mirror of
https://github.com/prometheus/prometheus.git
synced 2024-09-20 15:57:31 -07:00
47e3c90f9b
Only return an error where callers are doing something with it except simply logging and ignoring. All the errors touched in this commit flag the storage as dirty anyway, and that fact is logged anyway. So most of what is being removed here is just log spam. As discussed earlier, the class of errors that flags the storage as dirty signals fundamental corruption, no even bubbling up a one-time warning to the user (e.g. about incomplete results) isn't helping much because _anything_ happening in the storage has to be doubted from that point on (and in fact retroactively into the past, too). Flagging the storage dirty, and alerting on it (plus marking the state in the web UI) is the only way I can see right now. As a byproduct, I cleaned up the setDirty method a bit and improved the logged errors.
1622 lines
44 KiB
Go
1622 lines
44 KiB
Go
// Copyright 2014 The Prometheus Authors
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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package local
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import (
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"fmt"
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"hash/fnv"
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"math/rand"
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"os"
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"reflect"
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"testing"
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"testing/quick"
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"time"
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"github.com/prometheus/common/log"
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"github.com/prometheus/common/model"
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"github.com/prometheus/prometheus/storage/metric"
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"github.com/prometheus/prometheus/util/testutil"
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)
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func TestMatches(t *testing.T) {
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storage, closer := NewTestStorage(t, 1)
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defer closer.Close()
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samples := make([]*model.Sample, 100)
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fingerprints := make(model.Fingerprints, 100)
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for i := range samples {
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metric := model.Metric{
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model.MetricNameLabel: model.LabelValue(fmt.Sprintf("test_metric_%d", i)),
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"label1": model.LabelValue(fmt.Sprintf("test_%d", i/10)),
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"label2": model.LabelValue(fmt.Sprintf("test_%d", (i+5)/10)),
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"all": "const",
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}
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samples[i] = &model.Sample{
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Metric: metric,
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Timestamp: model.Time(i),
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Value: model.SampleValue(i),
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}
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fingerprints[i] = metric.FastFingerprint()
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}
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for _, s := range samples {
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storage.Append(s)
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}
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storage.WaitForIndexing()
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newMatcher := func(matchType metric.MatchType, name model.LabelName, value model.LabelValue) *metric.LabelMatcher {
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lm, err := metric.NewLabelMatcher(matchType, name, value)
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if err != nil {
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t.Fatalf("error creating label matcher: %s", err)
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}
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return lm
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}
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var matcherTests = []struct {
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matchers metric.LabelMatchers
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expected model.Fingerprints
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}{
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{
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matchers: metric.LabelMatchers{newMatcher(metric.Equal, "label1", "x")},
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expected: model.Fingerprints{},
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},
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{
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matchers: metric.LabelMatchers{newMatcher(metric.Equal, "label1", "test_0")},
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expected: fingerprints[:10],
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},
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{
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matchers: metric.LabelMatchers{
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newMatcher(metric.Equal, "label1", "test_0"),
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newMatcher(metric.Equal, "label2", "test_1"),
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},
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expected: fingerprints[5:10],
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},
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{
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matchers: metric.LabelMatchers{
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newMatcher(metric.Equal, "all", "const"),
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newMatcher(metric.NotEqual, "label1", "x"),
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},
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expected: fingerprints,
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},
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{
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matchers: metric.LabelMatchers{
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newMatcher(metric.Equal, "all", "const"),
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newMatcher(metric.NotEqual, "label1", "test_0"),
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},
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expected: fingerprints[10:],
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},
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{
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matchers: metric.LabelMatchers{
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newMatcher(metric.Equal, "all", "const"),
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newMatcher(metric.NotEqual, "label1", "test_0"),
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newMatcher(metric.NotEqual, "label1", "test_1"),
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newMatcher(metric.NotEqual, "label1", "test_2"),
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},
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expected: fingerprints[30:],
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},
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{
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matchers: metric.LabelMatchers{
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newMatcher(metric.Equal, "label1", ""),
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},
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expected: fingerprints[:0],
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},
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{
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matchers: metric.LabelMatchers{
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newMatcher(metric.NotEqual, "label1", "test_0"),
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newMatcher(metric.Equal, "label1", ""),
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},
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expected: fingerprints[:0],
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},
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{
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matchers: metric.LabelMatchers{
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newMatcher(metric.NotEqual, "label1", "test_0"),
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newMatcher(metric.Equal, "label2", ""),
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},
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expected: fingerprints[:0],
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},
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{
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matchers: metric.LabelMatchers{
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newMatcher(metric.Equal, "all", "const"),
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newMatcher(metric.NotEqual, "label1", "test_0"),
