prometheus/storage/metric/stochastic_test.go

// Copyright 2013 Prometheus Team
// 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 metric

import (
	"fmt"
	"github.com/prometheus/prometheus/coding"
	"github.com/prometheus/prometheus/coding/indexable"
	"github.com/prometheus/prometheus/model"
	dto "github.com/prometheus/prometheus/model/generated"
	"github.com/prometheus/prometheus/utility/test"
	"math"
	"math/rand"
	"testing"
	"testing/quick"
	"time"
)

const (
	stochasticMaximumVariance = 8
)

func BasicLifecycleTests(p MetricPersistence, t test.Tester) {
	if p == nil {
		t.Errorf("Received nil Metric Persistence.\n")
		return
	}
}

func ReadEmptyTests(p MetricPersistence, t test.Tester) {
	hasLabelPair := func(x int) (success bool) {
		name := model.LabelName(string(x))
		value := model.LabelValue(string(x))

		labelSet := model.LabelSet{
			name: value,
		}

		fingerprints, err := p.GetFingerprintsForLabelSet(labelSet)
		if err != nil {
			t.Error(err)
			return
		}

		success = len(fingerprints) == 0
		if !success {
			t.Errorf("unexpected fingerprint length %d, got %d", 0, len(fingerprints))
		}

		return
	}

	err := quick.Check(hasLabelPair, nil)
	if err != nil {
		t.Error(err)
		return
	}

	hasLabelName := func(x int) (success bool) {
		labelName := model.LabelName(string(x))

		fingerprints, err := p.GetFingerprintsForLabelName(labelName)
		if err != nil {
			t.Error(err)
			return
		}

		success = len(fingerprints) == 0
		if !success {
			t.Errorf("unexpected fingerprint length %d, got %d", 0, len(fingerprints))
		}

		return
	}

	err = quick.Check(hasLabelName, nil)
	if err != nil {
		t.Error(err)
		return
	}
}

func AppendSampleAsPureSparseAppendTests(p MetricPersistence, t test.Tester) {
	appendSample := func(x int) (success bool) {
		v := model.SampleValue(x)
		ts := time.Unix(int64(x), int64(x))
		labelName := model.LabelName(x)
		labelValue := model.LabelValue(x)
		l := model.Metric{labelName: labelValue}

		sample := model.Sample{
			Value:     v,
			Timestamp: ts,
			Metric:    l,
		}

		err := p.AppendSample(sample)

		success = err == nil
		if !success {
			t.Error(err)
		}

		return
	}

	if err := quick.Check(appendSample, nil); err != nil {
		t.Error(err)
	}
}

func AppendSampleAsSparseAppendWithReadsTests(p MetricPersistence, t test.Tester) {
	appendSample := func(x int) (success bool) {
		v := model.SampleValue(x)
		ts := time.Unix(int64(x), int64(x))
		labelName := model.LabelName(x)
		labelValue := model.LabelValue(x)
		l := model.Metric{labelName: labelValue}

		sample := model.Sample{
			Value:     v,
			Timestamp: ts,
			Metric:    l,
		}

		err := p.AppendSample(sample)
		if err != nil {
			t.Error(err)
			return
		}

		fingerprints, err := p.GetFingerprintsForLabelName(labelName)
		if err != nil {
			t.Error(err)
			return
		}
		if len(fingerprints) != 1 {
			t.Errorf("expected fingerprint count of %d, got %d", 1, len(fingerprints))
			return
		}

		fingerprints, err = p.GetFingerprintsForLabelSet(model.LabelSet{
			labelName: labelValue,
		})
		if err != nil {
			t.Error(err)
			return
		}
		if len(fingerprints) != 1 {
			t.Errorf("expected fingerprint count of %d, got %d", 1, len(fingerprints))
			return
		}

		return true
	}

	if err := quick.Check(appendSample, nil); err != nil {
		t.Error(err)
	}
}

func AppendSampleAsPureSingleEntityAppendTests(p MetricPersistence, t test.Tester) {
	appendSample := func(x int) bool {
		sample := model.Sample{
			Value:     model.SampleValue(x),
			Timestamp: time.Unix(int64(x), 0),
			Metric:    model.Metric{model.MetricNameLabel: "my_metric"},
		}

