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iter_test.go
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iter_test.go
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// Copyright (c) The Thanos Authors.
// Licensed under the Apache License 2.0.
package dedup
import (
"fmt"
"io"
"math"
"math/rand"
"sort"
"testing"
"github.com/prometheus/prometheus/model/histogram"
"github.com/prometheus/prometheus/model/labels"
"github.com/prometheus/prometheus/storage"
"github.com/prometheus/prometheus/tsdb/chunkenc"
"github.com/prometheus/prometheus/tsdb/tsdbutil"
"github.com/prometheus/prometheus/util/annotations"
"github.com/stretchr/testify/require"
"github.com/efficientgo/core/testutil"
"github.com/thanos-io/thanos/pkg/store/storepb"
"github.com/thanos-io/thanos/pkg/testutil/testiters"
)
type sample struct {
t int64
f float64
}
func (s sample) T() int64 {
return s.t
}
func (s sample) F() float64 {
return s.f
}
// TODO(rabenhorst): Needs to be implemented for native histogram support.
func (s sample) H() *histogram.Histogram {
panic("not implemented")
}
func (s sample) FH() *histogram.FloatHistogram {
panic("not implemented")
}
func (s sample) Type() chunkenc.ValueType {
return chunkenc.ValFloat
}
type series struct {
lset labels.Labels
samples []sample
}
func (s series) Labels() labels.Labels { return s.lset }
func (s series) Iterator(chunkenc.Iterator) chunkenc.Iterator {
return newMockedSeriesIterator(s.samples)
}
// TODO(bwplotka): Reuse SeriesSets from chunk iterators from Prometheus.
type mockedSeriesSet struct {
series []series
cur int
}
func (s *mockedSeriesSet) Next() bool {
s.cur++
return s.cur <= len(s.series)
}
func (s *mockedSeriesSet) At() storage.Series {
return s.series[s.cur-1]
}
func (s *mockedSeriesSet) Err() error { return nil }
func (s *mockedSeriesSet) Warnings() annotations.Annotations { return nil }
type mockedSeriesIterator struct {
cur int
samples []sample
}
func newMockedSeriesIterator(samples []sample) *mockedSeriesIterator {
return &mockedSeriesIterator{samples: samples, cur: -1}
}
// TODO(rabenhorst): Native histogram support needs to be added, currently hardcoded to float.
func (s *mockedSeriesIterator) Seek(t int64) chunkenc.ValueType {
s.cur = sort.Search(len(s.samples), func(n int) bool {
return s.samples[n].t >= t
})
if s.cur < len(s.samples) {
return chunkenc.ValFloat
}
return chunkenc.ValNone
}
func (s *mockedSeriesIterator) At() (t int64, v float64) {
sample := s.samples[s.cur]
return sample.t, sample.f
}
// TODO(rabenhorst): Needs to be implemented for native histogram support.
func (s *mockedSeriesIterator) AtHistogram(*histogram.Histogram) (int64, *histogram.Histogram) {
panic("not implemented")
}
func (s *mockedSeriesIterator) AtFloatHistogram(*histogram.FloatHistogram) (int64, *histogram.FloatHistogram) {
panic("not implemented")
}
func (s *mockedSeriesIterator) AtT() int64 {
return s.samples[s.cur].t
}
func (s *mockedSeriesIterator) Next() chunkenc.ValueType {
s.cur++
if s.cur < len(s.samples) {
return chunkenc.ValFloat
}
return chunkenc.ValNone
}
func (s *mockedSeriesIterator) Err() error { return nil }
var expectedRealSeriesWithStaleMarkerDeduplicatedForRate = []sample{
{t: 1587690005791, f: 461968}, {t: 1587690020791, f: 462151}, {t: 1587690035797, f: 462336}, {t: 1587690050791, f: 462650}, {t: 1587690065791, f: 462813}, {t: 1587690080791, f: 462987}, {t: 1587690095791, f: 463095}, {t: 1587690110791, f: 463247}, {t: 1587690125791, f: 463440}, {t: 1587690140791, f: 463642}, {t: 1587690155791, f: 463811}, {t: 1587690170791, f: 464027},
{t: 1587690185791, f: 464308}, {t: 1587690200791, f: 464514}, {t: 1587690215791, f: 464798}, {t: 1587690230791, f: 465018}, {t: 1587690245791, f: 465215}, {t: 1587690260813, f: 465431}, {t: 1587690275791, f: 465651}, {t: 1587690290791, f: 465870}, {t: 1587690305791, f: 466070}, {t: 1587690320792, f: 466248}, {t: 1587690335791, f: 466506}, {t: 1587690350791, f: 466766},
{t: 1587690365791, f: 466970}, {t: 1587690380791, f: 467123}, {t: 1587690395791, f: 467265}, {t: 1587690410791, f: 467383}, {t: 1587690425791, f: 467629}, {t: 1587690440791, f: 467931}, {t: 1587690455791, f: 468097}, {t: 1587690470791, f: 468281}, {t: 1587690485791, f: 468477}, {t: 1587690500791, f: 468649}, {t: 1587690515791, f: 468867}, {t: 1587690530791, f: 469150},
{t: 1587690545791, f: 469268}, {t: 1587690560791, f: 469488}, {t: 1587690575791, f: 469742}, {t: 1587690590791, f: 469951}, {t: 1587690605791, f: 470131}, {t: 1587690620791, f: 470337}, {t: 1587690635791, f: 470631}, {t: 1587690650791, f: 470832}, {t: 1587690665791, f: 471077}, {t: 1587690680791, f: 471311}, {t: 1587690695791, f: 471473}, {t: 1587690710791, f: 471728},
{t: 1587690725791, f: 472002}, {t: 1587690740791, f: 472158}, {t: 1587690755791, f: 472329}, {t: 1587690770791, f: 472722}, {t: 1587690785791, f: 472925}, {t: 1587690800791, f: 473220}, {t: 1587690815791, f: 473460}, {t: 1587690830791, f: 473748}, {t: 1587690845791, f: 473968}, {t: 1587690860791, f: 474261}, {t: 1587690875791, f: 474418}, {t: 1587690890791, f: 474726},