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newMatcher(metric.Equal, "not_existant", ""),
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},
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expected: fingerprints[10:],
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},
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{
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matchers: metric.LabelMatchers{
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newMatcher(metric.RegexMatch, "label1", `test_[3-5]`),
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},
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expected: fingerprints[30:60],
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},
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{
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matchers: metric.LabelMatchers{
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newMatcher(metric.Equal, "all", "const"),
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newMatcher(metric.RegexNoMatch, "label1", `test_[3-5]`),
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},
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expected: append(append(model.Fingerprints{}, fingerprints[:30]...), fingerprints[60:]...),
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},
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{
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matchers: metric.LabelMatchers{
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newMatcher(metric.RegexMatch, "label1", `test_[3-5]`),
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newMatcher(metric.RegexMatch, "label2", `test_[4-6]`),
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},
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expected: fingerprints[35:60],
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},
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{
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matchers: metric.LabelMatchers{
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newMatcher(metric.RegexMatch, "label1", `test_[3-5]`),
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newMatcher(metric.NotEqual, "label2", `test_4`),
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},
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expected: append(append(model.Fingerprints{}, fingerprints[30:35]...), fingerprints[45:60]...),
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},
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{
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matchers: metric.LabelMatchers{
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newMatcher(metric.Equal, "label1", `nonexistent`),
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newMatcher(metric.RegexMatch, "label2", `test`),
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},
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expected: model.Fingerprints{},
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},
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{
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matchers: metric.LabelMatchers{
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newMatcher(metric.Equal, "label1", `test_0`),
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newMatcher(metric.RegexMatch, "label2", `nonexistent`),
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},
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expected: model.Fingerprints{},
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},
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}
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for _, mt := range matcherTests {
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res := storage.MetricsForLabelMatchers(
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model.Earliest, model.Latest,
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mt.matchers...,
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)
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if len(mt.expected) != len(res) {
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t.Fatalf("expected %d matches for %q, found %d", len(mt.expected), mt.matchers, len(res))
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}
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for fp1 := range res {
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found := false
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for _, fp2 := range mt.expected {
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if fp1 == fp2 {
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found = true
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break
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}
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}
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if !found {
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t.Errorf("expected fingerprint %s for %q not in result", fp1, mt.matchers)
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}
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}
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}
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}
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func TestFingerprintsForLabels(t *testing.T) {
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storage, closer := NewTestStorage(t, 1)
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defer closer.Close()
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samples := make([]*model.Sample, 100)
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fingerprints := make(model.Fingerprints, 100)
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for i := range samples {
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metric := model.Metric{
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model.MetricNameLabel: model.LabelValue(fmt.Sprintf("test_metric_%d", i)),
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"label1": model.LabelValue(fmt.Sprintf("test_%d", i/10)),
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"label2": model.LabelValue(fmt.Sprintf("test_%d", (i+5)/10)),
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}
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samples[i] = &model.Sample{
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Metric: metric,
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Timestamp: model.Time(i),
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Value: model.SampleValue(i),
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}
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fingerprints[i] = metric.FastFingerprint()
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}
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for _, s := range samples {
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storage.Append(s)
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}
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storage.WaitForIndexing()
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var matcherTests = []struct {
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pairs []model.LabelPair
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expected model.Fingerprints
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}{
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{
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pairs: []model.LabelPair{{"label1", "x"}},
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expected: fingerprints[:0],
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},
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{
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pairs: []model.LabelPair{{"label1", "test_0"}},
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expected: fingerprints[:10],
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},
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{
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pairs: []model.LabelPair{
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{"label1", "test_0"},
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{"label1", "test_1"},
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},
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expected: fingerprints[:0],
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},
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{
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pairs: []model.LabelPair{
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{"label1", "test_0"},
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{"label2", "test_1"},
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},
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expected: fingerprints[5:10],
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},
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{
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pairs: []model.LabelPair{
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{"label1", "test_1"},
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{"label2", "test_2"},
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},
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expected: fingerprints[15:20],
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},
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}
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for _, mt := range matcherTests {
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resfps := storage.fingerprintsForLabelPairs(mt.pairs...)
