prometheus/storage/local/storage_test.go

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// Copyright 2014 The Prometheus Authors
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
package local
import (
"fmt"
"hash/fnv"
"math"
"math/rand"
"os"
"testing"
"testing/quick"
"time"
"github.com/prometheus/common/log"
"github.com/prometheus/common/model"
"github.com/prometheus/prometheus/storage/metric"
"github.com/prometheus/prometheus/util/testutil"
)
func TestMatches(t *testing.T) {
storage, closer := NewTestStorage(t, 2)
defer closer.Close()
storage.archiveHighWatermark = 90
samples := make([]*model.Sample, 100)
fingerprints := make(model.Fingerprints, 100)
for i := range samples {
metric := model.Metric{
model.MetricNameLabel: model.LabelValue(fmt.Sprintf("test_metric_%d", i)),
"label1": model.LabelValue(fmt.Sprintf("test_%d", i/10)),
"label2": model.LabelValue(fmt.Sprintf("test_%d", (i+5)/10)),
"all": "const",
}
samples[i] = &model.Sample{
Metric: metric,
Timestamp: model.Time(i),
Value: model.SampleValue(i),
}
fingerprints[i] = metric.FastFingerprint()
}
for _, s := range samples {
storage.Append(s)
}
storage.WaitForIndexing()
// Archive every tenth metric.
for i, fp := range fingerprints {
if i%10 != 0 {
continue
}
s, ok := storage.fpToSeries.get(fp)
if !ok {
t.Fatal("could not retrieve series for fp", fp)
}
storage.fpLocker.Lock(fp)
storage.persistence.archiveMetric(fp, s.metric, s.firstTime(), s.lastTime)
storage.fpLocker.Unlock(fp)
}
newMatcher := func(matchType metric.MatchType, name model.LabelName, value model.LabelValue) *metric.LabelMatcher {
lm, err := metric.NewLabelMatcher(matchType, name, value)
if err != nil {
t.Fatalf("error creating label matcher: %s", err)
}
return lm
}
var matcherTests = []struct {
matchers metric.LabelMatchers
expected model.Fingerprints
}{
{
matchers: metric.LabelMatchers{newMatcher(metric.Equal, "label1", "x")},
expected: model.Fingerprints{},
},
{
matchers: metric.LabelMatchers{newMatcher(metric.Equal, "label1", "test_0")},
expected: fingerprints[:10],
},
{
matchers: metric.LabelMatchers{
newMatcher(metric.Equal, "label1", "test_0"),
newMatcher(metric.Equal, "label2", "test_1"),
},
expected: fingerprints[5:10],
},
{
matchers: metric.LabelMatchers{
newMatcher(metric.Equal, "all", "const"),
newMatcher(metric.NotEqual, "label1", "x"),
},
expected: fingerprints,
},
{
matchers: metric.LabelMatchers{
newMatcher(metric.Equal, "all", "const"),
newMatcher(metric.NotEqual, "label1", "test_0"),
},
expected: fingerprints[10:],
},
{
matchers: metric.LabelMatchers{
newMatcher(metric.Equal, "all", "const"),
newMatcher(metric.NotEqual, "label1", "test_0"),
newMatcher(metric.NotEqual, "label1", "test_1"),
newMatcher(metric.NotEqual, "label1", "test_2"),
},
expected: fingerprints[30:],
},
{
matchers: metric.LabelMatchers{
newMatcher(metric.Equal, "label1", ""),
},
expected: fingerprints[:0],
},
{
matchers: metric.LabelMatchers{
newMatcher(metric.NotEqual, "label1", "test_0"),
newMatcher(metric.Equal, "label1", ""),
},
expected: fingerprints[:0],
},
{
matchers: metric.LabelMatchers{
newMatcher(metric.NotEqual, "label1", "test_0"),
newMatcher(metric.Equal, "label2", ""),
},
expected: fingerprints[:0],
},
{
matchers: metric.LabelMatchers{
newMatcher(metric.Equal, "all", "const"),
newMatcher(metric.NotEqual, "label1", "test_0"),
newMatcher(metric.Equal, "not_existent", ""),
},
expected: fingerprints[10:],
},
{
matchers: metric.LabelMatchers{
newMatcher(metric.RegexMatch, "label1", `test_[3-5]`),
},
expected: fingerprints[30:60],
},
{
matchers: metric.LabelMatchers{
newMatcher(metric.Equal, "all", "const"),
newMatcher(metric.RegexNoMatch, "label1", `test_[3-5]`),
},
expected: append(append(model.Fingerprints{}, fingerprints[:30]...), fingerprints[60:]...),
},
{
matchers: metric.LabelMatchers{
newMatcher(metric.RegexMatch, "label1", `test_[3-5]`),
newMatcher(metric.RegexMatch, "label2", `test_[4-6]`),
},
expected: fingerprints[35:60],
},
{
matchers: metric.LabelMatchers{
newMatcher(metric.RegexMatch, "label1", `test_[3-5]`),
newMatcher(metric.NotEqual, "label2", `test_4`),
},
expected: append(append(model.Fingerprints{}, fingerprints[30:35]...), fingerprints[45:60]...),
},
{
matchers: metric.LabelMatchers{
newMatcher(metric.Equal, "label1", `nonexistent`),
newMatcher(metric.RegexMatch, "label2", `test`),
},
expected: model.Fingerprints{},
},
{
matchers: metric.LabelMatchers{
newMatcher(metric.Equal, "label1", `test_0`),
newMatcher(metric.RegexMatch, "label2", `nonexistent`),
},
expected: model.Fingerprints{},
},
}
for _, mt := range matcherTests {
metrics, err := storage.MetricsForLabelMatchers(
model.Earliest, model.Latest,
mt.matchers,
)
if err != nil {
t.Fatal(err)
}
if len(mt.expected) != len(metrics) {
t.Fatalf("expected %d matches for %q, found %d", len(mt.expected), mt.matchers, len(metrics))
}
for _, m := range metrics {
fp1 := m.Metric.FastFingerprint()
found := false
for _, fp2 := range mt.expected {
if fp1 == fp2 {
found = true
break
}
}
if !found {
t.Errorf("expected fingerprint %s for %q not in result", fp1, mt.matchers)
}
}
// Smoketest for from/through.
metrics, err = storage.MetricsForLabelMatchers(
model.Earliest, -10000,
mt.matchers,
)
if err != nil {
t.Fatal(err)
}
if len(metrics) > 0 {
t.Error("expected no matches with 'through' older than any sample")
}
metrics, err = storage.MetricsForLabelMatchers(
10000, model.Latest,
mt.matchers,
)
if err != nil {
t.Fatal(err)
}
if len(metrics) > 0 {
t.Error("expected no matches with 'from' newer than any sample")
}
// Now the tricky one, cut out something from the middle.