{t: 1587690905791, f: 474913}, {t: 1587690920791, f: 475031}, {t: 1587690935791, f: 475284}, {t: 1587690950791, f: 475563}, {t: 1587690965791, f: 475762}, {t: 1587690980791, f: 475945}, {t: 1587690995791, f: 476302}, {t: 1587691010791, f: 476501}, {t: 1587691025791, f: 476849}, {t: 1587691040800, f: 477020}, {t: 1587691055791, f: 477280}, {t: 1587691070791, f: 477549},
{t: 1587691085791, f: 477758}, {t: 1587691100817, f: 477960}, {t: 1587691115791, f: 478261}, {t: 1587691130791, f: 478559}, {t: 1587691145791, f: 478704}, {t: 1587691160804, f: 478950}, {t: 1587691175791, f: 479173}, {t: 1587691190791, f: 479368}, {t: 1587691205791, f: 479625}, {t: 1587691220805, f: 479866}, {t: 1587691235791, f: 480008}, {t: 1587691250791, f: 480155},
{t: 1587691265791, f: 480472}, {t: 1587691280811, f: 480598}, {t: 1587691295791, f: 480771}, {t: 1587691310791, f: 480996}, {t: 1587691325791, f: 481200}, {t: 1587691340803, f: 481381}, {t: 1587691355791, f: 481584}, {t: 1587691370791, f: 481759}, {t: 1587691385791, f: 482003}, {t: 1587691400803, f: 482189}, {t: 1587691415791, f: 482457}, {t: 1587691430791, f: 482623},
{t: 1587691445791, f: 482768}, {t: 1587691460804, f: 483036}, {t: 1587691475791, f: 483322}, {t: 1587691490791, f: 483566}, {t: 1587691505791, f: 483709}, {t: 1587691520807, f: 483838}, {t: 1587691535791, f: 484091}, {t: 1587691550791, f: 484236}, {t: 1587691565791, f: 484454}, {t: 1587691580816, f: 484710}, {t: 1587691595791, f: 484978}, {t: 1587691610791, f: 485271},
{t: 1587691625791, f: 485476}, {t: 1587691640792, f: 485640}, {t: 1587691655791, f: 485921}, {t: 1587691670791, f: 486201}, {t: 1587691685791, f: 486555}, {t: 1587691700791, f: 486691}, {t: 1587691715791, f: 486831}, {t: 1587691730791, f: 487033}, {t: 1587691745791, f: 487268}, {t: 1587691760803, f: 487370}, {t: 1587691775791, f: 487571}, {t: 1587691790791, f: 487787},
{t: 1587691805791, f: 488036}, {t: 1587691820791, f: 488241}, {t: 1587691835791, f: 488411}, {t: 1587691850791, f: 488625}, {t: 1587691865791, f: 488868}, {t: 1587691880791, f: 489005}, {t: 1587691895791, f: 489237}, {t: 1587691910791, f: 489545}, {t: 1587691925791, f: 489750}, {t: 1587691940791, f: 489899}, {t: 1587691955791, f: 490048}, {t: 1587691970791, f: 490364},
{t: 1587691985791, f: 490485}, {t: 1587692000791, f: 490722}, {t: 1587692015791, f: 490866}, {t: 1587692030791, f: 491025}, {t: 1587692045791, f: 491286}, {t: 1587692060816, f: 491543}, {t: 1587692075791, f: 491787}, {t: 1587692090791, f: 492065}, {t: 1587692105791, f: 492223}, {t: 1587692120816, f: 492501}, {t: 1587692135791, f: 492767}, {t: 1587692150791, f: 492955},
{t: 1587692165791, f: 493194}, {t: 1587692180792, f: 493402}, {t: 1587692195791, f: 493647}, {t: 1587692210791, f: 493897}, {t: 1587692225791, f: 494117}, {t: 1587692240805, f: 494356}, {t: 1587692255791, f: 494620}, {t: 1587692270791, f: 494762}, {t: 1587692285791, f: 495001}, {t: 1587692300805, f: 495222}, {t: 1587692315791, f: 495393}, {t: 1587692330791, f: 495662},
{t: 1587692345791, f: 495875}, {t: 1587692360801, f: 496082}, {t: 1587692375791, f: 496196}, {t: 1587692390791, f: 496245}, {t: 1587692405791, f: 496295}, {t: 1587692420791, f: 496365}, {t: 1587692435791, f: 496401}, {t: 1587692450791, f: 496452}, {t: 1587692465791, f: 496491}, {t: 1587692480791, f: 496544}, {t: 1587692542149, f: 496544}, {t: 1587692557139, f: 496640},
{t: 1587692572139, f: 496851}, {t: 1587692587139, f: 497047}, {t: 1587692602144, f: 497264}, {t: 1587692617139, f: 497529}, {t: 1587692632139, f: 497717}, {t: 1587692647139, f: 497945}, {t: 1587692662154, f: 498179}, {t: 1587692677139, f: 498466}, {t: 1587692692139, f: 498642}, {t: 1587692707139, f: 498839}, {t: 1587692722139, f: 499021}, {t: 1587692737139, f: 499177},
{t: 1587692752139, f: 499345}, {t: 1587692767139, f: 499518}, {t: 1587692782149, f: 499726}, {t: 1587692797139, f: 499980}, {t: 1587692812139, f: 500196}, {t: 1587692827139, f: 500366}, {t: 1587692842139, f: 500524}, {t: 1587692857139, f: 500734}, {t: 1587692872139, f: 500966}, {t: 1587692887139, f: 501185}, {t: 1587692902139, f: 501253}, {t: 1587692917153, f: 501411},
{t: 1587692932139, f: 501670}, {t: 1587692947139, f: 501857}, {t: 1587692962139, f: 502110}, {t: 1587692977155, f: 502287}, {t: 1587692992139, f: 502569}, {t: 1587693007139, f: 502749}, {t: 1587693022139, f: 502938}, {t: 1587693037139, f: 503197}, {t: 1587693052139, f: 503435}, {t: 1587693067139, f: 503637}, {t: 1587693082139, f: 503880}, {t: 1587693097139, f: 504034},
{t: 1587693112139, f: 504186}, {t: 1587693127139, f: 504369}, {t: 1587693142139, f: 504597}, {t: 1587693157139, f: 504748}, {t: 1587693172139, f: 505063}, {t: 1587693187139, f: 505251}, {t: 1587693202139, f: 505443}, {t: 1587693217139, f: 505642}, {t: 1587693232139, f: 505943}, {t: 1587693247155, f: 506095}, {t: 1587693262139, f: 506316}, {t: 1587693277139, f: 506531},
