Merge pull request #1471 from fatsheep9146/native-histogram-exemplar
add native histogram exemplar support
This commit is contained in:
commit
542f7e6c6e
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@ -440,7 +440,7 @@ type HistogramOpts struct {
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// constant (or any negative float value).
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NativeHistogramZeroThreshold float64
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// The remaining fields define a strategy to limit the number of
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// The next three fields define a strategy to limit the number of
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// populated sparse buckets. If NativeHistogramMaxBucketNumber is left
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// at zero, the number of buckets is not limited. (Note that this might
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// lead to unbounded memory consumption if the values observed by the
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@ -473,6 +473,22 @@ type HistogramOpts struct {
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NativeHistogramMinResetDuration time.Duration
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NativeHistogramMaxZeroThreshold float64
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// NativeHistogramMaxExemplars limits the number of exemplars
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// that are kept in memory for each native histogram. If you leave it at
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// zero, a default value of 10 is used. If no exemplars should be kept specifically
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// for native histograms, set it to a negative value. (Scrapers can
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// still use the exemplars exposed for classic buckets, which are managed
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// independently.)
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NativeHistogramMaxExemplars int
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// NativeHistogramExemplarTTL is only checked once
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// NativeHistogramMaxExemplars is exceeded. In that case, the
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// oldest exemplar is removed if it is older than NativeHistogramExemplarTTL.
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// Otherwise, the older exemplar in the pair of exemplars that are closest
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// together (on an exponential scale) is removed.
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// If NativeHistogramExemplarTTL is left at its zero value, a default value of
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// 5m is used. To always delete the oldest exemplar, set it to a negative value.
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NativeHistogramExemplarTTL time.Duration
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// now is for testing purposes, by default it's time.Now.
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now func() time.Time
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@ -532,6 +548,7 @@ func newHistogram(desc *Desc, opts HistogramOpts, labelValues ...string) Histogr
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if opts.afterFunc == nil {
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opts.afterFunc = time.AfterFunc
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}
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h := &histogram{
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desc: desc,
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upperBounds: opts.Buckets,
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@ -556,6 +573,7 @@ func newHistogram(desc *Desc, opts HistogramOpts, labelValues ...string) Histogr
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h.nativeHistogramZeroThreshold = DefNativeHistogramZeroThreshold
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} // Leave h.nativeHistogramZeroThreshold at 0 otherwise.
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h.nativeHistogramSchema = pickSchema(opts.NativeHistogramBucketFactor)
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h.nativeExemplars = makeNativeExemplars(opts.NativeHistogramExemplarTTL, opts.NativeHistogramMaxExemplars)
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}
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for i, upperBound := range h.upperBounds {
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if i < len(h.upperBounds)-1 {
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@ -725,7 +743,8 @@ type histogram struct {
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// resetScheduled is protected by mtx. It is true if a reset is
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// scheduled for a later time (when nativeHistogramMinResetDuration has
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// passed).
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resetScheduled bool
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resetScheduled bool
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nativeExemplars nativeExemplars
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// now is for testing purposes, by default it's time.Now.
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now func() time.Time
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@ -742,6 +761,9 @@ func (h *histogram) Observe(v float64) {
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h.observe(v, h.findBucket(v))
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}
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// ObserveWithExemplar should not be called in a high-frequency setting
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// for a native histogram with configured exemplars. For this case,
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// the implementation isn't lock-free and might suffer from lock contention.
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func (h *histogram) ObserveWithExemplar(v float64, e Labels) {
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i := h.findBucket(v)
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h.observe(v, i)
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@ -821,6 +843,15 @@ func (h *histogram) Write(out *dto.Metric) error {
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Length: proto.Uint32(0),
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}}
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}
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// If exemplars are not configured, the cap will be 0.
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// So append is not needed in this case.
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if cap(h.nativeExemplars.exemplars) > 0 {
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h.nativeExemplars.Lock()
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his.Exemplars = append(his.Exemplars, h.nativeExemplars.exemplars...)
