mirror of
https://source.quilibrium.com/quilibrium/ceremonyclient.git
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609 lines
18 KiB
Go
609 lines
18 KiB
Go
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package main
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import (
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"bufio"
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"bytes"
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"cmp"
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"compress/bzip2"
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"compress/gzip"
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"encoding/json"
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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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"slices"
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"sort"
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"strings"
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"time"
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"github.com/cockroachdb/errors/oserror"
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"github.com/spf13/cobra"
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)
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// A note to the reader on nomenclature used in this command.
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//
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// The write-throughput benchmark is generated by a roachtest with a number of
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// independent worker VMs running the same benchmark (to allow for an average
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// value to be recorded).
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//
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// An instance of the roachtest on a given day, for a given workload type (e.g.
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// values of size 1024B, values of size 64B, etc.) is modelled as a `writeRun`.
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// Each worker VM in a `writeRun` produces data modelled as a `rawWriteRun`.
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// Each `rawWriteRun` contains the raw data points emitted periodically by the
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// VM and are modelled as `writePoint`s.
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//
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// A `writeWorkload` (i.e. singular) models all data for a particular type of
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// benchmark run (e.g. values of size 1024B), across all days. It is a mapping
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// of day to `writeRun`, which is a collection of `rawWriteRun`s.
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//
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// The `writeWorkloads` (i.e. plural) is a mapping from workload name to its
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// `writeWorkload`.
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//
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// The data can be thought of being modelled as follows:
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//
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// `writeWorkloads`---------\
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// - workload-name-A: `writeWorkload`-------\ |
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// - day-1: `writeRun`---------\ | |
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// - VM-1: `rawWriteRun`----\ | | |
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// [ ... raw data point ... ] `writePoint` x | | |
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// ... | | |
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// - VM-N: | | |
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// [ ... raw data point ... ] x | |
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// ... | |
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// - day-N: | |
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// - VM-1: | |
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// [ ... raw data point ... ] | |
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// ... | |
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// - VM-N: | |
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// [ ... raw data point ... ] x |
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// ... |
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// - workload-name-Z: |
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// - day-1: |
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// - VM-1: |
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// [ ... raw data point ... ] |
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// ... |
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// - VM-N: |
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// [ ... raw data point ... ] |
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// ... |
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// - day-N: |
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// - VM-1: |
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// [ ... raw data point ... ] |
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// ... |
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// - VM-N: |
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// [ ... raw data point ... ] x
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const (
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// summaryFilename is the filename for the top-level summary output.
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summaryFilename = "summary.json"
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// rawRunFmt is the format string for raw benchmark data.
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rawRunFmt = "BenchmarkRaw%s %d ops/sec %v pass %s elapsed %d bytes %d levels %f writeAmp"
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)
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func getWriteCommand() *cobra.Command {
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c := &cobra.Command{
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Use: "write",
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Short: "parse write throughput benchmark data",
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Long: `
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Parses write-throughput benchmark data into two sets of JSON "summary" files:
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1. A top-level summary.json file. Data in this file is reported per-day, per
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workload (i.e. values=1024, etc.), and is responsible for the top-level
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write-throughput visualizations on the Pebble benchmarks page.
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Each data-point for a time-series contains an ops/sec figure (measured as a
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simple average over all data points for that workload run), and a relative path
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to a per-run summary JSON file, containing the raw data for the run.
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2. A per-run *-summary.json file. Data in this file contains the raw data for
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each of the benchmark instances participating in the workload run on the given
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day. Each key in the file is the relative path to the original raw data file.
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Each data point contains the calculated optimal ops/sec for the instance of the
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run (see split.go for more detail on the algorithm), in addition to the raw data
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in CSV format.
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This command can be run without flags at the root of the directory containing
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the raw data. By default the raw data will be pulled from "data", and the
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resulting top-level and per-run summary files are written to "write-throughput".
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Both locations can be overridden with the --data-dir and --summary-dir flags,
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respectively.
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`,
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RunE: func(cmd *cobra.Command, args []string) error {
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dataDir, err := cmd.Flags().GetString("data-dir")
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if err != nil {
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return err
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}
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summaryDir, err := cmd.Flags().GetString("summary-dir")
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if err != nil {
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return err
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}
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return parseWrite(dataDir, summaryDir)
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},
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}
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c.Flags().String("data-dir", "data", "path to the raw data directory")
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c.Flags().String("summary-dir", "write-throughput", "output directory containing the summary files")
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c.SilenceUsage = true
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return c
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}
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// writePoint is a raw datapoint from an individual write-throughput benchmark
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// run.