{t: 1587693292139, f: 506807}, {t: 1587693307139, f: 507017}, {t: 1587693322139, f: 507293}, {t: 1587693337139, f: 507537}, {t: 1587693352139, f: 507788}, {t: 1587693367139, f: 507998}, {t: 1587693382139, f: 508317}, {t: 1587693397139, f: 508577}, {t: 1587693412139, f: 508777}, {t: 1587693427139, f: 508989}, {t: 1587693442163, f: 509281}, {t: 1587693457139, f: 509484},
{t: 1587693472139, f: 509720}, {t: 1587693487139, f: 509979}, {t: 1587693502139, f: 510189}, {t: 1587693517139, f: 510505}, {t: 1587693532139, f: 510661}, {t: 1587693547139, f: 510866}, {t: 1587693562139, f: 511131}, {t: 1587693577139, f: 511321}, {t: 1587693592139, f: 511495},
}
type chunkedSeries struct {
lset labels.Labels
chunks []storepb.AggrChunk
}
type chunkedSeriesSet struct {
series []chunkedSeries
i int
}
func newChunkedSeriesSet(s []chunkedSeries) *chunkedSeriesSet {
return &chunkedSeriesSet{series: s, i: -1}
}
func (s *chunkedSeriesSet) Next() bool {
if s.i >= len(s.series)-1 {
return false
}
s.i++
return true
}
func (*chunkedSeriesSet) Err() error {
return nil
}
func (s *chunkedSeriesSet) At() (labels.Labels, []storepb.AggrChunk) {
return s.series[s.i].lset, s.series[s.i].chunks
}
func toChunkedSeriesSlice(t testing.TB, set storepb.SeriesSet) []chunkedSeries {
var ret []chunkedSeries
for set.Next() {
lset, chunks := set.At()
ret = append(ret, chunkedSeries{
lset: lset, chunks: chunks,
})
}
testutil.Ok(t, set.Err())
return ret
}
func TestOverlapSplitSet(t *testing.T) {
input := []chunkedSeries{
{
lset: labels.FromStrings("a", "1_empty"),
},
{
lset: labels.FromStrings("a", "2_nonoverlap"),
chunks: []storepb.AggrChunk{{MinTime: 0, MaxTime: 20}, {MinTime: 21, MaxTime: 100}, {MinTime: 110, MaxTime: 300}},
},
{
lset: labels.FromStrings("a", "3_tworeplicas"),
chunks: []storepb.AggrChunk{{MinTime: 0, MaxTime: 20}, {MinTime: 0, MaxTime: 30}, {MinTime: 21, MaxTime: 50}, {MinTime: 31, MaxTime: 60}, {MinTime: 100, MaxTime: 160}},
},
{
lset: labels.FromStrings("a", "4_nonoverlap"),
chunks: []storepb.AggrChunk{{MinTime: 50, MaxTime: 55}, {MinTime: 56, MaxTime: 100}},
},
{
lset: labels.FromStrings("a", "5_minimaloverlap"),
chunks: []storepb.AggrChunk{{MinTime: 50, MaxTime: 55}, {MinTime: 55, MaxTime: 100}},
},
{
lset: labels.FromStrings("a", "6_fourreplica"),
chunks: []storepb.AggrChunk{{MinTime: 0, MaxTime: 20}, {MinTime: 0, MaxTime: 30}, {MinTime: 1, MaxTime: 15}, {MinTime: 2, MaxTime: 36}, {MinTime: 16, MaxTime: 200},
{MinTime: 21, MaxTime: 50}, {MinTime: 31, MaxTime: 60}, {MinTime: 100, MaxTime: 160}},
},
}
exp := []chunkedSeries{
{
lset: labels.FromStrings("a", "1_empty"),
},
{
lset: labels.FromStrings("a", "2_nonoverlap"),
chunks: []storepb.AggrChunk{{MinTime: 0, MaxTime: 20}, {MinTime: 21, MaxTime: 100}, {MinTime: 110, MaxTime: 300}},
},
{
lset: labels.FromStrings("a", "3_tworeplicas"),
chunks: []storepb.AggrChunk{{MinTime: 0, MaxTime: 20}, {MinTime: 21, MaxTime: 50}, {MinTime: 100, MaxTime: 160}},
},
{
lset: labels.FromStrings("a", "3_tworeplicas"),
chunks: []storepb.AggrChunk{{MinTime: 0, MaxTime: 30}, {MinTime: 31, MaxTime: 60}},
},
{
lset: labels.FromStrings("a", "4_nonoverlap"),
chunks: []storepb.AggrChunk{{MinTime: 50, MaxTime: 55}, {MinTime: 56, MaxTime: 100}},
},
{
lset: labels.FromStrings("a", "5_minimaloverlap"),
chunks: []storepb.AggrChunk{{MinTime: 50, MaxTime: 55}},
},
{
lset: labels.FromStrings("a", "5_minimaloverlap"),
chunks: []storepb.AggrChunk{{MinTime: 55, MaxTime: 100}},
},
{
lset: labels.FromStrings("a", "6_fourreplica"),
chunks: []storepb.AggrChunk{{MinTime: 0, MaxTime: 20}, {MinTime: 21, MaxTime: 50}, {MinTime: 100, MaxTime: 160}},
},
{
lset: labels.FromStrings("a", "6_fourreplica"),
chunks: []storepb.AggrChunk{{MinTime: 0, MaxTime: 30}, {MinTime: 31, MaxTime: 60}},
},
{
lset: labels.FromStrings("a", "6_fourreplica"),
chunks: []storepb.AggrChunk{{MinTime: 1, MaxTime: 15}, {MinTime: 16, MaxTime: 200}},
},
{
lset: labels.FromStrings("a", "6_fourreplica"),
chunks: []storepb.AggrChunk{{MinTime: 2, MaxTime: 36}},
},
}
got := toChunkedSeriesSlice(t, NewOverlapSplit(newChunkedSeriesSet(input)))
testutil.Equals(t, exp, got)
}
func TestDedupSeriesSet(t *testing.T) {
for _, tcase := range []struct {
name string
input []series
exp []series
isCounter bool
}{
{
name: "Single dedup label",
input: []series{
{
lset: labels.FromStrings("a", "1", "c", "3"),
samples: []sample{{10000, 1}, {20000, 2}},
}, {
lset: labels.FromStrings("a", "1", "c", "3"),
samples: []sample{{60000, 3}, {70000, 4}},
}, {
lset: labels.FromStrings("a", "1", "c", "3"),
samples: []sample{{200000, 5}, {210000, 6}},
}, {
lset: labels.FromStrings("a", "1", "c", "3"),
samples: []sample{{10000, 1}, {20000, 2}},
}, {
lset: labels.FromStrings("a", "1", "c", "3", "d", "4"),
samples: []sample{{10000, 1}, {20000, 2}},
}, {
lset: labels.FromStrings("a", "1", "c", "4"),
samples: []sample{{10000, 1}, {20000, 2}},
}, {
lset: labels.FromStrings("a", "2", "c", "3"),
samples: []sample{{10000, 1}, {20000, 2}},
}, {
lset: labels.FromStrings("a", "2", "c", "3"),
samples: []sample{{60000, 3}, {70000, 4}},
},
},
exp: []series{
{
lset: labels.FromStrings("a", "1", "c", "3"),
