Namespace: namespace,
			Subsystem: subsystem,
			Name:      "transformation_duration_seconds",
			Help:      "Latencies in seconds of value transformation operations.",
			// In-process transformations (ex. AES CBC) complete on the order of 20 microseconds. However, when
			// external KMS is involved latencies may climb into hundreds of milliseconds.
			Buckets:        metrics.ExponentialBuckets(5e-6, 2, 25),
			StabilityLevel: metrics.ALPHA,

			b.ReportMetric(float64(latencies[len(latencies)*90/100]), "p90-ns")
			b.ReportMetric(float64(latencies[len(latencies)*99/100]), "p99-ns")
		}
		return func(b *testing.B) {
			b.Run("idle", runOne)

			b.Run("loaded", func(b *testing.B) {
				stop := applyGCLoad(b)
				runOne(b)
				// Make sure to stop the timer before we wait! The load created above

}

type metricSet struct {
	latencies        *metrics.HistogramVec
	latenciesSummary *metrics.SummaryVec
}

// MustRegister registers all the prometheus metrics in the metricSet.
func (m *metricSet) mustRegister() {
	legacyregistry.MustRegister(m.latencies)
	if m.latenciesSummary != nil {
		legacyregistry.MustRegister(m.latenciesSummary)
	}
}

	b.ReportMetric(0, "ns/op")
	b.ReportMetric(0, "B/op")
	b.ReportMetric(0, "allocs/op")

	// Sort latencies then report percentiles.
	slices.Sort(latencies)
	b.ReportMetric(float64(latencies[len(latencies)*50/100]), "p50-ns")
	b.ReportMetric(float64(latencies[len(latencies)*90/100]), "p90-ns")
	b.ReportMetric(float64(latencies[len(latencies)*99/100]), "p99-ns")
}

func TestUserForcedGC(t *testing.T) {

// implementation which is buggy on NUMA systems, we have to use a simpler
// io.Writer implementation alone avoids also unnecessary buffer copies,
// and as such incurred latencies.
var Discard io.Writer = discard{}

// discard is /dev/null for Golang.
type discard struct{}

func (discard) Write(p []byte) (int, error) {
	return len(p), nil
}

// DiscardReader discarded reader

	b.ReportMetric(0, "allocs/op")

	// Sort latencies then report percentiles.
	sort.Slice(latencies, func(i, j int) bool {
		return latencies[i] < latencies[j]
	})
	b.ReportMetric(float64(latencies[len(latencies)*50/100]), "p50-ns")
	b.ReportMetric(float64(latencies[len(latencies)*90/100]), "p90-ns")
	b.ReportMetric(float64(latencies[len(latencies)*99/100]), "p99-ns")
}

var readMetricsSink [1024]interface{}

query results are printed to the console, organized by workload name.

All metrics returned are from server-side reports. This means that latencies
and error rates are from the perspective of the service itself and not of an
individual client (or aggregate set of clients). Rates and latencies are
calculated over a time interval of 1 minute.
`,
		Example: `  # Retrieve workload metrics for productpage-v1 workload

		in a runnable state before actually running. Bucket counts
		increase monotonically.

	/sched/pauses/stopping/gc:seconds
		Distribution of individual GC-related stop-the-world stopping
		latencies. This is the time it takes from deciding to stop the
		world until all Ps are stopped. This is a subset of the total
		GC-related stop-the-world time (/sched/pauses/total/gc:seconds).

		Kind:        KindFloat64Histogram,
		Cumulative:  true,
	},
	{
		Name:        "/sched/pauses/stopping/gc:seconds",

			elapsed:            time.Duration(10) * time.Second,
			metrics: []string{
				"apiserver_storage_transformation_duration_seconds",
			},
			want: `
			# HELP apiserver_storage_transformation_duration_seconds [ALPHA] Latencies in seconds of value transformation operations.
			# TYPE apiserver_storage_transformation_duration_seconds histogram