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if len(mt.expected) != len(resfps) {
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t.Fatalf("expected %d matches for %q, found %d", len(mt.expected), mt.pairs, len(resfps))
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}
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for fp1 := range resfps {
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found := false
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for _, fp2 := range mt.expected {
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if fp1 == fp2 {
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found = true
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break
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}
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}
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if !found {
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t.Errorf("expected fingerprint %s for %q not in result", fp1, mt.pairs)
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}
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}
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}
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}
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var benchLabelMatchingRes map[model.Fingerprint]metric.Metric
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func BenchmarkLabelMatching(b *testing.B) {
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s, closer := NewTestStorage(b, 1)
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defer closer.Close()
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h := fnv.New64a()
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lbl := func(x int) model.LabelValue {
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h.Reset()
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h.Write([]byte(fmt.Sprintf("%d", x)))
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return model.LabelValue(fmt.Sprintf("%d", h.Sum64()))
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}
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M := 32
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met := model.Metric{}
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for i := 0; i < M; i++ {
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met["label_a"] = lbl(i)
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for j := 0; j < M; j++ {
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met["label_b"] = lbl(j)
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for k := 0; k < M; k++ {
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met["label_c"] = lbl(k)
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for l := 0; l < M; l++ {
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met["label_d"] = lbl(l)
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s.Append(&model.Sample{
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Metric: met.Clone(),
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Timestamp: 0,
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Value: 1,
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})
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}
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}
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}
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}
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s.WaitForIndexing()
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newMatcher := func(matchType metric.MatchType, name model.LabelName, value model.LabelValue) *metric.LabelMatcher {
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lm, err := metric.NewLabelMatcher(matchType, name, value)
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if err != nil {
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b.Fatalf("error creating label matcher: %s", err)
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}
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return lm
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}
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var matcherTests = []metric.LabelMatchers{
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{
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newMatcher(metric.Equal, "label_a", lbl(1)),
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},
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{
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newMatcher(metric.Equal, "label_a", lbl(3)),
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newMatcher(metric.Equal, "label_c", lbl(3)),
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},
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{
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newMatcher(metric.Equal, "label_a", lbl(3)),
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newMatcher(metric.Equal, "label_c", lbl(3)),
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newMatcher(metric.NotEqual, "label_d", lbl(3)),
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},
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{
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newMatcher(metric.Equal, "label_a", lbl(3)),
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newMatcher(metric.Equal, "label_b", lbl(3)),
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newMatcher(metric.Equal, "label_c", lbl(3)),
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newMatcher(metric.NotEqual, "label_d", lbl(3)),
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},
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{
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newMatcher(metric.RegexMatch, "label_a", ".+"),
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},
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{
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newMatcher(metric.Equal, "label_a", lbl(3)),
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newMatcher(metric.RegexMatch, "label_a", ".+"),
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},
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{
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newMatcher(metric.Equal, "label_a", lbl(1)),
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newMatcher(metric.RegexMatch, "label_c", "("+lbl(3)+"|"+lbl(10)+")"),
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},
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{
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newMatcher(metric.Equal, "label_a", lbl(3)),
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newMatcher(metric.Equal, "label_a", lbl(4)),
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newMatcher(metric.RegexMatch, "label_c", "("+lbl(3)+"|"+lbl(10)+")"),
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},
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}
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b.ReportAllocs()
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b.ResetTimer()
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for i := 0; i < b.N; i++ {
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benchLabelMatchingRes = map[model.Fingerprint]metric.Metric{}
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for _, mt := range matcherTests {
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benchLabelMatchingRes = s.MetricsForLabelMatchers(
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model.Earliest, model.Latest,
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mt...,
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)
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}
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}
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// Stop timer to not count the storage closing.
