mirror of
https://github.com/prometheus/prometheus.git
synced 2024-12-28 15:09:39 -08:00
60e2bb0135
Improve promtool tsdb analyze - Make it more suitable for variable size float chunks. - Add support for histogram chunks. --------- Signed-off-by: Ziqi Zhao <zhaoziqi9146@gmail.com> Signed-off-by: Levi Harrison <git@leviharrison.dev>
805 lines
20 KiB
Go
805 lines
20 KiB
Go
// Copyright 2017 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 main
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import (
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"bufio"
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"context"
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"fmt"
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"io"
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"os"
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"path/filepath"
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"runtime"
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"runtime/pprof"
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"strconv"
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"strings"
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"sync"
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"text/tabwriter"
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"time"
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"github.com/alecthomas/units"
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"github.com/go-kit/log"
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"golang.org/x/exp/slices"
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"github.com/prometheus/prometheus/model/labels"
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"github.com/prometheus/prometheus/promql/parser"
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"github.com/prometheus/prometheus/storage"
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"github.com/prometheus/prometheus/tsdb"
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"github.com/prometheus/prometheus/tsdb/chunkenc"
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"github.com/prometheus/prometheus/tsdb/chunks"
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tsdb_errors "github.com/prometheus/prometheus/tsdb/errors"
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"github.com/prometheus/prometheus/tsdb/fileutil"
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"github.com/prometheus/prometheus/tsdb/index"
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)
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const timeDelta = 30000
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type writeBenchmark struct {
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outPath string
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samplesFile string
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cleanup bool
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numMetrics int
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storage *tsdb.DB
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cpuprof *os.File
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memprof *os.File
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blockprof *os.File
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mtxprof *os.File
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logger log.Logger
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}
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func benchmarkWrite(outPath, samplesFile string, numMetrics, numScrapes int) error {
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b := &writeBenchmark{
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outPath: outPath,
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samplesFile: samplesFile,
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numMetrics: numMetrics,
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logger: log.NewLogfmtLogger(log.NewSyncWriter(os.Stderr)),
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}
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if b.outPath == "" {
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dir, err := os.MkdirTemp("", "tsdb_bench")
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if err != nil {
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return err
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}
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b.outPath = dir
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b.cleanup = true
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}
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if err := os.RemoveAll(b.outPath); err != nil {
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return err
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}
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if err := os.MkdirAll(b.outPath, 0o777); err != nil {
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return err
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}
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dir := filepath.Join(b.outPath, "storage")
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l := log.With(b.logger, "ts", log.DefaultTimestampUTC, "caller", log.DefaultCaller)
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st, err := tsdb.Open(dir, l, nil, &tsdb.Options{
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RetentionDuration: int64(15 * 24 * time.Hour / time.Millisecond),
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MinBlockDuration: int64(2 * time.Hour / time.Millisecond),
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}, tsdb.NewDBStats())
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if err != nil {
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return err
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}
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st.DisableCompactions()
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b.storage = st
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var lbs []labels.Labels
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if _, err = measureTime("readData", func() error {
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f, err := os.Open(b.samplesFile)
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if err != nil {
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return err
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}
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defer f.Close()
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lbs, err = readPrometheusLabels(f, b.numMetrics)
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if err != nil {
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return err
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}
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return nil
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}); err != nil {
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return err
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}
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var total uint64
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dur, err := measureTime("ingestScrapes", func() error {
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if err := b.startProfiling(); err != nil {
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return err
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}
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total, err = b.ingestScrapes(lbs, numScrapes)
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if err != nil {
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return err
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}
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return nil
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})
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if err != nil {
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return err
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}
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fmt.Println(" > total samples:", total)
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fmt.Println(" > samples/sec:", float64(total)/dur.Seconds())
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if _, err = measureTime("stopStorage", func() error {
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if err := b.storage.Close(); err != nil {
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return err
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}
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return b.stopProfiling()
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}); err != nil {
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return err
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}
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return nil
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}
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func (b *writeBenchmark) ingestScrapes(lbls []labels.Labels, scrapeCount int) (uint64, error) {
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var mu sync.Mutex
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var total uint64
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for i := 0; i < scrapeCount; i += 100 {
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var wg sync.WaitGroup
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lbls := lbls
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for len(lbls) > 0 {
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l := 1000
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if len(lbls) < 1000 {
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l = len(lbls)
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}
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batch := lbls[:l]
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lbls = lbls[l:]
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wg.Add(1)
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go func() {
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n, err := b.ingestScrapesShard(batch, 100, int64(timeDelta*i))
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if err != nil {
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// exitWithError(err)
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fmt.Println(" err", err)
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}
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mu.Lock()