samples: []sample{{10000, 1}, {20000, 2}, {60000, 3}, {70000, 4}, {200000, 5}, {210000, 6}},
},
{
lset: labels.FromStrings("a", "1", "c", "3", "d", "4"),
samples: []sample{{10000, 1}, {20000, 2}},
},
{
lset: labels.FromStrings("a", "1", "c", "4"),
samples: []sample{{10000, 1}, {20000, 2}},
},
{
lset: labels.FromStrings("a", "2", "c", "3"),
samples: []sample{{10000, 1}, {20000, 2}, {60000, 3}, {70000, 4}},
},
},
},
{
name: "Multi dedup label",
input: []series{
{
lset: labels.FromStrings("a", "1", "c", "3"),
samples: []sample{{10000, 1}, {20000, 2}},
}, {
lset: labels.FromStrings("a", "1", "c", "3"),
samples: []sample{{60000, 3}, {70000, 4}},
}, {
lset: labels.FromStrings("a", "1", "c", "3"),
samples: []sample{{200000, 5}, {210000, 6}},
}, {
lset: labels.FromStrings("a", "1", "c", "3", "d", "4"),
samples: []sample{{10000, 1}, {20000, 2}},
}, {
lset: labels.FromStrings("a", "1", "c", "3"),
samples: []sample{{10000, 1}, {20000, 2}},
}, {
lset: labels.FromStrings("a", "1", "c", "4"),
samples: []sample{{10000, 1}, {20000, 2}},
}, {
lset: labels.FromStrings("a", "2", "c", "3"),
samples: []sample{{10000, 1}, {20000, 2}},
}, {
lset: labels.FromStrings("a", "2", "c", "3"),
samples: []sample{{60000, 3}, {70000, 4}},
},
},
exp: []series{
{
lset: labels.FromStrings("a", "1", "c", "3"),
samples: []sample{{10000, 1}, {20000, 2}, {60000, 3}, {70000, 4}, {200000, 5}, {210000, 6}},
},
{
lset: labels.FromStrings("a", "1", "c", "3", "d", "4"),
samples: []sample{{10000, 1}, {20000, 2}},
},
{
lset: labels.FromStrings("a", "1", "c", "3"),
samples: []sample{{10000, 1}, {20000, 2}},
},
{
lset: labels.FromStrings("a", "1", "c", "4"),
samples: []sample{{10000, 1}, {20000, 2}},
},
{
lset: labels.FromStrings("a", "2", "c", "3"),
samples: []sample{{10000, 1}, {20000, 2}, {60000, 3}, {70000, 4}},
},
},
},
{
name: "Multi dedup label - some series don't have all dedup labels",
input: []series{
{
lset: labels.FromStrings("a", "1", "c", "3"),
samples: []sample{{10000, 1}, {20000, 2}},
}, {
lset: labels.FromStrings("a", "1", "c", "3"),
samples: []sample{{60000, 3}, {70000, 4}},
},
},
exp: []series{
{
lset: labels.FromStrings("a", "1", "c", "3"),
samples: []sample{{10000, 1}, {20000, 2}, {60000, 3}, {70000, 4}},
},
},
},
{
// Regression test against https://github.com/thanos-io/thanos/issues/2401.
// Two counter series, when one (initially chosen) series is having hiccup (few dropped samples), while second is live.
// This also happens when 2 replicas scrape in different time (they usually do) and one sees later counter value then the other.
// Now, depending on what replica we look, we can see totally different counter value in total where total means
// after accounting for counter resets. We account for that in downsample.CounterSeriesIterator, mainly because
// we handle downsample Counter Aggregations specially (for detecting resets between chunks).
name: "Regression test against 2401",
isCounter: true,
input: []series{
{
lset: labels.FromStrings("a", "1"),
samples: []sample{
{10000, 8.0}, // Smaller timestamp, this will be chosen. CurrValue = 8.0.
{20000, 9.0}, // Same. CurrValue = 9.0.
// {Gap} app reset. No sample, because stale marker but removed by downsample.CounterSeriesIterator.
{50001, 9 + 1.0}, // Next after 20000+1 has a bit higher than timestamp then in second series. Penalty 5000 will be added.
{60000, 9 + 2.0},
{70000, 9 + 3.0},
{80000, 9 + 4.0},
{90000, 9 + 5.0}, // This should be now taken, and we expect 14 to be correct value now.
{100000, 9 + 6.0},
},
}, {
lset: labels.FromStrings("a", "1"),
samples: []sample{
{10001, 8.0}, // Penalty 5000 will be added.
// 20001 was app reset. No sample, because stale marker but removed by downsample.CounterSeriesIterator. Penalty 2 * (20000 - 10000) will be added.
// 30001 no sample. Within penalty, ignored.
{45001, 8 + 0.5}, // Smaller timestamp, this will be chosen. CurrValue = 8.5 which is smaller than last chosen value.
{55001, 8 + 1.5},
{65001, 8 + 2.5},
// {Gap} app reset. No sample, because stale marker but removed by downsample.CounterSeriesIterator.
},
},
},
exp: []series{
{
lset: labels.FromStrings("a", "1"),
samples: []sample{{10000, 8}, {20000, 9}, {45001, 9}, {55001, 10}, {65001, 11}, {90000, 14}, {100000, 15}},
},
},
},
{
// Same thing but not for counter should not adjust anything.
name: "Regression test with no counter adjustment",
isCounter: false,
input: []series{
{
lset: labels.FromStrings("a", "1"),
samples: []sample{
{10000, 8.0}, {20000, 9.0}, {50001, 9 + 1.0}, {60000, 9 + 2.0}, {70000, 9 + 3.0}, {80000, 9 + 4.0}, {90000, 9 + 5.0}, {100000, 9 + 6.0},
},
}, {
lset: labels.FromStrings("a", "1"),
samples: []sample{
{10001, 8.0}, {45001, 8 + 0.5}, {55001, 8 + 1.5}, {65001, 8 + 2.5},
},
},
},
exp: []series{
{
lset: labels.FromStrings("a", "1"),
samples: []sample{{10000, 8}, {20000, 9}, {45001, 8.5}, {55001, 9.5}, {65001, 10.5}, {90000, 14}, {100000, 15}},
},
},
},
{
// Regression test on real data against https://github.com/thanos-io/thanos/issues/2401.