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type writePoint struct {
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elapsedSecs int
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opsSec int
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passed bool
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size uint64
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levels int
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writeAmp float64
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}
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// formatCSV returns a comma-separated string representation of the datapoint.
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func (p writePoint) formatCSV() string {
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return fmt.Sprintf(
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"%d,%d,%v,%d,%d,%.2f",
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p.elapsedSecs, p.opsSec, p.passed, p.size, p.levels, p.writeAmp)
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}
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// rawWriteRun is a collection of datapoints from a single instance of a
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// benchmark run (i.e. datapoints comprising a single roachtest instance of a
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// write-throughput benchmark).
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type rawWriteRun struct {
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points []writePoint
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split int // memoized
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}
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// opsPerSecSplit returns an optimal-split point that divides the passes and
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// fails from the datapoints in a rawWriteRun.
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func (r *rawWriteRun) opsPerSecSplit() int {
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if r.split > 0 {
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return r.split
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}
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// Pre-process by partitioning the datapoint into passes and fails.
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var passes, fails []int
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for _, p := range r.points {
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if p.passed {
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passes = append(passes, p.opsSec)
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} else {
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fails = append(fails, p.opsSec)
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}
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}
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// Compute and cache the split point as we only need to calculate it once.
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split := findOptimalSplit(passes, fails)
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r.split = split
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return split
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}
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// writeAmp returns the value of the write-amplification at the end of the run.
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func (r *rawWriteRun) writeAmp() float64 {
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return r.points[len(r.points)-1].writeAmp
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}
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// formatCSV returns a comma-separated string representation of the rawWriteRun.
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// The value itself is a newline-delimited string value comprised of the CSV
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// representation of the individual writePoints.
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func (r rawWriteRun) formatCSV() string {
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var b bytes.Buffer
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for _, p := range r.points {
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_, _ = fmt.Fprintf(&b, "%s\n", p.formatCSV())
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}
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return b.String()
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}
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// writeRunSummary represents a single summary datapoint across all rawWriteRuns
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// that comprise a writeRun. The datapoint contains a summary ops-per-second
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// value, in addition to a path to the summary.json file with the combined data
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// for the run.
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type writeRunSummary struct {
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Name string `json:"name"`
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Date string `json:"date"`
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OpsSec int `json:"opsSec"`
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WriteAmp float64 `json:"writeAmp"`
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SummaryPath string `json:"summaryPath"`
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}
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// writeWorkloadSummary is an alias for a slice of writeRunSummaries.
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type writeWorkloadSummary []writeRunSummary
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// writeRun is a collection of one or more rawWriteRuns (i.e. the union of all
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// rawWriteRuns from each worker participating in the roachtest cluster used for
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// running the write-throughput benchmarks).
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type writeRun struct {
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// name is the benchmark workload name (i.e. "values=1024").
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name string
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// date is the date on which the writeRun took place.
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date string
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// dir is path to the directory containing the raw data. The path is
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// relative to the data-dir.
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dir string
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// rawRuns is a map from input data filename to its rawWriteRun data.
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rawRuns map[string]rawWriteRun
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}
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// summaryFilename returns the filename to be used for storing the summary
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// output for the writeRun. The filename preserves the original data source path
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// for ease of debugging / data-provenance.
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func (r writeRun) summaryFilename() string {
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parts := strings.Split(r.dir, string(os.PathSeparator))
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parts = append(parts, summaryFilename)
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return strings.Join(parts, "-")
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}
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// summarize computes a writeRunSummary datapoint for the writeRun.
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func (r writeRun) summarize() writeRunSummary {
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var (
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sumOpsSec int
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sumWriteAmp float64
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)
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for _, rr := range r.rawRuns {
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sumOpsSec += rr.opsPerSecSplit()
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sumWriteAmp += rr.writeAmp()
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}
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l := len(r.rawRuns)
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return writeRunSummary{
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Name: r.name,
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Date: r.date,
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SummaryPath: r.summaryFilename(),
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// Calculate an average across all raw runs in this run.
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// TODO(travers): test how this works in practice, after we have
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// gathered enough data.
