github.com/rcrowley/go-metrics v0.0.0-20250401214520-65e299d6c5c9: metrics.NewHistogram

Clean-room public code sample — generation instructions

Write a brand-new, minimal, self-contained code sample in this clean-room directory.
Do not copy, paraphrase, or reference any existing project source. Work only from this spec.

A csx.json manifest scaffold already exists. Do not recreate it from memory. Preserve its case.goal, packages and symbols; fill its empty case.contract with exact assertions and correct its environment, commands and verifierAdapter for the files you generate.

Goal: verify metrics.NewHistogram in pkg:golang/github.com/rcrowley/go-metrics@v0.0.0-20250401214520-65e299d6c5c9
Kind: HOW

Use EXACTLY these public packages and versions:
  - pkg:golang/github.com/rcrowley/go-metrics@v0.0.0-20250401214520-65e299d6c5c9
Demonstrate these symbols/APIs:
  - metrics.NewHistogram

Rules:
  - One focused purpose; the smallest project that proves the goal.
  - Include a contract test (test/contract.*) that runs OFFLINE and exits 0 exactly when the goal behavior works.
  - Pin every dependency with a lockfile so resolution is reproducible.
  - No secrets, credentials, or tokens. No real URLs (only example.com or localhost). No absolute paths.
  - No personal names, emails, company names, or project identifiers of any kind.
  - No binaries and no generated output (node_modules, dist, target, venv, .git, .env).
  - Keep it under 200 files and 256KB packed.

{"case":{"caseId":"case:sha256:9e016abf74a3297176260b00e4ff7ce83cc912b26640a727e2a14ccd941b167d","contract":["metrics.NewHistogram constructs a new histogram with the given sample reservoir","Histogram.Update records values into the reservoir and updates count, min, max, and sum","Histogram.Mean, Variance, and StdDev calculate statistical distribution measures","Histogram.Percentile and Percentiles calculate distribution percentiles","Histogram.Snapshot captures an immutable point-in-time snapshot with statistical metrics","Histogram.Clear resets all recorded statistics in the histogram"],"goal":"verify metrics.NewHistogram in pkg:golang/github.com/rcrowley/go-metrics@v0.0.0-20250401214520-65e299d6c5c9","kind":"HOW","packages":["pkg:golang/github.com/rcrowley/go-metrics@v0.0.0-20250401214520-65e299d6c5c9"],"schemaVersion":1,"symbols":["metrics.NewHistogram"]},"contractCommand":["go","run","./test"],"environment":{"arch":"x64","distro":"ubuntu","ecosystem":"golang","libc":"glibc","libcVersion":"2.39","os":"linux","osVersionBucket":"24","packageManager":"go","schemaVersion":1},"license":"MIT-0","packages":["pkg:golang/github.com/rcrowley/go-metrics@v0.0.0-20250401214520-65e299d6c5c9"],"schemaVersion":1,"subject":"pkg:golang/github.com/rcrowley/go-metrics@v0.0.0-20250401214520-65e299d6c5c9","symbols":["metrics.NewHistogram"],"verifierAdapter":"golang@1"}

module example.com/sample

go 1.22

require github.com/rcrowley/go-metrics v0.0.0-20250401214520-65e299d6c5c9

github.com/rcrowley/go-metrics v0.0.0-20250401214520-65e299d6c5c9 h1:bsUq1dX0N8AOIL7EB/X911+m4EHsnWEHeJ0c+3TTBrg=
github.com/rcrowley/go-metrics v0.0.0-20250401214520-65e299d6c5c9/go.mod h1:bCqnVzQkZxMG4s8nGwiZ5l3QUCyqpo9Y+/ZMZ9VjZe4=

package main

import (
	"fmt"

	"github.com/rcrowley/go-metrics"
)

func main() {
	sample := metrics.NewUniformSample(100)
	h := metrics.NewHistogram(sample)

	for i := int64(1); i <= 100; i++ {
		h.Update(i)
	}

	snap := h.Snapshot()
	fmt.Printf("Histogram count: %d, mean: %.2f, 50th: %.2f, 95th: %.2f\n",
		snap.Count(), snap.Mean(), snap.Percentile(0.50), snap.Percentile(0.95))
}

