qs.TgtXferStats[arn][Large] = XferStats{
			Avg:  v.XferRateLrg.Avg,
			Curr: lcurrTgt,
			Peak: math.Max(v.XferRateLrg.Peak, lcurrTgt),
		}
		qs.TgtXferStats[arn][Small] = XferStats{
			Avg:  v.XferRateSml.Avg,
			Curr: scurrTgt,
			Peak: math.Max(v.XferRateSml.Peak, scurrTgt),
		}
		if tcount > 0 {
			qs.TgtXferStats[arn][Total] = XferStats{
				Avg:  (v.XferRateLrg.Avg + v.XferRateSml.Avg) / float64(tcount),

}

type latencyStat struct {
	lastmin lastMinuteLatency
	curr    time.Duration
	avg     time.Duration
	peak    time.Duration
	N       int64
}

func (l *latencyStat) update(d time.Duration) {
	l.lastmin.add(d)
	l.N++
	if d > l.peak {
		l.peak = d
	}
	l.curr = l.lastmin.getTotal().avg()
	l.avg = time.Duration((int64(l.avg)*(l.N-1) + int64(l.curr)) / l.N)
}

        System.out.printf("Buffer Cache Performance: %d allocs, %d releases in %.2f ms%n", allocations.get(), releases.get(),
                overallTimeMs);
        System.out.printf("  Avg allocation time: %.2f ns%n", avgAllocTimeNs);
        System.out.printf("  Avg release time: %.2f ns%n", avgReleaseTimeNs);

        // Verify O(1) performance - should be reasonably fast (allowing for JVM overhead)

| `minio_node_if_rx_bytes_avg`  | Bytes received on the interface in 60s (avg) since uptime. |
| `minio_node_if_rx_bytes_max`  | Bytes received on the interface in 60s (max) since uptime. |
| `minio_node_if_rx_errors`     | Receive errors in 60s.                                     |
| `minio_node_if_rx_errors_avg` | Receive errors in 60s (avg).                               |

import org.opensearch.search.aggregations.bucket.histogram.Histogram;
import org.opensearch.search.aggregations.bucket.terms.Terms;
import org.opensearch.search.aggregations.metrics.Avg;
import org.opensearch.search.aggregations.metrics.Cardinality;

import jakarta.annotation.Resource;

/**
 * Service class for managing search logs and related analytics.
 *

			metric.TotalDowntime = epHealth.offlineDuration
			metric.LastOnline = epHealth.lastOnline
			metric.Online = epHealth.Online
			metric.Latency = madmin.LatencyStat{
				Curr: epHealth.latency.curr,
				Avg:  epHealth.latency.avg,
				Max:  epHealth.latency.peak,
			}
		}
		m[dID] = metric
	}
	return m
}

func (srs *SRStatus) updateXferRate(sz int64, duration time.Duration) {
	if sz > minLargeObjSize {

          "interval": "1m",
          "intervalFactor": 2,
          "legendFormat": "{{server}}",
          "refId": "A"
        }
      ],
      "title": "Avg. Active Workers",
      "type": "timeseries"
    },
    {
      "datasource": {
        "type": "prometheus",
        "uid": "${DS_PROMETHEUS}"
      },
      "fieldConfig": {
        "defaults": {

                assertTrue(variance < 50.0,
                        String.format(
                                "Extreme timing variance in concurrent operations: %.3f " + "(min: %d ns, max: %d ns, avg: %.1f ns). "
                                        + "Note: JVM timing variability is expected, constant-time implementation verified.",
                                variance, minTime, maxTime, avgTime));
            }

		{Name: "subquery_having_3", Age: 30},
		{Name: "subquery_having_4", Age: 40},
	}
	DB.Create(&users)

	var results []User
	DB.Select("AVG(age) as avgage").Where("name LIKE ?", "subquery_having%").Group("name").Having("AVG(age) > (?)", DB.
		Select("AVG(age)").Where("name LIKE ?", "subquery_having%").Table("users")).Find(&results)

	if len(results) != 2 {

				Value:       float64(qs.MRFStats.LastFailedCount),
			}

			if qs.QStats.Avg.Count > 0 || qs.QStats.Curr.Count > 0 {
				qt := qs.QStats
				currInQueueBytes.Value = qt.Curr.Bytes
				currInQueueCount.Value = qt.Curr.Count
				avgQueueBytes.Value = qt.Avg.Bytes
				avgQueueCount.Value = qt.Avg.Count
				maxQueueBytes.Value = qt.Max.Bytes
				maxQueueCount.Value = qt.Max.Count
			}