.02, 0.03, 0.06, 0.02, 0.05},
|
||||
NegativeSpans: []Span{{4, 2}, {1, 2}},
|
||||
NegativeBuckets: []float64{0.01, 0.01, 0.04, 0.04},
|
||||
},
|
||||
in2: &FloatHistogram{
|
||||
ZeroThreshold: 0.01,
|
||||
ZeroCount: 11,
|
||||
Count: 30,
|
||||
Sum: 2.345,
|
||||
PositiveSpans: []Span{{-2, 2}, {2, 3}},
|
||||
PositiveBuckets: []float64{1, 0, 3, 4, 7},
|
||||
NegativeSpans: []Span{{3, 2}, {3, 2}},
|
||||
NegativeBuckets: []float64{3, 1, 5, 6},
|
||||
},
|
||||
expectedSum: &FloatHistogram{
|
||||
ZeroThreshold: 1,
|
||||
ZeroCount: 29,
|
||||
Count: 51,
|
||||
Sum: 3.579,
|
||||
PositiveSpans: []Span{{1, 2}, {0, 3}},
|
||||
PositiveBuckets: []float64{2.02, 6.03, 10.06, 9.02, 5.05},
|
||||
NegativeSpans: []Span{{3, 3}, {1, 3}},
|
||||
NegativeBuckets: []float64{3, 2.01, 1.01, 4.04, 9.04, 6},
|
||||
},
|
||||
expErrMsg: "",
|
||||
expCounterResetCollision: false,
|
||||
expNHCBBoundsReconciled: false,
|
||||
},
|
||||
{
|
||||
name: "smaller zero bucket in first histogram",
|
||||
in1: &FloatHistogram{
|
||||
ZeroThreshold: 0.01,
|
||||
ZeroCount: 11,
|
||||
Count: 40,
|
||||
Sum: 2.345,
|
||||
PositiveSpans: []Span{{-2, 2}, {2, 3}},
|
||||
PositiveBuckets: []float64{1, 2, 3, 4, 7},
|
||||
NegativeSpans: []Span{{3, 2}, {3, 2}},
|
||||
NegativeBuckets: []float64{3, 1, 5, 6},
|
||||
},
|
||||
comp: &FloatHistogram{
|
||||
ZeroThreshold: 0.01,
|
||||
ZeroCount: 0,
|
||||
PositiveSpans: []Span{{-2, 2}, {2, 3}},
|
||||
PositiveBuckets: []float64{0.02, 0.03, 0.06, 0.07, 0.05},
|
||||
NegativeSpans: []Span{{3, 2}, {3, 2}},
|
||||
NegativeBuckets: []float64{0.01, 0.01, 0.04, 0.04},
|
||||
},
|
||||
in2: &FloatHistogram{
|
||||
ZeroThreshold: 1,
|
||||
ZeroCount: 17,
|
||||
Count: 11,
|
||||
Sum: 1.234,
|
||||
PositiveSpans: []Span{{1, 2}, {0, 3}},
|
||||
PositiveBuckets: []float64{2, 3, 6, 2, 5},
|
||||
NegativeSpans: []Span{{4, 2}, {1, 2}},
|
||||
NegativeBuckets: []float64{1, 1, 4, 4},
|
||||
},
|
||||
expectedSum: &FloatHistogram{
|
||||
ZeroThreshold: 1,
|
||||
ZeroCount: 31.05,
|
||||
Count: 51,
|
||||
Sum: 3.579,
|
||||
PositiveSpans: []Span{{1, 5}},
|
||||
PositiveBuckets: []float64{2, 6.06, 10.07, 9.05, 5},
|
||||
NegativeSpans: []Span{{3, 3}, {1, 3}},
|
||||
NegativeBuckets: []float64{3.01, 2.01, 1, 4, 9.04, 6.04},
|
||||
},
|
||||
expErrMsg: "",
|
||||
expCounterResetCollision: false,
|
||||
expNHCBBoundsReconciled: false,
|
||||
},
|
||||
{
|
||||
name: "first histogram contains zero buckets and Compact is called",
|
||||
in1: &FloatHistogram{
|
||||
ZeroThreshold: 0.01,
|
||||
ZeroCount: 11,
|
||||
Count: 30,
|
||||
Sum: 2.345,
|
||||
PositiveSpans: []Span{{-2, 2}, {1, 1}, {1, 3}},
|
||||
PositiveBuckets: []float64{1, 3, 3, 0, 7, -6},
|
||||
},
|
||||
comp: &FloatHistogram{
|
||||
ZeroThreshold: 0.01,
|
||||
PositiveSpans: []Span{{-2, 2}, {1, 1}, {1, 3}},
|
||||
PositiveBuckets: []float64{7, 2, 0.03, 0, 0.05, 0.06},
|
||||
},
|
||||
in2: &FloatHistogram{
|
||||
ZeroThreshold: 1,
|
||||
ZeroCount: 17,
|
||||
Count: 21,
|
||||
Sum: 1.234,
|
||||
PositiveSpans: []Span{{1, 2}, {1, 2}},
|
||||
PositiveBuckets: []float64{2, 3, 2, 5},
|
||||
},
|
||||
expectedSum: &FloatHistogram{
|
||||
ZeroThreshold: 1,
|
||||
ZeroCount: 41,
|
||||
Count: 51,
|
||||
Sum: 3.579,
|
||||
PositiveSpans: []Span{{1, 2}, {1, 2}},
|
||||
PositiveBuckets: []float64{5.03, 3, 9.05, -0.94},
|
||||
},
|
||||
expErrMsg: "",
|
||||
expCounterResetCollision: false,
|
||||
expNHCBBoundsReconciled: false,
|
||||
},
|
||||
{
|
||||
name: "reduce resolution",
|
||||
in1: &FloatHistogram{
|
||||
Schema: 2,
|
||||
ZeroThreshold: 0.01,
|
||||
ZeroCount: 11,
|
||||
Count: 30,
|
||||
Sum: 2.345,
|
||||
PositiveSpans: []Span{{-2, 2}, {1, 1}, {1, 3}},
|
||||
PositiveBuckets: []float64{1, 3, 1e100, 0, 7, -6},
|
||||
},
|
||||
comp: &FloatHistogram{
|
||||
Schema: 2,
|
||||
ZeroThreshold: 0.01,
|
||||
ZeroCount: 1,
|
||||
PositiveSpans: []Span{{-2, 2}, {1, 1}, {1, 3}},
|
||||
PositiveBuckets: []float64{7, 2, 0.03, 0, 0.05, 0.06},
|
||||
},
|
||||
in2: &FloatHistogram{
|
||||
Schema: 1,
|
||||
ZeroThreshold: 1,
|
||||
ZeroCount: 17,
|
||||
Count: 21,
|
||||
Sum: 1.234,
|
||||
PositiveSpans: []Span{{1, 2}, {1, 2}},
|
||||
PositiveBuckets: []float64{-1e100, 3, 2, 5},
|
||||
},
|
||||
expectedSum: &FloatHistogram{
|
||||
Schema: 1,
|
||||
ZeroThreshold: 1,
|
||||
ZeroCount: 42,
|
||||
Count: 51,
|
||||
Sum: 3.579,
|
||||
PositiveSpans: []Span{{1, 5}},
|
||||
PositiveBuckets: []float64{0.03, 10.05, -5.94, 2, 5},
|
||||
},
|
||||
expErrMsg: "",
|
||||
expCounterResetCollision: false,
|
||||
expNHCBBoundsReconciled: false,
|
||||
},
|
||||
{
|
||||
name: "reduce resolution of 'other' histogram",
|
||||
in1: &FloatHistogram{
|
||||
Schema: 0,
|
||||
ZeroThreshold: 1,
|
||||
ZeroCount: 17,
|
||||
Count: 21,
|
||||
Sum: 1.234,
|
||||
PositiveSpans: []Span{{1, 2}, {1, 2}},
|
||||
PositiveBuckets: []float64{2, 3, 2, 5},
|
||||
},
|
||||
comp: &FloatHistogram{
|
||||
Schema: 0,
|
||||
ZeroThreshold: 1,
|
||||
ZeroCount: 1,
|
||||
PositiveSpans: []Span{{1, 2}, {1, 2}},
|
||||
PositiveBuckets: []float64{17, 2, 0.03, 0},
|
||||
},
|
||||
in2: &FloatHistogram{
|
||||
Schema: 2,
|
||||
ZeroThreshold: 0.01,
|
||||
ZeroCount: 11,
|
||||
Count: 30,
|
||||
Sum: 2.345,
|
||||
PositiveSpans: []Span{{-2, 3}, {1, 1}, {1, 3}},
|
||||
PositiveBuckets: []float64{1e100, 4.1, -1e100, 2.1, 0, 7, -6},
|
||||
},
|
||||
expectedSum: &FloatHistogram{
|
||||
Schema: 0,
|
||||
ZeroThreshold: 1,
|
||||
ZeroCount: 33.1,
|
||||
Count: 51,
|
||||
Sum: 3.579,
|
||||
PositiveSpans: []Span{{1, 2}, {1, 2}},
|
||||
PositiveBuckets: []float64{21.1, 6, 2.03, 5},
|
||||
},
|
||||
expErrMsg: "",
|
||||
expCounterResetCollision: false,
|
||||
expNHCBBoundsReconciled: false,
|
||||
},
|
||||
{
|
||||
name: "warn on counter reset hint collision",
|
||||
in1: &FloatHistogram{
|
||||
Schema: CustomBucketsSchema,
|
||||
CounterResetHint: CounterReset,
|
||||
},
|
||||
in2: &FloatHistogram{
|
||||
Schema: CustomBucketsSchema,
|
||||
CounterResetHint: NotCounterReset,
|
||||
},
|
||||
expErrMsg: "",
|
||||
expCounterResetCollision: true,
|
||||
expNHCBBoundsReconciled: false,
|
||||
},
|
||||
}
|
||||
|
||||
for _, c := range cases {
|
||||
t.Run(c.name, func(t *testing.T) {
|
||||
testHistogramKahanAdd(t, c.in1, c.comp, c.in2, c.expectedSum, c.expErrMsg, c.expCounterResetCollision, c.expNHCBBoundsReconciled)
|
||||
})
|
||||
}
|
||||
}
|
||||
@ -2557,6 +2794,68 @@ func testHistogramAdd(t *testing.T, a, b, expected *FloatHistogram, expErrMsg st
|
||||
}
|
||||
}
|
||||
|
||||
func testHistogramKahanAdd(
|
||||
t *testing.T, a, c, b, expectedSum *FloatHistogram, expErrMsg string, expCounterResetCollision, expNHCBBoundsReconciled bool,
|
||||
) {
|
||||
var (
|
||||
aCopy = a.Copy()
|
||||
bCopy = b.Copy()
|
||||
cCopy *FloatHistogram
|
||||
expectedSumCopy *FloatHistogram
|
||||
)
|
||||
|
||||
if c != nil {
|
||||
cCopy = c.Copy()
|
||||
}
|
||||
|
||||
if expectedSum != nil {
|
||||
expectedSumCopy = expectedSum.Copy()
|
||||
}
|
||||
|
||||
comp, counterResetCollision, nhcbBoundsReconciled, err := aCopy.KahanAdd(bCopy, cCopy)
|
||||
if expErrMsg != "" {
|
||||
require.EqualError(t, err, expErrMsg)
|
||||
} else {
|
||||
require.NoError(t, err)
|
||||
}
|
||||
|
||||
var res *FloatHistogram
|
||||
if comp != nil {
|
||||
// Check that aCopy and its compensation histogram layouts match after addition.
