forked from mirror/client_golang
Merge pull request #1150 from prometheus/sparsehistogram
Merge sparsehistogram branch into main
This commit is contained in:
Björn Rabenstein
GitHub
commit
5f202eefdb
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@ -48,14 +48,22 @@ func NewMetrics(reg prometheus.Registerer, normMean, normDomain float64) *metric
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},
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[]string{"service"},
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),
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// The same as above, but now as a histogram, and only for the normal
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// distribution. The buckets are targeted to the parameters of the
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// normal distribution, with 20 buckets centered on the mean, each
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// half-sigma wide.
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// The same as above, but now as a histogram, and only for the
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// normal distribution. The histogram features both conventional
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// buckets as well as sparse buckets, the latter needed for the
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// experimental native histograms (ingested by a Prometheus
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// server v2.40 with the corresponding feature flag
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// enabled). The conventional buckets are targeted to the
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// parameters of the normal distribution, with 20 buckets
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// centered on the mean, each half-sigma wide. The sparse
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// buckets are always centered on zero, with a growth factor of
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// one bucket to the text of (at most) 1.1. (The precise factor
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// is 2^2^-3 = 1.0905077...)
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rpcDurationsHistogram: prometheus.NewHistogram(prometheus.HistogramOpts{
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Name: "rpc_durations_histogram_seconds",
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Help: "RPC latency distributions.",
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Buckets: prometheus.LinearBuckets(normMean-5*normDomain, .5*normDomain, 20),
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Name: "rpc_durations_histogram_seconds",
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Help: "RPC latency distributions.",
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Buckets: prometheus.LinearBuckets(normMean-5*normDomain, .5*normDomain, 20),
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NativeHistogramBucketFactor: 1.1,
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}),
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}
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reg.MustRegister(m.rpcDurations)
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2
go.mod
2
go.mod
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@ -8,7 +8,7 @@ require (
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github.com/davecgh/go-spew v1.1.1
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github.com/golang/protobuf v1.5.2
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github.com/json-iterator/go v1.1.12
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github.com/prometheus/client_model v0.2.0
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github.com/prometheus/client_model v0.3.0
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github.com/prometheus/common v0.37.0
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github.com/prometheus/procfs v0.8.0
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golang.org/x/sys v0.0.0-20220520151302-bc2c85ada10a
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3
go.sum
3
go.sum
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@ -134,8 +134,9 @@ github.com/mwitkow/go-conntrack v0.0.0-20190716064945-2f068394615f/go.mod h1:qRW
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github.com/pmezard/go-difflib v1.0.0 h1:4DBwDE0NGyQoBHbLQYPwSUPoCMWR5BEzIk/f1lZbAQM=
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github.com/pmezard/go-difflib v1.0.0/go.mod h1:iKH77koFhYxTK1pcRnkKkqfTogsbg7gZNVY4sRDYZ/4=
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github.com/prometheus/client_model v0.0.0-20190812154241-14fe0d1b01d4/go.mod h1:xMI15A0UPsDsEKsMN9yxemIoYk6Tm2C1GtYGdfGttqA=
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github.com/prometheus/client_model v0.2.0 h1:uq5h0d+GuxiXLJLNABMgp2qUWDPiLvgCzz2dUR+/W/M=
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github.com/prometheus/client_model v0.2.0/go.mod h1:xMI15A0UPsDsEKsMN9yxemIoYk6Tm2C1GtYGdfGttqA=
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github.com/prometheus/client_model v0.3.0 h1:UBgGFHqYdG/TPFD1B1ogZywDqEkwp3fBMvqdiQ7Xew4=
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github.com/prometheus/client_model v0.3.0/go.mod h1:LDGWKZIo7rky3hgvBe+caln+Dr3dPggB5dvjtD7w9+w=
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github.com/prometheus/common v0.37.0 h1:ccBbHCgIiT9uSoFY0vX8H3zsNR5eLt17/RQLUvn8pXE=
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github.com/prometheus/common v0.37.0/go.mod h1:phzohg0JFMnBEFGxTDbfu3QyL5GI8gTQJFhYO5B3mfA=