		err := p.AppendSample(sample)

		return err == nil
	}

	if err := quick.Check(appendSample, nil); err != nil {
		t.Error(err)
	}
}

func levelDBGetRangeValues(l *LevelDBMetricPersistence, fp model.Fingerprint, i model.Interval) (samples []model.SamplePair, err error) {
	begin := time.Now()

	defer func() {
		duration := time.Since(begin)

		recordOutcome(duration, err, map[string]string{operation: getRangeValues, result: success}, map[string]string{operation: getRangeValues, result: failure})
	}()

	k := &dto.SampleKey{
		Fingerprint: fp.ToDTO(),
		Timestamp:   indexable.EncodeTime(i.OldestInclusive),
	}

	e, err := coding.NewProtocolBuffer(k).Encode()
	if err != nil {
		return
	}

	iterator := l.metricSamples.NewIterator(true)
	defer iterator.Close()

	predicate := keyIsOlderThan(i.NewestInclusive)

	for valid := iterator.Seek(e); valid; valid = iterator.Next() {
		retrievedKey := &dto.SampleKey{}

		retrievedKey, err = extractSampleKey(iterator)
		if err != nil {
			return
		}

		if predicate(retrievedKey) {
			break
		}

		if !fingerprintsEqual(retrievedKey.Fingerprint, k.Fingerprint) {
			break
		}

		retrievedValue, err := extractSampleValues(iterator)
		if err != nil {
			return nil, err
		}

		samples = append(samples, model.SamplePair{
			Value:     model.SampleValue(*retrievedValue.Value[0].Value),
			Timestamp: indexable.DecodeTime(retrievedKey.Timestamp),
		})
	}

	return
}

func StochasticTests(persistenceMaker func() (MetricPersistence, test.Closer), t test.Tester) {
	stochastic := func(x int) (success bool) {
		p, closer := persistenceMaker()
		defer closer.Close()
		defer p.Close()

		seed := rand.NewSource(int64(x))
		random := rand.New(seed)

		numberOfMetrics := random.Intn(stochasticMaximumVariance) + 1
		numberOfSharedLabels := random.Intn(stochasticMaximumVariance)
		numberOfUnsharedLabels := random.Intn(stochasticMaximumVariance)
		numberOfSamples := random.Intn(stochasticMaximumVariance) + 2
		numberOfRangeScans := random.Intn(stochasticMaximumVariance)

		metricTimestamps := map[int]map[int64]bool{}
		metricEarliestSample := map[int]int64{}
		metricNewestSample := map[int]int64{}

		for metricIndex := 0; metricIndex < numberOfMetrics; metricIndex++ {
			sample := model.Sample{
				Metric: model.Metric{},
			}

			v := model.LabelValue(fmt.Sprintf("metric_index_%d", metricIndex))
			sample.Metric[model.MetricNameLabel] = v

			for sharedLabelIndex := 0; sharedLabelIndex < numberOfSharedLabels; sharedLabelIndex++ {
				l := model.LabelName(fmt.Sprintf("shared_label_%d", sharedLabelIndex))
				v := model.LabelValue(fmt.Sprintf("label_%d", sharedLabelIndex))

				sample.Metric[l] = v
			}

			for unsharedLabelIndex := 0; unsharedLabelIndex < numberOfUnsharedLabels; unsharedLabelIndex++ {
				l := model.LabelName(fmt.Sprintf("metric_index_%d_private_label_%d", metricIndex, unsharedLabelIndex))
				v := model.LabelValue(fmt.Sprintf("private_label_%d", unsharedLabelIndex))

				sample.Metric[l] = v
			}

			timestamps := map[int64]bool{}
			metricTimestamps[metricIndex] = timestamps
			var (
				newestSample  int64 = math.MinInt64
				oldestSample  int64 = math.MaxInt64
				nextTimestamp func() int64
			)