var (
from model.Time = 25
through model.Time = 75
)
metrics, err = storage.MetricsForLabelMatchers(
from, through,
mt.matchers,
)
if err != nil {
t.Fatal(err)
}
expected := model.Fingerprints{}
for _, fp := range mt.expected {
i := 0
for ; fingerprints[i] != fp && i < len(fingerprints); i++ {
}
if i == len(fingerprints) {
t.Fatal("expected fingerprint does not exist")
}
if !model.Time(i).Before(from) && !model.Time(i).After(through) {
expected = append(expected, fp)
}
}
if len(expected) != len(metrics) {
t.Errorf("expected %d range-limited matches for %q, found %d", len(expected), mt.matchers, len(metrics))
}
for _, m := range metrics {
fp1 := m.Metric.FastFingerprint()
found := false
for _, fp2 := range expected {
if fp1 == fp2 {
found = true
break
}
}
if !found {
t.Errorf("expected fingerprint %s for %q not in range-limited result", fp1, mt.matchers)
}
}
}
}
func TestFingerprintsForLabels(t *testing.T) {
storage, closer := NewTestStorage(t, 2)
defer closer.Close()
samples := make([]*model.Sample, 100)
fingerprints := make(model.Fingerprints, 100)
for i := range samples {
metric := model.Metric{
model.MetricNameLabel: model.LabelValue(fmt.Sprintf("test_metric_%d", i)),
"label1": model.LabelValue(fmt.Sprintf("test_%d", i/10)),
"label2": model.LabelValue(fmt.Sprintf("test_%d", (i+5)/10)),
}
samples[i] = &model.Sample{
Metric: metric,
Timestamp: model.Time(i),
Value: model.SampleValue(i),
}
fingerprints[i] = metric.FastFingerprint()
}
for _, s := range samples {
storage.Append(s)
}
storage.WaitForIndexing()
var matcherTests = []struct {
pairs []model.LabelPair
expected model.Fingerprints
}{
{
pairs: []model.LabelPair{{"label1", "x"}},
expected: fingerprints[:0],
},
{
pairs: []model.LabelPair{{"label1", "test_0"}},
expected: fingerprints[:10],
},
{
pairs: []model.LabelPair{
{"label1", "test_0"},
{"label1", "test_1"},
},
expected: fingerprints[:0],
},
{
pairs: []model.LabelPair{
{"label1", "test_0"},
{"label2", "test_1"},
},
expected: fingerprints[5:10],
},
{
pairs: []model.LabelPair{
{"label1", "test_1"},
{"label2", "test_2"},
},
expected: fingerprints[15:20],
},
}
for _, mt := range matcherTests {
var resfps map[model.Fingerprint]struct{}
for _, pair := range mt.pairs {
resfps = storage.fingerprintsForLabelPair(pair, nil, resfps)
}
if len(mt.expected) != len(resfps) {
t.Fatalf("expected %d matches for %q, found %d", len(mt.expected), mt.pairs, len(resfps))
}
for fp1 := range resfps {
found := false
for _, fp2 := range mt.expected {
if fp1 == fp2 {
found = true
break
}
}
if !found {
t.Errorf("expected fingerprint %s for %q not in result", fp1, mt.pairs)
}
}
}
}
var benchLabelMatchingRes []metric.Metric
func BenchmarkLabelMatching(b *testing.B) {
s, closer := NewTestStorage(b, 2)
defer closer.Close()
h := fnv.New64a()
lbl := func(x int) model.LabelValue {
h.Reset()
h.Write([]byte(fmt.Sprintf("%d", x)))
return model.LabelValue(fmt.Sprintf("%d", h.Sum64()))
}
M := 32
met := model.Metric{}
for i := 0; i < M; i++ {
met["label_a"] = lbl(i)
for j := 0; j < M; j++ {
met["label_b"] = lbl(j)
for k := 0; k < M; k++ {
met["label_c"] = lbl(k)
for l := 0; l < M; l++ {
met["label_d"] = lbl(l)
s.Append(&model.Sample{
Metric: met.Clone(),
Timestamp: 0,
Value: 1,
})
}
}
}
}
s.WaitForIndexing()
newMatcher := func(matchType metric.MatchType, name model.LabelName, value model.LabelValue) *metric.LabelMatcher {
lm, err := metric.NewLabelMatcher(matchType, name, value)
if err != nil {
b.Fatalf("error creating label matcher: %s", err)
}
return lm
}
var matcherTests = []metric.LabelMatchers{
{
newMatcher(metric.Equal, "label_a", lbl(1)),
},
{
newMatcher(metric.Equal, "label_a", lbl(3)),
newMatcher(metric.Equal, "label_c", lbl(3)),
},
{
newMatcher(metric.Equal, "label_a", lbl(3)),
newMatcher(metric.Equal, "label_c", lbl(3)),
newMatcher(metric.NotEqual, "label_d", lbl(3)),
},
{
newMatcher(metric.Equal, "label_a", lbl(3)),
newMatcher(metric.Equal, "label_b", lbl(3)),
newMatcher(metric.Equal, "label_c", lbl(3)),
newMatcher(metric.NotEqual, "label_d", lbl(3)),
},
{
newMatcher(metric.RegexMatch, "label_a", ".+"),
},
{
newMatcher(metric.Equal, "label_a", lbl(3)),
newMatcher(metric.RegexMatch, "label_a", ".+"),
},
{
newMatcher(metric.Equal, "label_a", lbl(1)),
newMatcher(metric.RegexMatch, "label_c", "("+lbl(3)+"|"+lbl(10)+")"),
},
{
newMatcher(metric.Equal, "label_a", lbl(3)),
newMatcher(metric.Equal, "label_a", lbl(4)),
newMatcher(metric.RegexMatch, "label_c", "("+lbl(3)+"|"+lbl(10)+")"),
},
}
b.ReportAllocs()
b.ResetTimer()
var err error
for i := 0; i < b.N; i++ {
benchLabelMatchingRes = []metric.Metric{}
for _, mt := range matcherTests {
benchLabelMatchingRes, err = s.MetricsForLabelMatchers(
model.Earliest, model.Latest,
mt,
)
if err != nil {
b.Fatal(err)
}
}
}
// Stop timer to not count the storage closing.
b.StopTimer()
}
func TestRetentionCutoff(t *testing.T) {
now := model.Now()
insertStart := now.Add(-2 * time.Hour)
s, closer := NewTestStorage(t, 2)
defer closer.Close()
// Stop maintenance loop to prevent actual purging.
close(s.loopStopping)
<-s.loopStopped
<-s.logThrottlingStopped
// Recreate channel to avoid panic when we really shut down.
s.loopStopping = make(chan struct{})
s.dropAfter = 1 * time.Hour
for i := 0; i < 120; i++ {
smpl := &model.Sample{
Metric: model.Metric{"job": "test"},
Timestamp: insertStart.Add(time.Duration(i) * time.Minute), // 1 minute intervals.
Value: 1,
}
s.Append(smpl)
}
s.WaitForIndexing()
lm, err := metric.NewLabelMatcher(metric.Equal, "job", "test")
if err != nil {
t.Fatalf("error creating label matcher: %s", err)
}
its, err := s.QueryRange(insertStart, now, lm)
if err != nil {
t.Fatal(err)
}
if len(its) != 1 {
t.Fatalf("expected one iterator but got %d", len(its))
}
val := its[0].ValueAtOrBeforeTime(now.Add(-61 * time.Minute))
if val.Timestamp != model.Earliest {
t.Errorf("unexpected result for timestamp before retention period")
}
vals := its[0].RangeValues(metric.Interval{OldestInclusive: insertStart, NewestInclusive: now})
// We get 59 values here because the model.Now() is slightly later
// than our now.