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OpsSec: sumOpsSec / l,
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WriteAmp: math.Round(100*sumWriteAmp/float64(l)) / 100, // round to 2dp.
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}
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}
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// cookedWriteRun is a representation of a previously parsed (or "cooked")
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// writeRun.
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type cookedWriteRun struct {
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OpsSec int `json:"opsSec"`
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Raw string `json:"rawData"`
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}
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// formatSummaryJSON returns a JSON representation of the combined raw data from
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// all rawWriteRuns that comprise the writeRun. It has the form:
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//
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// {
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// "original-raw-write-run-log-file-1.gz": {
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// "opsSec": ...,
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// "raw": ...,
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// },
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// ...
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// "original-raw-write-run-log-file-N.gz": {
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// "opsSec": ...,
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// "raw": ...,
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// },
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// }
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func (r writeRun) formatSummaryJSON() ([]byte, error) {
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m := make(map[string]cookedWriteRun)
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for name, data := range r.rawRuns {
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m[name] = cookedWriteRun{
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OpsSec: data.opsPerSecSplit(),
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Raw: data.formatCSV(),
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}
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}
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return prettyJSON(&m), nil
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}
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// write workload is a map from "day" to corresponding writeRun, for a given
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// write-throughput benchmark workload (i.e. values=1024).
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type writeWorkload struct {
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days map[string]*writeRun // map from day to runs for the given workload
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}
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// writeWorkloads is an alias for a map from workload name to its corresponding
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// map from day to writeRun.
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type writeWorkloads map[string]*writeWorkload
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// nameDay is a (name, day) tuple, used as a map key.
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type nameDay struct {
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name, day string
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}
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type writeLoader struct {
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// rootDir is the path to the root directory containing the data.
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dataDir string
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// summaryFilename is the name of the file containing the summary data.
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summaryDir string
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// workloads is a map from workload name to its corresponding data.
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workloads writeWorkloads
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// cooked is a "set" of (workload, day) tuples representing whether
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// previously parsed data was present for the (workload, day).
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cooked map[nameDay]bool
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// cookedSummaries is a map from workload name to previously generated data
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// for the workload. This data is "mixed-in" with new data when the summary
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// files are written out.
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cookedSummaries map[string]writeWorkloadSummary
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}
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// newWriteLoader returns a new writeLoader that can be used to generate the
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// summary files for write-throughput benchmarking data.
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func newWriteLoader(dataDir, summaryDir string) *writeLoader {
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return &writeLoader{
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dataDir: dataDir,
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summaryDir: summaryDir,
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workloads: make(writeWorkloads),
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cooked: make(map[nameDay]bool),
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cookedSummaries: make(map[string]writeWorkloadSummary),
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}
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}
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// loadCooked loads previously summarized write throughput benchmark data.
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func (l *writeLoader) loadCooked() error {
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b, err := os.ReadFile(filepath.Join(l.summaryDir, summaryFilename))
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if err != nil {
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// The first ever run will not find the summary file. Return early in
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// this case, and we'll start afresh.
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if oserror.IsNotExist(err) {
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return nil
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}
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return err
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}
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// Reconstruct the summary.
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summaries := make(map[string]writeWorkloadSummary)
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err = json.Unmarshal(b, &summaries)
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if err != nil {
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return err
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}
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// Populate the cooked map.
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l.cookedSummaries = summaries
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// Populate the set used for determining whether we can skip a raw file.
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for name, workloadSummary := range summaries {
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for _, runSummary := range workloadSummary {
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l.cooked[nameDay{name, runSummary.Date}] = true
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}
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}
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return nil
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}
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// loadRaw loads the raw data from the root data directory.
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func (l *writeLoader) loadRaw() error {
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walkFn := func(path, pathRel string, info os.FileInfo) error {
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// The relative directory structure is of the form:
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// $day/pebble/write/$name/$run/$file
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parts := strings.Split(pathRel, string(os.PathSeparator))
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if len(parts) < 6 {
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return nil // stumble forward on invalid paths
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}
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// Filter out files that aren't in write benchmark directories.
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if parts[2] != "write" {
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return nil
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}
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day := parts[0]
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f, err := os.Open(path)
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if err != nil {
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_, _ = fmt.Fprintf(os.Stderr, "%+v\n", err)
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return nil // stumble forward on error
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}
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defer func() { _ = f.Close() }()
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rd := io.Reader(f)
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if strings.HasSuffix(path, ".bz2") {
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rd = bzip2.NewReader(f)
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} else if strings.HasSuffix(path, ".gz") {
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var err error
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rd, err = gzip.NewReader(f)
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if err != nil {
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_, _ = fmt.Fprintf(os.Stderr, "%+v\n", err)
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return nil // stumble forward on error
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}
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}
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// Parse the data for this file and add to the appropriate workload.
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s := bufio.NewScanner(rd)
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r := rawWriteRun{}
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var name string
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for s.Scan() {
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line := s.Text()
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if !strings.HasPrefix(line, "BenchmarkRaw") {
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continue
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}
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var p writePoint
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var nameInner, elapsed string
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n, err := fmt.Sscanf(line, rawRunFmt,
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&nameInner, &p.opsSec, &p.passed, &elapsed, &p.size, &p.levels, &p.writeAmp)
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if err != nil || n != 7 {
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// Stumble forward on error.
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_, _ = fmt.Fprintf(os.Stderr, "%s: %v\n", s.Text(), err)
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continue
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}
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// The first datapoint we see in the file is assumed to be the same
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||
|
// for all datapoints.
|
||
|
if name == "" {
|
||
|
name = nameInner
|
||
|
|
||
|
// Skip files for (workload, day) pairs that have been parsed
|
||
|
// previously. Note that this relies on loadCooked having been
|
||
|
// called previously to seed the map with cooked data.
|
||
|
if ok := l.cooked[nameDay{name, day}]; ok {
|
||
|
_, _ = fmt.Fprintf(os.Stderr,
|
||
|
"skipping previously cooked data in file %s (workload=%q, day=%q)\n",
|
||
|
pathRel, name, day)
|
||
|
return nil
|
||
|
}
|
||
|
} else if name != nameInner {
|
||
|
_, _ = fmt.Fprintf(os.Stderr,
|
||
|
"WARN: benchmark name %q differs from previously seen name %q: %s",
|
||
|
nameInner, name, s.Text())
|
||
|
}
|
||
|
|
||
|
// Convert the elapsed time into seconds.
|
||
|
secs, err := time.ParseDuration(elapsed)
|
||
|
if err != nil {
|
||
|
// Stumble forward on error.
|
||
|
_, _ = fmt.Fprintf(os.Stderr, "%s: %v\n", s.Text(), err)
|
||
|
continue
|
||
|
}
|
||
|
p.elapsedSecs = int(secs.Seconds())
|
||
|
|
||
|
// Add this data point to the collection of points for this run.
|
||
|
r.points = append(r.points, p)
|
||
|
}
|
||
|
|
||
|
// Add the raw run to the map.
|
||
|
l.addRawRun(name, day, pathRel, r)
|
||
|
|
||
|
return nil
|
||
|
}
|
||
|
return walkDir(l.dataDir, walkFn)
|
||
|
}
|
||
|
|
||
|
// addRawRun adds a rawWriteRun to the corresponding datastructures by looking
|
||
|
// up the workload name (i.e. "values=1024"), then appending the rawWriteRun to
|
||
|
// the corresponding slice of all rawWriteRuns.
|
||
|
func (l *writeLoader) addRawRun(name, day, path string, raw rawWriteRun) {
|
||
|
// Skip files with no points (i.e. files that couldn't be parsed).
|
||
|
if len(raw.points) == 0 {
|
||
|
return
|
||
|
}
|
||
|
|
||
|
_, _ = fmt.Fprintf(
|
||
|
os.Stderr, "adding raw run: (workload=%q, day=%q); nPoints=%d; file=%s\n",
|
||
|
name, day, len(raw.points), path)
|
||
|
|
||
|
w := l.workloads[name]
|
||
|
if w == nil {
|
||
|
w = &writeWorkload{days: make(map[string]*writeRun)}
|
||
|
l.workloads[name] = w
|
||
|
}
|
||
|
|
||
|
r := w.days[day]
|
||
|
if r == nil {
|
||
|
r = &writeRun{
|
||
|
name: name,
|
||
|
date: day,
|
||
|
dir: filepath.Dir(path),
|
||
|
rawRuns: make(map[string]rawWriteRun),
|
||
|
}
|
||
|
w.days[day] = r
|
||
|
}
|
||
|
r.rawRuns[path] = raw
|
||
|
}
|
||
|
|
||
|
// cookSummary writes out the data in the loader to the summary file (new or
|
||
|
// existing).