|
||||
require.Equal(t, aCopy.Schema, comp.Schema)
|
||||
require.Equal(t, aCopy.ZeroThreshold, comp.ZeroThreshold)
|
||||
require.Equal(t, aCopy.PositiveSpans, comp.PositiveSpans)
|
||||
require.Equal(t, aCopy.NegativeSpans, comp.NegativeSpans)
|
||||
require.Len(t, aCopy.CustomValues, len(comp.CustomValues))
|
||||
require.Len(t, aCopy.PositiveBuckets, len(comp.PositiveBuckets))
|
||||
require.Len(t, aCopy.NegativeBuckets, len(comp.NegativeBuckets))
|
||||
|
||||
res, _, _, err = aCopy.Add(comp)
|
||||
if expErrMsg != "" {
|
||||
require.EqualError(t, err, expErrMsg)
|
||||
} else {
|
||||
require.NoError(t, err)
|
||||
}
|
||||
}
|
||||
|
||||
// Check that the warnings are correct.
|
||||
require.Equal(t, expCounterResetCollision, counterResetCollision)
|
||||
require.Equal(t, expNHCBBoundsReconciled, nhcbBoundsReconciled)
|
||||
|
||||
if expectedSum != nil {
|
||||
res.Compact(0)
|
||||
expectedSumCopy.Compact(0)
|
||||
|
||||
require.Equal(t, expectedSumCopy, res)
|
||||
|
||||
// Has it also happened in-place?
|
||||
require.Equal(t, expectedSumCopy, aCopy)
|
||||
|
||||
// Check that the argument was not mutated.
|
||||
require.Equal(t, b, bCopy)
|
||||
}
|
||||
}
|
||||
|
||||
func TestFloatHistogramSub(t *testing.T) {
|
||||
// This has fewer test cases than TestFloatHistogramAdd because Add and
|
||||
// Sub share most of the trickier code.
|
||||
|
||||
@ -230,14 +230,29 @@ func (b *baseBucketIterator[BC, IBC]) strippedAt() strippedBucket[BC] {
|
||||
// compactBuckets is a generic function used by both Histogram.Compact and
|
||||
// FloatHistogram.Compact. Set deltaBuckets to true if the provided buckets are
|
||||
// deltas. Set it to false if the buckets contain absolute counts.
|
||||
func compactBuckets[IBC InternalBucketCount](buckets []IBC, spans []Span, maxEmptyBuckets int, deltaBuckets bool) ([]IBC, []Span) {
|
||||
// For float histograms, deltaBuckets is always false.
|
||||
// primaryBuckets hold the main histogram values, while compensationBuckets (if provided) store
|
||||
// Kahan compensation values. compensationBuckets can only be provided for float histograms
|
||||
// and are processed in parallel with primaryBuckets to maintain synchronization.
|
||||
func compactBuckets[IBC InternalBucketCount](
|
||||
primaryBuckets []IBC, compensationBuckets []float64,
|
||||
spans []Span, maxEmptyBuckets int, deltaBuckets bool,
|
||||
) (updatedPrimaryBuckets []IBC, updatedCompensationBuckets []float64, updatedSpans []Span) {
|
||||
if deltaBuckets && compensationBuckets != nil {
|
||||
panic("histogram type mismatch: deltaBuckets cannot be true when compensationBuckets is provided")
|
||||
} else if compensationBuckets != nil && len(primaryBuckets) != len(compensationBuckets) {
|
||||
panic(fmt.Errorf(
|
||||
"primary buckets layout (%v) mismatch against associated compensation buckets layout (%v)",
|
||||
primaryBuckets, compensationBuckets),
|
||||
)
|
||||
}
|
||||
// Fast path: If there are no empty buckets AND no offset in any span is
|
||||
// <= maxEmptyBuckets AND no span has length 0, there is nothing to do and we can return
|
||||
// immediately. We check that first because it's cheap and presumably
|
||||
// common.
|
||||
nothingToDo := true
|
||||
var currentBucketAbsolute IBC
|
||||
for _, bucket := range buckets {
|
||||
for _, bucket := range primaryBuckets {
|
||||
if deltaBuckets {
|
||||
currentBucketAbsolute += bucket
|
||||
} else {
|
||||
@ -256,7 +271,7 @@ func compactBuckets[IBC InternalBucketCount](buckets []IBC, spans []Span, maxEmp
|
||||
}
|
||||
}
|
||||
if nothingToDo {
|
||||
return buckets, spans
|
||||
return primaryBuckets, compensationBuckets, spans
|
||||
}
|
||||
}
|
||||
|
||||
@ -268,12 +283,19 @@ func compactBuckets[IBC InternalBucketCount](buckets []IBC, spans []Span, maxEmp
|
||||
emptyBucketsHere := func() int {
|
||||
i := 0
|
||||
abs := currentBucketAbsolute
|
||||
for uint32(i)+posInSpan < spans[iSpan].Length && abs == 0 {
|
||||
comp := float64(0)
|
||||
if compensationBuckets != nil {
|
||||
comp = compensationBuckets[iBucket]
|
||||
}
|
||||
for uint32(i)+posInSpan < spans[iSpan].Length && abs == 0 && comp == 0 {
|
||||
i++
|
||||
if i+iBucket >= len(buckets) {
|
||||
if i+iBucket >= len(primaryBuckets) {
|
||||
break
|
||||
}
|
||||
abs = buckets[i+iBucket]
|
||||
abs = primaryBuckets[i+iBucket]
|
||||
if compensationBuckets != nil {
|
||||
comp = compensationBuckets[i+iBucket]
|
||||
}
|
||||
}
|
||||
return i
|
||||
}
|
||||
@ -313,11 +335,11 @@ func compactBuckets[IBC InternalBucketCount](buckets []IBC, spans []Span, maxEmp
|
||||
// Cut out empty buckets from start and end of spans, no matter
|
||||
// what. Also cut out empty buckets from the middle of a span but only
|
||||
// if there are more than maxEmptyBuckets consecutive empty buckets.
|
||||
for iBucket < len(buckets) {
|
||||
for iBucket < len(primaryBuckets) {
|
||||
if deltaBuckets {
|
||||
currentBucketAbsolute += buckets[iBucket]
|
||||
currentBucketAbsolute += primaryBuckets[iBucket]
|
||||
} else {
|
||||
currentBucketAbsolute = buckets[iBucket]
|
||||
currentBucketAbsolute = primaryBuckets[iBucket]
|
||||
}
|
||||
if nEmpty := emptyBucketsHere(); nEmpty > 0 {
|
||||
if posInSpan > 0 &&
|
||||
@ -334,11 +356,14 @@ func compactBuckets[IBC InternalBucketCount](buckets []IBC, spans []Span, maxEmp
|
||||
continue
|
||||
}
|
||||
// In all other cases, we cut out the empty buckets.
|
||||
if deltaBuckets && iBucket+nEmpty < len(buckets) {
|
||||
currentBucketAbsolute = -buckets[iBucket]
|
||||
buckets[iBucket+nEmpty] += buckets[iBucket]
|
||||
if deltaBuckets && iBucket+nEmpty < len(primaryBuckets) {
|
||||
currentBucketAbsolute = -primaryBuckets[iBucket]
|
||||
primaryBuckets[iBucket+nEmpty] += primaryBuckets[iBucket]
|
||||
}
|
||||
primaryBuckets = append(primaryBuckets[:iBucket], primaryBuckets[iBucket+nEmpty:]...)
|
||||
if compensationBuckets != nil {
|
||||
compensationBuckets = append(compensationBuckets[:iBucket], compensationBuckets[iBucket+nEmpty:]...)
|
||||
}
|
||||
buckets = append(buckets[:iBucket], buckets[iBucket+nEmpty:]...)
|
||||
if posInSpan == 0 {
|
||||
// Start of span.