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github.com/prometheus/procfs v0.8.0 h1:ODq8ZFEaYeCaZOJlZZdJA2AbQR98dSHSM1KW/You5mo=
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File diff suppressed because it is too large
Load Diff
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@ -20,6 +20,7 @@ import (
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"runtime"
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"sort"
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"sync"
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"sync/atomic"
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"testing"
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"testing/quick"
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"time"
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@ -167,7 +168,7 @@ func TestHistogramConcurrency(t *testing.T) {
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start.Add(1)
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end.Add(concLevel)
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sum := NewHistogram(HistogramOpts{
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his := NewHistogram(HistogramOpts{
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Name: "test_histogram",
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Help: "helpless",
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Buckets: testBuckets,
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@ -188,9 +189,9 @@ func TestHistogramConcurrency(t *testing.T) {
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start.Wait()
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for _, v := range vals {
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if n%2 == 0 {
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sum.Observe(v)
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his.Observe(v)
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} else {
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sum.(ExemplarObserver).ObserveWithExemplar(v, Labels{"foo": "bar"})
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his.(ExemplarObserver).ObserveWithExemplar(v, Labels{"foo": "bar"})
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}
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}
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end.Done()
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@ -201,7 +202,7 @@ func TestHistogramConcurrency(t *testing.T) {
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end.Wait()
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m := &dto.Metric{}
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sum.Write(m)
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his.Write(m)
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if got, want := int(*m.Histogram.SampleCount), total; got != want {
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t.Errorf("got sample count %d, want %d", got, want)
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}
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@ -467,3 +468,408 @@ func TestHistogramExemplar(t *testing.T) {
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}
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}
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}
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func TestSparseHistogram(t *testing.T) {
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scenarios := []struct {
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name string
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observations []float64 // With simulated interval of 1m.
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factor float64
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zeroThreshold float64
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maxBuckets uint32
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minResetDuration time.Duration
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maxZeroThreshold float64
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want string // String representation of protobuf.
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}{
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{
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name: "no sparse buckets",
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observations: []float64{1, 2, 3},
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factor: 1,
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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.
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},
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{
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name: "factor 1.1 results in schema 3",
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observations: []float64{0, 1, 2, 3},
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factor: 1.1,
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want: `sample_count:4 sample_sum:6 schema:3 zero_threshold:2.938735877055719e-39 zero_count:1 positive_span:<offset:0 length:1 > positive_span:<offset:7 length:1 > positive_span:<offset:4 length:1 > positive_delta:1 positive_delta:0 positive_delta:0 `,
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},
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{
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name: "factor 1.2 results in schema 2",
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observations: []float64{0, 1, 1.2, 1.4, 1.8, 2},
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factor: 1.2,