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total += n
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mu.Unlock()
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wg.Done()
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}()
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}
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wg.Wait()
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}
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fmt.Println("ingestion completed")
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return total, nil
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}
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func (b *writeBenchmark) ingestScrapesShard(lbls []labels.Labels, scrapeCount int, baset int64) (uint64, error) {
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ts := baset
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type sample struct {
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labels labels.Labels
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value int64
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ref *storage.SeriesRef
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}
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scrape := make([]*sample, 0, len(lbls))
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for _, m := range lbls {
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scrape = append(scrape, &sample{
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labels: m,
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value: 123456789,
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})
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}
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total := uint64(0)
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for i := 0; i < scrapeCount; i++ {
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app := b.storage.Appender(context.TODO())
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ts += timeDelta
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for _, s := range scrape {
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s.value += 1000
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var ref storage.SeriesRef
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if s.ref != nil {
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ref = *s.ref
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}
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ref, err := app.Append(ref, s.labels, ts, float64(s.value))
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if err != nil {
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panic(err)
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}
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if s.ref == nil {
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s.ref = &ref
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}
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total++
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}
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if err := app.Commit(); err != nil {
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return total, err
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}
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}
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return total, nil
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}
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func (b *writeBenchmark) startProfiling() error {
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var err error
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// Start CPU profiling.
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b.cpuprof, err = os.Create(filepath.Join(b.outPath, "cpu.prof"))
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if err != nil {
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return fmt.Errorf("bench: could not create cpu profile: %w", err)
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}
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if err := pprof.StartCPUProfile(b.cpuprof); err != nil {
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return fmt.Errorf("bench: could not start CPU profile: %w", err)
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}
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// Start memory profiling.
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b.memprof, err = os.Create(filepath.Join(b.outPath, "mem.prof"))
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if err != nil {
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return fmt.Errorf("bench: could not create memory profile: %w", err)
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}
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runtime.MemProfileRate = 64 * 1024
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// Start fatal profiling.
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b.blockprof, err = os.Create(filepath.Join(b.outPath, "block.prof"))
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if err != nil {
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return fmt.Errorf("bench: could not create block profile: %w", err)
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}
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runtime.SetBlockProfileRate(20)
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b.mtxprof, err = os.Create(filepath.Join(b.outPath, "mutex.prof"))
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if err != nil {
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return fmt.Errorf("bench: could not create mutex profile: %w", err)
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}
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runtime.SetMutexProfileFraction(20)
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return nil
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}
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func (b *writeBenchmark) stopProfiling() error {
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if b.cpuprof != nil {
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pprof.StopCPUProfile()
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b.cpuprof.Close()
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b.cpuprof = nil
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}
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if b.memprof != nil {
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if err := pprof.Lookup("heap").WriteTo(b.memprof, 0); err != nil {
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return fmt.Errorf("error writing mem profile: %w", err)
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}
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b.memprof.Close()
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b.memprof = nil
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}
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if b.blockprof != nil {
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if err := pprof.Lookup("block").WriteTo(b.blockprof, 0); err != nil {
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return fmt.Errorf("error writing block profile: %w", err)
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}
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b.blockprof.Close()
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b.blockprof = nil
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runtime.SetBlockProfileRate(0)
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}
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if b.mtxprof != nil {
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if err := pprof.Lookup("mutex").WriteTo(b.mtxprof, 0); err != nil {
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return fmt.Errorf("error writing mutex profile: %w", err)
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}
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b.mtxprof.Close()
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b.mtxprof = nil
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runtime.SetMutexProfileFraction(0)
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}
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return nil
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}
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func measureTime(stage string, f func() error) (time.Duration, error) {
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fmt.Printf(">> start stage=%s\n", stage)
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start := time.Now()
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if err := f(); err != nil {
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return 0, err
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}
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fmt.Printf(">> completed stage=%s duration=%s\n", stage, time.Since(start))
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return time.Since(start), nil
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}
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func readPrometheusLabels(r io.Reader, n int) ([]labels.Labels, error) {
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scanner := bufio.NewScanner(r)
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var mets []labels.Labels
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hashes := map[uint64]struct{}{}
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i := 0
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for scanner.Scan() && i < n {
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m := make([]labels.Label, 0, 10)
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r := strings.NewReplacer("\"", "", "{", "", "}", "")
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s := r.Replace(scanner.Text())
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labelChunks := strings.Split(s, ",")
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for _, labelChunk := range labelChunks {
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split := strings.Split(labelChunk, ":")
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m = append(m, labels.Label{Name: split[0], Value: split[1]})
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}
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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.