if len(vals) != 59 {
t.Errorf("expected 59 values but got %d", len(vals))
}
if expt := now.Add(-1 * time.Hour).Add(time.Minute); vals[0].Timestamp != expt {
t.Errorf("unexpected timestamp for first sample: %v, expected %v", vals[0].Timestamp.Time(), expt.Time())
}
}
func TestDropMetrics(t *testing.T) {
now := model.Now()
insertStart := now.Add(-2 * time.Hour)
s, closer := NewTestStorage(t, 2)
defer closer.Close()
chunkFileExists := func(fp model.Fingerprint) (bool, error) {
f, err := s.persistence.openChunkFileForReading(fp)
if err == nil {
f.Close()
return true, nil
}
if os.IsNotExist(err) {
return false, nil
}
return false, err
}
m1 := model.Metric{model.MetricNameLabel: "test", "n1": "v1"}
m2 := model.Metric{model.MetricNameLabel: "test", "n1": "v2"}
m3 := model.Metric{model.MetricNameLabel: "test", "n1": "v3"}
lm1, err := metric.NewLabelMatcher(metric.Equal, "n1", "v1")
if err != nil {
t.Fatal(err)
}
lmAll, err := metric.NewLabelMatcher(metric.Equal, model.MetricNameLabel, "test")
if err != nil {
t.Fatal(err)
}
N := 120000
for j, m := range []model.Metric{m1, m2, m3} {
for i := 0; i < N; i++ {
smpl := &model.Sample{
Metric: m,
Timestamp: insertStart.Add(time.Duration(i) * time.Millisecond), // 1 millisecond intervals.
Value: model.SampleValue(j),
}
s.Append(smpl)
}
}
s.WaitForIndexing()
// Archive m3, but first maintain it so that at least something is written to disk.
fpToBeArchived := m3.FastFingerprint()
s.maintainMemorySeries(fpToBeArchived, 0)
s.fpLocker.Lock(fpToBeArchived)
s.fpToSeries.del(fpToBeArchived)
s.persistence.archiveMetric(fpToBeArchived, m3, 0, insertStart.Add(time.Duration(N-1)*time.Millisecond))
s.fpLocker.Unlock(fpToBeArchived)
fps := s.fingerprintsForLabelPair(model.LabelPair{
Name: model.MetricNameLabel, Value: "test",
}, nil, nil)
if len(fps) != 3 {
t.Errorf("unexpected number of fingerprints: %d", len(fps))
}
fpList := model.Fingerprints{m1.FastFingerprint(), m2.FastFingerprint(), fpToBeArchived}
n, err := s.DropMetricsForLabelMatchers(lm1)
if err != nil {
t.Fatal(err)
}
if n != 1 {
t.Fatalf("expected 1 series to be dropped, got %d", n)
}
s.WaitForIndexing()
fps2 := s.fingerprintsForLabelPair(model.LabelPair{
Name: model.MetricNameLabel, Value: "test",
}, nil, nil)
if len(fps2) != 2 {
t.Errorf("unexpected number of fingerprints: %d", len(fps2))
}
it := s.preloadChunksForRange(fpList[0], model.Earliest, model.Latest)
if vals := it.RangeValues(metric.Interval{OldestInclusive: insertStart, NewestInclusive: now}); len(vals) != 0 {
t.Errorf("unexpected number of samples: %d", len(vals))
}
it = s.preloadChunksForRange(fpList[1], model.Earliest, model.Latest)
if vals := it.RangeValues(metric.Interval{OldestInclusive: insertStart, NewestInclusive: now}); len(vals) != N {
t.Errorf("unexpected number of samples: %d", len(vals))
}
exists, err := chunkFileExists(fpList[2])
if err != nil {
t.Fatal(err)
}
if !exists {
t.Errorf("chunk file does not exist for fp=%v", fpList[2])
}
n, err = s.DropMetricsForLabelMatchers(lmAll)
if err != nil {
t.Fatal(err)
}
if n != 2 {
t.Fatalf("expected 2 series to be dropped, got %d", n)
}
s.WaitForIndexing()
fps3 := s.fingerprintsForLabelPair(model.LabelPair{
Name: model.MetricNameLabel, Value: "test",
}, nil, nil)
if len(fps3) != 0 {
t.Errorf("unexpected number of fingerprints: %d", len(fps3))
}
it = s.preloadChunksForRange(fpList[0], model.Earliest, model.Latest)
if vals := it.RangeValues(metric.Interval{OldestInclusive: insertStart, NewestInclusive: now}); len(vals) != 0 {
t.Errorf("unexpected number of samples: %d", len(vals))
}
it = s.preloadChunksForRange(fpList[1], model.Earliest, model.Latest)
if vals := it.RangeValues(metric.Interval{OldestInclusive: insertStart, NewestInclusive: now}); len(vals) != 0 {
t.Errorf("unexpected number of samples: %d", len(vals))
}
exists, err = chunkFileExists(fpList[2])
if err != nil {
t.Fatal(err)
}
if exists {
t.Errorf("chunk file still exists for fp=%v", fpList[2])
}
}
func TestQuarantineMetric(t *testing.T) {
now := model.Now()
insertStart := now.Add(-2 * time.Hour)
s, closer := NewTestStorage(t, 2)
defer closer.Close()
chunkFileExists := func(fp model.Fingerprint) (bool, error) {
f, err := s.persistence.openChunkFileForReading(fp)
if err == nil {
f.Close()
return true, nil
}
if os.IsNotExist(err) {
return false, nil
}
return false, err
}
m1 := model.Metric{model.MetricNameLabel: "test", "n1": "v1"}
m2 := model.Metric{model.MetricNameLabel: "test", "n1": "v2"}
m3 := model.Metric{model.MetricNameLabel: "test", "n1": "v3"}
N := 120000
for j, m := range []model.Metric{m1, m2, m3} {
for i := 0; i < N; i++ {
smpl := &model.Sample{
Metric: m,
Timestamp: insertStart.Add(time.Duration(i) * time.Millisecond), // 1 millisecond intervals.
Value: model.SampleValue(j),
}
s.Append(smpl)
}
}
s.WaitForIndexing()
// Archive m3, but first maintain it so that at least something is written to disk.
fpToBeArchived := m3.FastFingerprint()
s.maintainMemorySeries(fpToBeArchived, 0)
s.fpLocker.Lock(fpToBeArchived)
s.fpToSeries.del(fpToBeArchived)
s.persistence.archiveMetric(fpToBeArchived, m3, 0, insertStart.Add(time.Duration(N-1)*time.Millisecond))
s.fpLocker.Unlock(fpToBeArchived)
// Corrupt the series file for m3.
f, err := os.Create(s.persistence.fileNameForFingerprint(fpToBeArchived))
if err != nil {
t.Fatal(err)
}
if _, err := f.WriteString("This is clearly not the content of a series file."); err != nil {
t.Fatal(err)
}
if f.Close(); err != nil {
t.Fatal(err)
}
fps := s.fingerprintsForLabelPair(model.LabelPair{
Name: model.MetricNameLabel, Value: "test",
}, nil, nil)
if len(fps) != 3 {
t.Errorf("unexpected number of fingerprints: %d", len(fps))
}
// This will access the corrupt file and lead to quarantining.
iter := s.preloadChunksForInstant(fpToBeArchived, now.Add(-2*time.Hour-1*time.Minute), now.Add(-2*time.Hour))
iter.Close()
time.Sleep(time.Second) // Give time to quarantine. TODO(beorn7): Find a better way to wait.
s.WaitForIndexing()
fps2 := s.fingerprintsForLabelPair(model.LabelPair{
Name: model.MetricNameLabel, Value: "test",
}, nil, nil)
if len(fps2) != 2 {
t.Errorf("unexpected number of fingerprints: %d", len(fps2))
}
exists, err := chunkFileExists(fpToBeArchived)
if err != nil {
t.Fatal(err)
}
if exists {
t.Errorf("chunk file exists for fp=%v", fpToBeArchived)
}
}
// TestLoop is just a smoke test for the loop method, if we can switch it on and
// off without disaster.
func TestLoop(t *testing.T) {
if testing.Short() {
t.Skip("Skipping test in short mode.")