|
||||
if nEmpty == int(spans[iSpan].Length) {
|
||||
@ -388,8 +413,8 @@ func compactBuckets[IBC InternalBucketCount](buckets []IBC, spans []Span, maxEmp
|
||||
iSpan++
|
||||
}
|
||||
}
|
||||
if maxEmptyBuckets == 0 || len(buckets) == 0 {
|
||||
return buckets, spans
|
||||
if maxEmptyBuckets == 0 || len(primaryBuckets) == 0 {
|
||||
return primaryBuckets, compensationBuckets, spans
|
||||
}
|
||||
|
||||
// Finally, check if any offsets between spans are small enough to merge
|
||||
@ -397,7 +422,7 @@ func compactBuckets[IBC InternalBucketCount](buckets []IBC, spans []Span, maxEmp
|
||||
iBucket = int(spans[0].Length)
|
||||
if deltaBuckets {
|
||||
currentBucketAbsolute = 0
|
||||
for _, bucket := range buckets[:iBucket] {
|
||||
for _, bucket := range primaryBuckets[:iBucket] {
|
||||
currentBucketAbsolute += bucket
|
||||
}
|
||||
}
|
||||
@ -406,7 +431,7 @@ func compactBuckets[IBC InternalBucketCount](buckets []IBC, spans []Span, maxEmp
|
||||
if int(spans[iSpan].Offset) > maxEmptyBuckets {
|
||||
l := int(spans[iSpan].Length)
|
||||
if deltaBuckets {
|
||||
for _, bucket := range buckets[iBucket : iBucket+l] {
|
||||
for _, bucket := range primaryBuckets[iBucket : iBucket+l] {
|
||||
currentBucketAbsolute += bucket
|
||||
}
|
||||
}
|
||||
@ -418,22 +443,28 @@ func compactBuckets[IBC InternalBucketCount](buckets []IBC, spans []Span, maxEmp
|
||||
offset := int(spans[iSpan].Offset)
|
||||
spans[iSpan-1].Length += uint32(offset) + spans[iSpan].Length
|
||||
spans = append(spans[:iSpan], spans[iSpan+1:]...)
|
||||
newBuckets := make([]IBC, len(buckets)+offset)
|
||||
copy(newBuckets, buckets[:iBucket])
|
||||
copy(newBuckets[iBucket+offset:], buckets[iBucket:])
|
||||
newPrimaryBuckets := make([]IBC, len(primaryBuckets)+offset)
|
||||
copy(newPrimaryBuckets, primaryBuckets[:iBucket])
|
||||
copy(newPrimaryBuckets[iBucket+offset:], primaryBuckets[iBucket:])
|
||||
if deltaBuckets {
|
||||
newBuckets[iBucket] = -currentBucketAbsolute
|
||||
newBuckets[iBucket+offset] += currentBucketAbsolute
|
||||
newPrimaryBuckets[iBucket] = -currentBucketAbsolute
|
||||
newPrimaryBuckets[iBucket+offset] += currentBucketAbsolute
|
||||
}
|
||||
primaryBuckets = newPrimaryBuckets
|
||||
if compensationBuckets != nil {
|
||||
newCompensationBuckets := make([]float64, len(compensationBuckets)+offset)
|
||||
copy(newCompensationBuckets, compensationBuckets[:iBucket])
|
||||
copy(newCompensationBuckets[iBucket+offset:], compensationBuckets[iBucket:])
|
||||
compensationBuckets = newCompensationBuckets
|
||||
}
|
||||
iBucket += offset
|
||||
buckets = newBuckets
|
||||
currentBucketAbsolute = buckets[iBucket]
|
||||
currentBucketAbsolute = primaryBuckets[iBucket]
|
||||
// Note that with many merges, it would be more efficient to
|
||||
// first record all the chunks of empty buckets to insert and
|
||||
// then do it in one go through all the buckets.
|
||||
}
|
||||
|
||||
return buckets, spans
|
||||
return primaryBuckets, compensationBuckets, spans
|
||||
}
|
||||
|
||||
func checkHistogramSpans(spans []Span, numBuckets int) error {
|
||||
|
||||
@ -349,11 +349,11 @@ func allEmptySpans(s []Span) bool {
|
||||
// Compact works like FloatHistogram.Compact. See there for detailed
|
||||
// explanations.
|
||||
func (h *Histogram) Compact(maxEmptyBuckets int) *Histogram {
|
||||
h.PositiveBuckets, h.PositiveSpans = compactBuckets(
|
||||
h.PositiveBuckets, h.PositiveSpans, maxEmptyBuckets, true,
|
||||
h.PositiveBuckets, _, h.PositiveSpans = compactBuckets(
|
||||
h.PositiveBuckets, nil, h.PositiveSpans, maxEmptyBuckets, true,
|
||||
)
|
||||
h.NegativeBuckets, h.NegativeSpans = compactBuckets(
|
||||
h.NegativeBuckets, h.NegativeSpans, maxEmptyBuckets, true,
|
||||
h.NegativeBuckets, _, h.NegativeSpans = compactBuckets(
|
||||
h.NegativeBuckets, nil, h.NegativeSpans, maxEmptyBuckets, true,
|
||||
)
|
||||
return h
|
||||
}
|
||||
|
||||
126
promql/engine.go
126
promql/engine.go
@ -50,6 +50,7 @@ import (
|
||||
"github.com/prometheus/prometheus/tsdb/chunkenc"
|
||||
"github.com/prometheus/prometheus/util/annotations"
|
||||
"github.com/prometheus/prometheus/util/features"
|
||||
"github.com/prometheus/prometheus/util/kahansum"
|
||||
"github.com/prometheus/prometheus/util/logging"
|
||||
"github.com/prometheus/prometheus/util/stats"
|
||||
"github.com/prometheus/prometheus/util/zeropool"
|
||||
@ -3239,23 +3240,26 @@ func vectorElemBinop(op parser.ItemType, lhs, rhs float64, hlhs, hrhs *histogram
|
||||
}
|
||||
|
||||
type groupedAggregation struct {
|
||||
floatValue float64
|
||||
histogramValue *histogram.FloatHistogram
|
||||
floatMean float64
|
||||
floatKahanC float64 // "Compensating value" for Kahan summation.
|
||||
groupCount float64
|
||||
heap vectorByValueHeap
|
||||
floatValue float64
|
||||
floatMean float64
|
||||
floatKahanC float64 // Compensation float for Kahan summation.
|
||||
histogramValue *histogram.FloatHistogram
|
||||
histogramMean *histogram.FloatHistogram
|
||||
histogramKahanC *histogram.FloatHistogram // Compensation histogram for Kahan summation.
|
||||
groupCount float64
|
||||
heap vectorByValueHeap
|
||||
|
||||
// All bools together for better packing within the struct.
|
||||
seen bool // Was this output groups seen in the input at this timestamp.
|
||||
hasFloat bool // Has at least 1 float64 sample aggregated.
|
||||
hasHistogram bool // Has at least 1 histogram sample aggregated.
|
||||
incompatibleHistograms bool // If true, group has seen mixed exponential and custom buckets.
|
||||
groupAggrComplete bool // Used by LIMITK to short-cut series loop when we've reached K elem on every group.
|
||||
incrementalMean bool // True after reverting to incremental calculation of the mean value.
|
||||
counterResetSeen bool // Counter reset hint CounterReset seen. Currently only used for histogram samples.
|
||||
notCounterResetSeen bool // Counter reset hint NotCounterReset seen. Currently only used for histogram samples.
|
||||
dropName bool // True if any sample in this group has DropName set.
|
||||
seen bool // Was this output groups seen in the input at this timestamp.
|
||||
hasFloat bool // Has at least 1 float64 sample aggregated.
|
||||
hasHistogram bool // Has at least 1 histogram sample aggregated.
|
||||
incompatibleHistograms bool // If true, group has seen mixed exponential and custom buckets.
|
||||
groupAggrComplete bool // Used by LIMITK to short-cut series loop when we've reached K elem on every group.
|
||||
floatIncrementalMean bool // True after reverting to incremental calculation for float-based mean value.
|
||||
histogramIncrementalMean bool // True after reverting to incremental calculation for histogram-based mean value.
|
||||
counterResetSeen bool // Counter reset hint CounterReset seen. Currently only used for histogram samples.
|
||||
notCounterResetSeen bool // Counter reset hint NotCounterReset seen. Currently only used for histogram samples.
|
||||
dropName bool // True if any sample in this group has DropName set.
|
||||
}
|
||||
|
||||
// aggregation evaluates sum, avg, count, stdvar, stddev or quantile at one timestep on inputMatrix.
|
||||
@ -3345,6 +3349,11 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, q float64, inputMatrix
|
||||
group.dropName = true
|
||||
}
|
||||
|
||||
var (
|
||||
nhcbBoundsReconciled bool
|
||||
err error
|
||||
)
|
||||
|
||||
switch op {
|
||||
case parser.SUM:
|
||||
if h != nil {
|
||||
@ -3356,7 +3365,7 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, q float64, inputMatrix
|
||||
case histogram.NotCounterReset:
|
||||
group.notCounterResetSeen = true
|
||||
}
|
||||
_, _, nhcbBoundsReconciled, err := group.histogramValue.Add(h)
|
||||
group.histogramKahanC, _, nhcbBoundsReconciled, err = group.histogramValue.KahanAdd(h, group.histogramKahanC)
|
||||
if err != nil {
|
||||
handleAggregationError(err, e, inputMatrix[si].Metric.Get(model.MetricNameLabel), &annos)
|
||||
group.incompatibleHistograms = true
|
||||
@ -3370,18 +3379,13 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, q float64, inputMatrix
|
||||
// point in copying the histogram in that case.
