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Promtool: Add support for compaction analysis (#8940)

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* Extend promtool to support compaction analysis

This commit extends the promtool tsdb analyze command to help
troubleshoot high Prometheus disk usage. The command now plots a
distribution of how full chunks are relative to the maximum capacity of
120 samples per chunk.

Signed-off-by: fpetkovski <filip.petkovsky@gmail.com>

* Update cmd/promtool/tsdb.go

Co-authored-by: Bartlomiej Plotka <bwplotka@gmail.com>

Co-authored-by: Bartlomiej Plotka <bwplotka@gmail.com>
This commit is contained in:
Filip Petkovski 2021-07-02 12:08:52 +02:00 committed by GitHub
parent 179b2155d1
commit 7c125aa5fb
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GPG key ID: 4AEE18F83AFDEB23
2 changed files with 57 additions and 1 deletions
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2
cmd/promtool/main.go
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@ -132,7 +132,7 @@ func main() {
benchWriteNumScrapes := tsdbBenchWriteCmd.Flag("scrapes", "Number of scrapes to simulate.").Default("3000").Int()
benchSamplesFile := tsdbBenchWriteCmd.Arg("file", "Input file with samples data, default is ("+filepath.Join("..", "..", "tsdb", "testdata", "20kseries.json")+").").Default(filepath.Join("..", "..", "tsdb", "testdata", "20kseries.json")).String()
tsdbAnalyzeCmd := tsdbCmd.Command("analyze", "Analyze churn, label pair cardinality.")
tsdbAnalyzeCmd := tsdbCmd.Command("analyze", "Analyze churn, label pair cardinality and compaction efficiency.")
analyzePath := tsdbAnalyzeCmd.Arg("db path", "Database path (default is "+defaultDBPath+").").Default(defaultDBPath).String()
analyzeBlockID := tsdbAnalyzeCmd.Arg("block id", "Block to analyze (default is the last block).").String()
analyzeLimit := tsdbAnalyzeCmd.Flag("limit", "How many items to show in each list.").Default("20").Int()

56
cmd/promtool/tsdb.go
View file

@ -17,8 +17,10 @@ import (
"bufio"
"context"
"fmt"
"github.com/prometheus/prometheus/tsdb/index"
"io"
"io/ioutil"
"math"
"os"
"path/filepath"
"runtime"
@ -561,6 +563,60 @@ func analyzeBlock(path, blockID string, limit int) error {
}
fmt.Printf("\nHighest cardinality metric names:\n")
printInfo(postingInfos)
return analyzeCompaction(block, ir)
}
func analyzeCompaction(block tsdb.BlockReader, indexr tsdb.IndexReader) (err error) {
postingsr, err := indexr.Postings(index.AllPostingsKey())
if err != nil {
return err
}
chunkr, err := block.Chunks()
if err != nil {
return err
}
defer func() {
err = tsdb_errors.NewMulti(err, chunkr.Close()).Err()
}()
const maxSamplesPerChunk = 120
nBuckets := 10
histogram := make([]int, nBuckets)
totalChunks := 0
for postingsr.Next() {
var lbsl = labels.Labels{}
var chks []chunks.Meta
if err := indexr.Series(postingsr.At(), &lbsl, &chks); err != nil {
return err
}
for _, chk := range chks {
// Load the actual data of the chunk.
chk, err := chunkr.Chunk(chk.Ref)
if err != nil {
return err
}
chunkSize := math.Min(float64(chk.NumSamples()), maxSamplesPerChunk)
// Calculate the bucket for the chunk and increment it in the histogram.
bucket := int(math.Ceil(float64(nBuckets)*chunkSize/maxSamplesPerChunk)) - 1
histogram[bucket]++
totalChunks++
}
}
fmt.Printf("\nCompaction analysis:\n")
fmt.Println("Fullness: Amount of samples in chunks (100% is 120 samples)")
// Normalize absolute counts to percentages and print them out.
for bucket, count := range histogram {
percentage := 100.0 * count / totalChunks
fmt.Printf("%7d%%: ", (bucket+1)*10)
for j := 0; j < percentage; j++ {
fmt.Printf("#")
}
fmt.Println()
}
return nil
}