}
samples := make(model.Samples, 1000)
for i := range samples {
samples[i] = &model.Sample{
Timestamp: model.Time(2 * i),
Value: model.SampleValue(float64(i) * 0.2),
}
}
directory := testutil.NewTemporaryDirectory("test_storage", t)
defer directory.Close()
o := &MemorySeriesStorageOptions{
MemoryChunks: 50,
MaxChunksToPersist: 1000000,
PersistenceRetentionPeriod: 24 * 7 * time.Hour,
PersistenceStoragePath: directory.Path(),
CheckpointInterval: 250 * time.Millisecond,
SyncStrategy: Adaptive,
MinShrinkRatio: 0.1,
}
storage := NewMemorySeriesStorage(o)
if err := storage.Start(); err != nil {
t.Errorf("Error starting storage: %s", err)
}
for _, s := range samples {
storage.Append(s)
}
storage.WaitForIndexing()
series, _ := storage.fpToSeries.get(model.Metric{}.FastFingerprint())
cdsBefore := len(series.chunkDescs)
time.Sleep(fpMaxWaitDuration + time.Second) // TODO(beorn7): Ugh, need to wait for maintenance to kick in.
cdsAfter := len(series.chunkDescs)
storage.Stop()
if cdsBefore <= cdsAfter {
t.Errorf(
"Number of chunk descriptors should have gone down by now. Got before %d, after %d.",
cdsBefore, cdsAfter,
)
}
}
func testChunk(t *testing.T, encoding chunkEncoding) {
samples := make(model.Samples, 500000)
for i := range samples {
samples[i] = &model.Sample{
Timestamp: model.Time(i),
Value: model.SampleValue(float64(i) * 0.2),
}
}
s, closer := NewTestStorage(t, encoding)
defer closer.Close()
for _, sample := range samples {
s.Append(sample)
}
s.WaitForIndexing()
for m := range s.fpToSeries.iter() {
s.fpLocker.Lock(m.fp)
defer s.fpLocker.Unlock(m.fp) // TODO remove, see below
var values []model.SamplePair
for _, cd := range m.series.chunkDescs {
if cd.isEvicted() {
continue
}
it := cd.c.newIterator()
for it.scan() {
values = append(values, it.value())
}
if it.err() != nil {
t.Error(it.err())
}
}
for i, v := range values {
if samples[i].Timestamp != v.Timestamp {
t.Errorf("%d. Got %v; want %v", i, v.Timestamp, samples[i].Timestamp)
}
if samples[i].Value != v.Value {
t.Errorf("%d. Got %v; want %v", i, v.Value, samples[i].Value)
}
}
//s.fpLocker.Unlock(m.fp)
}
log.Info("test done, closing")
}
func TestChunkType0(t *testing.T) {
testChunk(t, 0)
}
func TestChunkType1(t *testing.T) {
testChunk(t, 1)
}
func TestChunkType2(t *testing.T) {
testChunk(t, 2)
}
func testValueAtOrBeforeTime(t *testing.T, encoding chunkEncoding) {
samples := make(model.Samples, 10000)
for i := range samples {
samples[i] = &model.Sample{
Timestamp: model.Time(2 * i),
Value: model.SampleValue(float64(i) * 0.2),
}
}
s, closer := NewTestStorage(t, encoding)
defer closer.Close()
for _, sample := range samples {
s.Append(sample)
}
s.WaitForIndexing()
fp := model.Metric{}.FastFingerprint()
it := s.preloadChunksForRange(fp, model.Earliest, model.Latest)
// #1 Exactly on a sample.
for i, expected := range samples {
actual := it.ValueAtOrBeforeTime(expected.Timestamp)
if expected.Timestamp != actual.Timestamp {
t.Errorf("1.%d. Got %v; want %v", i, actual.Timestamp, expected.Timestamp)
}
if expected.Value != actual.Value {
t.Errorf("1.%d. Got %v; want %v", i, actual.Value, expected.Value)
}
}
// #2 Between samples.
for i, expected := range samples {
if i == len(samples)-1 {
continue
}
actual := it.ValueAtOrBeforeTime(expected.Timestamp + 1)
if expected.Timestamp != actual.Timestamp {
t.Errorf("2.%d. Got %v; want %v", i, actual.Timestamp, expected.Timestamp)
}
if expected.Value != actual.Value {
t.Errorf("2.%d. Got %v; want %v", i, actual.Value, expected.Value)
}
}
// #3 Corner cases: Just before the first sample, just after the last.
expected := &model.Sample{Timestamp: model.Earliest}
actual := it.ValueAtOrBeforeTime(samples[0].Timestamp - 1)
if expected.Timestamp != actual.Timestamp {
t.Errorf("3.1. Got %v; want %v", actual.Timestamp, expected.Timestamp)
}
if expected.Value != actual.Value {
t.Errorf("3.1. Got %v; want %v", actual.Value, expected.Value)
}
expected = samples[len(samples)-1]
actual = it.ValueAtOrBeforeTime(expected.Timestamp + 1)
if expected.Timestamp != actual.Timestamp {
t.Errorf("3.2. Got %v; want %v", actual.Timestamp, expected.Timestamp)
}
if expected.Value != actual.Value {
t.Errorf("3.2. Got %v; want %v", actual.Value, expected.Value)
}
}
func TestValueAtTimeChunkType0(t *testing.T) {
testValueAtOrBeforeTime(t, 0)
}
func TestValueAtTimeChunkType1(t *testing.T) {
testValueAtOrBeforeTime(t, 1)
}
func TestValueAtTimeChunkType2(t *testing.T) {
testValueAtOrBeforeTime(t, 2)
}
func benchmarkValueAtOrBeforeTime(b *testing.B, encoding chunkEncoding) {
samples := make(model.Samples, 10000)
for i := range samples {
samples[i] = &model.Sample{
Timestamp: model.Time(2 * i),
Value: model.SampleValue(float64(i) * 0.2),
}
}
s, closer := NewTestStorage(b, encoding)
defer closer.Close()
for _, sample := range samples {
s.Append(sample)
}
s.WaitForIndexing()
fp := model.Metric{}.FastFingerprint()
it := s.preloadChunksForRange(fp, model.Earliest, model.Latest)
b.ResetTimer()
for i := 0; i < b.N; i++ {
// #1 Exactly on a sample.
for i, expected := range samples {
actual := it.ValueAtOrBeforeTime(expected.Timestamp)
if expected.Timestamp != actual.Timestamp {
b.Errorf("1.%d. Got %v; want %v", i, actual.Timestamp, expected.Timestamp)
}
if expected.Value != actual.Value {
b.Errorf("1.%d. Got %v; want %v", i, actual.Value, expected.Value)
}
}
// #2 Between samples.