|
||||
} else {
|
||||
group.hasFloat = true
|
||||
group.floatValue, group.floatKahanC = kahanSumInc(f, group.floatValue, group.floatKahanC)
|
||||
group.floatValue, group.floatKahanC = kahansum.Inc(f, group.floatValue, group.floatKahanC)
|
||||
}
|
||||
|
||||
case parser.AVG:
|
||||
// For the average calculation of histograms, we use
|
||||
// incremental mean calculation without the help of
|
||||
// Kahan summation (but this should change, see
|
||||
// https://github.com/prometheus/prometheus/issues/14105
|
||||
// ). For floats, we improve the accuracy with the help
|
||||
// of Kahan summation. For a while, we assumed that
|
||||
// incremental mean calculation combined with Kahan
|
||||
// summation (see
|
||||
// We improve the accuracy with the help of Kahan summation.
|
||||
// For a while, we assumed that incremental mean calculation
|
||||
// combined with Kahan summation (see
|
||||
// https://stackoverflow.com/questions/61665473/is-it-beneficial-for-precision-to-calculate-the-incremental-mean-average
|
||||
// for inspiration) is generally the preferred solution.
|
||||
// However, it then turned out that direct mean
|
||||
@ -3416,20 +3420,37 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, q float64, inputMatrix
|
||||
case histogram.NotCounterReset:
|
||||
group.notCounterResetSeen = true
|
||||
}
|
||||
left := h.Copy().Div(group.groupCount)
|
||||
right := group.histogramValue.Copy().Div(group.groupCount)
|
||||
|
||||
toAdd, _, nhcbBoundsReconciled, err := left.Sub(right)
|
||||
if err != nil {
|
||||
handleAggregationError(err, e, inputMatrix[si].Metric.Get(model.MetricNameLabel), &annos)
|
||||
group.incompatibleHistograms = true
|
||||
continue
|
||||
if !group.histogramIncrementalMean {
|
||||
v := group.histogramValue.Copy()
|
||||
var c *histogram.FloatHistogram
|
||||
if group.histogramKahanC != nil {
|
||||
c = group.histogramKahanC.Copy()
|
||||
}
|
||||
c, _, nhcbBoundsReconciled, err = v.KahanAdd(h, c)
|
||||
if err != nil {
|
||||
handleAggregationError(err, e, inputMatrix[si].Metric.Get(model.MetricNameLabel), &annos)
|
||||
group.incompatibleHistograms = true
|
||||
continue
|
||||
}
|
||||
if nhcbBoundsReconciled {
|
||||
annos.Add(annotations.NewMismatchedCustomBucketsHistogramsInfo(e.Expr.PositionRange(), annotations.HistogramAgg))
|
||||
}
|
||||
if !v.HasOverflow() {
|
||||
group.histogramValue, group.histogramKahanC = v, c
|
||||
break
|
||||
}
|
||||
group.histogramIncrementalMean = true
|
||||
group.histogramMean = group.histogramValue.Copy().Div(group.groupCount - 1)
|
||||
if group.histogramKahanC != nil {
|
||||
group.histogramKahanC.Div(group.groupCount - 1)
|
||||
}
|
||||
}
|
||||
if nhcbBoundsReconciled {
|
||||
annos.Add(annotations.NewMismatchedCustomBucketsHistogramsInfo(e.Expr.PositionRange(), annotations.HistogramAgg))
|
||||
q := (group.groupCount - 1) / group.groupCount
|
||||
if group.histogramKahanC != nil {
|
||||
group.histogramKahanC.Mul(q)
|
||||
}
|
||||
|
||||
_, _, nhcbBoundsReconciled, err = group.histogramValue.Add(toAdd)
|
||||
toAdd := h.Copy().Div(group.groupCount)
|
||||
group.histogramKahanC, _, nhcbBoundsReconciled, err = group.histogramMean.Mul(q).KahanAdd(toAdd, group.histogramKahanC)
|
||||
if err != nil {
|
||||
handleAggregationError(err, e, inputMatrix[si].Metric.Get(model.MetricNameLabel), &annos)
|
||||
group.incompatibleHistograms = true
|
||||
@ -3444,8 +3465,8 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, q float64, inputMatrix
|
||||
// point in copying the histogram in that case.
|
||||
} else {
|
||||
group.hasFloat = true
|
||||
if !group.incrementalMean {
|
||||
newV, newC := kahanSumInc(f, group.floatValue, group.floatKahanC)
|
||||
if !group.floatIncrementalMean {
|
||||
newV, newC := kahansum.Inc(f, group.floatValue, group.floatKahanC)
|
||||
if !math.IsInf(newV, 0) {
|
||||
// The sum doesn't overflow, so we propagate it to the
|
||||
// group struct and continue with the regular
|
||||
@ -3456,12 +3477,12 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, q float64, inputMatrix
|
||||
// If we are here, we know that the sum _would_ overflow. So
|
||||
// instead of continue to sum up, we revert to incremental
|
||||
// calculation of the mean value from here on.
|
||||
group.incrementalMean = true
|
||||
group.floatIncrementalMean = true
|
||||
group.floatMean = group.floatValue / (group.groupCount - 1)
|
||||
group.floatKahanC /= group.groupCount - 1
|
||||
}
|
||||
q := (group.groupCount - 1) / group.groupCount
|
||||
group.floatMean, group.floatKahanC = kahanSumInc(
|
||||
group.floatMean, group.floatKahanC = kahansum.Inc(
|
||||
f/group.groupCount,
|
||||
q*group.floatMean,
|
||||
q*group.floatKahanC,
|
||||
@ -3536,8 +3557,24 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, q float64, inputMatrix
|
||||
case aggr.incompatibleHistograms:
|
||||
continue
|
||||
case aggr.hasHistogram:
|
||||
if aggr.histogramIncrementalMean {
|
||||
if aggr.histogramKahanC != nil {
|
||||
aggr.histogramValue, _, _, _ = aggr.histogramMean.Add(aggr.histogramKahanC)
|
||||
// Add can theoretically return ErrHistogramsIncompatibleSchema, but at
|
||||
// this stage errors should not occur if earlier KahanAdd calls succeeded.
|
||||
} else {
|
||||
aggr.histogramValue = aggr.histogramMean
|
||||
}
|
||||
} else {
|
||||
aggr.histogramValue.Div(aggr.groupCount)
|
||||
if aggr.histogramKahanC != nil {
|
||||
aggr.histogramValue, _, _, _ = aggr.histogramValue.Add(aggr.histogramKahanC.Div(aggr.groupCount))
|
||||
// Add can theoretically return ErrHistogramsIncompatibleSchema, but at
|
||||
// this stage errors should not occur if earlier KahanAdd calls succeeded.
|
||||
}
|
||||
}
|
||||
aggr.histogramValue = aggr.histogramValue.Compact(0)
|
||||
case aggr.incrementalMean:
|
||||
case aggr.floatIncrementalMean:
|
||||
aggr.floatValue = aggr.floatMean + aggr.floatKahanC
|
||||
default:
|
||||
aggr.floatValue = aggr.floatValue/aggr.groupCount + aggr.floatKahanC/aggr.groupCount
|
||||
@ -3565,6 +3602,11 @@ func (ev *evaluator) aggregation(e *parser.AggregateExpr, q float64, inputMatrix
|
||||
case aggr.incompatibleHistograms:
|
||||
continue
|
||||
case aggr.hasHistogram:
|
||||
if aggr.histogramKahanC != nil {
|
||||
aggr.histogramValue, _, _, _ = aggr.histogramValue.Add(aggr.histogramKahanC)
|
||||
// Add can theoretically return ErrHistogramsIncompatibleSchema, but at
|
||||
// this stage errors should not occur if earlier KahanAdd calls succeeded.
|
||||
}
|
||||
aggr.histogramValue.Compact(0)
|
||||
default:
|
||||
aggr.floatValue += aggr.floatKahanC
|
||||
|
||||
@ -33,6 +33,7 @@ import (
|
||||
"github.com/prometheus/prometheus/promql/parser/posrange"
|
||||
"github.com/prometheus/prometheus/schema"
|
||||
"github.com/prometheus/prometheus/util/annotations"
|
||||
"github.com/prometheus/prometheus/util/kahansum"
|
||||
)
|
||||
|
||||
// FunctionCall is the type of a PromQL function implementation
|
||||
@ -801,10 +802,7 @@ func funcAvgOverTime(_ []Vector, matrixVal Matrix, args parser.Expressions, enh
|
||||
if len(firstSeries.Floats) > 0 && len(firstSeries.Histograms) > 0 {
|
||||
return enh.Out, annotations.New().Add(annotations.NewMixedFloatsHistogramsWarning(getMetricName(firstSeries.Metric), args[0].PositionRange()))
|
||||
}
|
||||
// For the average calculation of histograms, we use incremental mean
|
||||
// calculation without the help of Kahan summation (but this should
|
||||
// change, see https://github.com/prometheus/prometheus/issues/14105 ).
|
||||
// For floats, we improve the accuracy with the help of Kahan summation.
|
||||
// We improve the accuracy with the help of Kahan summation.
