},
  "tags": {
    "objectLocation": {
      "name": "hosts",
      "poolId": 1,
      "setId": 1,
      "drives": [
        "/mnt/data1",
        "/mnt/data2",
        "/mnt/data3",
        "/mnt/data4"
      ]
    }
  },
  "accessKey": "minioadmin"
}
```

### Kafka Target

Assuming that you already have Apache Kafka configured and running.

```

                kind = annotation.getMemberValue("k")?.intValue ?: 1,
                metadataVersion = annotation.getMemberValue("mv")?.intArrayValue ?: IntArray(0),
                data1 = annotation.getMemberValue("d1")?.stringArrayValue ?: arrayOf(),
                data2 = annotation.getMemberValue("d2")?.stringArrayValue ?: arrayOf(),
                extraString = annotation.getMemberValue("xs")?.stringValue ?: "",

  {{end}}{{end}}
EXAMPLES:
  1. Start MinIO server on "/home/shared" directory.
     {{.Prompt}} {{.HelpName}} /home/shared

  2. Start single node server with 64 local drives "/mnt/data1" to "/mnt/data64".
     {{.Prompt}} {{.HelpName}} /mnt/data{1...64}

  3. Start distributed MinIO server on an 32 node setup with 32 drives each, run following command on all the nodes

      "accessResultData": {
        "properties": {
          "transformerName": {
            "type": "keyword"
          },
          "encoding": {
            "type": "keyword"
          },
          "data": {
            "type": "binary"
          }
        }
      },
      "contentLength": {
        "type": "long"
      },
      "lastModified": {
        "type": "long"
      },
      "ruleId": {

)

// initDataScanner will start the scanner in the background.
func initDataScanner(ctx context.Context, objAPI ObjectLayer) {
	go func() {
		r := rand.New(rand.NewSource(time.Now().UnixNano()))
		// Run the data scanner in a loop
		for {
			runDataScanner(ctx, objAPI)
			duration := time.Duration(r.Float64() * float64(scannerCycle.Load()))
			if duration < time.Second {

# Extra Data Types

Up to now, you have been using common data types, like:

* `int`
* `float`
* `str`
* `bool`

But you can also use more complex data types.

And you will still have the same features as seen up to now:

* Great editor support.
* Data conversion from incoming requests.
* Data conversion for response data.
* Data validation.
* Automatic annotation and documentation.

## Other data types

		hashPathCutSet = dataUsageRoot + string(filepath.Separator)
	}
}

// hashPath calculates a hash of the provided string.
func hashPath(data string) dataUsageHash {
	if data != dataUsageRoot {
		data = strings.Trim(data, hashPathCutSet)
	}
	return dataUsageHash(path.Clean(data))
}

//msgp:ignore dataUsageHashMap
type dataUsageHashMap map[string]struct{}

// DecodeMsg implements msgp.Decodable

    * In Requests und Responses wird es wie ein `float` behandelt.
* Sie können alle gültigen Pydantic-Datentypen hier überprüfen: <a href="https://docs.pydantic.dev/latest/usage/types/types/" class="external-link" target="_blank">Pydantic data types</a>.

## Beispiel

Hier ist ein Beispiel für eine *Pfadoperation* mit Parametern, die einige der oben genannten Typen verwenden.

//// tab | Python 3.10+

```Python hl_lines="1  3  12-16"

* `Decimal`:
    * 标准的 Python `Decimal`。
    * 在请求和响应中被当做 `float` 一样处理。
* 您可以在这里检查所有有效的pydantic数据类型: <a href="https://docs.pydantic.dev/latest/concepts/types/" class="external-link" target="_blank">Pydantic data types</a>.

## 例子

下面是一个*路径操作*的示例，其中的参数使用了上面的一些类型。

//// tab | Python 3.10+

```Python hl_lines="1  3  12-16"
{!> ../../docs_src/extra_data_types/tutorial001_an_py310.py!}
```

////

}

// DataUsageInfo represents data usage stats of the underlying Object API
type DataUsageInfo struct {
	TotalCapacity     uint64 `json:"capacity,omitempty"`
	TotalUsedCapacity uint64 `json:"usedCapacity,omitempty"`
	TotalFreeCapacity uint64 `json:"freeCapacity,omitempty"`

	// LastUpdate is the timestamp of when the data usage info was last updated.
	// This does not indicate a full scan.