for i, expected := range samples {
if i == len(samples)-1 {
continue
}
actual := it.ValueAtOrBeforeTime(expected.Timestamp + 1)
if expected.Timestamp != actual.Timestamp {
b.Errorf("2.%d. Got %v; want %v", i, actual.Timestamp, expected.Timestamp)
}
if expected.Value != actual.Value {
b.Errorf("2.%d. Got %v; want %v", i, actual.Value, expected.Value)
}
}
// #3 Corner cases: Just before the first sample, just after the last.
expected := &model.Sample{Timestamp: model.Earliest}
actual := it.ValueAtOrBeforeTime(samples[0].Timestamp - 1)
if expected.Timestamp != actual.Timestamp {
b.Errorf("3.1. Got %v; want %v", actual.Timestamp, expected.Timestamp)
}
if expected.Value != actual.Value {
b.Errorf("3.1. Got %v; want %v", actual.Value, expected.Value)
}
expected = samples[len(samples)-1]
actual = it.ValueAtOrBeforeTime(expected.Timestamp + 1)
if expected.Timestamp != actual.Timestamp {
b.Errorf("3.2. Got %v; want %v", actual.Timestamp, expected.Timestamp)
}
if expected.Value != actual.Value {
b.Errorf("3.2. Got %v; want %v", actual.Value, expected.Value)
}
}
}
func BenchmarkValueAtOrBeforeTimeChunkType0(b *testing.B) {
benchmarkValueAtOrBeforeTime(b, 0)
}
func BenchmarkValueAtTimeChunkType1(b *testing.B) {
benchmarkValueAtOrBeforeTime(b, 1)
}
func BenchmarkValueAtTimeChunkType2(b *testing.B) {
benchmarkValueAtOrBeforeTime(b, 2)
}
func testRangeValues(t *testing.T, encoding chunkEncoding) {
samples := make(model.Samples, 10000)
for i := range samples {
samples[i] = &model.Sample{
Timestamp: model.Time(2 * i),
Value: model.SampleValue(float64(i) * 0.2),
}
}
s, closer := NewTestStorage(t, encoding)
defer closer.Close()
for _, sample := range samples {
s.Append(sample)
}
s.WaitForIndexing()
fp := model.Metric{}.FastFingerprint()
it := s.preloadChunksForRange(fp, model.Earliest, model.Latest)
// #1 Zero length interval at sample.
for i, expected := range samples {
actual := it.RangeValues(metric.Interval{
OldestInclusive: expected.Timestamp,
NewestInclusive: expected.Timestamp,
})
if len(actual) != 1 {
t.Fatalf("1.%d. Expected exactly one result, got %d.", i, len(actual))
}
if expected.Timestamp != actual[0].Timestamp {
t.Errorf("1.%d. Got %v; want %v.", i, actual[0].Timestamp, expected.Timestamp)
}
if expected.Value != actual[0].Value {
t.Errorf("1.%d. Got %v; want %v.", i, actual[0].Value, expected.Value)
}
}
// #2 Zero length interval off sample.
for i, expected := range samples {
actual := it.RangeValues(metric.Interval{
OldestInclusive: expected.Timestamp + 1,
NewestInclusive: expected.Timestamp + 1,
})
if len(actual) != 0 {
t.Fatalf("2.%d. Expected no result, got %d.", i, len(actual))
}
}
// #3 2sec interval around sample.
for i, expected := range samples {
actual := it.RangeValues(metric.Interval{
OldestInclusive: expected.Timestamp - 1,
NewestInclusive: expected.Timestamp + 1,
})
if len(actual) != 1 {
t.Fatalf("3.%d. Expected exactly one result, got %d.", i, len(actual))
}
if expected.Timestamp != actual[0].Timestamp {
t.Errorf("3.%d. Got %v; want %v.", i, actual[0].Timestamp, expected.Timestamp)
}
if expected.Value != actual[0].Value {
t.Errorf("3.%d. Got %v; want %v.", i, actual[0].Value, expected.Value)
}
}
// #4 2sec interval sample to sample.
for i, expected1 := range samples {
if i == len(samples)-1 {
continue
}
expected2 := samples[i+1]
actual := it.RangeValues(metric.Interval{
OldestInclusive: expected1.Timestamp,
NewestInclusive: expected1.Timestamp + 2,
})
if len(actual) != 2 {
t.Fatalf("4.%d. Expected exactly 2 results, got %d.", i, len(actual))
}
if expected1.Timestamp != actual[0].Timestamp {
t.Errorf("4.%d. Got %v for 1st result; want %v.", i, actual[0].Timestamp, expected1.Timestamp)
}
if expected1.Value != actual[0].Value {
t.Errorf("4.%d. Got %v for 1st result; want %v.", i, actual[0].Value, expected1.Value)
}
if expected2.Timestamp != actual[1].Timestamp {
t.Errorf("4.%d. Got %v for 2nd result; want %v.", i, actual[1].Timestamp, expected2.Timestamp)
}
if expected2.Value != actual[1].Value {
t.Errorf("4.%d. Got %v for 2nd result; want %v.", i, actual[1].Value, expected2.Value)
}
}
// #5 corner cases: Interval ends at first sample, interval starts
// at last sample, interval entirely before/after samples.
expected := samples[0]
actual := it.RangeValues(metric.Interval{
OldestInclusive: expected.Timestamp - 2,
NewestInclusive: expected.Timestamp,
})
if len(actual) != 1 {
t.Fatalf("5.1. Expected exactly one result, got %d.", len(actual))
}
if expected.Timestamp != actual[0].Timestamp {
t.Errorf("5.1. Got %v; want %v.", actual[0].Timestamp, expected.Timestamp)
}
if expected.Value != actual[0].Value {
t.Errorf("5.1. Got %v; want %v.", actual[0].Value, expected.Value)
}
expected = samples[len(samples)-1]
actual = it.RangeValues(metric.Interval{
OldestInclusive: expected.Timestamp,
NewestInclusive: expected.Timestamp + 2,
})
if len(actual) != 1 {
t.Fatalf("5.2. Expected exactly one result, got %d.", len(actual))
}
if expected.Timestamp != actual[0].Timestamp {
t.Errorf("5.2. Got %v; want %v.", actual[0].Timestamp, expected.Timestamp)
}
if expected.Value != actual[0].Value {
t.Errorf("5.2. Got %v; want %v.", actual[0].Value, expected.Value)
}
firstSample := samples[0]
actual = it.RangeValues(metric.Interval{
OldestInclusive: firstSample.Timestamp - 4,
NewestInclusive: firstSample.Timestamp - 2,
})
if len(actual) != 0 {
t.Fatalf("5.3. Expected no results, got %d.", len(actual))
}
lastSample := samples[len(samples)-1]
actual = it.RangeValues(metric.Interval{
OldestInclusive: lastSample.Timestamp + 2,
NewestInclusive: lastSample.Timestamp + 4,
})
if len(actual) != 0 {
t.Fatalf("5.3. Expected no results, got %d.", len(actual))
}
}
func TestRangeValuesChunkType0(t *testing.T) {
testRangeValues(t, 0)
}
func TestRangeValuesChunkType1(t *testing.T) {
testRangeValues(t, 1)
}
func TestRangeValuesChunkType2(t *testing.T) {
testRangeValues(t, 2)
}
func benchmarkRangeValues(b *testing.B, encoding chunkEncoding) {
samples := make(model.Samples, 10000)
for i := range samples {
samples[i] = &model.Sample{
Timestamp: model.Time(2 * i),
Value: model.SampleValue(float64(i) * 0.2),
}
}
s, closer := NewTestStorage(b, encoding)
defer closer.Close()
for _, sample := range samples {
s.Append(sample)
}
s.WaitForIndexing()
fp := model.Metric{}.FastFingerprint()
it := s.preloadChunksForRange(fp, model.Earliest, model.Latest)
b.ResetTimer()
for i := 0; i < b.N; i++ {
for _, sample := range samples {
actual := it.RangeValues(metric.Interval{
OldestInclusive: sample.Timestamp - 20,
NewestInclusive: sample.Timestamp + 20,
})
if len(actual) < 10 {
b.Fatalf("not enough samples found")
}
}
}
}
func BenchmarkRangeValuesChunkType0(b *testing.B) {
benchmarkRangeValues(b, 0)
}
func BenchmarkRangeValuesChunkType1(b *testing.B) {
benchmarkRangeValues(b, 1)
}
func BenchmarkRangeValuesChunkType2(b *testing.B) {
benchmarkRangeValues(b, 2)
}
func testEvictAndPurgeSeries(t *testing.T, encoding chunkEncoding) {
samples := make(model.Samples, 10000)
for i := range samples {
samples[i] = &model.Sample{
Timestamp: model.Time(2 * i),
Value: model.SampleValue(float64(i * i)),
}
}
s, closer := NewTestStorage(t, encoding)
defer closer.Close()
for _, sample := range samples {
s.Append(sample)
}
s.WaitForIndexing()
fp := model.Metric{}.FastFingerprint()
// Drop ~half of the chunks.