|
||||
// For a while, we assumed that incremental mean calculation combined
|
||||
// with Kahan summation (see
|
||||
// https://stackoverflow.com/questions/61665473/is-it-beneficial-for-precision-to-calculate-the-incremental-mean-average
|
||||
@ -847,23 +845,47 @@ func funcAvgOverTime(_ []Vector, matrixVal Matrix, args parser.Expressions, enh
|
||||
}
|
||||
}()
|
||||
|
||||
mean := s.Histograms[0].H.Copy()
|
||||
trackCounterReset(mean)
|
||||
var (
|
||||
sum = s.Histograms[0].H.Copy()
|
||||
mean, kahanC *histogram.FloatHistogram
|
||||
count float64
|
||||
incrementalMean bool
|
||||
nhcbBoundsReconciled bool
|
||||
err error
|
||||
)
|
||||
trackCounterReset(sum)
|
||||
for i, h := range s.Histograms[1:] {
|
||||
trackCounterReset(h.H)
|
||||
count := float64(i + 2)
|
||||
left := h.H.Copy().Div(count)
|
||||
right := mean.Copy().Div(count)
|
||||
|
||||
toAdd, _, nhcbBoundsReconciled, err := left.Sub(right)
|
||||
if err != nil {
|
||||
return mean, err
|
||||
count = float64(i + 2)
|
||||
if !incrementalMean {
|
||||
sumCopy := sum.Copy()
|
||||
var cCopy *histogram.FloatHistogram
|
||||
if kahanC != nil {
|
||||
cCopy = kahanC.Copy()
|
||||
}
|
||||
cCopy, _, nhcbBoundsReconciled, err = sumCopy.KahanAdd(h.H, cCopy)
|
||||
if err != nil {
|
||||
return sumCopy.Div(count), err
|
||||
}
|
||||
if nhcbBoundsReconciled {
|
||||
nhcbBoundsReconciledSeen = true
|
||||
}
|
||||
if !sumCopy.HasOverflow() {
|
||||
sum, kahanC = sumCopy, cCopy
|
||||
continue
|
||||
}
|
||||
incrementalMean = true
|
||||
mean = sum.Copy().Div(count - 1)
|
||||
if kahanC != nil {
|
||||
kahanC.Div(count - 1)
|
||||
}
|
||||
}
|
||||
if nhcbBoundsReconciled {
|
||||
nhcbBoundsReconciledSeen = true
|
||||
q := (count - 1) / count
|
||||
if kahanC != nil {
|
||||
kahanC.Mul(q)
|
||||
}
|
||||
|
||||
_, _, nhcbBoundsReconciled, err = mean.Add(toAdd)
|
||||
toAdd := h.H.Copy().Div(count)
|
||||
kahanC, _, nhcbBoundsReconciled, err = mean.Mul(q).KahanAdd(toAdd, kahanC)
|
||||
if err != nil {
|
||||
return mean, err
|
||||
}
|
||||
@ -871,7 +893,18 @@ func funcAvgOverTime(_ []Vector, matrixVal Matrix, args parser.Expressions, enh
|
||||
nhcbBoundsReconciledSeen = true
|
||||
}
|
||||
}
|
||||
return mean, nil
|
||||
if incrementalMean {
|
||||
if kahanC != nil {
|
||||
_, _, _, err := mean.Add(kahanC)
|
||||
return mean, err
|
||||
}
|
||||
return mean, nil
|
||||
}
|
||||
if kahanC != nil {
|
||||
_, _, _, err := sum.Div(count).Add(kahanC.Div(count))
|
||||
return sum, err
|
||||
}
|
||||
return sum.Div(count), nil
|
||||
})
|
||||
if err != nil {
|
||||
if errors.Is(err, histogram.ErrHistogramsIncompatibleSchema) {
|
||||
@ -890,7 +923,7 @@ func funcAvgOverTime(_ []Vector, matrixVal Matrix, args parser.Expressions, enh
|
||||
for i, f := range s.Floats[1:] {
|
||||
count = float64(i + 2)
|
||||
if !incrementalMean {
|
||||
newSum, newC := kahanSumInc(f.F, sum, kahanC)
|
||||
newSum, newC := kahansum.Inc(f.F, sum, kahanC)
|
||||
// Perform regular mean calculation as long as
|
||||
// the sum doesn't overflow.
|
||||
if !math.IsInf(newSum, 0) {
|
||||
@ -904,7 +937,7 @@ func funcAvgOverTime(_ []Vector, matrixVal Matrix, args parser.Expressions, enh
|
||||
kahanC /= (count - 1)
|
||||
}
|
||||
q := (count - 1) / count
|
||||
mean, kahanC = kahanSumInc(f.F/count, q*mean, q*kahanC)
|
||||
mean, kahanC = kahansum.Inc(f.F/count, q*mean, q*kahanC)
|
||||
}
|
||||
if incrementalMean {
|
||||
return mean + kahanC
|
||||
@ -1145,9 +1178,14 @@ func funcSumOverTime(_ []Vector, matrixVal Matrix, args parser.Expressions, enh
|
||||
|
||||
sum := s.Histograms[0].H.Copy()
|
||||
trackCounterReset(sum)
|
||||
var (
|
||||
comp *histogram.FloatHistogram
|
||||
nhcbBoundsReconciled bool
|
||||
err error
|
||||
)
|
||||
for _, h := range s.Histograms[1:] {
|
||||
trackCounterReset(h.H)
|
||||
_, _, nhcbBoundsReconciled, err := sum.Add(h.H)
|
||||
comp, _, nhcbBoundsReconciled, err = sum.KahanAdd(h.H, comp)
|
||||
if err != nil {
|
||||
return sum, err
|
||||
}
|
||||
@ -1155,7 +1193,16 @@ func funcSumOverTime(_ []Vector, matrixVal Matrix, args parser.Expressions, enh
|
||||
nhcbBoundsReconciledSeen = true
|
||||
}
|
||||
}
|
||||
return sum, nil
|
||||
if comp != nil {
|
||||
sum, _, nhcbBoundsReconciled, err = sum.Add(comp)
|
||||
if err != nil {
|
||||
return sum, err
|
||||
}
|
||||
if nhcbBoundsReconciled {
|
||||
nhcbBoundsReconciledSeen = true
|
||||
}
|
||||
}
|
||||
return sum, err
|
||||
})
|
||||
if err != nil {
|
||||
if errors.Is(err, histogram.ErrHistogramsIncompatibleSchema) {
|
||||
@ -1167,7 +1214,7 @@ func funcSumOverTime(_ []Vector, matrixVal Matrix, args parser.Expressions, enh
|
||||
return aggrOverTime(matrixVal, enh, func(s Series) float64 {
|
||||
var sum, c float64
|
||||
for _, f := range s.Floats {
|
||||
sum, c = kahanSumInc(f.F, sum, c)
|
||||
sum, c = kahansum.Inc(f.F, sum, c)
|
||||
}
|
||||
if math.IsInf(sum, 0) {
|
||||
return sum
|
||||
@ -1220,8 +1267,8 @@ func varianceOverTime(matrixVal Matrix, args parser.Expressions, enh *EvalNodeHe
|
||||
for _, f := range s.Floats {
|
||||
count++
|
||||
delta := f.F - (mean + cMean)
|
||||
mean, cMean = kahanSumInc(delta/count, mean, cMean)
|
||||
aux, cAux = kahanSumInc(delta*(f.F-(mean+cMean)), aux, cAux)
|
||||
mean, cMean = kahansum.Inc(delta/count, mean, cMean)
|
||||
aux, cAux = kahansum.Inc(delta*(f.F-(mean+cMean)), aux, cAux)
|
||||
}
|
||||
variance := (aux + cAux) / count
|
||||
if varianceToResult == nil {
|
||||
@ -1434,24 +1481,6 @@ func funcTimestamp(vectorVals []Vector, _ Matrix, _ parser.Expressions, enh *Eva
|
||||
return enh.Out, nil
|
||||
}
|
||||
|
||||
// We get incorrect results if this function is inlined; see https://github.com/prometheus/prometheus/issues/16714.
|
||||
//
|
||||
//go:noinline
|
||||
func kahanSumInc(inc, sum, c float64) (newSum, newC float64) {
|
||||
t := sum + inc
|
||||
switch {
|
||||
case math.IsInf(t, 0):
|
||||
c = 0
|
||||
|
||||
// Using Neumaier improvement, swap if next term larger than sum.
|
||||
case math.Abs(sum) >= math.Abs(inc):
|
||||
c += (sum - t) + inc
|
||||
default:
|
||||
c += (inc - t) + sum
|
||||
}
|
||||
return t, c
|
||||
}
|
||||
|
||||
// linearRegression performs a least-square linear regression analysis on the
|
||||
// provided SamplePairs. It returns the slope, and the intercept value at the
|
||||
// provided time.
|
||||
@ -1474,10 +1503,10 @@ func linearRegression(samples []FPoint, interceptTime int64) (slope, intercept f
|
||||
}
|
||||
n += 1.0
|
||||
x := float64(sample.T-interceptTime) / 1e3
|
||||
sumX, cX = kahanSumInc(x, sumX, cX)
|
||||
sumY, cY = kahanSumInc(sample.F, sumY, cY)
|
||||
sumXY, cXY = kahanSumInc(x*sample.F, sumXY, cXY)
|
||||
sumX2, cX2 = kahanSumInc(x*x, sumX2, cX2)
|
||||
sumX, cX = kahansum.Inc(x, sumX, cX)
|
||||
sumY, cY = kahansum.Inc(sample.F, sumY, cY)
|
||||
sumXY, cXY = kahansum.Inc(x*sample.F, sumXY, cXY)
|
||||
sumX2, cX2 = kahansum.Inc(x*x, sumX2, cX2)
|
||||
}
|
||||
if constY {
|
||||
if math.IsInf(initY, 0) {
|
||||
@ -1613,7 +1642,7 @@ func histogramVariance(vectorVals []Vector, enh *EvalNodeHelper, varianceToResul
|
||||
}
|
||||
}
|
||||
delta := val - mean
|
||||
variance, cVariance = kahanSumInc(bucket.Count*delta*delta, variance, cVariance)
|
||||
variance, cVariance = kahansum.Inc(bucket.Count*delta*delta, variance, cVariance)
|
||||
}
|
||||
variance += cVariance
|
||||
variance /= h.Count
|
||||
|
||||
@ -24,6 +24,7 @@ import (
|
||||
"github.com/prometheus/prometheus/model/histogram"
|
||||
"github.com/prometheus/prometheus/model/labels"
|
||||
"github.com/prometheus/prometheus/promql/parser/posrange"
|
||||
"github.com/prometheus/prometheus/util/kahansum"
|
||||
)
|
||||
|
||||
func TestHistogramRateCounterResetHint(t *testing.T) {
|
||||
@ -79,7 +80,7 @@ func TestKahanSumInc(t *testing.T) {
|
||||
|
||||
runTest := func(t *testing.T, a, b, expected float64) {
|
||||
t.Run(fmt.Sprintf("%v + %v = %v", a, b, expected), func(t *testing.T) {
|
||||
sum, c := kahanSumInc(b, a, 0)
|
||||
sum, c := kahansum.Inc(b, a, 0)
|
||||
result := sum + c
|
||||
|
||||
if math.IsNaN(expected) {
|
||||
|
||||
5
promql/promqltest/testdata/aggregators.test
vendored
5
promql/promqltest/testdata/aggregators.test
vendored
@ -687,6 +687,11 @@ load 10s
|
||||
eval instant at 1m sum(data{test="ten"})