			nextTimestamp = func() int64 {
				var candidate int64
				candidate = random.Int63n(math.MaxInt32 - 1)

				if _, has := timestamps[candidate]; has {
					// WART
					candidate = nextTimestamp()
				}

				timestamps[candidate] = true

				if candidate < oldestSample {
					oldestSample = candidate
				}

				if candidate > newestSample {
					newestSample = candidate
				}

				return candidate
			}

			for sampleIndex := 0; sampleIndex < numberOfSamples; sampleIndex++ {
				sample.Timestamp = time.Unix(nextTimestamp(), 0)
				sample.Value = model.SampleValue(sampleIndex)

				err := p.AppendSample(sample)

				if err != nil {
					t.Error(err)
					return
				}
			}

			metricEarliestSample[metricIndex] = oldestSample
			metricNewestSample[metricIndex] = newestSample

			for sharedLabelIndex := 0; sharedLabelIndex < numberOfSharedLabels; sharedLabelIndex++ {
				labelPair := model.LabelSet{
					model.LabelName(fmt.Sprintf("shared_label_%d", sharedLabelIndex)): model.LabelValue(fmt.Sprintf("label_%d", sharedLabelIndex)),
				}

				fingerprints, err := p.GetFingerprintsForLabelSet(labelPair)
				if err != nil {
					t.Error(err)
					return
				}
				if len(fingerprints) == 0 {
					t.Errorf("expected fingerprint count of %d, got %d", 0, len(fingerprints))
					return
				}

				labelName := model.LabelName(fmt.Sprintf("shared_label_%d", sharedLabelIndex))
				fingerprints, err = p.GetFingerprintsForLabelName(labelName)
				if err != nil {
					t.Error(err)
					return
				}
				if len(fingerprints) == 0 {
					t.Errorf("expected fingerprint count of %d, got %d", 0, len(fingerprints))
					return
				}
			}
		}

		for sharedIndex := 0; sharedIndex < numberOfSharedLabels; sharedIndex++ {
			labelName := model.LabelName(fmt.Sprintf("shared_label_%d", sharedIndex))
			fingerprints, err := p.GetFingerprintsForLabelName(labelName)
			if err != nil {
				t.Error(err)
				return
			}

			if len(fingerprints) != numberOfMetrics {
				t.Errorf("expected fingerprint count of %d, got %d", numberOfMetrics, len(fingerprints))
				return
			}
		}

		for metricIndex := 0; metricIndex < numberOfMetrics; metricIndex++ {
			for unsharedLabelIndex := 0; unsharedLabelIndex < numberOfUnsharedLabels; unsharedLabelIndex++ {
				labelName := model.LabelName(fmt.Sprintf("metric_index_%d_private_label_%d", metricIndex, unsharedLabelIndex))
				labelValue := model.LabelValue(fmt.Sprintf("private_label_%d", unsharedLabelIndex))
				labelSet := model.LabelSet{
					labelName: labelValue,
				}

				fingerprints, err := p.GetFingerprintsForLabelSet(labelSet)
				if err != nil {
					t.Error(err)
					return
				}
				if len(fingerprints) != 1 {
					t.Errorf("expected fingerprint count of %d, got %d", 1, len(fingerprints))
					return
				}

				fingerprints, err = p.GetFingerprintsForLabelName(labelName)
				if err != nil {
					t.Error(err)
					return
				}
				if len(fingerprints) != 1 {
					t.Errorf("expected fingerprint count of %d, got %d", 1, len(fingerprints))
					return
				}
			}

			metric := model.Metric{}
			metric[model.MetricNameLabel] = model.LabelValue(fmt.Sprintf("metric_index_%d", metricIndex))

			for i := 0; i < numberOfSharedLabels; i++ {
				l := model.LabelName(fmt.Sprintf("shared_label_%d", i))
				v := model.LabelValue(fmt.Sprintf("label_%d", i))

				metric[l] = v
			}

			for i := 0; i < numberOfUnsharedLabels; i++ {
				l := model.LabelName(fmt.Sprintf("metric_index_%d_private_label_%d", metricIndex, i))
				v := model.LabelValue(fmt.Sprintf("private_label_%d", i))

				metric[l] = v
			}

			for i := 0; i < numberOfRangeScans; i++ {
				timestamps := metricTimestamps[metricIndex]

				var first int64 = 0
				var second int64 = 0

				for {
					firstCandidate := random.Int63n(int64(len(timestamps)))
					secondCandidate := random.Int63n(int64(len(timestamps)))

					smallest := int64(-1)
					largest := int64(-1)

					if firstCandidate == secondCandidate {
						continue
					} else if firstCandidate > secondCandidate {
						largest = firstCandidate
						smallest = secondCandidate
					} else {
						largest = secondCandidate
						smallest = firstCandidate
					}

					j := int64(0)
					for i := range timestamps {
						if j == smallest {
							first = i
						} else if j == largest {
							second = i
							break
						}
						j++
					}

					break
				}

				begin := first
				end := second

				if second < first {
					begin, end = second, first
				}

				interval := model.Interval{
					OldestInclusive: time.Unix(begin, 0),
					NewestInclusive: time.Unix(end, 0),
				}

				samples := []model.SamplePair{}
				fp := model.NewFingerprintFromMetric(metric)
				switch persistence := p.(type) {
				case *LevelDBMetricPersistence:
					var err error
					samples, err = levelDBGetRangeValues(persistence, fp, interval)
					if err != nil {
						t.Fatal(err)
						return
					}
				default:
					samples = p.GetRangeValues(fp, interval)
				}

				if len(samples) < 2 {
					t.Errorf("expected sample count less than %d, got %d", 2, len(samples))
					return
				}
			}
		}

		return true
	}

	if err := quick.Check(stochastic, nil); err != nil {
		t.Error(err)
	}
}

// Test Definitions Follow

var testLevelDBBasicLifecycle = buildLevelDBTestPersistence("basic_lifecycle", BasicLifecycleTests)

func TestLevelDBBasicLifecycle(t *testing.T) {
	testLevelDBBasicLifecycle(t)
}

func BenchmarkLevelDBBasicLifecycle(b *testing.B) {
	for i := 0; i < b.N; i++ {
		testLevelDBBasicLifecycle(b)
	}
}

var testLevelDBReadEmpty = buildLevelDBTestPersistence("read_empty", ReadEmptyTests)

func TestLevelDBReadEmpty(t *testing.T) {
	testLevelDBReadEmpty(t)
}

func BenchmarkLevelDBReadEmpty(b *testing.B) {
	for i := 0; i < b.N; i++ {
		testLevelDBReadEmpty(b)
	}
}

var testLevelDBAppendSampleAsPureSparseAppend = buildLevelDBTestPersistence("append_sample_as_pure_sparse_append", AppendSampleAsPureSparseAppendTests)

func TestLevelDBAppendSampleAsPureSparseAppend(t *testing.T) {
	testLevelDBAppendSampleAsPureSparseAppend(t)
}

func BenchmarkLevelDBAppendSampleAsPureSparseAppend(b *testing.B) {
	for i := 0; i < b.N; i++ {
		testLevelDBAppendSampleAsPureSparseAppend(b)
	}
}

var testLevelDBAppendSampleAsSparseAppendWithReads = buildLevelDBTestPersistence("append_sample_as_sparse_append_with_reads", AppendSampleAsSparseAppendWithReadsTests)

func TestLevelDBAppendSampleAsSparseAppendWithReads(t *testing.T) {
	testLevelDBAppendSampleAsSparseAppendWithReads(t)
}

func BenchmarkLevelDBAppendSampleAsSparseAppendWithReads(b *testing.B) {
	for i := 0; i < b.N; i++ {
		testLevelDBAppendSampleAsSparseAppendWithReads(b)
	}
}

var testLevelDBAppendSampleAsPureSingleEntityAppend = buildLevelDBTestPersistence("append_sample_as_pure_single_entity_append", AppendSampleAsPureSingleEntityAppendTests)

