}

// exponentialMovingAverage calculates the exponential moving average
func exponentialMovingAverage(beta, previousAvg, incrementAvg float64) float64 {
	return (1-beta)*incrementAvg + beta*previousAvg
}

// getExpMovingAvgBytesPerSecond returns the exponential moving average for the bucket in bytes
func (m *bucketMeasurement) getExpMovingAvgBytesPerSecond() float64 {
	m.lock.Lock()

var (
	replicationAverageActiveWorkersMD = NewGaugeMD(replicationAverageActiveWorkers,
		"Average number of active replication workers")
	replicationAverageQueuedBytesMD = NewGaugeMD(replicationAverageQueuedBytes,
		"Average number of bytes queued for replication since server start")
	replicationAverageQueuedCountMD = NewGaugeMD(replicationAverageQueuedCount,
		"Average number of objects queued for replication since server start")

		cpuNice:           "CPU nice time",
		cpuLoad1:          "CPU load average 1min",
		cpuLoad5:          "CPU load average 5min",
		cpuLoad15:         "CPU load average 15min",
		cpuLoad1Perc:      "CPU load average 1min (percentage)",
		cpuLoad5Perc:      "CPU load average 5min (percentage)",
		cpuLoad15Perc:     "CPU load average 15min (percentage)",
	}
	resourceMetricsGroups = []*MetricsGroupV2{

		allDriveLabels...)
	driveWaitingIOMD = NewGaugeMD(driveWaitingIO,
		"Total waiting I/O operations on a drive", allDriveLabels...)
	driveAPILatencyMD = NewGaugeMD(driveAPILatencyMicros,
		"Average last minute latency in µs for drive API storage operations",
		append(allDriveLabels, apiL)...)
	driveHealthMD = NewGaugeMD(driveHealth,
		"Drive health (0 = offline, 1 = healthy, 2 = healing)", allDriveLabels...)

}

// exponentialMovingAverage calculates the exponential moving average
func exponentialMovingAverage(beta, previousAvg, incrementAvg float64) float64 {
	return (1-beta)*incrementAvg + beta*previousAvg
}

// getExpMovingAvgBytesPerSecond returns the exponential moving average for the bucket/target in bytes
func (m *rateMeasurement) getExpMovingAvgBytesPerSecond() float64 {
	m.lock.Lock()

            // Analyze timing consistency across threads
            if (!timings.isEmpty()) {
                double avgTime = timings.stream().mapToLong(Long::longValue).average().orElse(0.0);
                long maxTime = timings.stream().mapToLong(Long::longValue).max().orElse(0L);
                long minTime = timings.stream().mapToLong(Long::longValue).min().orElse(0L);

	internodeDialedErrorsTotalMD = NewCounterMD(internodeDialErrorsTotal,
		"Total number of internode TCP dial timeouts and errors")
	internodeDialAvgTimeNanosMD = NewGaugeMD(internodeDialAvgTimeNanos,
		"Average dial time of internode TCP calls in nanoseconds")
	internodeSentBytesTotalMD = NewCounterMD(internodeSentBytesTotal,
		"Total number of bytes sent to other peer nodes")

	dialect := DB.Dialector.Name()
	switch dialect {
	case "mysql", "sqlserver":
		// these dialects do not support the "returning" clause
		return
	default:
		// This user struct will leverage the existing users table, but override
		// the Name field to default to null.
		type user struct {
			gorm.Model
			Name string `gorm:"default:null"`
		}
		u1 := user{}

    public static final String LOG_TYPE_SEARCH_USER_DAY = "search_user_day_agg";

    /** Log type constant for hourly average request time aggregation. */
    public static final String LOG_TYPE_SEARCH_REQTIMEAVG_HOUR = "search_reqtimeavg_hour_agg";

    /** Log type constant for daily average request time aggregation. */
    public static final String LOG_TYPE_SEARCH_REQTIMEAVG_DAY = "search_reqtimeavg_day_agg";

What would have happened if Gradle was limited to _only Java 11_ at the time?

## Decision

1. The Gradle distribution will never include a Java runtime.
   Instead, Gradle will leverage Java toolchains for the Daemon and worker processes.
2. The Gradle Daemon, Worker processes, and Tooling API client will continue to support running on different Java versions during a single Gradle invocation.