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want: `sample_count:6 sample_sum:7.4 schema:2 zero_threshold:2.938735877055719e-39 zero_count:1 positive_span:<offset:0 length:5 > positive_delta:1 positive_delta:-1 positive_delta:2 positive_delta:-2 positive_delta:2 `,
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},
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{
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name: "factor 4 results in schema -1",
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observations: []float64{
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0.5, 1, // Bucket 0: (0.25, 1]
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1.5, 2, 3, 3.5, // Bucket 1: (1, 4]
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5, 6, 7, // Bucket 2: (4, 16]
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33.33, // Bucket 3: (16, 64]
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},
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factor: 4,
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want: `sample_count:10 sample_sum:62.83 schema:-1 zero_threshold:2.938735877055719e-39 zero_count:0 positive_span:<offset:0 length:4 > positive_delta:2 positive_delta:2 positive_delta:-1 positive_delta:-2 `,
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},
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{
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name: "factor 17 results in schema -2",
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observations: []float64{
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0.5, 1, // Bucket 0: (0.0625, 1]
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1.5, 2, 3, 3.5, 5, 6, 7, // Bucket 1: (1, 16]
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33.33, // Bucket 2: (16, 256]
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},
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factor: 17,
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want: `sample_count:10 sample_sum:62.83 schema:-2 zero_threshold:2.938735877055719e-39 zero_count:0 positive_span:<offset:0 length:3 > positive_delta:2 positive_delta:5 positive_delta:-6 `,
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},
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{
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name: "negative buckets",
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observations: []float64{0, -1, -1.2, -1.4, -1.8, -2},
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factor: 1.2,
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want: `sample_count:6 sample_sum:-7.4 schema:2 zero_threshold:2.938735877055719e-39 zero_count:1 negative_span:<offset:0 length:5 > negative_delta:1 negative_delta:-1 negative_delta:2 negative_delta:-2 negative_delta:2 `,
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},
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{
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name: "negative and positive buckets",
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observations: []float64{0, -1, -1.2, -1.4, -1.8, -2, 1, 1.2, 1.4, 1.8, 2},
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factor: 1.2,
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want: `sample_count:11 sample_sum:0 schema:2 zero_threshold:2.938735877055719e-39 zero_count:1 negative_span:<offset:0 length:5 > negative_delta:1 negative_delta:-1 negative_delta:2 negative_delta:-2 negative_delta:2 positive_span:<offset:0 length:5 > positive_delta:1 positive_delta:-1 positive_delta:2 positive_delta:-2 positive_delta:2 `,
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},
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{
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name: "wide zero bucket",
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observations: []float64{0, -1, -1.2, -1.4, -1.8, -2, 1, 1.2, 1.4, 1.8, 2},
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factor: 1.2,
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zeroThreshold: 1.4,
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want: `sample_count:11 sample_sum:0 schema:2 zero_threshold:1.4 zero_count:7 negative_span:<offset:4 length:1 > negative_delta:2 positive_span:<offset:4 length:1 > positive_delta:2 `,
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},
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{
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name: "NaN observation",
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observations: []float64{0, 1, 1.2, 1.4, 1.8, 2, math.NaN()},
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factor: 1.2,
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want: `sample_count:7 sample_sum:nan schema:2 zero_threshold:2.938735877055719e-39 zero_count:1 positive_span:<offset:0 length:5 > positive_delta:1 positive_delta:-1 positive_delta:2 positive_delta:-2 positive_delta:2 `,
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},
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{
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name: "+Inf observation",
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observations: []float64{0, 1, 1.2, 1.4, 1.8, 2, math.Inf(+1)},
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factor: 1.2,
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want: `sample_count:7 sample_sum:inf schema:2 zero_threshold:2.938735877055719e-39 zero_count:1 positive_span:<offset:0 length:5 > positive_span:<offset:4092 length:1 > positive_delta:1 positive_delta:-1 positive_delta:2 positive_delta:-2 positive_delta:2 positive_delta:-1 `,
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},
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{
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name: "-Inf observation",