|
||||
{} 10
|
||||
|
||||
# Plain addition doesn't use Kahan summation, so operations involving very large magnitudes
|
||||
# (±1e+100) lose precision. The smaller values are absorbed, leading to an incorrect result.
|
||||
# eval instant at 1m sum(data{test="ten",point="a"}) + sum(data{test="ten",point="b"}) + sum(data{test="ten",point="c"}) + sum(data{test="ten",point="d"})
|
||||
# {} 10
|
||||
|
||||
eval instant at 1m avg(data{test="ten"})
|
||||
{} 2.5
|
||||
|
||||
|
||||
134
promql/promqltest/testdata/native_histograms.test
vendored
134
promql/promqltest/testdata/native_histograms.test
vendored
@ -1388,22 +1388,28 @@ clear
|
||||
|
||||
# Test native histograms with sum, count, avg.
|
||||
load 10m
|
||||
histogram_sum{idx="0"} {{schema:0 count:25 sum:1234.5 z_bucket:4 z_bucket_w:0.001 buckets:[1 2 0 1 1] n_buckets:[2 4 0 0 1 9]}}x1
|
||||
histogram_sum{idx="1"} {{schema:0 count:41 sum:2345.6 z_bucket:5 z_bucket_w:0.001 buckets:[1 3 1 2 1 1 1] n_buckets:[0 1 4 2 7 0 0 0 0 5 5 2]}}x1
|
||||
histogram_sum{idx="2"} {{schema:0 count:41 sum:1111.1 z_bucket:5 z_bucket_w:0.001 buckets:[1 3 1 2 1 1 1] n_buckets:[0 1 4 2 7 0 0 0 0 5 5 2]}}x1
|
||||
histogram_sum{idx="3"} {{schema:1 count:0}}x1
|
||||
histogram_sum{idx="0"} {{schema:0 count:25 sum:3.1 z_bucket:4 z_bucket_w:0.001 buckets:[1 2 0 1 1] n_buckets:[2 4 0 0 1 9]}}x1
|
||||
histogram_sum{idx="1"} {{schema:0 count:41 sum:1e100 z_bucket:5 z_bucket_w:0.001 buckets:[1 3 1 2 1 1 1] n_buckets:[0 1 4 2 7 0 0 0 0 5 5 2]}}x1
|
||||
histogram_sum{idx="2"} {{schema:0 count:41 sum:-1e100 z_bucket:5 z_bucket_w:0.001 buckets:[1 3 1 2 1 1 1] n_buckets:[0 1 4 2 7 0 0 0 0 5 5 2]}}x1
|
||||
histogram_sum{idx="3"} {{schema:1 count:0 sum:1.3 z_bucket:3 z_bucket_w:0.001 buckets:[2 4 2 3 2 2] n_buckets:[1 2 5 3 8 1 1 1 1 6 3]}}x1
|
||||
histogram_sum_float{idx="0"} 42.0x1
|
||||
|
||||
eval instant at 10m sum(histogram_sum)
|
||||
expect no_warn
|
||||
{} {{schema:0 count:107 sum:4691.2 z_bucket:14 z_bucket_w:0.001 buckets:[3 8 2 5 3 2 2] n_buckets:[2 6 8 4 15 9 0 0 0 10 10 4]}}
|
||||
{} {{schema:0 count:107 sum:4.4 z_bucket:17 z_bucket_w:0.001 buckets:[5 14 7 7 3 2 2] n_buckets:[3 13 19 6 17 18 0 0 0 10 10 4]}}
|
||||
|
||||
eval instant at 10m sum({idx="0"})
|
||||
expect warn
|
||||
|
||||
eval instant at 10m sum(histogram_sum{idx="0"} + ignoring(idx) histogram_sum{idx="1"} + ignoring(idx) histogram_sum{idx="2"} + ignoring(idx) histogram_sum{idx="3"})
|
||||
eval instant at 10m sum(histogram_sum{idx="0"} + ignoring(idx) histogram_sum{idx="3"})
|
||||
expect no_warn
|
||||
{} {{schema:0 count:107 sum:4691.2 z_bucket:14 z_bucket_w:0.001 buckets:[3 8 2 5 3 2 2] n_buckets:[2 6 8 4 15 9 0 0 0 10 10 4]}}
|
||||
{} {{schema:0 count:25 sum:4.4 z_bucket:7 z_bucket_w:0.001 buckets:[3 8 5 3 1] n_buckets:[3 11 11 2 3 18]}}
|
||||
|
||||
# Plain addition doesn't use Kahan summation, so operations involving very large magnitudes
|
||||
# (±1e+100) lose precision. The smaller values are absorbed, leading to an incorrect result.
|
||||
# eval instant at 10m sum(histogram_sum{idx="0"} + ignoring(idx) histogram_sum{idx="1"} + ignoring(idx) histogram_sum{idx="2"} + ignoring(idx) histogram_sum{idx="3"})
|
||||
# expect no_warn
|
||||
# {} {{schema:0 count:107 sum:4.4 z_bucket:14 z_bucket_w:0.001 buckets:[3 8 2 5 3 2 2] n_buckets:[2 6 8 4 15 9 0 0 0 10 10 4]}}
|
||||
|
||||
eval instant at 10m count(histogram_sum)
|
||||
expect no_warn
|
||||
@ -1411,13 +1417,63 @@ eval instant at 10m count(histogram_sum)
|
||||
|
||||
eval instant at 10m avg(histogram_sum)
|
||||
expect no_warn
|
||||
{} {{schema:0 count:26.75 sum:1172.8 z_bucket:3.5 z_bucket_w:0.001 buckets:[0.75 2 0.5 1.25 0.75 0.5 0.5] n_buckets:[0.5 1.5 2 1 3.75 2.25 0 0 0 2.5 2.5 1]}}
|
||||
{} {{schema:0 count:26.75 sum:1.1 z_bucket:4.25 z_bucket_w:0.001 buckets:[1.25 3.5 1.75 1.75 0.75 0.5 0.5] n_buckets:[0.75 3.25 4.75 1.5 4.25 4.5 0 0 0 2.5 2.5 1]}}
|
||||
|
||||
clear
|
||||
|
||||
# Test native histograms with incremental avg calulation.
|
||||
# Very large floats involved trigger incremental avg calculation, as direct avg calculation would overflow float64.
|
||||
load 10m
|
||||
histogram_avg_incremental{idx="0"} {{schema:0 count:1.7976931348623157e+308 sum:5.30921651659898 z_bucket:1.78264e50 z_bucket_w:0.001 buckets:[1.78264E+50 1.78264E+215 1.78264E+219 3363.5121756487] n_buckets:[1178.20696291113 731.697776280323 715.201503759399 1386.11378876781 855.572553278132]}}x1
|
||||
histogram_avg_incremental{idx="1"} {{schema:0 count:1e308 sum:0.961118537914768 z_bucket:0.76342771 z_bucket_w:0.001 buckets:[0.76342771 0.76342771 0.76342771 195.70084087969] n_buckets:[421.30382970055 0 450441.779]}}x1
|
||||
histogram_avg_incremental{idx="2"} {{schema:0 count:1e-6 sum:1.62091361305318 z_bucket:1.9592258 z_bucket_w:0.001 buckets:[1.9592258 1.9592258 1.9592258 1135.74692279] n_buckets:[0 4504.41779 588.599358265103 40.3760942760943]}}x1
|
||||
histogram_avg_incremental{idx="3"} {{schema:0 count:1e-6 sum:0.865089463758091 z_bucket:7.69805412 z_bucket_w:0.001 buckets:[2.258E+220 2.258E+220 2.3757689E+217 1078.68071312804] n_buckets:[349.905284031261 0 0 0.161173466838949 588.599358]}}x1
|
||||
histogram_avg_incremental{idx="4"} {{schema:0 count:1e-6 sum:0.323055185914577 z_bucket:458.90154 z_bucket_w:0.001 buckets:[7.69805412 7.69805412 2.258E+220 3173.28218135701]}}x1
|
||||
histogram_avg_incremental{idx="5"} {{schema:0 count:1e-6 sum:0.951811357687154 z_bucket:1.78264e50 z_bucket_w:0.001 buckets:[458.90154 458.90154 7.69805412 2178.35] n_buckets:[2054.92644438789 844.560108898123]}}x1
|
||||
histogram_avg_incremental{idx="6"} {{schema:0 count:1e-6 sum:0 z_bucket:5 z_bucket_w:0.001 buckets:[0 0 1.78264E+219 376.770478890989]}}x1
|
||||
histogram_avg_incremental{idx="7"} {{schema:0 count:1e-6 sum:0 z_bucket:0 z_bucket_w:0.001 buckets:[0 0 458.90154 250325.5] n_buckets:[0 0.0000000011353 0 608.697257]}}x1