// Real data with stale marker after downsample.CounterSeriesIterator (required for downsampling + rate).
name: "Regression test on real data against 2401",
isCounter: true,
input: []series{
{
lset: labels.FromStrings("a", "1"),
samples: []sample{
{t: 1587690005791, f: 461968}, {t: 1587690020791, f: 462151}, {t: 1587690035797, f: 462336}, {t: 1587690050791, f: 462650}, {t: 1587690065791, f: 462813}, {t: 1587690080791, f: 462987}, {t: 1587690095791, f: 463095}, {t: 1587690110791, f: 463247}, {t: 1587690125791, f: 463440}, {t: 1587690140791, f: 463642}, {t: 1587690155791, f: 463811},
{t: 1587690170791, f: 464027}, {t: 1587690185791, f: 464308}, {t: 1587690200791, f: 464514}, {t: 1587690215791, f: 464798}, {t: 1587690230791, f: 465018}, {t: 1587690245791, f: 465215}, {t: 1587690260813, f: 465431}, {t: 1587690275791, f: 465651}, {t: 1587690290791, f: 465870}, {t: 1587690305791, f: 466070}, {t: 1587690320792, f: 466248},
{t: 1587690335791, f: 466506}, {t: 1587690350791, f: 466766}, {t: 1587690365791, f: 466970}, {t: 1587690380791, f: 467123}, {t: 1587690395791, f: 467265}, {t: 1587690410791, f: 467383}, {t: 1587690425791, f: 467629}, {t: 1587690440791, f: 467931}, {t: 1587690455791, f: 468097}, {t: 1587690470791, f: 468281}, {t: 1587690485791, f: 468477},
{t: 1587690500791, f: 468649}, {t: 1587690515791, f: 468867}, {t: 1587690530791, f: 469150}, {t: 1587690545791, f: 469268}, {t: 1587690560791, f: 469488}, {t: 1587690575791, f: 469742}, {t: 1587690590791, f: 469951}, {t: 1587690605791, f: 470131}, {t: 1587690620791, f: 470337}, {t: 1587690635791, f: 470631}, {t: 1587690650791, f: 470832},
{t: 1587690665791, f: 471077}, {t: 1587690680791, f: 471311}, {t: 1587690695791, f: 471473}, {t: 1587690710791, f: 471728}, {t: 1587690725791, f: 472002}, {t: 1587690740791, f: 472158}, {t: 1587690755791, f: 472329}, {t: 1587690770791, f: 472722}, {t: 1587690785791, f: 472925}, {t: 1587690800791, f: 473220}, {t: 1587690815791, f: 473460},
{t: 1587690830791, f: 473748}, {t: 1587690845791, f: 473968}, {t: 1587690860791, f: 474261}, {t: 1587690875791, f: 474418}, {t: 1587690890791, f: 474726}, {t: 1587690905791, f: 474913}, {t: 1587690920791, f: 475031}, {t: 1587690935791, f: 475284}, {t: 1587690950791, f: 475563}, {t: 1587690965791, f: 475762}, {t: 1587690980791, f: 475945},
{t: 1587690995791, f: 476302}, {t: 1587691010791, f: 476501}, {t: 1587691025791, f: 476849}, {t: 1587691040800, f: 477020}, {t: 1587691055791, f: 477280}, {t: 1587691070791, f: 477549}, {t: 1587691085791, f: 477758}, {t: 1587691100817, f: 477960}, {t: 1587691115791, f: 478261}, {t: 1587691130791, f: 478559}, {t: 1587691145791, f: 478704},
{t: 1587691160804, f: 478950}, {t: 1587691175791, f: 479173}, {t: 1587691190791, f: 479368}, {t: 1587691205791, f: 479625}, {t: 1587691220805, f: 479866}, {t: 1587691235791, f: 480008}, {t: 1587691250791, f: 480155}, {t: 1587691265791, f: 480472}, {t: 1587691280811, f: 480598}, {t: 1587691295791, f: 480771}, {t: 1587691310791, f: 480996},
{t: 1587691325791, f: 481200}, {t: 1587691340803, f: 481381}, {t: 1587691355791, f: 481584}, {t: 1587691370791, f: 481759}, {t: 1587691385791, f: 482003}, {t: 1587691400803, f: 482189}, {t: 1587691415791, f: 482457}, {t: 1587691430791, f: 482623}, {t: 1587691445791, f: 482768}, {t: 1587691460804, f: 483036}, {t: 1587691475791, f: 483322},
{t: 1587691490791, f: 483566}, {t: 1587691505791, f: 483709}, {t: 1587691520807, f: 483838}, {t: 1587691535791, f: 484091}, {t: 1587691550791, f: 484236}, {t: 1587691565791, f: 484454}, {t: 1587691580816, f: 484710}, {t: 1587691595791, f: 484978}, {t: 1587691610791, f: 485271}, {t: 1587691625791, f: 485476}, {t: 1587691640792, f: 485640},
{t: 1587691655791, f: 485921}, {t: 1587691670791, f: 486201}, {t: 1587691685791, f: 486555}, {t: 1587691700791, f: 486691}, {t: 1587691715791, f: 486831}, {t: 1587691730791, f: 487033}, {t: 1587691745791, f: 487268}, {t: 1587691760803, f: 487370}, {t: 1587691775791, f: 487571}, {t: 1587691790791, f: 487787}, {t: 1587691805791, f: 488036},
{t: 1587691820791, f: 488241}, {t: 1587691835791, f: 488411}, {t: 1587691850791, f: 488625}, {t: 1587691865791, f: 488868}, {t: 1587691880791, f: 489005}, {t: 1587691895791, f: 489237}, {t: 1587691910791, f: 489545}, {t: 1587691925791, f: 489750}, {t: 1587691940791, f: 489899}, {t: 1587691955791, f: 490048}, {t: 1587691970791, f: 490364},
{t: 1587691985791, f: 490485}, {t: 1587692000791, f: 490722}, {t: 1587692015791, f: 490866}, {t: 1587692030791, f: 491025}, {t: 1587692045791, f: 491286}, {t: 1587692060816, f: 491543}, {t: 1587692075791, f: 491787}, {t: 1587692090791, f: 492065}, {t: 1587692105791, f: 492223}, {t: 1587692120816, f: 492501}, {t: 1587692135791, f: 492767},
{t: 1587692150791, f: 492955}, {t: 1587692165791, f: 493194}, {t: 1587692180792, f: 493402}, {t: 1587692195791, f: 493647}, {t: 1587692210791, f: 493897}, {t: 1587692225791, f: 494117}, {t: 1587692240805, f: 494356}, {t: 1587692255791, f: 494620}, {t: 1587692270791, f: 494762}, {t: 1587692285791, f: 495001}, {t: 1587692300805, f: 495222},
{t: 1587692315791, f: 495393}, {t: 1587692330791, f: 495662}, {t: 1587692345791, f: 495875}, {t: 1587692360801, f: 496082}, {t: 1587692375791, f: 496196}, {t: 1587692390791, f: 496245}, {t: 1587692405791, f: 496295}, {t: 1587692420791, f: 496365}, {t: 1587692435791, f: 496401}, {t: 1587692450791, f: 496452}, {t: 1587692465791, f: 496491},
