* 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"

  fun multiline() {
    consumeEvents(
      """
      |data: YHOO
      |data: +2
      |data: 10
      |
      |
      """.trimMargin(),
    )
    assertThat(callbacks.remove()).isEqualTo(Event(null, null, "YHOO\n+2\n10"))
  }

  @Test
  fun multilineCr() {
    consumeEvents(
      """
      |data: YHOO
      |data: +2
      |data: 10
      |
      |
      """.trimMargin().replace("\n", "\r"),

  }

  data class CallStart(
    override val timestampNs: Long,
    override val call: Call,
  ) : CallEvent()

  data class CallEnd(
    override val timestampNs: Long,
    override val call: Call,
  ) : CallEvent() {
    override fun closes(event: CallEvent): Boolean = event is CallStart && call == event.call
  }

  data class CallFailed(
    override val timestampNs: Long,

Lambda

MinIO's Object Lambda implementation allows for transforming your data to serve unique data format requirements for each application. For example, a dataset created by an ecommerce application might include personally identifiable information (PII). When the same data is processed for analytics, PII should be redacted. However, if the same dataset is used for a marketing campaign, you might need to enrich the data with additional details, such as information from the customer loyalty database....

It doesn't return a large `str` containing the data in JSON format (as a string). It returns a Python standard data structure (e.g. a `dict`) with values and sub-values that are all compatible with JSON.

/// note

`jsonable_encoder` is actually used by **FastAPI** internally to convert data. But it is useful in many other scenarios.

- MinIO exports Prometheus compatible data by default as an authorized endpoint at `/minio/v2/metrics/cluster`. 

* Read the body of the request as JSON.
* Convert the corresponding types (if needed).
* Validate the data.
    * If the data is invalid, it will return a nice and clear error, indicating exactly where and what was the incorrect data.
* Give you the received data in the parameter `item`.

/**
 * A bidirectional sequence of data frames exchanged between client and server.
 */
@ExperimentalOkHttpApi
interface Stream {
  val requestBody: BufferedSource
  val responseBody: BufferedSink

  /**
   * Terminate the stream so that no further data is transmitted or received. Note that
   * [requestBody] may return data after this call; that is the buffered data received before this
   * stream was canceled.
   *

    assertRoundTrip(buf.toByteString())
  }

  private fun assertRoundTrip(data: ByteString) {
    val encodeBuffer = Buffer()
    encode(data, encodeBuffer)
    assertThat(encodedLength(data).toLong()).isEqualTo(encodeBuffer.size)
    val decodeBuffer = Buffer()
    decode(encodeBuffer, encodeBuffer.size, decodeBuffer)
    assertEquals(data, decodeBuffer.readByteString())
  }

## Recap

With **FastAPI**, by using short, intuitive and standard Python type declarations, you get:

* Editor support: error checks, autocompletion, etc.
* Data "<abbr title="converting the string that comes from an HTTP request into Python data">parsing</abbr>"
* Data validation
* API annotation and automatic documentation

And you only have to declare them once.