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h := ml.Hash()
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if _, ok := hashes[h]; ok {
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continue
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}
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mets = append(mets, ml)
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hashes[h] = struct{}{}
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i++
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}
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return mets, nil
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}
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func listBlocks(path string, humanReadable bool) error {
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db, err := tsdb.OpenDBReadOnly(path, nil)
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if err != nil {
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return err
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}
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defer func() {
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err = tsdb_errors.NewMulti(err, db.Close()).Err()
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}()
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blocks, err := db.Blocks()
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if err != nil {
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return err
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}
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printBlocks(blocks, true, humanReadable)
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return nil
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}
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func printBlocks(blocks []tsdb.BlockReader, writeHeader, humanReadable bool) {
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tw := tabwriter.NewWriter(os.Stdout, 13, 0, 2, ' ', 0)
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defer tw.Flush()
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if writeHeader {
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fmt.Fprintln(tw, "BLOCK ULID\tMIN TIME\tMAX TIME\tDURATION\tNUM SAMPLES\tNUM CHUNKS\tNUM SERIES\tSIZE")
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}
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for _, b := range blocks {
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meta := b.Meta()
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fmt.Fprintf(tw,
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"%v\t%v\t%v\t%v\t%v\t%v\t%v\t%v\n",
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meta.ULID,
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getFormatedTime(meta.MinTime, humanReadable),
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getFormatedTime(meta.MaxTime, humanReadable),
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time.Duration(meta.MaxTime-meta.MinTime)*time.Millisecond,
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meta.Stats.NumSamples,
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meta.Stats.NumChunks,
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meta.Stats.NumSeries,
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getFormatedBytes(b.Size(), humanReadable),
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)
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}
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}
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func getFormatedTime(timestamp int64, humanReadable bool) string {
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if humanReadable {
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return time.Unix(timestamp/1000, 0).UTC().String()
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}
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return strconv.FormatInt(timestamp, 10)
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}
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func getFormatedBytes(bytes int64, humanReadable bool) string {
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if humanReadable {
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return units.Base2Bytes(bytes).String()
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}
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return strconv.FormatInt(bytes, 10)
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}
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func openBlock(path, blockID string) (*tsdb.DBReadOnly, tsdb.BlockReader, error) {
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db, err := tsdb.OpenDBReadOnly(path, nil)
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if err != nil {
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return nil, nil, err
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}
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if blockID == "" {
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blockID, err = db.LastBlockID()
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if err != nil {
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return nil, nil, err
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}
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}
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b, err := db.Block(blockID)
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if err != nil {
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return nil, nil, err
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}
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return db, b, nil
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}
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func analyzeBlock(ctx context.Context, path, blockID string, limit int, runExtended bool, matchers string) error {
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var (
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selectors []*labels.Matcher
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err error
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)
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if len(matchers) > 0 {
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selectors, err = parser.ParseMetricSelector(matchers)
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if err != nil {
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return err
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}
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}
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db, block, err := openBlock(path, blockID)
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if err != nil {
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return err
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}
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defer func() {
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err = tsdb_errors.NewMulti(err, db.Close()).Err()
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}()
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meta := block.Meta()
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fmt.Printf("Block ID: %s\n", meta.ULID)
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// Presume 1ms resolution that Prometheus uses.
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fmt.Printf("Duration: %s\n", (time.Duration(meta.MaxTime-meta.MinTime) * 1e6).String())
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fmt.Printf("Total Series: %d\n", meta.Stats.NumSeries)
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if len(matchers) > 0 {
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fmt.Printf("Matcher: %s\n", matchers)
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}
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ir, err := block.Index()
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if err != nil {
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return err
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}
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defer ir.Close()
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allLabelNames, err := ir.LabelNames(ctx, selectors...)
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if err != nil {
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return err
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}
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fmt.Printf("Label names: %d\n", len(allLabelNames))
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type postingInfo struct {
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key string
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metric uint64
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}
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postingInfos := []postingInfo{}
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printInfo := func(postingInfos []postingInfo) {
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slices.SortFunc(postingInfos, func(a, b postingInfo) int { return int(b.metric) - int(a.metric) })
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for i, pc := range postingInfos {
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if i >= limit {
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break
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}
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fmt.Printf("%d %s\n", pc.metric, pc.key)
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}
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}
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labelsUncovered := map[string]uint64{}
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labelpairsUncovered := map[string]uint64{}
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labelpairsCount := map[string]uint64{}
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entries := 0
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var (
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p index.Postings
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refs []storage.SeriesRef
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)
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if len(matchers) > 0 {
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p, err = tsdb.PostingsForMatchers(ctx, ir, selectors...)