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h.nativeExemplars.Unlock()
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}
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}
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addAndResetCounts(hotCounts, coldCounts)
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return nil
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@ -1091,8 +1122,10 @@ func (h *histogram) resetCounts(counts *histogramCounts) {
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deleteSyncMap(&counts.nativeHistogramBucketsPositive)
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}
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// updateExemplar replaces the exemplar for the provided bucket. With empty
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// labels, it's a no-op. It panics if any of the labels is invalid.
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// updateExemplar replaces the exemplar for the provided classic bucket.
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// With empty labels, it's a no-op. It panics if any of the labels is invalid.
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// If histogram is native, the exemplar will be cached into nativeExemplars,
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// which has a limit, and will remove one exemplar when limit is reached.
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func (h *histogram) updateExemplar(v float64, bucket int, l Labels) {
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if l == nil {
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return
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@ -1102,6 +1135,10 @@ func (h *histogram) updateExemplar(v float64, bucket int, l Labels) {
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panic(err)
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}
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h.exemplars[bucket].Store(e)
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doSparse := h.nativeHistogramSchema > math.MinInt32 && !math.IsNaN(v)
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if doSparse {
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h.nativeExemplars.addExemplar(e)
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}
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}
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// HistogramVec is a Collector that bundles a set of Histograms that all share the
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@ -1575,3 +1612,142 @@ func addAndResetCounts(hot, cold *histogramCounts) {
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atomic.AddUint64(&hot.nativeHistogramZeroBucket, atomic.LoadUint64(&cold.nativeHistogramZeroBucket))
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atomic.StoreUint64(&cold.nativeHistogramZeroBucket, 0)
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}
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type nativeExemplars struct {
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sync.Mutex
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ttl time.Duration
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exemplars []*dto.Exemplar
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}
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func makeNativeExemplars(ttl time.Duration, maxCount int) nativeExemplars {
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if ttl == 0 {
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ttl = 5 * time.Minute
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}
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if maxCount == 0 {
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maxCount = 10
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}
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if maxCount < 0 {
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maxCount = 0
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}
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return nativeExemplars{
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ttl: ttl,
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exemplars: make([]*dto.Exemplar, 0, maxCount),
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}
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}
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func (n *nativeExemplars) addExemplar(e *dto.Exemplar) {
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if cap(n.exemplars) == 0 {
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return
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}
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n.Lock()
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defer n.Unlock()
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// The index where to insert the new exemplar.
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var nIdx int = -1
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// When the number of exemplars has not yet exceeded or
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// is equal to cap(n.exemplars), then
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// insert the new exemplar directly.
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if len(n.exemplars) < cap(n.exemplars) {
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for nIdx = 0; nIdx < len(n.exemplars); nIdx++ {
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if *e.Value < *n.exemplars[nIdx].Value {
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break
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}
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}
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n.exemplars = append(n.exemplars[:nIdx], append([]*dto.Exemplar{e}, n.exemplars[nIdx:]...)...)
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return
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}
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// When the number of exemplars exceeds the limit, remove one exemplar.
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var (
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rIdx int // The index where to remove the old exemplar.
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ot = time.Now() // Oldest timestamp seen.
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otIdx = -1 // Index of the exemplar with the oldest timestamp.
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md = -1.0 // Logarithm of the delta of the closest pair of exemplars.
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mdIdx = -1 // Index of the older exemplar within the closest pair.
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cLog float64 // Logarithm of the current exemplar.
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pLog float64 // Logarithm of the previous exemplar.
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)
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for i, exemplar := range n.exemplars {
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// Find the exemplar with the oldest timestamp.
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if otIdx == -1 || exemplar.Timestamp.AsTime().Before(ot) {
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ot = exemplar.Timestamp.AsTime()
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otIdx = i
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}
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// Find the index at which to insert new the exemplar.
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if *e.Value <= *exemplar.Value && nIdx == -1 {
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nIdx = i
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}
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// Find the two closest exemplars and pick the one the with older timestamp.
