sink.writeInt(errorCode.httpCode)
      sink.flush()
    }
  }

  /** The maximum size of bytes that may be sent in a single call to [data]. */
  fun maxDataLength(): Int = maxFrameSize

  /**
   * `source.length` may be longer than the max length of the variant's data frame. Implementations
   * must send multiple frames as necessary.
   *
   * @param source the buffer to draw bytes from. May be null if byteCount is 0.

  ) {
    if (streamId == 0) throw IOException("PROTOCOL_ERROR: TYPE_DATA streamId == 0")

    // TODO: checkState open or half-closed (local) or raise STREAM_CLOSED
    val inFinished = flags and FLAG_END_STREAM != 0
    val gzipped = flags and FLAG_COMPRESSED != 0
    if (gzipped) {
      throw IOException("PROTOCOL_ERROR: FLAG_COMPRESSED without SETTINGS_COMPRESS_DATA")
    }

    ```Python hl_lines="3  77-79"
    {!> ../../../docs_src/security/tutorial003.py!}
    ```

### Check the password

At this point we have the user data from our database, but we haven't checked the password.

Let's put that data in the Pydantic `UserInDB` model first.

You should never save plaintext passwords, so, we'll use the (fake) password hashing system.

wss:example.com/  s:wss h:example.com p:/
data:example.com/  s:data p:example.com/
javascript:example.com/  s:javascript p:example.com/
mailto:example.com/  s:mailto p:example.com/
/a/b/c  s:http h:example.org p:/a/b/c
/a/\s/c  s:http h:example.org p:/a/%20/c
/a%2fc  s:http h:example.org p:/a%2fc
/a/%2f/c  s:http h:example.org p:/a/%2f/c
\#\u03B2  s:http h:example.org p:/foo/bar f:#\u03B2
data:text/html,test#test  s:data p:text/html,test f:#test

e.g.:
To download `2021/taxes.csv` archived in `financial.zip` and stored under a bucket named `company-data`, you can issue a GET request using the following path 'company-data/financial.zip/2021/taxes.csv`

## Contents properties

## Why distributed MinIO?

MinIO in distributed mode can help you setup a highly-available storage system with a single object storage deployment. With distributed MinIO, you can optimally use storage devices, irrespective of their location in a network.

### Data protection

which is a fitness factor for a lossless data compression.

Pre-compressed input typically compresses in excess of 2GiB/s per core,
so performance impact should be minimal even if precompressed data is re-compressed.
Decompressing incompressible data has no significant performance impact.

```Python
my_user: User = User(id=3, name="John Doe", joined="2018-07-19")

second_user_data = {
    "id": 4,
    "name": "Mary",
    "joined": "2018-11-30",
}

my_second_user: User = User(**second_user_data)
```

!!! info
    `**second_user_data` یعنی:

    کلید ها و مقادیر دیکشنری `second_user_data` را مستقیما به عنوان ارگومان های key-value بفرست، که معادل است با : `User(id=4, name="Mary", joined="2018-11-30")`

    @VisibleForTesting
    data class AcceptedApiChange(
        val type: String?,
        val member: String?,
        val acceptation: String?,
        val changes: List<String>?
    ) {
        override fun toString(): String = "Type: '$type', Member: '$member'"
    }

    @VisibleForTesting
    data class AcceptedApiChanges(
        val acceptedApiChanges: List<AcceptedApiChange>?

!!! note "Hinweis Deutsche Übersetzung"