{t: 1587692480791, f: 496544}, {t: 1587692555791, f: 496619}, {t: 1587692570791, f: 496852}, {t: 1587692585791, f: 497052}, {t: 1587692600791, f: 497245}, {t: 1587692615791, f: 497529}, {t: 1587692630791, f: 497697}, {t: 1587692645791, f: 497909}, {t: 1587692660791, f: 498156}, {t: 1587692675803, f: 498466}, {t: 1587692690791, f: 498647},
{t: 1587692705791, f: 498805}, {t: 1587692720791, f: 499013}, {t: 1587692735805, f: 499169}, {t: 1587692750791, f: 499345}, {t: 1587692765791, f: 499499}, {t: 1587692780791, f: 499731}, {t: 1587692795806, f: 499972}, {t: 1587692810791, f: 500201}, {t: 1587692825791, f: 500354}, {t: 1587692840791, f: 500512}, {t: 1587692855791, f: 500739},
{t: 1587692870791, f: 500958}, {t: 1587692885791, f: 501190}, {t: 1587692900791, f: 501233}, {t: 1587692915791, f: 501391}, {t: 1587692930791, f: 501649}, {t: 1587692945791, f: 501853}, {t: 1587692960791, f: 502065}, {t: 1587692975791, f: 502239}, {t: 1587692990810, f: 502554}, {t: 1587693005791, f: 502754}, {t: 1587693020791, f: 502938},
{t: 1587693035791, f: 503141}, {t: 1587693050791, f: 503416}, {t: 1587693065791, f: 503642}, {t: 1587693080791, f: 503873}, {t: 1587693095791, f: 504014}, {t: 1587693110791, f: 504178}, {t: 1587693125821, f: 504374}, {t: 1587693140791, f: 504578}, {t: 1587693155791, f: 504753}, {t: 1587693170791, f: 505043}, {t: 1587693185791, f: 505232},
{t: 1587693200791, f: 505437}, {t: 1587693215791, f: 505596}, {t: 1587693230791, f: 505923}, {t: 1587693245791, f: 506088}, {t: 1587693260791, f: 506307}, {t: 1587693275791, f: 506518}, {t: 1587693290791, f: 506786}, {t: 1587693305791, f: 507008}, {t: 1587693320803, f: 507260}, {t: 1587693335791, f: 507519}, {t: 1587693350791, f: 507776},
{t: 1587693365791, f: 508003}, {t: 1587693380791, f: 508322}, {t: 1587693395804, f: 508551}, {t: 1587693410791, f: 508750}, {t: 1587693425791, f: 508994}, {t: 1587693440791, f: 509237}, {t: 1587693455791, f: 509452}, {t: 1587693470791, f: 509702}, {t: 1587693485791, f: 509971}, {t: 1587693500791, f: 510147}, {t: 1587693515791, f: 510471},
{t: 1587693530816, f: 510666}, {t: 1587693545791, f: 510871}, {t: 1587693560791, f: 511123}, {t: 1587693575791, f: 511303}, {t: 1587693590791, f: 511500},
},
}, {
lset: labels.FromStrings("a", "1"),
samples: []sample{
{t: 1587690007139, f: 461993}, {t: 1587690022139, f: 462164}, {t: 1587690037139, f: 462409}, {t: 1587690052139, f: 462662}, {t: 1587690067139, f: 462824}, {t: 1587690082139, f: 462987}, {t: 1587690097155, f: 463108}, {t: 1587690112139, f: 463261}, {t: 1587690127139, f: 463465}, {t: 1587690142139, f: 463642},
{t: 1587690157139, f: 463823}, {t: 1587690172139, f: 464065}, {t: 1587690187139, f: 464333}, {t: 1587690202139, f: 464566}, {t: 1587690217139, f: 464811}, {t: 1587690232140, f: 465032}, {t: 1587690247139, f: 465229}, {t: 1587690262139, f: 465445}, {t: 1587690277139, f: 465700}, {t: 1587690292139, f: 465884},
{t: 1587690307139, f: 466083}, {t: 1587690322139, f: 466250}, {t: 1587690337150, f: 466534}, {t: 1587690352139, f: 466791}, {t: 1587690367139, f: 466970}, {t: 1587690382139, f: 467149}, {t: 1587690397139, f: 467265}, {t: 1587690412139, f: 467383}, {t: 1587690427139, f: 467647}, {t: 1587690442139, f: 467943},
{t: 1587690457139, f: 468121}, {t: 1587690472139, f: 468294}, {t: 1587690487139, f: 468545}, {t: 1587690502139, f: 468676}, {t: 1587690517139, f: 468879}, {t: 1587690532139, f: 469154}, {t: 1587690547139, f: 469281}, {t: 1587690562139, f: 469512}, {t: 1587690577139, f: 469783}, {t: 1587690592139, f: 469964},
{t: 1587690607139, f: 470171}, {t: 1587690622139, f: 470355}, {t: 1587690637139, f: 470656}, {t: 1587690652139, f: 470845}, {t: 1587690667139, f: 471077}, {t: 1587690682139, f: 471315}, {t: 1587690697139, f: 471535}, {t: 1587690712139, f: 471766}, {t: 1587690727139, f: 472002}, {t: 1587690742139, f: 472171},
{t: 1587690757139, f: 472354}, {t: 1587690772139, f: 472736}, {t: 1587690787139, f: 472948}, {t: 1587690802139, f: 473259}, {t: 1587690817139, f: 473460}, {t: 1587690832139, f: 473753}, {t: 1587690847139, f: 474007}, {t: 1587690862139, f: 474286}, {t: 1587690877139, f: 474423}, {t: 1587690892139, f: 474788},
{t: 1587690907139, f: 474925}, {t: 1587690922139, f: 475031}, {t: 1587690937139, f: 475316}, {t: 1587690952139, f: 475573}, {t: 1587690967139, f: 475784}, {t: 1587690982139, f: 475992}, {t: 1587690997139, f: 476341}, {t: 1587691012139, f: 476541}, {t: 1587691027139, f: 476890}, {t: 1587691042139, f: 477033},
{t: 1587691057139, f: 477305}, {t: 1587691072139, f: 477577}, {t: 1587691087139, f: 477771}, {t: 1587691102139, f: 478012}, {t: 1587691117139, f: 478296}, {t: 1587691132139, f: 478559}, {t: 1587691147139, f: 478744}, {t: 1587691162139, f: 478950}, {t: 1587691177139, f: 479201}, {t: 1587691192139, f: 479388},
{t: 1587691207139, f: 479638}, {t: 1587691222154, f: 479907}, {t: 1587691237139, f: 480008}, {t: 1587691252139, f: 480167}, {t: 1587691267139, f: 480472}, {t: 1587691282157, f: 480615}, {t: 1587691297139, f: 480771}, {t: 1587691312139, f: 481027}, {t: 1587691327139, f: 481212}, {t: 1587691342159, f: 481395},
{t: 1587691357139, f: 481598}, {t: 1587691372139, f: 481786}, {t: 1587691387139, f: 482003}, {t: 1587691402141, f: 482236}, {t: 1587691417139, f: 482508}, {t: 1587691432139, f: 482636}, {t: 1587691447139, f: 482780}, {t: 1587691462139, f: 483059}, {t: 1587691477139, f: 483357}, {t: 1587691492139, f: 483566},
{t: 1587691507139, f: 483711}, {t: 1587691522139, f: 483838}, {t: 1587691537139, f: 484091}, {t: 1587691552139, f: 484254}, {t: 1587691567139, f: 484479}, {t: 1587691582139, f: 484748}, {t: 1587691597139, f: 484978}, {t: 1587691612139, f: 485271}, {t: 1587691627139, f: 485488}, {t: 1587691642139, f: 485700},
{t: 1587691657139, f: 485945}, {t: 1587691672139, f: 486228}, {t: 1587691687139, f: 486588}, {t: 1587691702139, f: 486691}, {t: 1587691717139, f: 486881}, {t: 1587691732139, f: 487046}, {t: 1587691747139, f: 487291}, {t: 1587691762177, f: 487410}, {t: 1587691777139, f: 487571}, {t: 1587691792139, f: 487799},
{t: 1587691807139, f: 488050}, {t: 1587691822139, f: 488241}, {t: 1587691837139, f: 488424}, {t: 1587691852139, f: 488629}, {t: 1587691867139, f: 488875}, {t: 1587691882139, f: 489017}, {t: 1587691897139, f: 489254}, {t: 1587691912139, f: 489545}, {t: 1587691927139, f: 489778}, {t: 1587691942139, f: 489912},
{t: 1587691957139, f: 490084}, {t: 1587691972139, f: 490364}, {t: 1587691987139, f: 490510}, {t: 1587692002139, f: 490744}, {t: 1587692017139, f: 490880}, {t: 1587692032139, f: 491025}, {t: 1587692047139, f: 491297}, {t: 1587692062155, f: 491557}, {t: 1587692077139, f: 491839}, {t: 1587692092139, f: 492065},
{t: 1587692107139, f: 492234}, {t: 1587692122139, f: 492526}, {t: 1587692137139, f: 492767}, {t: 1587692152139, f: 492967}, {t: 1587692167139, f: 493218}, {t: 1587692182139, f: 493442}, {t: 1587692197139, f: 493647}, {t: 1587692212139, f: 493920}, {t: 1587692227139, f: 494170}, {t: 1587692242139, f: 494358},
{t: 1587692257139, f: 494632}, {t: 1587692272139, f: 494800}, {t: 1587692287139, f: 495026}, {t: 1587692302139, f: 495222}, {t: 1587692317139, f: 495433}, {t: 1587692332139, f: 495677}, {t: 1587692347139, f: 495901}, {t: 1587692362139, f: 496107}, {t: 1587692377139, f: 496196}, {t: 1587692392139, f: 496245},
{t: 1587692407139, f: 496300}, {t: 1587692422159, f: 496365}, {t: 1587692437139, f: 496401}, {t: 1587692452139, f: 496452}, {t: 1587692467139, f: 496532}, {t: 1587692542149, f: 496537}, {t: 1587692557139, f: 496633}, {t: 1587692572139, f: 496844}, {t: 1587692587139, f: 497040}, {t: 1587692602144, f: 497257},
{t: 1587692617139, f: 497522}, {t: 1587692632139, f: 497710}, {t: 1587692647139, f: 497938}, {t: 1587692662154, f: 498172}, {t: 1587692677139, f: 498459}, {t: 1587692692139, f: 498635}, {t: 1587692707139, f: 498832}, {t: 1587692722139, f: 499014}, {t: 1587692737139, f: 499170}, {t: 1587692752139, f: 499338},
{t: 1587692767139, f: 499511}, {t: 1587692782149, f: 499719}, {t: 1587692797139, f: 499973}, {t: 1587692812139, f: 500189}, {t: 1587692827139, f: 500359}, {t: 1587692842139, f: 500517}, {t: 1587692857139, f: 500727}, {t: 1587692872139, f: 500959}, {t: 1587692887139, f: 501178}, {t: 1587692902139, f: 501246},
{t: 1587692917153, f: 501404}, {t: 1587692932139, f: 501663}, {t: 1587692947139, f: 501850}, {t: 1587692962139, f: 502103}, {t: 1587692977155, f: 502280}, {t: 1587692992139, f: 502562}, {t: 1587693007139, f: 502742}, {t: 1587693022139, f: 502931}, {t: 1587693037139, f: 503190}, {t: 1587693052139, f: 503428},
{t: 1587693067139, f: 503630}, {t: 1587693082139, f: 503873}, {t: 1587693097139, f: 504027}, {t: 1587693112139, f: 504179}, {t: 1587693127139, f: 504362}, {t: 1587693142139, f: 504590}, {t: 1587693157139, f: 504741}, {t: 1587693172139, f: 505056}, {t: 1587693187139, f: 505244}, {t: 1587693202139, f: 505436},
{t: 1587693217139, f: 505635}, {t: 1587693232139, f: 505936}, {t: 1587693247155, f: 506088}, {t: 1587693262139, f: 506309}, {t: 1587693277139, f: 506524}, {t: 1587693292139, f: 506800}, {t: 1587693307139, f: 507010}, {t: 1587693322139, f: 507286}, {t: 1587693337139, f: 507530}, {t: 1587693352139, f: 507781},
{t: 1587693367139, f: 507991}, {t: 1587693382139, f: 508310}, {t: 1587693397139, f: 508570}, {t: 1587693412139, f: 508770}, {t: 1587693427139, f: 508982}, {t: 1587693442163, f: 509274}, {t: 1587693457139, f: 509477}, {t: 1587693472139, f: 509713}, {t: 1587693487139, f: 509972}, {t: 1587693502139, f: 510182},
{t: 1587693517139, f: 510498}, {t: 1587693532139, f: 510654}, {t: 1587693547139, f: 510859}, {t: 1587693562139, f: 511124}, {t: 1587693577139, f: 511314}, {t: 1587693592139, f: 511488},
},
},
},
exp: []series{
{
lset: labels.FromStrings("a", "1"),
samples: expectedRealSeriesWithStaleMarkerDeduplicatedForRate,
},
},
},
} {
t.Run(tcase.name, func(t *testing.T) {
// If it is a counter then pass a function which expects a counter.
f := ""
if tcase.isCounter {
f = "rate"
}
dedupSet := NewSeriesSet(&mockedSeriesSet{series: tcase.input}, f)
var ats []storage.Series
for dedupSet.Next() {
ats = append(ats, dedupSet.At())
}
testutil.Ok(t, dedupSet.Err())
testutil.Equals(t, len(tcase.exp), len(ats))
for i, s := range ats {
testutil.Equals(t, tcase.exp[i].lset, s.Labels(), "labels mismatch for series %v", i)
res := expandSeries(t, s.Iterator(nil))
testutil.Equals(t, tcase.exp[i].samples, res, "values mismatch for series :%v", i)
}
})
}
}
func TestDedupSeriesIterator(t *testing.T) {
// The deltas between timestamps should be at least 10000 to not be affected
// by the initial penalty of 5000, that will cause the second iterator to seek
// ahead this far at least once.
