enhance promtool tsdb analyze command (#12869)
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>
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@ -18,7 +18,6 @@ import (
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"context"
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"fmt"
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"io"
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"math"
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"os"
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"path/filepath"
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"runtime"
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@ -620,10 +619,12 @@ func analyzeCompaction(ctx context.Context, block tsdb.BlockReader, indexr tsdb.
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err = tsdb_errors.NewMulti(err, chunkr.Close()).Err()
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}()
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const maxSamplesPerChunk = 120
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nBuckets := 10
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histogram := make([]int, nBuckets)
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totalChunks := 0
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floatChunkSamplesCount := make([]int, 0)
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floatChunkSize := make([]int, 0)
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histogramChunkSamplesCount := make([]int, 0)
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histogramChunkSize := make([]int, 0)
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histogramChunkBucketsCount := make([]int, 0)
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var builder labels.ScratchBuilder
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for postingsr.Next() {
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var chks []chunks.Meta
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@ -637,26 +638,56 @@ func analyzeCompaction(ctx context.Context, block tsdb.BlockReader, indexr tsdb.
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if err != nil {
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return err
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}
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chunkSize := math.Min(float64(chk.NumSamples()), maxSamplesPerChunk)
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// Calculate the bucket for the chunk and increment it in the histogram.
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bucket := int(math.Ceil(float64(nBuckets)*chunkSize/maxSamplesPerChunk)) - 1
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histogram[bucket]++
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switch chk.Encoding() {
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case chunkenc.EncXOR:
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floatChunkSamplesCount = append(floatChunkSamplesCount, chk.NumSamples())
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floatChunkSize = append(floatChunkSize, len(chk.Bytes()))
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case chunkenc.EncFloatHistogram:
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histogramChunkSamplesCount = append(histogramChunkSamplesCount, chk.NumSamples())
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histogramChunkSize = append(histogramChunkSize, len(chk.Bytes()))
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fhchk, ok := chk.(*chunkenc.FloatHistogramChunk)
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if !ok {
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return fmt.Errorf("chunk is not FloatHistogramChunk")
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}
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it := fhchk.Iterator(nil)
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bucketCount := 0
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for it.Next() == chunkenc.ValFloatHistogram {
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_, f := it.AtFloatHistogram()
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bucketCount += len(f.PositiveBuckets)
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bucketCount += len(f.NegativeBuckets)
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}
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histogramChunkBucketsCount = append(histogramChunkBucketsCount, bucketCount)
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case chunkenc.EncHistogram:
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histogramChunkSamplesCount = append(histogramChunkSamplesCount, chk.NumSamples())
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histogramChunkSize = append(histogramChunkSize, len(chk.Bytes()))
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hchk, ok := chk.(*chunkenc.HistogramChunk)
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if !ok {
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return fmt.Errorf("chunk is not HistogramChunk")
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}
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it := hchk.Iterator(nil)
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bucketCount := 0
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for it.Next() == chunkenc.ValHistogram {
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_, f := it.AtHistogram()
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bucketCount += len(f.PositiveBuckets)
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bucketCount += len(f.NegativeBuckets)
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}
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histogramChunkBucketsCount = append(histogramChunkBucketsCount, bucketCount)
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}
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totalChunks++
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}
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}
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fmt.Printf("\nCompaction analysis:\n")
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fmt.Println("Fullness: Amount of samples in chunks (100% is 120 samples)")
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// Normalize absolute counts to percentages and print them out.
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for bucket, count := range histogram {
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percentage := 100.0 * count / totalChunks
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fmt.Printf("%7d%%: ", (bucket+1)*10)
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for j := 0; j < percentage; j++ {
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fmt.Printf("#")
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}
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fmt.Println()
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}
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fmt.Println()
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displayHistogram("samples per float chunk", floatChunkSamplesCount, totalChunks)
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displayHistogram("bytes per float chunk", floatChunkSize, totalChunks)
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displayHistogram("samples per histogram chunk", histogramChunkSamplesCount, totalChunks)
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displayHistogram("bytes per histogram chunk", histogramChunkSize, totalChunks)
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displayHistogram("buckets per histogram chunk", histogramChunkBucketsCount, totalChunks)
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return nil
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}
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@ -732,3 +763,42 @@ func backfillOpenMetrics(path, outputDir string, humanReadable, quiet bool, maxB
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return checkErr(backfill(5000, inputFile.Bytes(), outputDir, humanReadable, quiet, maxBlockDuration))
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}
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func displayHistogram(dataType string, datas []int, total int) {
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slices.Sort(datas)
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start, end, step := generateBucket(datas[0], datas[len(datas)-1])
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sum := 0
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buckets := make([]int, (end-start)/step+1)
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maxCount := 0
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for _, c := range datas {
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sum += c
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buckets[(c-start)/step]++
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if buckets[(c-start)/step] > maxCount {
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maxCount = buckets[(c-start)/step]
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}
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}
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avg := sum / len(datas)
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fmt.Printf("%s (min/avg/max): %d/%d/%d\n", dataType, datas[0], avg, datas[len(datas)-1])
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maxLeftLen := strconv.Itoa(len(fmt.Sprintf("%d", end)))
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maxRightLen := strconv.Itoa(len(fmt.Sprintf("%d", end+step)))
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maxCountLen := strconv.Itoa(len(fmt.Sprintf("%d", maxCount)))
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for bucket, count := range buckets {
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percentage := 100.0 * count / total
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fmt.Printf("[%"+maxLeftLen+"d, %"+maxRightLen+"d]: %"+maxCountLen+"d %s\n", bucket*step+start+1, (bucket+1)*step+start, count, strings.Repeat("#", percentage))
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}
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fmt.Println()
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}
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func generateBucket(min, max int) (start, end, step int) {
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s := (max - min) / 10
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step = 10
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for step < s && step <= 10000 {
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step *= 10
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}
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start = min - min%step
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end = max - max%step + step
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return
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}
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@ -0,0 +1,43 @@
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// 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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"testing"
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"github.com/stretchr/testify/require"
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)
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func TestGenerateBucket(t *testing.T) {
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tcs := []struct {
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min, max int
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start, end, step int
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}{
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{
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min: 101,
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max: 141,
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start: 100,
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end: 150,
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step: 10,
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},
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}
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for _, tc := range tcs {
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start, end, step := generateBucket(tc.min, tc.max)
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require.Equal(t, tc.start, start)
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require.Equal(t, tc.end, end)
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require.Equal(t, tc.step, step)
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}
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}
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