alias kv-pairify='yq ".configuration[]" | jq ".[]" | jq -r ".name + \"=\" + .value"'
```

Let's take for example a set of 12 compute nodes with an aggregate memory of _1.2TiB_, we need to do following settings for optimal results. Add the following optimal entries for _core-site.xml_ to configure _s3a_ with **MinIO**. Most important options here are

```
cat ${HADOOP_CONF_DIR}/core-site.xml | kv-pairify | grep "mapred"

type endpointSet struct {
	argPatterns []ellipses.ArgPattern
	endpoints   []string   // Endpoints saved from previous GetEndpoints().
	setIndexes  [][]uint64 // All the sets.
}

// Supported set sizes this is used to find the optimal
// single set size.
var setSizes = []uint64{2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}

// getDivisibleSize - returns a greatest common divisor of
// all the ellipses sizes.

list_quorum                     (string)    set the acceptable quorum expected for list operations e.g. "optimal", "reduced", "disk", "strict", "auto" (default: 'strict')
replication_priority            (string)    set replication priority (default: 'auto')

		return cfg, err
	}
	cfg.RemoteTransportDeadline = remoteTransportDeadline

	listQuorum := env.Get(EnvAPIListQuorum, kvs.GetWithDefault(apiListQuorum, DefaultKVS))
	switch listQuorum {
	case "strict", "optimal", "reduced", "disk", "auto":
	default:
		return cfg, fmt.Errorf("invalid value %v for list_quorum: will default to 'strict'", listQuorum)
	}
	cfg.ListQuorum = listQuorum

	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 {

// 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 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 {

	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

            return Optional.of(Boolean.TRUE);
        }
        return Optional.empty();
    }

    @Override
    public Optional<Boolean> updateSnapshots() {
        if (commandLine.hasOption(CLIManager.UPDATE_SNAPSHOTS)) {
            return Optional.of(Boolean.TRUE);
        }
        return Optional.empty();
    }

    @Override
    public Optional<List<String>> activatedProfiles() {