cases := []struct {
a, b, exp []sample
}{
{ // Generally prefer the first series.
a: []sample{{10000, 10}, {20000, 11}, {30000, 12}, {40000, 13}},
b: []sample{{10000, 20}, {20000, 21}, {30000, 22}, {40000, 23}},
exp: []sample{{10000, 10}, {20000, 11}, {30000, 12}, {40000, 13}},
},
{ // Prefer b if it starts earlier.
a: []sample{{10100, 1}, {20100, 1}, {30100, 1}, {40100, 1}},
b: []sample{{10000, 2}, {20000, 2}, {30000, 2}, {40000, 2}},
exp: []sample{{10000, 2}, {20000, 2}, {30000, 2}, {40000, 2}},
},
{ // Don't switch series on a single delta sized gap.
a: []sample{{10000, 1}, {20000, 1}, {40000, 1}},
b: []sample{{10000, 2}, {20000, 2}, {30000, 2}, {40000, 2}},
exp: []sample{{10000, 1}, {20000, 1}, {40000, 1}},
},
{
a: []sample{{10000, 1}, {20000, 1}, {40000, 1}},
b: []sample{{15000, 2}, {25000, 2}, {35000, 2}, {45000, 2}},
exp: []sample{{10000, 1}, {20000, 1}, {40000, 1}},
},
{ // Once the gap gets bigger than 2 deltas, switch and stay with the new series.
a: []sample{{10000, 1}, {20000, 1}, {30000, 1}, {60000, 1}, {70000, 1}},
b: []sample{{10100, 2}, {20100, 2}, {30100, 2}, {40100, 2}, {50100, 2}, {60100, 2}},
exp: []sample{{10000, 1}, {20000, 1}, {30000, 1}, {50100, 2}, {60100, 2}},
},
}
for i, c := range cases {
t.Logf("case %d:", i)
it := newDedupSeriesIterator(
noopAdjustableSeriesIterator{newMockedSeriesIterator(c.a)},
noopAdjustableSeriesIterator{newMockedSeriesIterator(c.b)},
)
res := expandSeries(t, noopAdjustableSeriesIterator{it})
testutil.Equals(t, c.exp, res)
}
}
func TestDedupSeriesIterator_NativeHistograms(t *testing.T) {
hs := tsdbutil.GenerateTestHistograms(1)
casesMixed := []struct {
a []sample
b []*testiters.HistogramPair
exp []any
}{
{
a: []sample{{t: 0, f: 0}},
b: []*testiters.HistogramPair{{T: 10000, H: hs[0]}, {T: 20000, H: hs[0]}, {T: 30000, H: hs[0]}, {T: 40000, H: hs[0]}},
exp: []any{&sample{t: 0, f: 0}, &testiters.HistogramPair{T: 10000, H: hs[0]}, &testiters.HistogramPair{T: 20000, H: hs[0]}, &testiters.HistogramPair{T: 30000, H: hs[0]}, &testiters.HistogramPair{T: 40000, H: hs[0]}},
},
}
for i, c := range casesMixed {
c := c
t.Run(fmt.Sprintf("mixed-%d", i), func(t *testing.T) {
t.Parallel()
it := newDedupSeriesIterator(
noopAdjustableSeriesIterator{testiters.NewHistogramIterator(c.b)},
noopAdjustableSeriesIterator{newMockedSeriesIterator(c.a)},
)
res := expandHistogramSeries(t, noopAdjustableSeriesIterator{it})
require.EqualValues(t, c.exp, res)
it = newDedupSeriesIterator(
noopAdjustableSeriesIterator{newMockedSeriesIterator(c.a)},
noopAdjustableSeriesIterator{testiters.NewHistogramIterator(c.b)},
)
res = expandHistogramSeries(t, noopAdjustableSeriesIterator{it})
require.EqualValues(t, c.exp, res)
})
}
}
func BenchmarkDedupSeriesIterator(b *testing.B) {
run := func(b *testing.B, s1, s2 []sample) {
it := newDedupSeriesIterator(
noopAdjustableSeriesIterator{newMockedSeriesIterator(s1)},
noopAdjustableSeriesIterator{newMockedSeriesIterator(s2)},
)
b.ResetTimer()
var total int64
for it.Next() != chunkenc.ValNone {
t, _ := it.At()
total += t
}
fmt.Fprint(io.Discard, total)
}
b.Run("equal", func(b *testing.B) {
var s1, s2 []sample
for i := 0; i < b.N; i++ {
s1 = append(s1, sample{t: int64(i * 10000), f: 1})
}
for i := 0; i < b.N; i++ {
s2 = append(s2, sample{t: int64(i * 10000), f: 2})
}
run(b, s1, s2)
})
b.Run("fixed-delta", func(b *testing.B) {
var s1, s2 []sample
for i := 0; i < b.N; i++ {
s1 = append(s1, sample{t: int64(i * 10000), f: 1})
}
for i := 0; i < b.N; i++ {
s2 = append(s2, sample{t: int64(i*10000) + 10, f: 2})
}
run(b, s1, s2)
})
b.Run("minor-rand-delta", func(b *testing.B) {
var s1, s2 []sample
for i := 0; i < b.N; i++ {
s1 = append(s1, sample{t: int64(i*10000) + rand.Int63n(5000), f: 1})
}
for i := 0; i < b.N; i++ {
s2 = append(s2, sample{t: int64(i*10000) + +rand.Int63n(5000), f: 2})
}
run(b, s1, s2)
})
}
const hackyStaleMarker = float64(-99999999)
func expandSeries(t testing.TB, it chunkenc.Iterator) (res []sample) {
for it.Next() != chunkenc.ValNone {
t, v := it.At()
// Nan != Nan, so substitute for another value.
// This is required for testutil.Equals to work deterministically.
if math.IsNaN(v) {
v = hackyStaleMarker
}
res = append(res, sample{t, v})
}
testutil.Ok(t, it.Err())
return res
}
func expandHistogramSeries(t testing.TB, it chunkenc.Iterator) (res []any) {
for {
nextVal := it.Next()
if nextVal == chunkenc.ValNone {
break
}
if nextVal == chunkenc.ValHistogram {
t, h := it.AtHistogram(nil)
res = append(res, &testiters.HistogramPair{T: t, H: h})
} else {
t, f := it.At()
res = append(res, &sample{t: t, f: f})
}
}
testutil.Ok(t, it.Err())
return res
}