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if err != nil {
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return err
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}
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// Expand refs first and cache in memory.
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// So later we don't have to expand again.
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refs, err = index.ExpandPostings(p)
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if err != nil {
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return err
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}
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fmt.Printf("Matched series: %d\n", len(refs))
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p = index.NewListPostings(refs)
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} else {
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p, err = ir.Postings(ctx, "", "") // The special all key.
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if err != nil {
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return err
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}
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}
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chks := []chunks.Meta{}
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builder := labels.ScratchBuilder{}
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for p.Next() {
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if err = ir.Series(p.At(), &builder, &chks); err != nil {
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return err
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}
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// Amount of the block time range not covered by this series.
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uncovered := uint64(meta.MaxTime-meta.MinTime) - uint64(chks[len(chks)-1].MaxTime-chks[0].MinTime)
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builder.Labels().Range(func(lbl labels.Label) {
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key := lbl.Name + "=" + lbl.Value
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labelsUncovered[lbl.Name] += uncovered
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labelpairsUncovered[key] += uncovered
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labelpairsCount[key]++
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entries++
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})
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}
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if p.Err() != nil {
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return p.Err()
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}
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fmt.Printf("Postings (unique label pairs): %d\n", len(labelpairsUncovered))
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fmt.Printf("Postings entries (total label pairs): %d\n", entries)
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postingInfos = postingInfos[:0]
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for k, m := range labelpairsUncovered {
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postingInfos = append(postingInfos, postingInfo{k, uint64(float64(m) / float64(meta.MaxTime-meta.MinTime))})
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}
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fmt.Printf("\nLabel pairs most involved in churning:\n")
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printInfo(postingInfos)
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postingInfos = postingInfos[:0]
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for k, m := range labelsUncovered {
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postingInfos = append(postingInfos, postingInfo{k, uint64(float64(m) / float64(meta.MaxTime-meta.MinTime))})
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}
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fmt.Printf("\nLabel names most involved in churning:\n")
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printInfo(postingInfos)
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postingInfos = postingInfos[:0]
|
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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
|
|
}
|
|
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, err := chunkr.Chunk(chk)
|
|
if err != nil {
|
|
return err
|
|
}
|
|
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 fmt.Errorf("chunk is not FloatHistogramChunk")
|
|
}
|
|
it := fhchk.Iterator(nil)
|
|
bucketCount := 0
|
|
for it.Next() == chunkenc.ValFloatHistogram {
|
|
_, f := it.AtFloatHistogram()
|
|
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 fmt.Errorf("chunk is not HistogramChunk")
|
|
}
|
|
it := hchk.Iterator(nil)
|
|
bucketCount := 0
|
|
for it.Next() == chunkenc.ValHistogram {
|
|
_, f := it.AtHistogram()
|
|
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
|
|
}
|
|
|
|
func dumpSamples(ctx context.Context, path string, mint, maxt int64, match string) (err error) {
|
|
db, err := tsdb.OpenDBReadOnly(path, 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()
|
|
|
|
matchers, err := parser.ParseMetricSelector(match)
|
|
if err != nil {
|
|
return err
|
|
}
|
|
ss := q.Select(ctx, false, nil, matchers...)
|
|
|
|
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()
|
|
fmt.Printf("%s %s %d\n", lbs, fh.String(), ts)
|
|
}
|
|
for it.Next() == chunkenc.ValHistogram {
|
|
ts, h := it.AtHistogram()
|
|
fmt.Printf("%s %s %d\n", lbs, h.String(), ts)
|
|
}
|
|
if it.Err() != nil {
|
|
return ss.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 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) 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))
|
|
}
|
|
|
|
func displayHistogram(dataType string, datas []int, total int) {
|
|
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(fmt.Sprintf("%d", end)))
|
|
maxRightLen := strconv.Itoa(len(fmt.Sprintf("%d", end+step)))
|
|
maxCountLen := strconv.Itoa(len(fmt.Sprintf("%d", 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(min, max int) (start, end, step int) {
|
|
s := (max - min) / 10
|
|
|
|
step = 10
|
|
for step < s && step <= 10000 {
|
|
step *= 10
|
|
}
|
|
|
|
start = min - min%step
|
|
end = max - max%step + step
|
|
|
|
return
|
|
}
|