{
  "schemaVersion": 1,
  "goal": "verify metrics.NewHistogram in pkg:golang/github.com/rcrowley/go-metrics@v0.0.0-20250401214520-65e299d6c5c9",
  "kind": "HOW",
  "packages": [
    "pkg:golang/github.com/rcrowley/go-metrics@v0.0.0-20250401214520-65e299d6c5c9"
  ],
  "symbols": [
    "metrics.NewHistogram"
  ]
}

package main

import (
	"fmt"
	"math"
	"os"

	"github.com/rcrowley/go-metrics"
)

func main() {
	// 1. metrics.NewHistogram constructs a new histogram with the given sample reservoir
	s := metrics.NewUniformSample(100)
	h := metrics.NewHistogram(s)
	if h == nil {
		fmt.Fprintf(os.Stderr, "expected non-nil histogram\n")
		os.Exit(1)
	}
	if h.Count() != 0 || h.Min() != 0 || h.Max() != 0 || h.Sum() != 0 {
		fmt.Fprintf(os.Stderr, "expected initial count=0 min=0 max=0 sum=0\n")
		os.Exit(1)
	}

	// 2. Histogram.Update records values into the reservoir and updates count, min, max, and sum
	h.Update(10)
	h.Update(20)
	h.Update(30)
	if h.Count() != 3 {
		fmt.Fprintf(os.Stderr, "expected count 3, got %d\n", h.Count())
		os.Exit(1)
	}
	if h.Min() != 10 || h.Max() != 30 || h.Sum() != 60 {
		fmt.Fprintf(os.Stderr, "expected min 10, max 30, sum 60; got min %d, max %d, sum %d\n", h.Min(), h.Max(), h.Sum())
		os.Exit(1)
	}

	// 3. Histogram.Mean, Variance, and StdDev calculate statistical distribution measures
	if math.Abs(h.Mean()-20.0) > 0.001 {
		fmt.Fprintf(os.Stderr, "expected mean 20.0, got %f\n", h.Mean())
		os.Exit(1)
	}
	if h.Variance() <= 0.0 || h.StdDev() <= 0.0 {
		fmt.Fprintf(os.Stderr, "expected positive variance and stddev, got var %f, stddev %f\n", h.Variance(), h.StdDev())
		os.Exit(1)
	}

	// 4. Histogram.Percentile and Percentiles calculate distribution percentiles
	p50 := h.Percentile(0.50)
	p95 := h.Percentile(0.95)
	if p50 <= 0 || p95 <= 0 || p50 > p95 {
		fmt.Fprintf(os.Stderr, "unexpected percentiles: p50 %f, p95 %f\n", p50, p95)
		os.Exit(1)
	}
	percentiles := h.Percentiles([]float64{0.25, 0.50, 0.75, 0.99})
	if len(percentiles) != 4 {
		fmt.Fprintf(os.Stderr, "expected 4 percentiles, got %d\n", len(percentiles))
		os.Exit(1)
	}
	for i := 1; i < len(percentiles); i++ {
		if percentiles[i-1] > percentiles[i] {
			fmt.Fprintf(os.Stderr, "percentiles not monotonic: %v\n", percentiles)
			os.Exit(1)
		}
	}

	// 5. Histogram.Snapshot captures an immutable point-in-time snapshot with statistical metrics
	snap := h.Snapshot()
	if snap == nil {
		fmt.Fprintf(os.Stderr, "expected non-nil snapshot\n")
		os.Exit(1)
	}
	if snap.Count() != 3 {
		fmt.Fprintf(os.Stderr, "expected snapshot count 3, got %d\n", snap.Count())
		os.Exit(1)
	}
	h.Update(100)
	if snap.Count() != 3 {
		fmt.Fprintf(os.Stderr, "snapshot count mutated: got %d, expected 3\n", snap.Count())
		os.Exit(1)
	}
	snapPanicked := false
	func() {
		defer func() {
			if recover() != nil {
				snapPanicked = true
			}
		}()
		snap.Update(50)
	}()
	if !snapPanicked {
		fmt.Fprintf(os.Stderr, "expected snapshot.Update to panic on immutable snapshot\n")
		os.Exit(1)
	}

	// 6. Histogram.Clear resets all recorded statistics in the histogram
	h.Clear()
	if h.Count() != 0 || h.Min() != 0 || h.Max() != 0 || h.Sum() != 0 {
		fmt.Fprintf(os.Stderr, "expected cleared histogram with count=0 min=0 max=0 sum=0\n")
		os.Exit(1)
	}

	fmt.Println("All metrics.NewHistogram contract assertions passed.")
}