s.maintainMemorySeries(fp, 10000)
it := s.preloadChunksForRange(fp, model.Earliest, model.Latest)
actual := it.RangeValues(metric.Interval{
OldestInclusive: 0,
NewestInclusive: 100000,
})
if len(actual) < 4000 {
t.Fatalf("expected more than %d results after purging half of series, got %d", 4000, len(actual))
}
if actual[0].Timestamp < 6000 || actual[0].Timestamp > 10000 {
t.Errorf("1st timestamp out of expected range: %v", actual[0].Timestamp)
}
want := model.Time(19998)
if actual[len(actual)-1].Timestamp != want {
t.Errorf("2nd timestamp: want %v, got %v", want, actual[1].Timestamp)
}
// Drop everything.
s.maintainMemorySeries(fp, 100000)
it = s.preloadChunksForRange(fp, model.Earliest, model.Latest)
actual = it.RangeValues(metric.Interval{
OldestInclusive: 0,
NewestInclusive: 100000,
})
if len(actual) != 0 {
t.Fatal("expected zero results after purging the whole series")
}
// Recreate series.
for _, sample := range samples {
s.Append(sample)
}
s.WaitForIndexing()
series, ok := s.fpToSeries.get(fp)
if !ok {
t.Fatal("could not find series")
}
// Persist head chunk so we can safely archive.
series.headChunkClosed = true
s.maintainMemorySeries(fp, model.Earliest)
// Archive metrics.
s.fpToSeries.del(fp)
lastTime, err := series.head().lastTime()
if err != nil {
t.Fatal(err)
}
s.persistence.archiveMetric(fp, series.metric, series.firstTime(), lastTime)
archived, _, _ := s.persistence.hasArchivedMetric(fp)
if !archived {
t.Fatal("not archived")
}
// Drop ~half of the chunks of an archived series.
s.maintainArchivedSeries(fp, 10000)
archived, _, _ = s.persistence.hasArchivedMetric(fp)
if !archived {
t.Fatal("archived series purged although only half of the chunks dropped")
}
// Drop everything.
s.maintainArchivedSeries(fp, 100000)
archived, _, _ = s.persistence.hasArchivedMetric(fp)
if archived {
t.Fatal("archived series not dropped")
}
// Recreate series.
for _, sample := range samples {
s.Append(sample)
}
s.WaitForIndexing()
series, ok = s.fpToSeries.get(fp)
if !ok {
t.Fatal("could not find series")
}
// Persist head chunk so we can safely archive.
series.headChunkClosed = true
s.maintainMemorySeries(fp, model.Earliest)
// Archive metrics.
s.fpToSeries.del(fp)
lastTime, err = series.head().lastTime()
if err != nil {
t.Fatal(err)
}
s.persistence.archiveMetric(fp, series.metric, series.firstTime(), lastTime)
archived, _, _ = s.persistence.hasArchivedMetric(fp)
if !archived {
t.Fatal("not archived")
}
// Unarchive metrics.
s.getOrCreateSeries(fp, model.Metric{})
series, ok = s.fpToSeries.get(fp)
if !ok {
t.Fatal("could not find series")
}
archived, _, _ = s.persistence.hasArchivedMetric(fp)
if archived {
t.Fatal("archived")
}
// Set archiveHighWatermark to a low value so that we can see it increase.
s.archiveHighWatermark = 42
// This will archive again, but must not drop it completely, despite the
// memorySeries being empty.
s.maintainMemorySeries(fp, 10000)
archived, _, _ = s.persistence.hasArchivedMetric(fp)
if !archived {
t.Fatal("series purged completely")
}
// archiveHighWatermark must have been set by maintainMemorySeries.
if want, got := model.Time(19998), s.archiveHighWatermark; want != got {
t.Errorf("want archiveHighWatermark %v, got %v", want, got)
}
}
func TestEvictAndPurgeSeriesChunkType0(t *testing.T) {
testEvictAndPurgeSeries(t, 0)
}
func TestEvictAndPurgeSeriesChunkType1(t *testing.T) {
testEvictAndPurgeSeries(t, 1)
}
func TestEvictAndPurgeSeriesChunkType2(t *testing.T) {
testEvictAndPurgeSeries(t, 2)
}
func testEvictAndLoadChunkDescs(t *testing.T, encoding chunkEncoding) {
samples := make(model.Samples, 10000)
for i := range samples {
samples[i] = &model.Sample{
Timestamp: model.Time(2 * i),
Value: model.SampleValue(float64(i * i)),
}
}
// Give last sample a timestamp of now so that the head chunk will not
// be closed (which would then archive the time series later as
// everything will get evicted).
samples[len(samples)-1] = &model.Sample{
Timestamp: model.Now(),
Value: model.SampleValue(3.14),
}
s, closer := NewTestStorage(t, encoding)
defer closer.Close()
// Adjust memory chunks to lower value to see evictions.
s.maxMemoryChunks = 1
for _, sample := range samples {
s.Append(sample)
}
s.WaitForIndexing()
fp := model.Metric{}.FastFingerprint()
series, ok := s.fpToSeries.get(fp)
if !ok {
t.Fatal("could not find series")
}
oldLen := len(series.chunkDescs)
// Maintain series without any dropped chunks.
s.maintainMemorySeries(fp, 0)
// Give the evict goroutine an opportunity to run.
time.Sleep(250 * time.Millisecond)
// Maintain series again to trigger chunkDesc eviction
s.maintainMemorySeries(fp, 0)
if oldLen <= len(series.chunkDescs) {
t.Errorf("Expected number of chunkDescs to decrease, old number %d, current number %d.", oldLen, len(series.chunkDescs))
}
// Load everything back.
it := s.preloadChunksForRange(fp, 0, 100000)
if oldLen != len(series.chunkDescs) {
t.Errorf("Expected number of chunkDescs to have reached old value again, old number %d, current number %d.", oldLen, len(series.chunkDescs))
}
it.Close()
// Now maintain series with drops to make sure nothing crazy happens.