|
||||
# This test fails due to float64 rounding in the incremental average calculation.
|
||||
# For large intermediate means (e.g. ~1e99), multiplying by a fractional weight like (n-1)/n
|
||||
# produces values such as 2.0000000000000002e99 instead of the mathematically exact 2e99.
|
||||
# While the relative error is tiny, subtracting nearly equal high-magnitude values later
|
||||
# result in a large absolute error. The outcome also depends on the (effectively random) order
|
||||
# in which input series are processed which makes the test flaky.
|
||||
# histogram_avg_incremental_2{idx="0"} {{schema:0 count:1.7976931348623157e+308 sum:5.3 z_bucket:1.78264e50 z_bucket_w:0.001 buckets:[1.78264E+50 1.78264E+215 1.78264E+219 3363.5121756487] n_buckets:[1178.20696291113 731.697776280323 715.201503759399 1386.11378876781 855.572553278132]}}x1
|
||||
# histogram_avg_incremental_2{idx="1"} {{schema:0 count:1e308 sum:1e100 z_bucket:0.76342771 z_bucket_w:0.001 buckets:[0.76342771 0.76342771 0.76342771 195.70084087969] n_buckets:[421.30382970055 0 450441.779]}}x1
|
||||
# histogram_avg_incremental_2{idx="2"} {{schema:0 count:1e-6 sum:1 z_bucket:1.9592258 z_bucket_w:0.001 buckets:[1.9592258 1.9592258 1.9592258 1135.74692279] n_buckets:[0 4504.41779 588.599358265103 40.3760942760943]}}x1
|
||||
# histogram_avg_incremental_2{idx="3"} {{schema:0 count:1e-6 sum:-1e100 z_bucket:7.69805412 z_bucket_w:0.001 buckets:[2.258E+220 2.258E+220 2.3757689E+217 1078.68071312804] n_buckets:[349.905284031261 0 0 0.161173466838949 588.599358]}}x1
|
||||
# histogram_avg_incremental_2{idx="4"} {{schema:0 count:1e-6 sum:1 z_bucket:458.90154 z_bucket_w:0.001 buckets:[7.69805412 7.69805412 2.258E+220 3173.28218135701]}}x1
|
||||
# histogram_avg_incremental_2{idx="5"} {{schema:0 count:1e-6 sum:1 z_bucket:1.78264e50 z_bucket_w:0.001 buckets:[458.90154 458.90154 7.69805412 2178.35] n_buckets:[2054.92644438789 844.560108898123]}}x1
|
||||
# histogram_avg_incremental_2{idx="6"} {{schema:0 count:1e-6 sum:0 z_bucket:5 z_bucket_w:0.001 buckets:[0 0 1.78264E+219 376.770478890989]}}x1
|
||||
# histogram_avg_incremental_2{idx="7"} {{schema:0 count:1e-6 sum:0 z_bucket:0 z_bucket_w:0.001 buckets:[0 0 458.90154 250325.5] n_buckets:[0 0.0000000011353 0 608.697257]}}x1
|
||||
|
||||
eval instant at 10m avg(histogram_avg_incremental)
|
||||
{} {{schema:0 count:3.497116418577895e+307 sum:1.2539005843658437 z_bucket:4.4566e49 z_bucket_w:0.001 buckets:[2.8225e+219 2.822522283e+219 3.271129711125e+219 32728.442914086805] n_buckets:[500.5428151288539 760.0844593974477 56468.19748275306 254.4185391888429 180.5214889097665]}}
|
||||
|
||||
# This test doesn't work, see the load section above for reasoning.
|
||||
# eval instant at 10m avg(histogram_avg_incremental_2)
|
||||
# {} {{schema:0 count:3.497116418577895e+307 sum:1.0375 z_bucket:4.4566e49 z_bucket_w:0.001 buckets:[2.8225e+219 2.822522283e+219 3.271129711125e+219 32728.442914086805] n_buckets:[500.5428151288539 760.0844593974477 56468.19748275306 254.4185391888429 180.5214889097665]}}
|
||||
|
||||
clear
|
||||
|
||||
# Test native histograms with sum_over_time, avg_over_time.
|
||||
load 1m
|
||||
histogram_sum_over_time {{schema:0 count:25 sum:1234.5 z_bucket:4 z_bucket_w:0.001 buckets:[1 2 0 1 1] n_buckets:[2 4 0 0 1 9]}} {{schema:0 count:41 sum:2345.6 z_bucket:5 z_bucket_w:0.001 buckets:[1 3 1 2 1 1 1] n_buckets:[0 1 4 2 7 0 0 0 0 5 5 2]}} {{schema:0 count:41 sum:1111.1 z_bucket:5 z_bucket_w:0.001 buckets:[1 3 1 2 1 1 1] n_buckets:[0 1 4 2 7 0 0 0 0 5 5 2]}} {{schema:1 count:0}}
|
||||
histogram_sum_over_time_2 {{schema:0 count:1e10 sum:5.30921651659898 z_bucket:1.78264e50 z_bucket_w:0.001 buckets:[1.78264E+50 1.78264E+215 1.78264E+219 3363.5121756487] n_buckets:[1178.20696291113 731.697776280323 715.201503759399 1386.11378876781 855.572553278132]}} {{schema:0 count:1e-6 sum:0.961118537914768 z_bucket:0.76342771 z_bucket_w:0.001 buckets:[0.76342771 0.76342771 0.76342771 195.70084087969] n_buckets:[421.30382970055 0 450441.779]}} {{schema:0 count:1e-6 sum:1.62091361305318 z_bucket:1.9592258 z_bucket_w:0.001 buckets:[1.9592258 1.9592258 1.9592258 1135.74692279] n_buckets:[0 4504.41779 588.599358265103 40.3760942760943]}} {{schema:0 count:1e-6 sum:0.865089463758091 z_bucket:7.69805412 z_bucket_w:0.001 buckets:[2.258E+220 2.258E+220 2.3757689E+217 1078.68071312804] n_buckets:[349.905284031261 0 0 0.161173466838949 588.599358]}} {{schema:0 count:1e-6 sum:0.323055185914577 z_bucket:458.90154 z_bucket_w:0.001 buckets:[7.69805412 7.69805412 2.258E+220 3173.28218135701]}} {{schema:0 count:1e-6 sum:0.951811357687154 z_bucket:1.78264e50 z_bucket_w:0.001 buckets:[458.90154 458.90154 7.69805412 2178.35] n_buckets:[2054.92644438789 844.560108898123]}} {{schema:0 count:1e-6 sum:0 z_bucket:5 z_bucket_w:0.001 buckets:[0 0 1.78264E+219 376.770478890989]}} {{schema:0 count:1e-6 sum:0 z_bucket:0 z_bucket_w:0.001 buckets:[0 0 458.90154 250325.5] n_buckets:[0 0.0000000011353 0 608.697257]}}
|
||||
histogram_sum_over_time_3 {{schema:0 count:1 sum:1}} {{schema:0 count:2 sum:1e100}} {{schema:0 count:3 sum:1}} {{schema:0 count:4 sum:-1e100}}
|
||||
histogram_sum_over_time_4 {{schema:0 count:1 sum:5.3}} {{schema:0 count:2 sum:1e100}} {{schema:0 count:3 sum:1}} {{schema:0 count:4 sum:-1e100}} {{schema:0 count:5 sum:2}} {{schema:0 count:6 sum:1e50}} {{schema:0 count:7 sum:-1e50}}
|
||||
histogram_sum_over_time_incremental {{schema:0 count:1.7976931348623157e+308 sum:5.30921651659898 z_bucket:1.78264e50 z_bucket_w:0.001 buckets:[1.78264E+50 1.78264E+215 1.78264E+219 3363.5121756487] n_buckets:[1178.20696291113 731.697776280323 715.201503759399 1386.11378876781 855.572553278132]}} {{schema:0 count:1e308 sum:0.961118537914768 z_bucket:0.76342771 z_bucket_w:0.001 buckets:[0.76342771 0.76342771 0.76342771 195.70084087969] n_buckets:[421.30382970055 0 450441.779]}} {{schema:0 count:1e-6 sum:1.62091361305318 z_bucket:1.9592258 z_bucket_w:0.001 buckets:[1.9592258 1.9592258 1.9592258 1135.74692279] n_buckets:[0 4504.41779 588.599358265103 40.3760942760943]}} {{schema:0 count:1e-6 sum:0.865089463758091 z_bucket:7.69805412 z_bucket_w:0.001 buckets:[2.258E+220 2.258E+220 2.3757689E+217 1078.68071312804] n_buckets:[349.905284031261 0 0 0.161173466838949 588.599358]}} {{schema:0 count:1e-6 sum:0.323055185914577 z_bucket:458.90154 z_bucket_w:0.001 buckets:[7.69805412 7.69805412 2.258E+220 3173.28218135701]}} {{schema:0 count:1e-6 sum:0.951811357687154 z_bucket:1.78264e50 z_bucket_w:0.001 buckets:[458.90154 458.90154 7.69805412 2178.35] n_buckets:[2054.92644438789 844.560108898123]}} {{schema:0 count:1e-6 sum:0 z_bucket:5 z_bucket_w:0.001 buckets:[0 0 1.78264E+219 376.770478890989]}} {{schema:0 count:1e-6 sum:0 z_bucket:0 z_bucket_w:0.001 buckets:[0 0 458.90154 250325.5] n_buckets:[0 0.0000000011353 0 608.697257]}}
|
||||
histogram_sum_over_time_incremental_2 {{schema:0 count:1.7976931348623157e+308 sum:5.3}} {{schema:0 count:1e308 sum:1e100}} {{schema:0 count:1e-6 sum:1}} {{schema:0 count:1e-6 sum:-1e100}} {{schema:0 count:1e-6 sum:2}} {{schema:0 count:1e-6 sum:0}} {{schema:0 count:1e-6 sum:0}}
|
||||
histogram_sum_over_time_incremental_3 {{schema:0 count:1.7976931348623157e+308 sum:5.3}} {{schema:0 count:1e308 sum:1e100}} {{schema:0 count:1e-6 sum:-1e100}} {{schema:0 count:1e-6 sum:1}} {{schema:0 count:1e-6 sum:1e100}} {{schema:0 count:1e-6 sum:-1e100}} {{schema:0 count:1e-6 sum:0}}
|
||||
histogram_sum_over_time_incremental_4 {{schema:0 count:1.7976931348623157e+308 sum:5.3}} {{schema:0 count:1e308 sum:1e100}} {{schema:0 count:1e-6 sum:-1e100}} {{schema:0 count:1e-6 sum:1}} {{schema:0 count:1e-6 sum:1e50}} {{schema:0 count:1e-6 sum:-1e50}} {{schema:0 count:1e-6 sum:0}}
|
||||
histogram_sum_over_time_incremental_6 {{schema:0 count:1.7976931348623157e+308 sum:1}} {{schema:0 count:1e308 sum:1e100}} {{schema:0 count:1e-6 sum:1}} {{schema:0 count:1e-6 sum:-1e100}}
