/**
     * Share capability indicating scale-out support.
     */
    public static final int SMB2_SHARE_CAP_SCALEOUT = 0x20;

    /**
     * Share capability indicating cluster support.
     */
    public static final int SMB2_SHARE_CAP_CLUSTER = 0x40;

    /**
     * Share capability indicating asymmetric support.
     */
    public static final int SMB2_SHARE_CAP_ASYMMETRIC = 0x80;

			Optional:    true,
			Type:        "path",
			Sensitive:   true,
		},
		config.HelpKV{
			Key:         KafkaVersion,
			Description: "specify the version of the Kafka cluster",
			Optional:    true,
			Type:        "string",
		},
		config.HelpKV{
			Key:         QueueSize,
			Description: "configure channel queue size for Kafka targets",
			Optional:    true,

		return
	}

	if globalSiteReplicationSys.isEnabled() && !v.Enabled() {
		writeErrorResponse(ctx, w, APIError{
			Code:           "InvalidBucketState",
			Description:    "Cluster replication is enabled on this site, versioning cannot be suspended on bucket.",
			HTTPStatusCode: http.StatusBadRequest,
		}, r.URL)
		return
	}

```

In a given setup, there are `n` MinIO instances talking to `m` KES servers but only `1` central KMS. The most simple setup consists of `1` MinIO server or cluster talking to `1` KMS via `1` KES server.

The main difference between various MinIO-KMS deployments is the KMS implementation. The following table helps you select the right option for your use case:

// 5. Start the GRPC server for worker 0 using C.
// 6. Create a context D using the full cluster server def E.
// 7. Read the variable in D.
TEST(CAPI, SingleHostServerDefV1Works) {
  // Create a server def that represents a 2-process cluster and a client.
  // Example:
  //
  // cluster { job { name: "worker"
  //                 tasks { key: 0 value: "localhost:14522" }

      id = 'elasticsearch.internal-available-ports'
      implementationClass = 'org.elasticsearch.gradle.internal.InternalAvailableTcpPortProviderPlugin'
    }
    internalClusterTest {
      id = 'elasticsearch.internal-cluster-test'
      implementationClass = 'org.elasticsearch.gradle.internal.test.InternalClusterTestPlugin'
    }
    internalDistributionArchiveCheck {
      id = 'elasticsearch.internal-distribution-archive-check'

## Enabling Parquet Format

Parquet is DISABLED by default since hostile crafted input can easily crash the server.

If you are in a controlled environment where it is safe to assume no hostile content can be uploaded to your cluster you can safely enable Parquet.
To enable Parquet set the environment variable `MINIO_API_SELECT_PARQUET=on`.

## Example using Python API

### 1. Prerequisites

	loadDataUsage := func(ctx context.Context) (u DataUsageInfo, err error) {
		objLayer := newObjectLayerFn()
		if objLayer == nil {
			return u, err
		}

		// Collect cluster level object metrics.
		u, err = loadDataUsageFromBackend(GlobalContext, objLayer)
		return u, err
	}
	return cachevalue.NewFromFunc(1*time.Minute,
		cachevalue.Opts{ReturnLastGood: true},
		loadDataUsage)
}

            test.addTestListener(listener);

            /*
             * We use lazy-evaluated strings in order to configure system properties whose value will not be known until
             * execution time (e.g. cluster port numbers). Adding these via the normal DSL doesn't work as these get treated
             * as task inputs and therefore Gradle attempts to snapshot them before/after task execution. This fails due

	LastFailedCount uint64 `json:"failedCount_last5min"`
	// Count of unreplicated entries that were dropped after MRF retry limit reached since cluster start.
	TotalDroppedCount uint64 `json:"droppedCount_since_uptime"`
	// Bytes of unreplicated entries that were dropped after MRF retry limit reached since cluster start.
	TotalDroppedBytes uint64 `json:"droppedBytes_since_uptime"`
}

// SMA struct for calculating simple moving average