var (
	driveUsedBytesMD = NewGaugeMD(driveUsedBytes,
		"Total storage used on a drive in bytes", allDriveLabels...)
	driveFreeBytesMD = NewGaugeMD(driveFreeBytes,
		"Total storage free on a drive in bytes", allDriveLabels...)
	driveTotalBytesMD = NewGaugeMD(driveTotalBytes,
		"Total storage available on a drive in bytes", allDriveLabels...)
	driveUsedInodesMD = NewGaugeMD(driveUsedInodes,
		"Total used inodes on a drive", allDriveLabels...)

        print("transferProgressed", buffer.toString());
    }

    private String getStatus(long complete, long total) {
        if (total >= 1024) {
            return toKB(complete) + "/" + toKB(total) + " KB ";
        } else if (total >= 0) {
            return complete + "/" + total + " B ";
        } else if (complete >= 1024) {
            return toKB(complete) + " KB ";
        } else {

        <version>5.1.8</version>
        <executions>
          <execution>
            <id>bundle-manifest</id>
            <phase>process-classes</phase>
            <goals>
              <goal>manifest</goal>
            </goals>
          </execution>
        </executions>
        <configuration>
          <instructions>
            <Export-Package>
              !com.google.common.base.internal,

example with 1024 drives - 4, 8, 16 are GCD factors. With 16 drives we get a total of 64 possible sets, with 8 drives we get a total of 128 possible sets, with 4 drives we get a total of 256 possible sets. So algorithm automatically chooses 64 sets, which is *16* 64 = 1024* drives in total.

- *If total number of nodes are of odd number then GCD algorithm provides affinity towards odd number erasure sets to provide for uniform distribution across nodes*. This is to ensure that same number of drives...

		},
		andSet: true,
	}

	tests := []struct {
		filter   Filter
		userTags string
		want     bool
	}{
		{
			filter:   noTags,
			userTags: "",
			want:     true,
		},
		{
			filter:   noTags,
			userTags: "A=3",
			want:     true,
		},
		{
			filter:   oneTag,
			userTags: "A=3",
			want:     false,
		},
		{
			filter:   oneTag,

public class StopwatchBenchmark {
  @Benchmark
  long stopwatch(int reps) {
    long total = 0;
    for (int i = 0; i < reps; i++) {
      Stopwatch s = Stopwatch.createStarted();
      // here is where you would do something
      total += s.elapsed(NANOSECONDS);
    }
    return total;
  }

  @Benchmark
  long manual(int reps) {
    long total = 0;
    for (int i = 0; i < reps; i++) {
      long start = System.nanoTime();

	usageTotalBytesMD = NewGaugeMD(usageTotalBytes,
		"Total cluster usage in bytes")
	usageObjectsCountMD = NewGaugeMD(usageObjectsCount,
		"Total cluster objects count")
	usageVersionsCountMD = NewGaugeMD(usageVersionsCount,
		"Total cluster object versions (including delete markers) count")
	usageDeleteMarkersCountMD = NewGaugeMD(usageDeleteMarkersCount,
		"Total cluster delete markers count")

      panels.timeSeries.statPercent('Success Rate', queries.globalRequestSuccessRate, 'Total success rate of requests in the cluster'),
      panels.timeSeries.statRps('4xxs', queries.globalRequest4xx, 'Total 4xx requests in in the cluster'),
      panels.timeSeries.statRps('5xxs', queries.globalRequest5xx, 'Total 5xx requests in in the cluster'),
    ]),

	processCPUTotalSecondsMD          = NewCounterMD(processCPUTotalSeconds, "Total user and system CPU time spent in seconds")
	processGoRoutineTotalMD           = NewGaugeMD(processGoRoutineTotal, "Total number of go routines running")
	processIORCharBytesMD             = NewCounterMD(processIORCharBytes, "Total bytes read by the process from the underlying storage system including cache, /proc/[pid]/io rchar")

)

var (
	auditFailedMessagesMD = NewCounterMD(auditFailedMessages,
		"Total number of messages that failed to send since start",
		targetID)
	auditTargetQueueLengthMD = NewGaugeMD(auditTargetQueueLength,
		"Number of unsent messages in queue for target",
		targetID)
	auditTotalMessagesMD = NewCounterMD(auditTotalMessages,
		"Total number of messages sent since start",
		targetID)
)