* from uniform), and amortized since some operations can trigger a hash table resize.
 *
 * <p>Unlike {@code java.util.HashMap}, iteration is only proportional to the actual {@code size()},
 * which is optimal, and <i>not</i> the size of the internal hashtable, which could be much larger
 * than {@code size()}. Furthermore, this structure places significantly reduced load on the garbage

 * from uniform), and amortized since some operations can trigger a hash table resize.
 *
 * <p>Unlike {@code java.util.HashMap}, iteration is only proportional to the actual {@code size()},
 * which is optimal, and <i>not</i> the size of the internal hashtable, which could be much larger
 * than {@code size()}. Furthermore, this structure places significantly reduced load on the garbage

 * Second, it is always backed by an array large enough to hold a value for every possible
 * combination of row and column keys. (This is rarely optimal unless the table is extremely dense.)
 * Finally, every possible combination of row and column keys is always considered to have a value
 * associated with it: It is not possible to "remove" a value, only to replace it with {@code null},

		if v == nil {
			field.ReflectValueOf(ctx, value).Set(reflect.New(field.FieldType).Elem())
		} else {
			reflectV := reflect.ValueOf(v)
			// Optimal value type acquisition for v
			reflectValType := reflectV.Type()

			if reflectValType.AssignableTo(field.FieldType) {
				if reflectV.Kind() == reflect.Ptr && reflectV.Elem().Kind() == reflect.Ptr {

	return c(ctx, network, address)
}

const (
	defaultOutQueue    = 65535    // kind of close to max open fds per user
	readBufferSize     = 32 << 10 // 32 KiB is the most optimal on Linux
	writeBufferSize    = 32 << 10 // 32 KiB is the most optimal on Linux
	defaultDialTimeout = 2 * time.Second
	connPingInterval   = 10 * time.Second
	connWriteTimeout   = 3 * time.Second
)

type connectionParams struct {

 * Second, it is always backed by an array large enough to hold a value for every possible
 * combination of row and column keys. (This is rarely optimal unless the table is extremely dense.)
 * Finally, every possible combination of row and column keys is always considered to have a value
 * associated with it: It is not possible to "remove" a value, only to replace it with {@code null},

// acceptable quorum expected for list operations
func getListQuorum(quorum string, driveCount int) int {
	switch quorum {
	case "disk":
		return 1
	case "reduced":
		return 2
	case "optimal":
		return (driveCount + 1) / 2
	case "auto":
		return -1
	}
	// defaults to 'strict'
	return driveCount
}

	}
	if buffered := wr.Buffered(); buffered != wantBuffered {
		t.Fatalf("Buffered = %v; want %v", buffered, wantBuffered)
	}
}

func BenchmarkReaderCopyOptimal(b *testing.B) {
	// Optimal case is where the underlying reader implements io.WriterTo
	srcBuf := bytes.NewBuffer(make([]byte, 8192))
	src := NewReader(srcBuf)
	dstBuf := new(bytes.Buffer)
	dst := onlyWriter{dstBuf}
	for i := 0; i < b.N; i++ {

	maxProcs := runtime.GOMAXPROCS(0)
	cpuProcs := runtime.NumCPU()
	if maxProcs < cpuProcs {
		warnings = append(warnings, color.YellowBold("Detected GOMAXPROCS(%d) < NumCPU(%d), please make sure to provide all PROCS to MinIO for optimal performance",
			maxProcs, cpuProcs))
	}

	// Initialize grid
	bootstrapTrace("initGrid", func() {
		logger.FatalIf(initGlobalGrid(GlobalContext, globalEndpoints), "Unable to configure server grid RPC services")

	// in the 10 node shard would coordinate between other 9 shards to get the
	// rest of the `99*9` requests.
	//
	// This essentially splits the workload properly and also allows for network
	// utilization to be optimal, instead of blindly throttling the way we are
	// doing below. However the changes that are needed here are a bit involved,
	// further discussion advised. Remove this comment and remove the worker model