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b.StopTimer()
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}
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func TestRetentionCutoff(t *testing.T) {
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now := model.Now()
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insertStart := now.Add(-2 * time.Hour)
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s, closer := NewTestStorage(t, 1)
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defer closer.Close()
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// Stop maintenance loop to prevent actual purging.
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close(s.loopStopping)
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<-s.loopStopped
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<-s.logThrottlingStopped
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// Recreate channel to avoid panic when we really shut down.
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s.loopStopping = make(chan struct{})
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s.dropAfter = 1 * time.Hour
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for i := 0; i < 120; i++ {
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smpl := &model.Sample{
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Metric: model.Metric{"job": "test"},
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Timestamp: insertStart.Add(time.Duration(i) * time.Minute), // 1 minute intervals.
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Value: 1,
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}
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s.Append(smpl)
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}
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s.WaitForIndexing()
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var fp model.Fingerprint
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for f := range s.fingerprintsForLabelPairs(model.LabelPair{Name: "job", Value: "test"}) {
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fp = f
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break
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}
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pl := s.NewPreloader()
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defer pl.Close()
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// Preload everything.
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it := pl.PreloadRange(fp, insertStart, now)
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val := it.ValueAtOrBeforeTime(now.Add(-61 * time.Minute))
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if val.Timestamp != model.Earliest {
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t.Errorf("unexpected result for timestamp before retention period")
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}
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vals := it.RangeValues(metric.Interval{OldestInclusive: insertStart, NewestInclusive: now})
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// We get 59 values here because the model.Now() is slightly later
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// than our now.
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if len(vals) != 59 {
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t.Errorf("expected 59 values but got %d", len(vals))
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}
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if expt := now.Add(-1 * time.Hour).Add(time.Minute); vals[0].Timestamp != expt {
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t.Errorf("unexpected timestamp for first sample: %v, expected %v", vals[0].Timestamp.Time(), expt.Time())
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}
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}
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func TestDropMetrics(t *testing.T) {
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now := model.Now()
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insertStart := now.Add(-2 * time.Hour)
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s, closer := NewTestStorage(t, 1)
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defer closer.Close()
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chunkFileExists := func(fp model.Fingerprint) (bool, error) {
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f, err := s.persistence.openChunkFileForReading(fp)
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if err == nil {
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f.Close()
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return true, nil
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}
|
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if os.IsNotExist(err) {
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return false, nil
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}
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return false, err
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}
|
|
|
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m1 := model.Metric{model.MetricNameLabel: "test", "n1": "v1"}
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m2 := model.Metric{model.MetricNameLabel: "test", "n1": "v2"}
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m3 := model.Metric{model.MetricNameLabel: "test", "n1": "v3"}
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|
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N := 120000
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|
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for j, m := range []model.Metric{m1, m2, m3} {
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for i := 0; i < N; i++ {
|
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smpl := &model.Sample{
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Metric: m,
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Timestamp: insertStart.Add(time.Duration(i) * time.Millisecond), // 1 millisecond intervals.
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Value: model.SampleValue(j),
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}
|
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s.Append(smpl)
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}
|
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}
|
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s.WaitForIndexing()
|
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|
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// Archive m3, but first maintain it so that at least something is written to disk.
|
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fpToBeArchived := m3.FastFingerprint()
|
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s.maintainMemorySeries(fpToBeArchived, 0)
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s.fpLocker.Lock(fpToBeArchived)
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s.fpToSeries.del(fpToBeArchived)
|
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s.persistence.archiveMetric(fpToBeArchived, m3, 0, insertStart.Add(time.Duration(N-1)*time.Millisecond))
|
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s.fpLocker.Unlock(fpToBeArchived)
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|
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fps := s.fingerprintsForLabelPairs(model.LabelPair{Name: model.MetricNameLabel, Value: "test"})
|
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if len(fps) != 3 {
|
|
t.Errorf("unexpected number of fingerprints: %d", len(fps))
|
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}
|
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|
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fpList := model.Fingerprints{m1.FastFingerprint(), m2.FastFingerprint(), fpToBeArchived}
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|
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s.DropMetricsForFingerprints(fpList[0])
|
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s.WaitForIndexing()
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|
|
fps2 := s.fingerprintsForLabelPairs(model.LabelPair{
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Name: model.MetricNameLabel, Value: "test",
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})
|
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if len(fps2) != 2 {
|
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t.Errorf("unexpected number of fingerprints: %d", len(fps2))
|
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}
|
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|
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_, it := s.preloadChunksForRange(fpList[0], model.Earliest, model.Latest)
|
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if vals := it.RangeValues(metric.Interval{OldestInclusive: insertStart, NewestInclusive: now}); len(vals) != 0 {
|
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t.Errorf("unexpected number of samples: %d", len(vals))
|
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}
|
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|
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_, it = s.preloadChunksForRange(fpList[1], model.Earliest, model.Latest)
|
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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])
|
|
}
|
|
|
|
s.DropMetricsForFingerprints(fpList...)