|
||||
# Kahan summation only compensates reliably across two magnitude scales. In following inputs, the
|
||||
# series contains three distinct magnitude groups (≈1, ≈1e50, and ≈1e100). When these magnitudes
|
||||
# are interleaved, rounding error can't be fully compensated, causing smaller values to be lost.
|
||||
# However, when values are ordered so that cancellation within one magnitude group
|
||||
# occurs first, followed by cancellation of the next group, the outcome remains accurate.
|
||||
# histogram_sum_over_time_5 {{schema:0 count:1 sum:5.3}} {{schema:0 count:2 sum:1e100}} {{schema:0 count:3 sum:1}} {{schema:0 count:4 sum:1e50}} {{schema:0 count:5 sum:2}} {{schema:0 count:6 sum:-1e100}} {{schema:0 count:7 sum:-1e50}}
|
||||
# histogram_sum_over_time_incremental_5 {{schema:0 count:1.7976931348623157e+308 sum:5.3}} {{schema:0 count:1e308 sum:1e100}} {{schema:0 count:1e-6 sum:1e50}} {{schema:0 count:1e-6 sum:1}} {{schema:0 count:1e-6 sum:-1e100}} {{schema:0 count:1e-6 sum:-1e50}} {{schema:0 count:1e-6 sum:0}}
|
||||
|
||||
eval instant at 3m sum_over_time(histogram_sum_over_time[4m:1m])
|
||||
{} {{schema:0 count:107 sum:4691.2 z_bucket:14 z_bucket_w:0.001 buckets:[3 8 2 5 3 2 2] n_buckets:[2 6 8 4 15 9 0 0 0 10 10 4]}}
|
||||
@ -1425,6 +1481,68 @@ eval instant at 3m sum_over_time(histogram_sum_over_time[4m:1m])
|
||||
eval instant at 3m avg_over_time(histogram_sum_over_time[4m:1m])
|
||||
{} {{schema:0 count:26.75 sum:1172.8 z_bucket:3.5 z_bucket_w:0.001 buckets:[0.75 2 0.5 1.25 0.75 0.5 0.5] n_buckets:[0.5 1.5 2 1 3.75 2.25 0 0 0 2.5 2.5 1]}}
|
||||
|
||||
eval instant at 7m sum_over_time(histogram_sum_over_time_2[8m:1m])
|
||||
{} {{schema:0 count:10000000000.000008 sum:10.03120467492675 z_bucket:3.56528e+50 z_bucket_w:0.001 buckets:[2.258e+220 2.2580178264e+220 2.6169037689e+220 261827.54331269444] n_buckets:[4004.342521030831 6080.675675179582 451745.57986202446 2035.3483135107433 1444.171911278132]}}
|
||||
|
||||
eval instant at 7m avg_over_time(histogram_sum_over_time_2[8m:1m])
|
||||
{} {{schema:0 count:1250000000.000001 sum:1.2539005843658437 z_bucket:4.4566e49 z_bucket_w:0.001 buckets:[2.8225e+219 2.822522283e+219 3.271129711125e+219 32728.442914086805] n_buckets:[500.5428151288539 760.0844593974477 56468.19748275306 254.4185391888429 180.5214889097665]}}
|
||||
|
||||
eval instant at 3m sum_over_time(histogram_sum_over_time_3[4m:1m])
|
||||
{} {{schema:0 count:10 sum:2}}
|
||||
|
||||
eval instant at 3m avg_over_time(histogram_sum_over_time_3[4m:1m])
|
||||
{} {{schema:0 count:2.5 sum:0.5}}
|
||||
|
||||
eval instant at 6m sum_over_time(histogram_sum_over_time_4[7m:1m])
|
||||
{} {{schema:0 count:28 sum:8.3}}
|
||||
|
||||
eval instant at 6m avg_over_time(histogram_sum_over_time_4[7m:1m])
|
||||
{} {{schema:0 count:4 sum:1.1857142857142857}}
|
||||
|
||||
# These tests don't work, see the load section above for reasoning.
|
||||
# eval instant at 6m sum_over_time(histogram_sum_over_time_5[7m:1m])
|
||||
# {} {{schema:0 count:28 sum:8.3}}
|
||||
#
|
||||
# eval instant at 6m avg_over_time(histogram_sum_over_time_5[7m:1m])
|
||||
# {} {{schema:0 count:4 sum:1.1857142857142857}}
|
||||
|
||||
eval instant at 7m sum_over_time(histogram_sum_over_time_incremental[8m:1m])
|
||||
{} {{schema:0 count:Inf sum:10.03120467492675 z_bucket:3.56528e+50 z_bucket_w:0.001 buckets:[2.258e+220 2.2580178264e+220 2.6169037689e+220 261827.54331269444] n_buckets:[4004.342521030831 6080.675675179582 451745.57986202446 2035.3483135107433 1444.171911278132]}}
|
||||
|
||||
eval instant at 7m avg_over_time(histogram_sum_over_time_incremental[8m:1m])
|
||||
{} {{schema:0 count:3.497116418577895e+307 sum:1.2539005843658437 z_bucket:4.4566e49 z_bucket_w:0.001 buckets:[2.8225e+219 2.822522283e+219 3.271129711125e+219 32728.442914086805] n_buckets:[500.5428151288539 760.0844593974477 56468.19748275306 254.4185391888429 180.5214889097665]}}
|
||||
|
||||
eval instant at 6m sum_over_time(histogram_sum_over_time_incremental_2[7m:1m])
|
||||
{} {{schema:0 count:Inf sum:8.3}}
|
||||
|
||||
eval instant at 6m avg_over_time(histogram_sum_over_time_incremental_2[7m:1m])
|
||||
{} {{schema:0 count:3.9967044783747367e+307 sum:1.1857142857142857}}
|
||||
|
||||
eval instant at 6m sum_over_time(histogram_sum_over_time_incremental_3[7m:1m])
|
||||
{} {{schema:0 count:Inf sum:6.3}}
|
||||
|
||||
eval instant at 6m avg_over_time(histogram_sum_over_time_incremental_3[7m:1m])
|
||||
{} {{schema:0 count:3.9967044783747367e+307 sum:0.9}}
|
||||
|
||||
eval instant at 6m sum_over_time(histogram_sum_over_time_incremental_4[7m:1m])
|
||||
{} {{schema:0 count:Inf sum:6.3}}
|
||||
|
||||
eval instant at 6m avg_over_time(histogram_sum_over_time_incremental_4[7m:1m])
|
||||
{} {{schema:0 count:3.9967044783747367e+307 sum:0.9}}
|
||||
|
||||
# These tests don't work, see the load section above for reasoning.
|
||||
# eval instant at 6m sum_over_time(histogram_sum_over_time_incremental_5[7m:1m])
|
||||
# {} {{schema:0 count:Inf sum:6.3}}
|
||||
#
|
||||
# eval instant at 6m avg_over_time(histogram_sum_over_time_incremental_5[7m:1m])
|
||||
# {} {{schema:0 count:3.9967044783747367e+307 sum:0.9}}
|
||||
|
||||
eval instant at 3m sum_over_time(histogram_sum_over_time_incremental_6[4m:1m])
|
||||
{} {{schema:0 count:Inf sum:2}}
|
||||
|
||||
eval instant at 3m avg_over_time(histogram_sum_over_time_incremental_6[4m:1m])
|
||||
{} {{schema:0 count:6.99423283715579e+307 sum:0.5}}
|
||||
|
||||
clear
|
||||
|
||||
# Test native histograms with sub operator.
|
||||
|
||||
39
util/kahansum/kahansum.go
Normal file
39
util/kahansum/kahansum.go
Normal file
@ -0,0 +1,39 @@
|
||||
// Copyright 2024 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 kahansum
|
||||
|
||||
import "math"
|
||||
|
||||
// Inc performs addition of two floating-point numbers using the Kahan summation algorithm.
|
||||
// We get incorrect results if this function is inlined; see https://github.com/prometheus/prometheus/issues/16714.
|
||||
//
|
||||
//go:noinline
|
||||
func Inc(inc, sum, c float64) (newSum, newC float64) {
|
||||
t := sum + inc
|
||||
switch {
|
||||
case math.IsInf(t, 0):
|
||||
c = 0
|
||||
|
||||
// Using Neumaier improvement, swap if next term larger than sum.
|
||||
case math.Abs(sum) >= math.Abs(inc):
|
||||
c += (sum - t) + inc
|
||||
default:
|
||||
c += (inc - t) + sum
|
||||
}
|
||||
return t, c
|
||||
}
|
||||
|
||||
func Dec(dec, sum, c float64) (newSum, newC float64) {
|
||||
return Inc(-dec, sum, c)
|
||||
}
|
||||
Loading…
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Reference in New Issue
Block a user