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pLog = cLog
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cLog = math.Log(exemplar.GetValue())
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if i == 0 {
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continue
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}
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diff := math.Abs(cLog - pLog)
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if md == -1 || diff < md {
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md = diff
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if n.exemplars[i].Timestamp.AsTime().Before(n.exemplars[i-1].Timestamp.AsTime()) {
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mdIdx = i
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} else {
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mdIdx = i - 1
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}
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}
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}
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// If all existing exemplar are smaller than new exemplar,
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// then the exemplar should be inserted at the end.
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if nIdx == -1 {
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nIdx = len(n.exemplars)
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}
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if otIdx != -1 && e.Timestamp.AsTime().Sub(ot) > n.ttl {
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rIdx = otIdx
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} else {
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// In the previous for loop, when calculating the closest pair of exemplars,
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// we did not take into account the newly inserted exemplar.
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// So we need to calculate with the newly inserted exemplar again.
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elog := math.Log(e.GetValue())
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if nIdx > 0 {
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diff := math.Abs(elog - math.Log(n.exemplars[nIdx-1].GetValue()))
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if diff < md {
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md = diff
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mdIdx = nIdx
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if n.exemplars[nIdx-1].Timestamp.AsTime().Before(e.Timestamp.AsTime()) {
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mdIdx = nIdx - 1
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}
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}
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}
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if nIdx < len(n.exemplars) {
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diff := math.Abs(math.Log(n.exemplars[nIdx].GetValue()) - elog)
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if diff < md {
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mdIdx = nIdx
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if n.exemplars[nIdx].Timestamp.AsTime().Before(e.Timestamp.AsTime()) {
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mdIdx = nIdx
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}
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}
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}
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rIdx = mdIdx
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}
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// Adjust the slice according to rIdx and nIdx.
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switch {
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case rIdx == nIdx:
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n.exemplars[nIdx] = e
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case rIdx < nIdx:
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n.exemplars = append(n.exemplars[:rIdx], append(n.exemplars[rIdx+1:nIdx], append([]*dto.Exemplar{e}, n.exemplars[nIdx:]...)...)...)
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case rIdx > nIdx:
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n.exemplars = append(n.exemplars[:nIdx], append([]*dto.Exemplar{e}, append(n.exemplars[nIdx:rIdx], n.exemplars[rIdx+1:]...)...)...)
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}
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}
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@ -1271,3 +1271,158 @@ func TestHistogramVecCreatedTimestampWithDeletes(t *testing.T) {
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now = now.Add(1 * time.Hour)
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expectCTsForMetricVecValues(t, histogramVec.MetricVec, dto.MetricType_HISTOGRAM, expected)
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}
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func TestNativeHistogramExemplar(t *testing.T) {
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// Test the histogram with positive NativeHistogramExemplarTTL and NativeHistogramMaxExemplars
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h := NewHistogram(HistogramOpts{
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Name: "test",
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Help: "test help",
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Buckets: []float64{1, 2, 3, 4},
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NativeHistogramBucketFactor: 1.1,
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NativeHistogramMaxExemplars: 3,
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NativeHistogramExemplarTTL: 10 * time.Second,
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}).(*histogram)
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tcs := []struct {
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name string
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addFunc func(*histogram)
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expectedValues []float64
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}{
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{
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name: "add exemplars to the limit",
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addFunc: func(h *histogram) {
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h.ObserveWithExemplar(1, Labels{"id": "1"})
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h.ObserveWithExemplar(3, Labels{"id": "1"})
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h.ObserveWithExemplar(5, Labels{"id": "1"})
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},
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expectedValues: []float64{1, 3, 5},
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},
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{
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name: "remove exemplar in closest pair, the removed index equals to inserted index",
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addFunc: func(h *histogram) {
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h.ObserveWithExemplar(4, Labels{"id": "1"})
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},
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expectedValues: []float64{1, 3, 4},
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},
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{
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name: "remove exemplar in closest pair, the removed index is bigger than inserted index",
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addFunc: func(h *histogram) {
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h.ObserveWithExemplar(0, Labels{"id": "1"})
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},
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expectedValues: []float64{0, 1, 4},
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},
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{
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name: "remove exemplar with oldest timestamp, the removed index is smaller than inserted index",
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addFunc: func(h *histogram) {
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h.now = func() time.Time { return time.Now().Add(time.Second * 11) }
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h.ObserveWithExemplar(6, Labels{"id": "1"})
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},
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expectedValues: []float64{0, 4, 6},
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},
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}
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for _, tc := range tcs {
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t.Run(tc.name, func(t *testing.T) {
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tc.addFunc(h)
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compareNativeExemplarValues(t, h.nativeExemplars.exemplars, tc.expectedValues)
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})
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}
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// Test the histogram with negative NativeHistogramExemplarTTL
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h = NewHistogram(HistogramOpts{
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Name: "test",
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Help: "test help",
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Buckets: []float64{1, 2, 3, 4},
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NativeHistogramBucketFactor: 1.1,
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NativeHistogramMaxExemplars: 3,
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NativeHistogramExemplarTTL: -1 * time.Second,
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}).(*histogram)
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tcs = []struct {
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name string
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addFunc func(*histogram)
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expectedValues []float64
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}{
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{
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name: "add exemplars to the limit",
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addFunc: func(h *histogram) {
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h.ObserveWithExemplar(1, Labels{"id": "1"})
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h.ObserveWithExemplar(3, Labels{"id": "1"})
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h.ObserveWithExemplar(5, Labels{"id": "1"})
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},
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expectedValues: []float64{1, 3, 5},
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},
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{
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name: "remove exemplar with oldest timestamp, the removed index is smaller than inserted index",
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addFunc: func(h *histogram) {
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h.ObserveWithExemplar(4, Labels{"id": "1"})
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},
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expectedValues: []float64{3, 4, 5},
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},
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{
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name: "remove exemplar with oldest timestamp, the removed index equals to inserted index",
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addFunc: func(h *histogram) {
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h.ObserveWithExemplar(0, Labels{"id": "1"})
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},
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expectedValues: []float64{0, 4, 5},
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},
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{
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name: "remove exemplar with oldest timestamp, the removed index is bigger than inserted index",
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addFunc: func(h *histogram) {
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h.ObserveWithExemplar(3, Labels{"id": "1"})
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},
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expectedValues: []float64{0, 3, 4},
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},
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}
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for _, tc := range tcs {
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t.Run(tc.name, func(t *testing.T) {
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tc.addFunc(h)
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compareNativeExemplarValues(t, h.nativeExemplars.exemplars, tc.expectedValues)
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})
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}
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// Test the histogram with negative NativeHistogramMaxExemplars
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h = NewHistogram(HistogramOpts{
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Name: "test",
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Help: "test help",
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Buckets: []float64{1, 2, 3, 4},
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NativeHistogramBucketFactor: 1.1,
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NativeHistogramMaxExemplars: -1,
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NativeHistogramExemplarTTL: -1 * time.Second,
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}).(*histogram)
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tcs = []struct {
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name string
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addFunc func(*histogram)
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expectedValues []float64
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}{
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{
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name: "add exemplars to the limit, but no effect",
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addFunc: func(h *histogram) {
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h.ObserveWithExemplar(1, Labels{"id": "1"})
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h.ObserveWithExemplar(3, Labels{"id": "1"})
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h.ObserveWithExemplar(5, Labels{"id": "1"})
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},
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expectedValues: []float64{},
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},
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}
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for _, tc := range tcs {
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t.Run(tc.name, func(t *testing.T) {
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tc.addFunc(h)
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compareNativeExemplarValues(t, h.nativeExemplars.exemplars, tc.expectedValues)
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})
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}
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}
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func compareNativeExemplarValues(t *testing.T, exps []*dto.Exemplar, values []float64) {
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if len(exps) != len(values) {
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t.Errorf("the count of exemplars is not %d", len(values))
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}
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for i, e := range exps {
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if e.GetValue() != values[i] {
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t.Errorf("the %dth exemplar value %v is not as expected: %v", i, e.GetValue(), values[i])
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}
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}
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}
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