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observations: []float64{0, 1, 1.2, 1.4, 1.8, 2, math.Inf(-1)},
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factor: 1.2,
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want: `sample_count:7 sample_sum:-inf schema:2 zero_threshold:2.938735877055719e-39 zero_count:1 negative_span:<offset:4097 length:1 > negative_delta:1 positive_span:<offset:0 length:5 > positive_delta:1 positive_delta:-1 positive_delta:2 positive_delta:-2 positive_delta:2 `,
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},
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{
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name: "limited buckets but nothing triggered",
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observations: []float64{0, 1, 1.2, 1.4, 1.8, 2},
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factor: 1.2,
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maxBuckets: 4,
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want: `sample_count:6 sample_sum:7.4 schema:2 zero_threshold:2.938735877055719e-39 zero_count:1 positive_span:<offset:0 length:5 > positive_delta:1 positive_delta:-1 positive_delta:2 positive_delta:-2 positive_delta:2 `,
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},
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{
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name: "buckets limited by halving resolution",
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observations: []float64{0, 1, 1.1, 1.2, 1.4, 1.8, 2, 3},
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factor: 1.2,
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maxBuckets: 4,
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want: `sample_count:8 sample_sum:11.5 schema:1 zero_threshold:2.938735877055719e-39 zero_count:1 positive_span:<offset:0 length:5 > positive_delta:1 positive_delta:2 positive_delta:-1 positive_delta:-2 positive_delta:1 `,
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},
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{
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name: "buckets limited by widening the zero bucket",
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observations: []float64{0, 1, 1.1, 1.2, 1.4, 1.8, 2, 3},
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factor: 1.2,
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maxBuckets: 4,
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maxZeroThreshold: 1.2,
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want: `sample_count:8 sample_sum:11.5 schema:2 zero_threshold:1 zero_count:2 positive_span:<offset:1 length:7 > positive_delta:1 positive_delta:1 positive_delta:-2 positive_delta:2 positive_delta:-2 positive_delta:0 positive_delta:1 `,
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},
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{
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name: "buckets limited by widening the zero bucket twice",
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observations: []float64{0, 1, 1.1, 1.2, 1.4, 1.8, 2, 3, 4},
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factor: 1.2,
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maxBuckets: 4,
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maxZeroThreshold: 1.2,
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want: `sample_count:9 sample_sum:15.5 schema:2 zero_threshold:1.189207115002721 zero_count:3 positive_span:<offset:2 length:7 > positive_delta:2 positive_delta:-2 positive_delta:2 positive_delta:-2 positive_delta:0 positive_delta:1 positive_delta:0 `,
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},
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{
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name: "buckets limited by reset",
|
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observations: []float64{0, 1, 1.1, 1.2, 1.4, 1.8, 2, 3, 4},
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factor: 1.2,
|
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maxBuckets: 4,
|
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maxZeroThreshold: 1.2,
|
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minResetDuration: 5 * time.Minute,
|
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want: `sample_count:2 sample_sum:7 schema:2 zero_threshold:2.938735877055719e-39 zero_count:0 positive_span:<offset:7 length:2 > positive_delta:1 positive_delta:0 `,
|
||||
},
|
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{
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name: "limited buckets but nothing triggered, negative observations",
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observations: []float64{0, -1, -1.2, -1.4, -1.8, -2},
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factor: 1.2,
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maxBuckets: 4,
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want: `sample_count:6 sample_sum:-7.4 schema:2 zero_threshold:2.938735877055719e-39 zero_count:1 negative_span:<offset:0 length:5 > negative_delta:1 negative_delta:-1 negative_delta:2 negative_delta:-2 negative_delta:2 `,
|
||||
},
|
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{
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name: "buckets limited by halving resolution, negative observations",
|
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observations: []float64{0, -1, -1.1, -1.2, -1.4, -1.8, -2, -3},
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factor: 1.2,
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maxBuckets: 4,
