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Allow a zero threshold of zero 

Signed-off-by: beorn7 <beorn@grafana.com>
This commit is contained in:
beorn7 2021-07-20 19:01:13 +02:00
parent aa6f67a9e6
commit 9ef5f90a76
3 changed files with 29 additions and 24 deletions
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2
prometheus/examples_test.go
View File

@ -538,8 +538,6 @@ func ExampleHistogram() {
// cumulative_count: 816
// upper_bound: 40
// >
// sb_schema: 0
// sb_zero_threshold: 0
// >
}

33
prometheus/histogram.go
View File

@ -369,8 +369,13 @@ type HistogramOpts struct {
// SparseBucketsZeroThreshold are accumulated into a “zero” bucket. For
// best results, this should be close to a bucket boundary. This is
// usually the case if picking a power of two. If
// SparseBucketsZeroThreshold is left at zero (or set to a negative
// value), DefSparseBucketsZeroThreshold is used as the threshold.
// SparseBucketsZeroThreshold is left at zero,
// DefSparseBucketsZeroThreshold is used as the threshold. If it is set
// to a negative value, a threshold of zero is used, i.e. only
// observations of precisely zero will go into the zero
// bucket. (TODO(beorn7): That's obviously weird and just a consequence
// of making the zero value of HistogramOpts meaningful. Has to be
// solved more elegantly in the final version.)
SparseBucketsZeroThreshold float64
// TODO(beorn7): Need a setting to limit total bucket count and to
// configure a strategy to enforce the limit, e.g. if minimum duration
@ -415,7 +420,6 @@ func newHistogram(desc *Desc, opts HistogramOpts, labelValues ...string) Histogr
h := &histogram{
desc: desc,
upperBounds: opts.Buckets,
sparseThreshold: opts.SparseBucketsZeroThreshold,
labelPairs: MakeLabelPairs(desc, labelValues),
counts: [2]*histogramCounts{{}, {}},
now: time.Now,
@ -423,12 +427,15 @@ func newHistogram(desc *Desc, opts HistogramOpts, labelValues ...string) Histogr
if len(h.upperBounds) == 0 && opts.SparseBucketsFactor <= 1 {
h.upperBounds = DefBuckets
}
if h.sparseThreshold <= 0 {
h.sparseThreshold = DefSparseBucketsZeroThreshold
}
if opts.SparseBucketsFactor <= 1 {
h.sparseThreshold = 0 // To mark that there are no sparse buckets.
h.sparseSchema = math.MinInt32 // To mark that there are no sparse buckets.
} else {
switch {
case opts.SparseBucketsZeroThreshold > 0:
h.sparseThreshold = opts.SparseBucketsZeroThreshold
case opts.SparseBucketsZeroThreshold == 0:
h.sparseThreshold = DefSparseBucketsZeroThreshold
} // Leave h.sparseThreshold at 0 otherwise.
h.sparseSchema = pickSparseSchema(opts.SparseBucketsFactor)
}
for i, upperBound := range h.upperBounds {
@ -559,8 +566,8 @@ type histogram struct {
upperBounds []float64
labelPairs []*dto.LabelPair
exemplars []atomic.Value // One more than buckets (to include +Inf), each a *dto.Exemplar.
sparseSchema int32
sparseThreshold float64 // This is zero iff no sparse buckets are used.
sparseSchema int32 // Set to math.MinInt32 if no sparse buckets are used.
sparseThreshold float64
now func() time.Time // To mock out time.Now() for testing.
}
@ -607,8 +614,6 @@ func (h *histogram) Write(out *dto.Metric) error {
Bucket: make([]*dto.Bucket, len(h.upperBounds)),
SampleCount: proto.Uint64(count),
SampleSum: proto.Float64(math.Float64frombits(atomic.LoadUint64(&coldCounts.sumBits))),
SbSchema: &h.sparseSchema,
SbZeroThreshold: &h.sparseThreshold,
}
out.Histogram = his
out.Label = h.labelPairs
@ -648,7 +653,9 @@ func (h *histogram) Write(out *dto.Metric) error {
atomic.AddUint64(&hotCounts.buckets[i], atomic.LoadUint64(&coldCounts.buckets[i]))
atomic.StoreUint64(&coldCounts.buckets[i], 0)
}
if h.sparseThreshold != 0 {
if h.sparseSchema > math.MinInt32 {
his.SbZeroThreshold = &h.sparseThreshold
his.SbSchema = &h.sparseSchema
zeroBucket := atomic.LoadUint64(&coldCounts.sparseZeroBucket)
defer func() {
@ -749,7 +756,7 @@ func (h *histogram) findBucket(v float64) int {
// observe is the implementation for Observe without the findBucket part.
func (h *histogram) observe(v float64, bucket int) {
// Do not add to sparse buckets for NaN observations.
doSparse := h.sparseThreshold != 0 && !math.IsNaN(v)
doSparse := h.sparseSchema > math.MinInt32 && !math.IsNaN(v)
var whichSparse, sparseKey int
if doSparse {
switch {

2
prometheus/histogram_test.go
View File

@ -470,7 +470,7 @@ func TestSparseHistogram(t *testing.T) {
name: "no sparse buckets",
observations: []float64{1, 2, 3},
factor: 1,
want: `sample_count:3 sample_sum:6 bucket:<cumulative_count:0 upper_bound:0.005 > bucket:<cumulative_count:0 upper_bound:0.01 > bucket:<cumulative_count:0 upper_bound:0.025 > bucket:<cumulative_count:0 upper_bound:0.05 > bucket:<cumulative_count:0 upper_bound:0.1 > bucket:<cumulative_count:0 upper_bound:0.25 > bucket:<cumulative_count:0 upper_bound:0.5 > bucket:<cumulative_count:1 upper_bound:1 > bucket:<cumulative_count:2 upper_bound:2.5 > bucket:<cumulative_count:3 upper_bound:5 > bucket:<cumulative_count:3 upper_bound:10 > sb_schema:0 sb_zero_threshold:0 `, // Has conventional buckets because there are no sparse buckets.
want: `sample_count:3 sample_sum:6 bucket:<cumulative_count:0 upper_bound:0.005 > bucket:<cumulative_count:0 upper_bound:0.01 > bucket:<cumulative_count:0 upper_bound:0.025 > bucket:<cumulative_count:0 upper_bound:0.05 > bucket:<cumulative_count:0 upper_bound:0.1 > bucket:<cumulative_count:0 upper_bound:0.25 > bucket:<cumulative_count:0 upper_bound:0.5 > bucket:<cumulative_count:1 upper_bound:1 > bucket:<cumulative_count:2 upper_bound:2.5 > bucket:<cumulative_count:3 upper_bound:5 > bucket:<cumulative_count:3 upper_bound:10 > `, // Has conventional buckets because there are no sparse buckets.
},
{
name: "factor 1.1 results in schema 3",