|
|
s.WaitForIndexing()
|
|
|
|
fps3 := s.fingerprintsForLabelPairs(model.LabelPair{
|
|
Name: model.MetricNameLabel, Value: "test",
|
|
})
|
|
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, 1)
|
|
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.fingerprintsForLabelPairs(model.LabelPair{Name: model.MetricNameLabel, Value: "test"})
|
|
if len(fps) != 3 {
|
|
t.Errorf("unexpected number of fingerprints: %d", len(fps))
|
|
}
|
|
|
|
pl := s.NewPreloader()
|
|
// This will access the corrupt file and lead to quarantining.
|
|
pl.PreloadInstant(fpToBeArchived, now.Add(-2*time.Hour), time.Minute)
|
|
pl.Close()
|
|
time.Sleep(time.Second) // Give time to quarantine. TODO(beorn7): Find a better way to wait.
|
|
s.WaitForIndexing()
|
|
|
|
fps2 := s.fingerprintsForLabelPairs(model.LabelPair{
|
|
Name: model.MetricNameLabel, Value: "test",
|
|
})
|
|
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.(*memorySeriesStorage).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)
|
|
|
|
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 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 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 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 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 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")
|
|
}
|
|
|
|
// 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")
|
|
}
|
|
}
|
|
|
|
func TestEvictAndPurgeSeriesChunkType0(t *testing.T) {
|
|
testEvictAndPurgeSeries(t, 0)
|
|
}
|
|
|
|
func TestEvictAndPurgeSeriesChunkType1(t *testing.T) {
|
|
testEvictAndPurgeSeries(t, 1)
|
|
}
|
|
|
|
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(50 * 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.
|
|
p := s.NewPreloader()
|
|
p.PreloadRange(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))
|
|
}
|
|
|
|
p.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)
|
|
}
|
|
|
|
// 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)
|
|
}
|
|
return verifyStorage(t, s, samples, 24*7*time.Hour)
|
|
}
|
|
|
|
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)
|
|
}
|
|
|
|
// 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)
|
|
}
|
|
verifyStorage(b, s.(*memorySeriesStorage), samples[:middle], o.PersistenceRetentionPeriod)
|
|
for _, sample := range samples[middle:end] {
|
|
s.Append(sample)
|
|
}
|
|
verifyStorage(b, s.(*memorySeriesStorage), samples[:end], o.PersistenceRetentionPeriod)
|
|
}
|
|
}
|
|
|
|
func BenchmarkFuzzChunkType0(b *testing.B) {
|
|
benchmarkFuzz(b, 0)
|
|
}
|
|
|
|
func BenchmarkFuzzChunkType1(b *testing.B) {
|
|
benchmarkFuzz(b, 1)
|
|
}
|
|
|
|
func createRandomSamples(metricName string, minLen int) model.Samples {
|
|
type valueCreator func() model.SampleValue
|
|
type deltaApplier func(model.SampleValue) model.SampleValue
|
|
|
|
var (
|
|
maxMetrics = 5
|
|
maxStreakLength = 500
|
|
maxTimeDelta = 10000
|
|
maxTimeDeltaFactor = 10
|
|
timestamp = model.Now() - model.Time(maxTimeDelta*maxTimeDeltaFactor*minLen/4) // 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())
|
|
},
|
|
},
|
|
},
|
|
}
|
|
)
|
|
|
|
// 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 {
|
|
// Pick a metric for this cycle.