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want: `sample_count:8 sample_sum:-11.5 schema:1 zero_threshold:2.938735877055719e-39 zero_count:1 negative_span:<offset:0 length:5 > negative_delta:1 negative_delta:2 negative_delta:-1 negative_delta:-2 negative_delta:1 `,
|
||||
},
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{
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name: "buckets limited by widening the zero bucket, negative observations",
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observations: []float64{0, -1, -1.1, -1.2, -1.4, -1.8, -2, -3},
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factor: 1.2,
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maxBuckets: 4,
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maxZeroThreshold: 1.2,
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want: `sample_count:8 sample_sum:-11.5 schema:2 zero_threshold:1 zero_count:2 negative_span:<offset:1 length:7 > negative_delta:1 negative_delta:1 negative_delta:-2 negative_delta:2 negative_delta:-2 negative_delta:0 negative_delta:1 `,
|
||||
},
|
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{
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name: "buckets limited by widening the zero bucket twice, negative observations",
|
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observations: []float64{0, -1, -1.1, -1.2, -1.4, -1.8, -2, -3, -4},
|
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factor: 1.2,
|
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maxBuckets: 4,
|
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maxZeroThreshold: 1.2,
|
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want: `sample_count:9 sample_sum:-15.5 schema:2 zero_threshold:1.189207115002721 zero_count:3 negative_span:<offset:2 length:7 > negative_delta:2 negative_delta:-2 negative_delta:2 negative_delta:-2 negative_delta:0 negative_delta:1 negative_delta:0 `,
|
||||
},
|
||||
{
|
||||
name: "buckets limited by reset, negative observations",
|
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observations: []float64{0, -1, -1.1, -1.2, -1.4, -1.8, -2, -3, -4},
|
||||
factor: 1.2,
|
||||
maxBuckets: 4,
|
||||
maxZeroThreshold: 1.2,
|
||||
minResetDuration: 5 * time.Minute,
|
||||
want: `sample_count:2 sample_sum:-7 schema:2 zero_threshold:2.938735877055719e-39 zero_count:0 negative_span:<offset:7 length:2 > negative_delta:1 negative_delta:0 `,
|
||||
},
|
||||
{
|
||||
name: "buckets limited by halving resolution, then reset",
|
||||
observations: []float64{0, 1, 1.1, 1.2, 1.4, 1.8, 2, 5, 5.1, 3, 4},
|
||||
factor: 1.2,
|
||||
maxBuckets: 4,
|
||||
minResetDuration: 9 * time.Minute,
|
||||
want: `sample_count:2 sample_sum:7 schema:2 zero_threshold:2.938735877055719e-39 zero_count:0 positive_span:<offset:7 length:2 > positive_delta:1 positive_delta:0 `,
|
||||
},
|
||||
{
|
||||
name: "buckets limited by widening the zero bucket, then reset",
|
||||
observations: []float64{0, 1, 1.1, 1.2, 1.4, 1.8, 2, 5, 5.1, 3, 4},
|
||||
factor: 1.2,
|
||||
maxBuckets: 4,
|
||||
maxZeroThreshold: 1.2,
|
||||
minResetDuration: 9 * time.Minute,
|
||||
want: `sample_count:2 sample_sum:7 schema:2 zero_threshold:2.938735877055719e-39 zero_count:0 positive_span:<offset:7 length:2 > positive_delta:1 positive_delta:0 `,
|
||||
},
|
||||
}
|
||||
|
||||
for _, s := range scenarios {
|
||||
t.Run(s.name, func(t *testing.T) {
|
||||
his := NewHistogram(HistogramOpts{
|
||||
Name: "name",
|
||||
Help: "help",
|
||||
NativeHistogramBucketFactor: s.factor,
|
||||
NativeHistogramZeroThreshold: s.zeroThreshold,
|
||||
NativeHistogramMaxBucketNumber: s.maxBuckets,
|
||||
NativeHistogramMinResetDuration: s.minResetDuration,
|
||||
NativeHistogramMaxZeroThreshold: s.maxZeroThreshold,
|
||||
})
|
||||
ts := time.Now().Add(30 * time.Second)
|
||||
now := func() time.Time {
|
||||
return ts
|
||||
}
|
||||
his.(*histogram).now = now
|
||||
for _, o := range s.observations {
|
||||
his.Observe(o)
|
||||
ts = ts.Add(time.Minute)
|
||||
}
|
||||
m := &dto.Metric{}
|
||||
if err := his.Write(m); err != nil {
|
||||
t.Fatal("unexpected error writing metric", err)
|
||||
}
|
||||
got := m.Histogram.String()
|
||||
if s.want != got {
|
||||
t.Errorf("want histogram %q, got %q", s.want, got)
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
func TestSparseHistogramConcurrency(t *testing.T) {
|
||||
if testing.Short() {
|
||||
t.Skip("Skipping test in short mode.")
|
||||
}
|
||||
|
||||
rand.Seed(42)
|
||||
|
||||
it := func(n uint32) bool {
|
||||
mutations := int(n%1e4 + 1e4)
|
||||
concLevel := int(n%5 + 1)
|
||||
total := mutations * concLevel
|
||||
|
||||
var start, end sync.WaitGroup
|
||||
start.Add(1)
|
||||
end.Add(concLevel)
|
||||
|
||||
his := NewHistogram(HistogramOpts{
|
||||
Name: "test_sparse_histogram",
|
||||
Help: "This help is sparse.",
|
||||
NativeHistogramBucketFactor: 1.05,
|
||||
NativeHistogramZeroThreshold: 0.0000001,
|
||||
NativeHistogramMaxBucketNumber: 50,
|
||||
NativeHistogramMinResetDuration: time.Hour, // Comment out to test for totals below.
|
||||
NativeHistogramMaxZeroThreshold: 0.001,
|
||||
})
|
||||
|
||||
ts := time.Now().Add(30 * time.Second).Unix()
|
||||
now := func() time.Time {
|
||||
return time.Unix(atomic.LoadInt64(&ts), 0)
|
||||
}
|
||||
his.(*histogram).now = now
|
||||
|
||||
allVars := make([]float64, total)
|
||||
var sampleSum float64
|
||||
for i := 0; i < concLevel; i++ {
|
||||
vals := make([]float64, mutations)
|
||||
for j := 0; j < mutations; j++ {
|
||||
v := rand.NormFloat64()
|
||||
vals[j] = v
|
||||
allVars[i*mutations+j] = v
|
||||
sampleSum += v
|
||||
}
|
||||
|
||||
go func(vals []float64) {
|
||||
start.Wait()
|
||||
for _, v := range vals {
|
||||
// An observation every 1 to 10 seconds.
