// Copyright 2017 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 main import ( "bufio" "bytes" "context" "errors" "fmt" "io" "log/slog" "os" "path/filepath" "runtime" "runtime/pprof" "slices" "strconv" "strings" "sync" "text/tabwriter" "time" "github.com/alecthomas/units" "go.uber.org/atomic" "github.com/prometheus/common/promslog" "github.com/prometheus/prometheus/model/labels" "github.com/prometheus/prometheus/promql/parser" "github.com/prometheus/prometheus/storage" "github.com/prometheus/prometheus/tsdb" "github.com/prometheus/prometheus/tsdb/chunkenc" "github.com/prometheus/prometheus/tsdb/chunks" tsdb_errors "github.com/prometheus/prometheus/tsdb/errors" "github.com/prometheus/prometheus/tsdb/fileutil" "github.com/prometheus/prometheus/tsdb/index" ) const timeDelta = 30000 type writeBenchmark struct { outPath string samplesFile string cleanup bool numMetrics int storage *tsdb.DB cpuprof *os.File memprof *os.File blockprof *os.File mtxprof *os.File logger *slog.Logger } func benchmarkWrite(outPath, samplesFile string, numMetrics, numScrapes int) error { b := &writeBenchmark{ outPath: outPath, samplesFile: samplesFile, numMetrics: numMetrics, logger: promslog.New(&promslog.Config{}), } if b.outPath == "" { dir, err := os.MkdirTemp("", "tsdb_bench") if err != nil { return err } b.outPath = dir b.cleanup = true } if err := os.RemoveAll(b.outPath); err != nil { return err } if err := os.MkdirAll(b.outPath, 0o777); err != nil { return err } dir := filepath.Join(b.outPath, "storage") st, err := tsdb.Open(dir, b.logger, nil, &tsdb.Options{ RetentionDuration: int64(15 * 24 * time.Hour / time.Millisecond), MinBlockDuration: int64(2 * time.Hour / time.Millisecond), }, tsdb.NewDBStats()) if err != nil { return err } st.DisableCompactions() b.storage = st var lbs []labels.Labels if _, err = measureTime("readData", func() error { f, err := os.Open(b.samplesFile) if err != nil { return err } defer f.Close() lbs, err = readPrometheusLabels(f, b.numMetrics) if err != nil { return err } return nil }); err != nil { return err } var total uint64 dur, err := measureTime("ingestScrapes", func() error { if err := b.startProfiling(); err != nil { return err } total, err = b.ingestScrapes(lbs, numScrapes) if err != nil { return err } return nil }) if err != nil { return err } fmt.Println(" > total samples:", total) fmt.Println(" > samples/sec:", float64(total)/dur.Seconds()) if _, err = measureTime("stopStorage", func() error { if err := b.storage.Close(); err != nil { return err } return b.stopProfiling() }); err != nil { return err } return nil } func (b *writeBenchmark) ingestScrapes(lbls []labels.Labels, scrapeCount int) (uint64, error) { var total atomic.Uint64 for i := 0; i < scrapeCount; i += 100 { var wg sync.WaitGroup lbls := lbls for len(lbls) > 0 { l := 1000 if len(lbls) < 1000 { l = len(lbls) } batch := lbls[:l] lbls = lbls[l:] wg.Add(1) go func() { defer wg.Done() n, err := b.ingestScrapesShard(batch, 100, int64(timeDelta*i)) if err != nil { // exitWithError(err) fmt.Println(" err", err) } total.Add(n) }() } wg.Wait() } fmt.Println("ingestion completed") return total.Load(), nil } func (b *writeBenchmark) ingestScrapesShard(lbls []labels.Labels, scrapeCount int, baset int64) (uint64, error) { ts := baset type sample struct { labels labels.Labels value int64 ref *storage.SeriesRef } scrape := make([]*sample, 0, len(lbls)) for _, m := range lbls { scrape = append(scrape, &sample{ labels: m, value: 123456789, }) } total := uint64(0) for i := 0; i < scrapeCount; i++ { app := b.storage.Appender(context.TODO()) ts += timeDelta for _, s := range scrape { s.value += 1000 var ref storage.SeriesRef if s.ref != nil { ref = *s.ref } ref, err := app.Append(ref, s.labels, ts, float64(s.value)) if err != nil { panic(err) } if s.ref == nil { s.ref = &ref } total++ } if err := app.Commit(); err != nil { return total, err } } return total, nil } func (b *writeBenchmark) startProfiling() error { var err error // Start CPU profiling. b.cpuprof, err = os.Create(filepath.Join(b.outPath, "cpu.prof")) if err != nil { return fmt.Errorf("bench: could not create cpu profile: %w", err) } if err := pprof.StartCPUProfile(b.cpuprof); err != nil { return fmt.Errorf("bench: could not start CPU profile: %w", err) } // Start memory profiling. b.memprof, err = os.Create(filepath.Join(b.outPath, "mem.prof")) if err != nil { return fmt.Errorf("bench: could not create memory profile: %w", err) } runtime.MemProfileRate = 64 * 1024 // Start fatal profiling. b.blockprof, err = os.Create(filepath.Join(b.outPath, "block.prof")) if err != nil { return fmt.Errorf("bench: could not create block profile: %w", err) } runtime.SetBlockProfileRate(20) b.mtxprof, err = os.Create(filepath.Join(b.outPath, "mutex.prof")) if err != nil { return fmt.Errorf("bench: could not create mutex profile: %w", err) } runtime.SetMutexProfileFraction(20) return nil } func (b *writeBenchmark) stopProfiling() error { if b.cpuprof != nil { pprof.StopCPUProfile() b.cpuprof.Close() b.cpuprof = nil } if b.memprof != nil { if err := pprof.Lookup("heap").WriteTo(b.memprof, 0); err != nil { return fmt.Errorf("error writing mem profile: %w", err) } b.memprof.Close() b.memprof = nil } if b.blockprof != nil { if err := pprof.Lookup("block").WriteTo(b.blockprof, 0); err != nil { return fmt.Errorf("error writing block profile: %w", err) } b.blockprof.Close() b.blockprof = nil runtime.SetBlockProfileRate(0) } if b.mtxprof != nil { if err := pprof.Lookup("mutex").WriteTo(b.mtxprof, 0); err != nil { return fmt.Errorf("error writing mutex profile: %w", err) } b.mtxprof.Close() b.mtxprof = nil runtime.SetMutexProfileFraction(0) } return nil } func measureTime(stage string, f func() error) (time.Duration, error) { fmt.Printf(">> start stage=%s\n", stage) start := time.Now() if err := f(); err != nil { return 0, err } fmt.Printf(">> completed stage=%s duration=%s\n", stage, time.Since(start)) return time.Since(start), nil } func readPrometheusLabels(r io.Reader, n int) ([]labels.Labels, error) { scanner := bufio.NewScanner(r) var mets []labels.Labels hashes := map[uint64]struct{}{} i := 0 for scanner.Scan() && i < n { m := make([]labels.Label, 0, 10) r := strings.NewReplacer("\"", "", "{", "", "}", "") s := r.Replace(scanner.Text()) labelChunks := strings.Split(s, ",") for _, labelChunk := range labelChunks { split := strings.Split(labelChunk, ":") m = append(m, labels.Label{Name: split[0], Value: split[1]}) } ml := labels.New(m...) // This sorts by name - order of the k/v labels matters, don't assume we'll always receive them already sorted. h := ml.Hash() if _, ok := hashes[h]; ok { continue } mets = append(mets, ml) hashes[h] = struct{}{} i++ } return mets, nil } func listBlocks(path string, humanReadable bool) error { db, err := tsdb.OpenDBReadOnly(path, "", nil) if err != nil { return err } defer func() { err = tsdb_errors.NewMulti(err, db.Close()).Err() }() blocks, err := db.Blocks() if err != nil { return err } printBlocks(blocks, true, humanReadable) return nil } func printBlocks(blocks []tsdb.BlockReader, writeHeader, humanReadable bool) { tw := tabwriter.NewWriter(os.Stdout, 13, 0, 2, ' ', 0) defer tw.Flush() if writeHeader { fmt.Fprintln(tw, "BLOCK ULID\tMIN TIME\tMAX TIME\tDURATION\tNUM SAMPLES\tNUM CHUNKS\tNUM SERIES\tSIZE") } for _, b := range blocks { meta := b.Meta() fmt.Fprintf(tw, "%v\t%v\t%v\t%v\t%v\t%v\t%v\t%v\n", meta.ULID, getFormattedTime(meta.MinTime, humanReadable), getFormattedTime(meta.MaxTime, humanReadable), time.Duration(meta.MaxTime-meta.MinTime)*time.Millisecond, meta.Stats.NumSamples, meta.Stats.NumChunks, meta.Stats.NumSeries, getFormattedBytes(b.Size(), humanReadable), ) } } func getFormattedTime(timestamp int64, humanReadable bool) string { if humanReadable { return time.Unix(timestamp/1000, 0).UTC().String() } return strconv.FormatInt(timestamp, 10) } func getFormattedBytes(bytes int64, humanReadable bool) string { if humanReadable { return units.Base2Bytes(bytes).String() } return strconv.FormatInt(bytes, 10) } func openBlock(path, blockID string) (*tsdb.DBReadOnly, tsdb.BlockReader, error) { db, err := tsdb.OpenDBReadOnly(path, "", nil) if err != nil { return nil, nil, err } if blockID == "" { blockID, err = db.LastBlockID() if err != nil { return nil, nil, err } } b, err := db.Block(blockID, tsdb.DefaultPostingsDecoderFactory) if err != nil { return nil, nil, err } return db, b, nil } func analyzeBlock(ctx context.Context, path, blockID string, limit int, runExtended bool, matchers string) error { var ( selectors []*labels.Matcher err error ) if len(matchers) > 0 { selectors, err = parser.ParseMetricSelector(matchers) if err != nil { return err } } db, block, err := openBlock(path, blockID) if err != nil { return err } defer func() { err = tsdb_errors.NewMulti(err, db.Close()).Err() }() meta := block.Meta() fmt.Printf("Block ID: %s\n", meta.ULID) // Presume 1ms resolution that Prometheus uses. fmt.Printf("Duration: %s\n", (time.Duration(meta.MaxTime-meta.MinTime) * 1e6).String()) fmt.Printf("Total Series: %d\n", meta.Stats.NumSeries) if len(matchers) > 0 { fmt.Printf("Matcher: %s\n", matchers) } ir, err := block.Index() if err != nil { return err } defer ir.Close() allLabelNames, err := ir.LabelNames(ctx, selectors...) if err != nil { return err } fmt.Printf("Label names: %d\n", len(allLabelNames)) type postingInfo struct { key string metric uint64 } postingInfos := []postingInfo{} printInfo := func(postingInfos []postingInfo) { slices.SortFunc(postingInfos, func(a, b postingInfo) int { switch { case b.metric < a.metric: return -1 case b.metric > a.metric: return 1 default: return 0 } }) for i, pc := range postingInfos { if i >= limit { break } fmt.Printf("%d %s\n", pc.metric, pc.key) } } labelsUncovered := map[string]uint64{} labelpairsUncovered := map[string]uint64{} labelpairsCount := map[string]uint64{} entries := 0 var ( p index.Postings refs []storage.SeriesRef ) if len(matchers) > 0 { p, err = tsdb.PostingsForMatchers(ctx, ir, selectors...) if err != nil { return err } // Expand refs first and cache in memory. // So later we don't have to expand again. refs, err = index.ExpandPostings(p) if err != nil { return err } fmt.Printf("Matched series: %d\n", len(refs)) p = index.NewListPostings(refs) } else { p, err = ir.Postings(ctx, "", "") // The special all key. if err != nil { return err } } chks := []chunks.Meta{} builder := labels.ScratchBuilder{} for p.Next() { if err = ir.Series(p.At(), &builder, &chks); err != nil { return err } // Amount of the block time range not covered by this series. uncovered := uint64(meta.MaxTime-meta.MinTime) - uint64(chks[len(chks)-1].MaxTime-chks[0].MinTime) builder.Labels().Range(func(lbl labels.Label) { key := lbl.Name + "=" + lbl.Value labelsUncovered[lbl.Name] += uncovered labelpairsUncovered[key] += uncovered labelpairsCount[key]++ entries++ }) } if p.Err() != nil { return p.Err() } fmt.Printf("Postings (unique label pairs): %d\n", len(labelpairsUncovered)) fmt.Printf("Postings entries (total label pairs): %d\n", entries) postingInfos = postingInfos[:0] for k, m := range labelpairsUncovered { postingInfos = append(postingInfos, postingInfo{k, uint64(float64(m) / float64(meta.MaxTime-meta.MinTime))}) } fmt.Printf("\nLabel pairs most involved in churning:\n") printInfo(postingInfos) postingInfos = postingInfos[:0] for k, m := range labelsUncovered { postingInfos = append(postingInfos, postingInfo{k, uint64(float64(m) / float64(meta.MaxTime-meta.MinTime))}) } fmt.Printf("\nLabel names most involved in churning:\n") printInfo(postingInfos) postingInfos = postingInfos[:0] for k, m := range labelpairsCount { postingInfos = append(postingInfos, postingInfo{k, m}) } fmt.Printf("\nMost common label pairs:\n") printInfo(postingInfos) postingInfos = postingInfos[:0] for _, n := range allLabelNames { values, err := ir.SortedLabelValues(ctx, n, selectors...) if err != nil { return err } var cumulativeLength uint64 for _, str := range values { cumulativeLength += uint64(len(str)) } postingInfos = append(postingInfos, postingInfo{n, cumulativeLength}) } fmt.Printf("\nLabel names with highest cumulative label value length:\n") printInfo(postingInfos) postingInfos = postingInfos[:0] for _, n := range allLabelNames { lv, err := ir.SortedLabelValues(ctx, n, selectors...) if err != nil { return err } postingInfos = append(postingInfos, postingInfo{n, uint64(len(lv))}) } fmt.Printf("\nHighest cardinality labels:\n") printInfo(postingInfos) postingInfos = postingInfos[:0] lv, err := ir.SortedLabelValues(ctx, "__name__", selectors...) if err != nil { return err } for _, n := range lv { postings, err := ir.Postings(ctx, "__name__", n) if err != nil { return err } // Only intersect postings if matchers are specified. if len(matchers) > 0 { postings = index.Intersect(postings, index.NewListPostings(refs)) } count := 0 for postings.Next() { count++ } if postings.Err() != nil { return postings.Err() } postingInfos = append(postingInfos, postingInfo{n, uint64(count)}) } fmt.Printf("\nHighest cardinality metric names:\n") printInfo(postingInfos) if runExtended { return analyzeCompaction(ctx, block, ir, selectors) } return nil } func analyzeCompaction(ctx context.Context, block tsdb.BlockReader, indexr tsdb.IndexReader, matchers []*labels.Matcher) (err error) { var postingsr index.Postings if len(matchers) > 0 { postingsr, err = tsdb.PostingsForMatchers(ctx, indexr, matchers...) } else { n, v := index.AllPostingsKey() postingsr, err = indexr.Postings(ctx, n, v) } if err != nil { return err } chunkr, err := block.Chunks() if err != nil { return err } defer func() { err = tsdb_errors.NewMulti(err, chunkr.Close()).Err() }() totalChunks := 0 floatChunkSamplesCount := make([]int, 0) floatChunkSize := make([]int, 0) histogramChunkSamplesCount := make([]int, 0) histogramChunkSize := make([]int, 0) histogramChunkBucketsCount := make([]int, 0) var builder labels.ScratchBuilder for postingsr.Next() { var chks []chunks.Meta if err := indexr.Series(postingsr.At(), &builder, &chks); err != nil { return err } for _, chk := range chks { // Load the actual data of the chunk. chk, iterable, err := chunkr.ChunkOrIterable(chk) if err != nil { return err } // Chunks within blocks should not need to be re-written, so an // iterable is not expected to be returned from the chunk reader. if iterable != nil { return errors.New("ChunkOrIterable should not return an iterable when reading a block") } switch chk.Encoding() { case chunkenc.EncXOR: floatChunkSamplesCount = append(floatChunkSamplesCount, chk.NumSamples()) floatChunkSize = append(floatChunkSize, len(chk.Bytes())) case chunkenc.EncFloatHistogram: histogramChunkSamplesCount = append(histogramChunkSamplesCount, chk.NumSamples()) histogramChunkSize = append(histogramChunkSize, len(chk.Bytes())) fhchk, ok := chk.(*chunkenc.FloatHistogramChunk) if !ok { return errors.New("chunk is not FloatHistogramChunk") } it := fhchk.Iterator(nil) bucketCount := 0 for it.Next() == chunkenc.ValFloatHistogram { _, f := it.AtFloatHistogram(nil) bucketCount += len(f.PositiveBuckets) bucketCount += len(f.NegativeBuckets) } histogramChunkBucketsCount = append(histogramChunkBucketsCount, bucketCount) case chunkenc.EncHistogram: histogramChunkSamplesCount = append(histogramChunkSamplesCount, chk.NumSamples()) histogramChunkSize = append(histogramChunkSize, len(chk.Bytes())) hchk, ok := chk.(*chunkenc.HistogramChunk) if !ok { return errors.New("chunk is not HistogramChunk") } it := hchk.Iterator(nil) bucketCount := 0 for it.Next() == chunkenc.ValHistogram { _, f := it.AtHistogram(nil) bucketCount += len(f.PositiveBuckets) bucketCount += len(f.NegativeBuckets) } histogramChunkBucketsCount = append(histogramChunkBucketsCount, bucketCount) } totalChunks++ } } fmt.Printf("\nCompaction analysis:\n") fmt.Println() displayHistogram("samples per float chunk", floatChunkSamplesCount, totalChunks) displayHistogram("bytes per float chunk", floatChunkSize, totalChunks) displayHistogram("samples per histogram chunk", histogramChunkSamplesCount, totalChunks) displayHistogram("bytes per histogram chunk", histogramChunkSize, totalChunks) displayHistogram("buckets per histogram chunk", histogramChunkBucketsCount, totalChunks) return nil } type SeriesSetFormatter func(series storage.SeriesSet) error func dumpSamples(ctx context.Context, dbDir, sandboxDirRoot string, mint, maxt int64, match []string, formatter SeriesSetFormatter) (err error) { db, err := tsdb.OpenDBReadOnly(dbDir, sandboxDirRoot, nil) if err != nil { return err } defer func() { err = tsdb_errors.NewMulti(err, db.Close()).Err() }() q, err := db.Querier(mint, maxt) if err != nil { return err } defer q.Close() matcherSets, err := parser.ParseMetricSelectors(match) if err != nil { return err } var ss storage.SeriesSet if len(matcherSets) > 1 { var sets []storage.SeriesSet for _, mset := range matcherSets { sets = append(sets, q.Select(ctx, true, nil, mset...)) } ss = storage.NewMergeSeriesSet(sets, 0, storage.ChainedSeriesMerge) } else { ss = q.Select(ctx, false, nil, matcherSets[0]...) } err = formatter(ss) if err != nil { return err } if ws := ss.Warnings(); len(ws) > 0 { return tsdb_errors.NewMulti(ws.AsErrors()...).Err() } if ss.Err() != nil { return ss.Err() } return nil } func formatSeriesSet(ss storage.SeriesSet) error { for ss.Next() { series := ss.At() lbs := series.Labels() it := series.Iterator(nil) for it.Next() == chunkenc.ValFloat { ts, val := it.At() fmt.Printf("%s %g %d\n", lbs, val, ts) } for it.Next() == chunkenc.ValFloatHistogram { ts, fh := it.AtFloatHistogram(nil) fmt.Printf("%s %s %d\n", lbs, fh.String(), ts) } for it.Next() == chunkenc.ValHistogram { ts, h := it.AtHistogram(nil) fmt.Printf("%s %s %d\n", lbs, h.String(), ts) } if it.Err() != nil { return ss.Err() } } return nil } // CondensedString is labels.Labels.String() without spaces after the commas. func CondensedString(ls labels.Labels) string { var b bytes.Buffer b.WriteByte('{') i := 0 ls.Range(func(l labels.Label) { if i > 0 { b.WriteByte(',') } b.WriteString(l.Name) b.WriteByte('=') b.WriteString(strconv.Quote(l.Value)) i++ }) b.WriteByte('}') return b.String() } func formatSeriesSetOpenMetrics(ss storage.SeriesSet) error { for ss.Next() { series := ss.At() lbs := series.Labels() metricName := lbs.Get(labels.MetricName) lbs = lbs.DropMetricName() it := series.Iterator(nil) for it.Next() == chunkenc.ValFloat { ts, val := it.At() fmt.Printf("%s%s %g %.3f\n", metricName, CondensedString(lbs), val, float64(ts)/1000) } if it.Err() != nil { return ss.Err() } } fmt.Println("# EOF") return nil } func checkErr(err error) int { if err != nil { fmt.Fprintln(os.Stderr, err) return 1 } return 0 } func backfillOpenMetrics(path, outputDir string, humanReadable, quiet bool, maxBlockDuration time.Duration, customLabels map[string]string) int { inputFile, err := fileutil.OpenMmapFile(path) if err != nil { return checkErr(err) } defer inputFile.Close() if err := os.MkdirAll(outputDir, 0o777); err != nil { return checkErr(fmt.Errorf("create output dir: %w", err)) } return checkErr(backfill(5000, inputFile.Bytes(), outputDir, humanReadable, quiet, maxBlockDuration, customLabels)) } func displayHistogram(dataType string, datas []int, total int) { if len(datas) == 0 { fmt.Printf("%s: N/A\n\n", dataType) return } slices.Sort(datas) start, end, step := generateBucket(datas[0], datas[len(datas)-1]) sum := 0 buckets := make([]int, (end-start)/step+1) maxCount := 0 for _, c := range datas { sum += c buckets[(c-start)/step]++ if buckets[(c-start)/step] > maxCount { maxCount = buckets[(c-start)/step] } } avg := sum / len(datas) fmt.Printf("%s (min/avg/max): %d/%d/%d\n", dataType, datas[0], avg, datas[len(datas)-1]) maxLeftLen := strconv.Itoa(len(strconv.Itoa(end))) maxRightLen := strconv.Itoa(len(strconv.Itoa(end + step))) maxCountLen := strconv.Itoa(len(strconv.Itoa(maxCount))) for bucket, count := range buckets { percentage := 100.0 * count / total fmt.Printf("[%"+maxLeftLen+"d, %"+maxRightLen+"d]: %"+maxCountLen+"d %s\n", bucket*step+start+1, (bucket+1)*step+start, count, strings.Repeat("#", percentage)) } fmt.Println() } func generateBucket(minVal, maxVal int) (start, end, step int) { s := (maxVal - minVal) / 10 step = 10 for step < s && step <= 10000 { step *= 10 } start = minVal - minVal%step end = maxVal - maxVal%step + step return }