|
|
metric := metrics[rand.Intn(len(metrics))]
|
|
timeDelta := rand.Intn(maxTimeDelta) + 1
|
|
generator := generators[rand.Intn(len(generators))]
|
|
createValue := generator.createValue
|
|
applyDelta := generator.applyDelta[rand.Intn(len(generator.applyDelta))]
|
|
incTimestamp := func() { timestamp += model.Time(timeDelta * (rand.Intn(maxTimeDeltaFactor) + 1)) }
|
|
switch rand.Intn(4) {
|
|
case 0: // A single sample.
|
|
result = append(result, &model.Sample{
|
|
Metric: metric,
|
|
Value: createValue(),
|
|
Timestamp: timestamp,
|
|
})
|
|
incTimestamp()
|
|
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,
|
|
})
|
|
incTimestamp()
|
|
}
|
|
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,
|
|
})
|
|
incTimestamp()
|
|
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,
|
|
})
|
|
incTimestamp()
|
|
}
|
|
}
|
|
}
|
|
|
|
return result
|
|
}
|
|
|
|
func verifyStorage(t testing.TB, s *memorySeriesStorage, samples model.Samples, maxAge time.Duration) bool {
|
|
s.WaitForIndexing()
|
|
result := true
|
|
for _, i := range rand.Perm(len(samples)) {
|
|
sample := samples[i]
|
|
if sample.Timestamp.Before(model.TimeFromUnixNano(time.Now().Add(-maxAge).UnixNano())) {
|
|
continue
|
|
// TODO: Once we have a guaranteed cutoff at the
|
|
// retention period, we can verify here that no results
|
|
// are returned.
|
|
}
|
|
fp, err := s.mapper.mapFP(sample.Metric.FastFingerprint(), sample.Metric)
|
|
if err != nil {
|
|
t.Fatal(err)
|
|
}
|
|
p := s.NewPreloader()
|
|
it := p.PreloadRange(fp, sample.Timestamp, sample.Timestamp)
|
|
found := it.ValueAtOrBeforeTime(sample.Timestamp)
|
|
if found.Timestamp == model.Earliest {
|
|
t.Errorf("Sample %#v: Expected sample not found.", sample)
|
|
result = false
|
|
p.Close()
|
|
continue
|
|
}
|
|
if sample.Value != found.Value || sample.Timestamp != found.Timestamp {
|
|
t.Errorf(
|
|
"Value (or timestamp) mismatch, want %f (at time %v), got %f (at time %v).",
|
|
sample.Value, sample.Timestamp, found.Value, found.Timestamp,
|
|
)
|
|
result = false
|
|
}
|
|
p.Close()
|
|
}
|
|
return result
|
|
}
|
|
|
|
func TestAppendOutOfOrder(t *testing.T) {
|
|
s, closer := NewTestStorage(t, 1)
|
|
defer closer.Close()
|
|
|
|
m := model.Metric{
|
|
model.MetricNameLabel: "out_of_order",
|
|
}
|
|
|
|
for i, t := range []int{0, 2, 2, 1} {
|
|
s.Append(&model.Sample{
|
|
Metric: m,
|
|
Timestamp: model.Time(t),
|
|
Value: model.SampleValue(i),
|
|
})
|
|
}
|
|
|
|
fp, err := s.mapper.mapFP(m.FastFingerprint(), m)
|
|
if err != nil {
|
|
t.Fatal(err)
|
|
}
|
|
|
|
pl := s.NewPreloader()
|
|
defer pl.Close()
|
|
|
|
it := pl.PreloadRange(fp, 0, 2)
|
|
|
|
want := []model.SamplePair{
|
|
{
|
|
Timestamp: 0,
|
|
Value: 0,
|
|
},
|
|
{
|
|
Timestamp: 2,
|
|
Value: 1,
|
|
},
|
|
}
|
|
got := it.RangeValues(metric.Interval{OldestInclusive: 0, NewestInclusive: 2})
|
|
if !reflect.DeepEqual(want, got) {
|
|
t.Fatalf("want %v, got %v", want, got)
|
|
}
|
|
}
|