|
||||
atomic.AddInt64(&ts, rand.Int63n(10)+1)
|
||||
his.Observe(v)
|
||||
}
|
||||
end.Done()
|
||||
}(vals)
|
||||
}
|
||||
sort.Float64s(allVars)
|
||||
start.Done()
|
||||
end.Wait()
|
||||
|
||||
m := &dto.Metric{}
|
||||
his.Write(m)
|
||||
|
||||
// Uncomment these tests for totals only if you have disabled histogram resets above.
|
||||
//
|
||||
// if got, want := int(*m.Histogram.SampleCount), total; got != want {
|
||||
// t.Errorf("got sample count %d, want %d", got, want)
|
||||
// }
|
||||
// if got, want := *m.Histogram.SampleSum, sampleSum; math.Abs((got-want)/want) > 0.001 {
|
||||
// t.Errorf("got sample sum %f, want %f", got, want)
|
||||
// }
|
||||
|
||||
sumBuckets := int(m.Histogram.GetZeroCount())
|
||||
current := 0
|
||||
for _, delta := range m.Histogram.GetNegativeDelta() {
|
||||
current += int(delta)
|
||||
if current < 0 {
|
||||
t.Fatalf("negative bucket population negative: %d", current)
|
||||
}
|
||||
sumBuckets += current
|
||||
}
|
||||
current = 0
|
||||
for _, delta := range m.Histogram.GetPositiveDelta() {
|
||||
current += int(delta)
|
||||
if current < 0 {
|
||||
t.Fatalf("positive bucket population negative: %d", current)
|
||||
}
|
||||
sumBuckets += current
|
||||
}
|
||||
if got, want := sumBuckets, int(*m.Histogram.SampleCount); got != want {
|
||||
t.Errorf("got bucket population sum %d, want %d", got, want)
|
||||
}
|
||||
|
||||
return true
|
||||
}
|
||||
|
||||
if err := quick.Check(it, nil); err != nil {
|
||||
t.Error(err)
|
||||
}
|
||||
}
|
||||
|
||||
func TestGetLe(t *testing.T) {
|
||||
scenarios := []struct {
|
||||
key int
|
||||
schema int32
|
||||
want float64
|
||||
}{
|
||||
{
|
||||
key: -1,
|
||||
schema: -1,
|
||||
want: 0.25,
|
||||
},
|
||||
{
|
||||
key: 0,
|
||||
schema: -1,
|
||||
want: 1,
|
||||
},
|
||||
{
|
||||
key: 1,
|
||||
schema: -1,
|
||||
want: 4,
|
||||
},
|
||||
{
|
||||
key: 512,
|
||||
schema: -1,
|
||||
want: math.MaxFloat64,
|
||||
},
|
||||
{
|
||||
key: 513,
|
||||
schema: -1,
|
||||
want: math.Inf(+1),
|
||||
},
|
||||
{
|
||||
key: -1,
|
||||
schema: 0,
|
||||
want: 0.5,
|
||||
},
|
||||
{
|
||||
key: 0,
|
||||
schema: 0,
|
||||
want: 1,
|
||||
},
|
||||
{
|
||||
key: 1,
|
||||
schema: 0,
|
||||
want: 2,
|
||||
},
|
||||
{
|
||||
key: 1024,
|
||||
schema: 0,
|
||||
want: math.MaxFloat64,
|
||||
},
|
||||
{
|
||||
key: 1025,
|
||||
schema: 0,
|
||||
want: math.Inf(+1),
|
||||
},
|
||||
{
|
||||
key: -1,
|
||||
schema: 2,
|
||||
want: 0.8408964152537144,
|
||||
},
|
||||
{
|
||||
key: 0,
|
||||
schema: 2,
|
||||
want: 1,
|
||||
},
|
||||
{
|
||||
key: 1,
|
||||
schema: 2,
|
||||
want: 1.189207115002721,
|
||||
},
|
||||
{
|
||||
key: 4096,
|
||||
schema: 2,
|
||||
want: math.MaxFloat64,
|
||||
},
|
||||
{
|
||||
key: 4097,
|
||||
schema: 2,
|
||||
want: math.Inf(+1),
|
||||
},
|
||||
}
|
||||
|
||||
for i, s := range scenarios {
|
||||
got := getLe(s.key, s.schema)
|
||||
if s.want != got {
|
||||
t.Errorf("%d. key %d, schema %d, want upper bound of %g, got %g", i, s.key, s.schema, s.want, got)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue