StabilityLevel: metrics.ALPHA,
		},
		[]string{"protocol", "transport", "stage"},
	)

	legacyregistry.MustRegister(starts)
	legacyregistry.MustRegister(latencies)
	legacyregistry.MustRegister(failures)
	return &DialMetrics{starts: starts, latencies: latencies, failures: failures, clock: clock.RealClock{}}
}

// Clock returns the clock.
func (m *DialMetrics) Clock() clock.Clock {
	return m.clock
}

			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,

}

var _ crConverterInterface = &converterMetric{}

type converterMetric struct {
	delegate  crConverterInterface
	latencies *metrics.HistogramVec
	crdName   string
}

func (c *converterMetricFactory) addMetrics(crdName string, converter crConverterInterface) (crConverterInterface, error) {
	c.factoryLock.Lock()
	defer c.factoryLock.Unlock()

// 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

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

// Add  a new duration data
func (l *lastMinuteLatency) addAll(sec int64, a AccElem) {
	l.forwardTo(sec)
	winIdx := sec % 60
	l.Totals[winIdx].merge(a)
	l.LastSec = sec
}

// Merge all recorded latencies of last minute into one
func (l *lastMinuteLatency) getTotal() AccElem {
	var res AccElem
	sec := time.Now().Unix()
	l.forwardTo(sec)
	for _, elem := range l.Totals[:] {
		res.merge(elem)
	}
	return res

		uerr = errors.Unwrap(leafErr)
		depth++
		if depth == unwrapErrsDepth {
			// If we have reached enough depth we
			// do not further recurse down, this
			// is done to avoid any unnecessary
			// latencies this might bring.
			break
		}
	}
	if uerr == nil {
		leafErr = err
	}
	return leafErr
}

// One log message per error.

	m.Set(driveAvailabilityErrorsTotal, float64(disk.Metrics.TotalErrorsAvailability), labels...)
	m.Set(driveWaitingIO, float64(disk.Metrics.TotalWaiting), labels...)

	// Append the api label for the drive API latencies.
	labels = append(labels, "api", "")
	lastIdx := len(labels) - 1
	for apiName, latency := range disk.Metrics.LastMinute {
		labels[lastIdx] = "storage." + apiName

// heuristic, they may be put into the same cluster and it can introduce
// artificial dependencies and incur great performance loss.  In this example,
// Node_Y becomes dependent on IteratorGetNext and the latencies add up if
// Node_X and Node_Y are in the same cluster.
//
// IteratorGetNext -> Node_X -> Stage
//
// Unstage -> Node_Y
//
Status ClusterScopingPassImpl::ScopingForPipelineStages() {

	currentPaths sync.Map

	cycleInfoMu sync.Mutex
	cycleInfo   *currentScannerCycle
}

var globalScannerMetrics scannerMetrics

const (
	// START Realtime metrics, that only to records
	// last minute latencies and total operation count.
	scannerMetricReadMetadata scannerMetric = iota
	scannerMetricCheckMissing
	scannerMetricSaveUsage
	scannerMetricApplyAll
	scannerMetricApplyVersion
	scannerMetricTierObjSweep