|
||
|
func (l *writeLoader) cookSummary() error {
|
||
|
summary := make(map[string]writeWorkloadSummary)
|
||
|
for name, w := range l.workloads {
|
||
|
summary[name] = cookWriteSummary(w)
|
||
|
}
|
||
|
|
||
|
// Mix in the previously cooked values.
|
||
|
for name, cooked := range l.cookedSummaries {
|
||
|
existing, ok := summary[name]
|
||
|
if !ok {
|
||
|
summary[name] = cooked
|
||
|
} else {
|
||
|
// We must merge and re-sort by date.
|
||
|
existing = append(existing, cooked...)
|
||
|
slices.SortFunc(existing, func(a, b writeRunSummary) int {
|
||
|
return cmp.Compare(a.Date, b.Date)
|
||
|
})
|
||
|
summary[name] = existing
|
||
|
}
|
||
|
}
|
||
|
b := prettyJSON(&summary)
|
||
|
b = append(b, '\n')
|
||
|
|
||
|
outputPath := filepath.Join(l.summaryDir, summaryFilename)
|
||
|
err := os.WriteFile(outputPath, b, 0644)
|
||
|
if err != nil {
|
||
|
return err
|
||
|
}
|
||
|
|
||
|
return nil
|
||
|
}
|
||
|
|
||
|
// cookWriteSummary is a helper that generates the summary for a write workload
|
||
|
// by computing the per-day summaries across all runs.
|
||
|
func cookWriteSummary(w *writeWorkload) writeWorkloadSummary {
|
||
|
days := make([]string, 0, len(w.days))
|
||
|
for day := range w.days {
|
||
|
days = append(days, day)
|
||
|
}
|
||
|
sort.Strings(days)
|
||
|
|
||
|
var summary writeWorkloadSummary
|
||
|
for _, day := range days {
|
||
|
r := w.days[day]
|
||
|
summary = append(summary, r.summarize())
|
||
|
}
|
||
|
|
||
|
return summary
|
||
|
}
|
||
|
|
||
|
// cookWriteRunSummaries writes out the per-run summary files.
|
||
|
func (l *writeLoader) cookWriteRunSummaries() error {
|
||
|
for _, w := range l.workloads {
|
||
|
for _, r := range w.days {
|
||
|
// Write out files preserving the original directory structure for
|
||
|
// ease of understanding / debugging.
|
||
|
outputPath := filepath.Join(l.summaryDir, r.summaryFilename())
|
||
|
if err := outputWriteRunSummary(r, outputPath); err != nil {
|
||
|
return err
|
||
|
}
|
||
|
}
|
||
|
}
|
||
|
return nil
|
||
|
}
|
||
|
|
||
|
// outputWriteRunSummary is a helper that generates the summary JSON for the
|
||
|
// writeRun and writes it to the given output path.
|
||
|
func outputWriteRunSummary(r *writeRun, outputPath string) error {
|
||
|
f, err := os.OpenFile(outputPath, os.O_CREATE|os.O_TRUNC|os.O_WRONLY, 0644)
|
||
|
if err != nil {
|
||
|
return err
|
||
|
}
|
||
|
defer func() { _ = f.Close() }()
|
||
|
|
||
|
b, err := r.formatSummaryJSON()
|
||
|
if err != nil {
|
||
|
return err
|
||
|
}
|
||
|
b = append(b, '\n')
|
||
|
|
||
|
_, err = f.Write(b)
|
||
|
return err
|
||
|
}
|
||
|
|
||
|
// parseWrite parses the raw write-throughput benchmark data and writes out the
|
||
|
// summary files.
|
||
|
func parseWrite(dataDir, summaryDir string) error {
|
||
|
l := newWriteLoader(dataDir, summaryDir)
|
||
|
if err := l.loadCooked(); err != nil {
|
||
|
return err
|
||
|
}
|
||
|
|
||
|
if err := l.loadRaw(); err != nil {
|
||
|
return err
|
||
|
}
|
||
|
|
||
|
if err := l.cookSummary(); err != nil {
|
||
|
return err
|
||
|
}
|
||
|
|
||
|
return l.cookWriteRunSummaries()
|
||
|
}
|