func TestLevelDBAppendSampleAsPureSingleEntityAppend(t *testing.T) {
	testLevelDBAppendSampleAsPureSingleEntityAppend(t)
}

func BenchmarkLevelDBAppendSampleAsPureSingleEntityAppend(b *testing.B) {
	for i := 0; i < b.N; i++ {
		testLevelDBAppendSampleAsPureSingleEntityAppend(b)
	}
}

func testLevelDBStochastic(t test.Tester) {
	persistenceMaker := func() (MetricPersistence, test.Closer) {
		temporaryDirectory := test.NewTemporaryDirectory("test_leveldb_stochastic", t)

		p, err := NewLevelDBMetricPersistence(temporaryDirectory.Path())
		if err != nil {
			t.Errorf("Could not start up LevelDB: %q\n", err)
		}

		return p, temporaryDirectory
	}

	StochasticTests(persistenceMaker, t)
}

func TestLevelDBStochastic(t *testing.T) {
	testLevelDBStochastic(t)
}

func BenchmarkLevelDBStochastic(b *testing.B) {
	for i := 0; i < b.N; i++ {
		testLevelDBStochastic(b)
	}
}

var testMemoryBasicLifecycle = buildMemoryTestPersistence(BasicLifecycleTests)

func TestMemoryBasicLifecycle(t *testing.T) {
	testMemoryBasicLifecycle(t)
}

func BenchmarkMemoryBasicLifecycle(b *testing.B) {
	for i := 0; i < b.N; i++ {
		testMemoryBasicLifecycle(b)
	}
}

var testMemoryReadEmpty = buildMemoryTestPersistence(ReadEmptyTests)

func TestMemoryReadEmpty(t *testing.T) {
	testMemoryReadEmpty(t)
}

func BenchmarkMemoryReadEmpty(b *testing.B) {
	for i := 0; i < b.N; i++ {
		testMemoryReadEmpty(b)
	}
}

var testMemoryAppendSampleAsPureSparseAppend = buildMemoryTestPersistence(AppendSampleAsPureSparseAppendTests)

func TestMemoryAppendSampleAsPureSparseAppend(t *testing.T) {
	testMemoryAppendSampleAsPureSparseAppend(t)
}

func BenchmarkMemoryAppendSampleAsPureSparseAppend(b *testing.B) {
	for i := 0; i < b.N; i++ {
		testMemoryAppendSampleAsPureSparseAppend(b)
	}
}

var testMemoryAppendSampleAsSparseAppendWithReads = buildMemoryTestPersistence(AppendSampleAsSparseAppendWithReadsTests)

func TestMemoryAppendSampleAsSparseAppendWithReads(t *testing.T) {
	testMemoryAppendSampleAsSparseAppendWithReads(t)
}

func BenchmarkMemoryAppendSampleAsSparseAppendWithReads(b *testing.B) {
	for i := 0; i < b.N; i++ {
		testMemoryAppendSampleAsSparseAppendWithReads(b)
	}
}

var testMemoryAppendSampleAsPureSingleEntityAppend = buildMemoryTestPersistence(AppendSampleAsPureSingleEntityAppendTests)

func TestMemoryAppendSampleAsPureSingleEntityAppend(t *testing.T) {
	testMemoryAppendSampleAsPureSingleEntityAppend(t)
}

func BenchmarkMemoryAppendSampleAsPureSingleEntityAppend(b *testing.B) {
	for i := 0; i < b.N; i++ {
		testMemoryAppendSampleAsPureSingleEntityAppend(b)
	}
}

func testMemoryStochastic(t test.Tester) {
	persistenceMaker := func() (MetricPersistence, test.Closer) {
		return NewMemorySeriesStorage(), test.NilCloser
	}

	StochasticTests(persistenceMaker, t)
}

func TestMemoryStochastic(t *testing.T) {
	testMemoryStochastic(t)
}

func BenchmarkMemoryStochastic(b *testing.B) {
	for i := 0; i < b.N; i++ {
		testMemoryStochastic(b)
	}
}