}

func concatNaive(ss ...string) string {
	rs := ss[0]
	for i := 1; i < len(ss); i++ {
		rs += ss[i]
	}
	return rs
}

func benchmark(b *testing.B, data []string) {
	b.Run("concat naive", func(b *testing.B) {
		b.ResetTimer()
		b.ReportAllocs()
		for i := 0; i < b.N; i++ {
			concatNaive(data...)
		}
	})
	b.Run("concat fast", func(b *testing.B) {
		b.ResetTimer()
		b.ReportAllocs()

## Why is MinIO cloud-native?

MinIO is a high performance distributed object storage server, designed for large-scale private cloud infrastructure. MinIO is designed in a cloud-native manner to scale sustainably in multi-tenant environments. Orchestration platforms like Kubernetes provide perfect cloud-native environment to deploy and scale MinIO.

## MinIO Deployment on Kubernetes

There are multiple options to deploy MinIO on Kubernetes:

        assert data == "Hello, router with prefix!"


def test_native_prefix_router():
    client = TestClient(app)
    with client.websocket_connect("/native/") as websocket:
        data = websocket.receive_text()
        assert data == "Hello, router with native prefix!"


def test_router2():
    client = TestClient(app)
    with client.websocket_connect("/router2") as websocket:

# Ignore arch dependent warnings, we chose the right libs on start
fess binary: arch-dependent-file-in-usr-share
# Please check our changelog at https://github.com/codelibs/fess

    # o isAvailableDatabaseNativeJDBC: (NotRequired - Default false)
    #   [true]
    #     Use classes of database native JDBC on generated classes to get best performances of DB access.
    #     Your project needs to refer to database native JDBC.
    #
    #; isAvailableDatabaseNativeJDBC = false
    # - - - - - - - - - -/

    # /- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

# **Disaggregated HDP Spark and Hive with MinIO**

## **1. Cloud-native Architecture**

![cloud-native](https://github.com/minio/minio/blob/master/docs/bigdata/images/image1.png?raw=true "cloud native architecture")

Kubernetes manages stateless Spark and Hive containers elastically on the compute nodes. Spark has native scheduler integration with Kubernetes. Hive, for legacy reasons, uses YARN scheduler on top of Kubernetes.

        #}
    }
    # - - - - - - - - - -/
}
# ----------------/
#
# Default mapping as follows:
# --------------------------------------------------------
# | JDBC Type     | Java Native          | CSharp Native |
# | ------------------------------------------------------
# | CHAR          | java.lang.String     | String        |
# | VARCHAR       | java.lang.String     | String        |

And to communicate using WebSockets with your backend you would probably use your frontend's utilities.

Or you might have a native mobile application that communicates with your WebSocket backend directly, in native code.

Or you might have any other way to communicate with the WebSocket endpoint.

---

			return checkRequestAuthType(ctx, r, action, bucket, name)
		}
	}
	urlValues := r.Form

	// Extract all the listBucketVersions query params to their native values.
	prefix, marker, delimiter, maxkeys, encodingType, versionIDMarker, errCode := getListBucketObjectVersionsArgs(urlValues)
	if errCode != ErrNone {
		writeErrorResponse(ctx, w, errorCodes.ToAPIErr(errCode), r.URL)