s.maintainMemorySeries(fp, 100000)
if len(series.chunkDescs) != 1 {
t.Errorf("Expected exactly one chunkDesc left, got %d.", len(series.chunkDescs))
}
}
func TestEvictAndLoadChunkDescsType0(t *testing.T) {
testEvictAndLoadChunkDescs(t, 0)
}
func TestEvictAndLoadChunkDescsType1(t *testing.T) {
testEvictAndLoadChunkDescs(t, 1)
}
func benchmarkAppend(b *testing.B, encoding chunkEncoding) {
samples := make(model.Samples, b.N)
for i := range samples {
samples[i] = &model.Sample{
Metric: model.Metric{
model.MetricNameLabel: model.LabelValue(fmt.Sprintf("test_metric_%d", i%10)),
"label1": model.LabelValue(fmt.Sprintf("test_metric_%d", i%10)),
"label2": model.LabelValue(fmt.Sprintf("test_metric_%d", i%10)),
},
Timestamp: model.Time(i),
Value: model.SampleValue(i),
}
}
b.ResetTimer()
s, closer := NewTestStorage(b, encoding)
defer closer.Close()
for _, sample := range samples {
s.Append(sample)
}
}
func BenchmarkAppendType0(b *testing.B) {
benchmarkAppend(b, 0)
}
func BenchmarkAppendType1(b *testing.B) {
benchmarkAppend(b, 1)
}
func BenchmarkAppendType2(b *testing.B) {
benchmarkAppend(b, 2)
}
// Append a large number of random samples and then check if we can get them out
// of the storage alright.
func testFuzz(t *testing.T, encoding chunkEncoding) {
if testing.Short() {
t.Skip("Skipping test in short mode.")
}
check := func(seed int64) bool {
rand.Seed(seed)
s, c := NewTestStorage(t, encoding)
defer c.Close()
samples := createRandomSamples("test_fuzz", 10000)
for _, sample := range samples {
s.Append(sample)
}
if !verifyStorageRandom(t, s, samples) {
return false
}
return verifyStorageSequential(t, s, samples)
}
if err := quick.Check(check, nil); err != nil {
t.Fatal(err)
}
}
func TestFuzzChunkType0(t *testing.T) {
testFuzz(t, 0)
}
func TestFuzzChunkType1(t *testing.T) {
testFuzz(t, 1)
}
func TestFuzzChunkType2(t *testing.T) {
testFuzz(t, 2)
}
// benchmarkFuzz is the benchmark version of testFuzz. The storage options are
// set such that evictions, checkpoints, and purging will happen concurrently,
// too. This benchmark will have a very long runtime (up to minutes). You can
// use it as an actual benchmark. Run it like this:
//
// go test -cpu 1,2,4,8 -run=NONE -bench BenchmarkFuzzChunkType -benchmem
//
// You can also use it as a test for races. In that case, run it like this (will
// make things even slower):
//
// go test -race -cpu 8 -short -bench BenchmarkFuzzChunkType
func benchmarkFuzz(b *testing.B, encoding chunkEncoding) {
DefaultChunkEncoding = encoding
const samplesPerRun = 100000
rand.Seed(42)
directory := testutil.NewTemporaryDirectory("test_storage", b)
defer directory.Close()
o := &MemorySeriesStorageOptions{
MemoryChunks: 100,
MaxChunksToPersist: 1000000,
PersistenceRetentionPeriod: time.Hour,
PersistenceStoragePath: directory.Path(),
CheckpointInterval: time.Second,
SyncStrategy: Adaptive,
MinShrinkRatio: 0.1,
}
s := NewMemorySeriesStorage(o)
if err := s.Start(); err != nil {
b.Fatalf("Error starting storage: %s", err)
}
s.Start()
defer s.Stop()
samples := createRandomSamples("benchmark_fuzz", samplesPerRun*b.N)
b.ResetTimer()
for i := 0; i < b.N; i++ {
start := samplesPerRun * i
end := samplesPerRun * (i + 1)
middle := (start + end) / 2
for _, sample := range samples[start:middle] {
s.Append(sample)
}
verifyStorageRandom(b, s, samples[:middle])
for _, sample := range samples[middle:end] {
s.Append(sample)
}
verifyStorageRandom(b, s, samples[:end])
verifyStorageSequential(b, s, samples)
}
}
func BenchmarkFuzzChunkType0(b *testing.B) {
benchmarkFuzz(b, 0)
}
func BenchmarkFuzzChunkType1(b *testing.B) {
benchmarkFuzz(b, 1)
}
func BenchmarkFuzzChunkType2(b *testing.B) {
benchmarkFuzz(b, 2)
}
func createRandomSamples(metricName string, minLen int) model.Samples {
type valueCreator func() model.SampleValue
type deltaApplier func(model.SampleValue) model.SampleValue
var (
maxMetrics = 5
maxStreakLength = 2000
maxTimeDelta = 10000
timestamp = model.Now() - model.Time(maxTimeDelta*minLen) // So that some timestamps are in the future.
generators = []struct {
createValue valueCreator
applyDelta []deltaApplier
}{
{ // "Boolean".
createValue: func() model.SampleValue {
return model.SampleValue(rand.Intn(2))
},
applyDelta: []deltaApplier{
func(_ model.SampleValue) model.SampleValue {
return model.SampleValue(rand.Intn(2))
},
},
},
{ // Integer with int deltas of various byte length.
createValue: func() model.SampleValue {
return model.SampleValue(rand.Int63() - 1<<62)
},
applyDelta: []deltaApplier{
func(v model.SampleValue) model.SampleValue {
return model.SampleValue(rand.Intn(1<<8) - 1<<7 + int(v))
},
func(v model.SampleValue) model.SampleValue {
return model.SampleValue(rand.Intn(1<<16) - 1<<15 + int(v))
},
func(v model.SampleValue) model.SampleValue {
return model.SampleValue(rand.Int63n(1<<32) - 1<<31 + int64(v))
},
},
},
{ // Float with float32 and float64 deltas.
createValue: func() model.SampleValue {
return model.SampleValue(rand.NormFloat64())
},
applyDelta: []deltaApplier{
func(v model.SampleValue) model.SampleValue {
return v + model.SampleValue(float32(rand.NormFloat64()))
},
func(v model.SampleValue) model.SampleValue {
return v + model.SampleValue(rand.NormFloat64())
},
},
},
}
timestampIncrementers = []func(baseDelta model.Time) model.Time{
// Regular increments.
func(delta model.Time) model.Time {
return delta
},
// Jittered increments. σ is 1/100 of delta, e.g. 10ms for 10s scrape interval.
func(delta model.Time) model.Time {
return delta + model.Time(rand.NormFloat64()*float64(delta)/100)
},
// Regular increments, but missing a scrape with 10% chance.
func(delta model.Time) model.Time {
i := rand.Intn(100)
if i < 90 {
return delta
}
if i < 99 {
return 2 * delta
}
return 3 * delta
// Ignoring the case with more than two missed scrapes in a row.
},
}
)
// Prefill result with two samples with colliding metrics (to test fingerprint mapping).
result := model.Samples{
&model.Sample{
Metric: model.Metric{
"instance": "ip-10-33-84-73.l05.ams5.s-cloud.net:24483",
"status": "503",
},
Value: 42,
Timestamp: timestamp,
},
&model.Sample{
Metric: model.Metric{
"instance": "ip-10-33-84-73.l05.ams5.s-cloud.net:24480",
"status": "500",
},
Value: 2010,
Timestamp: timestamp + 1,
},
}
metrics := []model.Metric{}
for n := rand.Intn(maxMetrics); n >= 0; n-- {
metrics = append(metrics, model.Metric{
model.MetricNameLabel: model.LabelValue(metricName),
model.LabelName(fmt.Sprintf("labelname_%d", n+1)): model.LabelValue(fmt.Sprintf("labelvalue_%d", rand.Int())),
})
}
for len(result) < minLen {
var (
// Pick a metric for this cycle.
metric = metrics[rand.Intn(len(metrics))]
timeDelta = model.Time(rand.Intn(maxTimeDelta) + 1)
generator = generators[rand.Intn(len(generators))]
createValue = generator.createValue
applyDelta = generator.applyDelta[rand.Intn(len(generator.applyDelta))]
incTimestamp = timestampIncrementers[rand.Intn(len(timestampIncrementers))]
)
switch rand.Intn(4) {
case 0: // A single sample.
result = append(result, &model.Sample{
Metric: metric,
Value: createValue(),
Timestamp: timestamp,
})
timestamp += incTimestamp(timeDelta)
case 1: // A streak of random sample values.
for n := rand.Intn(maxStreakLength); n >= 0; n-- {
result = append(result, &model.Sample{
Metric: metric,
Value: createValue(),
Timestamp: timestamp,
})
timestamp += incTimestamp(timeDelta)
}
case 2: // A streak of sample values with incremental changes.
value := createValue()
for n := rand.Intn(maxStreakLength); n >= 0; n-- {
result = append(result, &model.Sample{
Metric: metric,
Value: value,
Timestamp: timestamp,
})
timestamp += incTimestamp(timeDelta)
value = applyDelta(value)
}
case 3: // A streak of constant sample values.
value := createValue()
for n := rand.Intn(maxStreakLength); n >= 0; n-- {
result = append(result, &model.Sample{
Metric: metric,
Value: value,
Timestamp: timestamp,
})
timestamp += incTimestamp(timeDelta)
}
}
}
return result
}
func verifyStorageRandom(t testing.TB, s *MemorySeriesStorage, samples model.Samples) bool {
s.WaitForIndexing()
result := true
for _, i := range rand.Perm(len(samples)) {
sample := samples[i]
fp := s.mapper.mapFP(sample.Metric.FastFingerprint(), sample.Metric)
it := s.preloadChunksForInstant(fp, sample.Timestamp, sample.Timestamp)
found := it.ValueAtOrBeforeTime(sample.Timestamp)
startTime := it.(*boundedIterator).start
switch {
case found.Timestamp != model.Earliest && sample.Timestamp.Before(startTime):
t.Errorf("Sample #%d %#v: Expected outdated sample to be excluded.", i, sample)
result = false
case found.Timestamp == model.Earliest && !sample.Timestamp.Before(startTime):
t.Errorf("Sample #%d %#v: Expected sample not found.", i, sample)
result = false
case found.Timestamp == model.Earliest && sample.Timestamp.Before(startTime):
// All good. Outdated sample dropped.
case sample.Value != found.Value || sample.Timestamp != found.Timestamp:
t.Errorf(
"Sample #%d %#v: Value (or timestamp) mismatch, want %f (at time %v), got %f (at time %v).",
i, sample, sample.Value, sample.Timestamp, found.Value, found.Timestamp,
)
result = false
}
it.Close()
}
return result
}
func verifyStorageSequential(t testing.TB, s *MemorySeriesStorage, samples model.Samples) bool {
s.WaitForIndexing()
var (
result = true
fp model.Fingerprint
it SeriesIterator
r []model.SamplePair
j int
)
defer func() {
it.Close()
}()
for i, sample := range samples {
newFP := s.mapper.mapFP(sample.Metric.FastFingerprint(), sample.Metric)
if it == nil || newFP != fp {
fp = newFP
if it != nil {
it.Close()
}
it = s.preloadChunksForRange(fp, sample.Timestamp, model.Latest)
r = it.RangeValues(metric.Interval{
OldestInclusive: sample.Timestamp,
NewestInclusive: model.Latest,
})
j = -1
}
startTime := it.(*boundedIterator).start
if sample.Timestamp.Before(startTime) {
continue
}
j++
if j >= len(r) {
t.Errorf(
"Sample #%d %v not found.",
i, sample,
)
result = false
continue
}
found := r[j]
if sample.Value != found.Value || sample.Timestamp != found.Timestamp {
t.Errorf(
"Sample #%d %v: Value (or timestamp) mismatch, want %f (at time %v), got %f (at time %v).",
i, sample, sample.Value, sample.Timestamp, found.Value, found.Timestamp,
)
result = false
}
}
return result
}
func TestAppendOutOfOrder(t *testing.T) {
s, closer := NewTestStorage(t, 2)
defer closer.Close()
m := model.Metric{
model.MetricNameLabel: "out_of_order",
}
tests := []struct {
name string
timestamp model.Time
value model.SampleValue
wantErr error
}{
{
name: "1st sample",
timestamp: 0,
value: 0,
wantErr: nil,
},
{
name: "regular append",
timestamp: 2,
value: 1,
wantErr: nil,
},
{
name: "same timestamp, same value (no-op)",
timestamp: 2,
value: 1,
wantErr: nil,
},
{
name: "same timestamp, different value",
timestamp: 2,
value: 2,
wantErr: ErrDuplicateSampleForTimestamp,
},
{
name: "earlier timestamp, same value",
timestamp: 1,
value: 2,
wantErr: ErrOutOfOrderSample,
},
{
name: "earlier timestamp, different value",
timestamp: 1,
value: 3,
wantErr: ErrOutOfOrderSample,
},
{
name: "regular append of NaN",
timestamp: 3,
value: model.SampleValue(math.NaN()),
wantErr: nil,
},
{
name: "no-op append of NaN",
timestamp: 3,
value: model.SampleValue(math.NaN()),
wantErr: nil,
},
{
name: "append of NaN with earlier timestamp",
timestamp: 2,
value: model.SampleValue(math.NaN()),
wantErr: ErrOutOfOrderSample,
},
{
name: "append of normal sample after NaN with same timestamp",
timestamp: 3,
value: 3.14,
wantErr: ErrDuplicateSampleForTimestamp,
},
}
for _, test := range tests {
gotErr := s.Append(&model.Sample{
Metric: m,
Timestamp: test.timestamp,
Value: test.value,
})
if gotErr != test.wantErr {
t.Errorf("%s: got %q, want %q", test.name, gotErr, test.wantErr)
}
}
fp := s.mapper.mapFP(m.FastFingerprint(), m)
it := s.preloadChunksForRange(fp, 0, 2)
defer it.Close()
want := []model.SamplePair{
{
Timestamp: 0,
Value: 0,
},
{
Timestamp: 2,
Value: 1,
},
{
Timestamp: 3,
Value: model.SampleValue(math.NaN()),
},
}
got := it.RangeValues(metric.Interval{OldestInclusive: 0, NewestInclusive: 3})
// Note that we cannot just reflect.DeepEqual(want, got) because it has
// the semantics of NaN != NaN.
for i, gotSamplePair := range got {
wantSamplePair := want[i]
if !wantSamplePair.Equal(&gotSamplePair) {
t.Fatalf("want %v, got %v", wantSamplePair, gotSamplePair)
}
}
}