# 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

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

=== "Python 3.9+"

    ```Python hl_lines="1  3  12-16"
    {!> ../../../docs_src/extra_data_types/tutorial001_an_py39.py!}
    ```

=== "Python 3.8+"

    ```Python hl_lines="1  3  13-17"
    {!> ../../../docs_src/extra_data_types/tutorial001_an.py!}
    ```

=== "Python 3.10+ non-Annotated"

=== "Python 3.8 и выше"

    ```Python hl_lines="1  3  12-16"
    {!> ../../../docs_src/extra_data_types/tutorial001.py!}
    ```

=== "Python 3.10 и выше"

    ```Python hl_lines="1  2  11-15"
    {!> ../../../docs_src/extra_data_types/tutorial001_py310.py!}
    ```

```Python hl_lines="1  3  12-16"
{!../../../docs_src/extra_data_types/tutorial001.py!}
```

Note que os parâmetros dentro da função tem seu tipo de dados natural, e você pode, por exemplo, realizar manipulações normais de data, como:

```Python hl_lines="18-19"
{!../../../docs_src/extra_data_types/tutorial001.py!}

  @Test
  fun largeRead() {
    val data = randomByteString(1000000)
    write(data)
    val operator =
      FileOperator(
        randomAccessFile!!.getChannel(),
      )
    val buffer = Buffer()
    operator.read(0, buffer, data.size.toLong())
    assertThat(buffer.readByteString()).isEqualTo(data)
  }

  @Test
  fun largeWrite() {
    val data = randomByteString(1000000)
    val operator =

    `JSONResponse` itself is a sub-class of `Response`.

And when you return a `Response`, **FastAPI** will pass it directly.

It won't do any data conversion with Pydantic models, it won't convert the contents to any type, etc.

This gives you a lot of flexibility. You can return any data type, override any data declaration or validation, etc.

## Using the `jsonable_encoder` in a `Response`

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....

(A SQL separator is semicolon ";")

take-finally-*.sql:
SQL statements for check loaded data (or DDL after data loading)
You should write your own SQL statements in this file.
(basically same specifications as replace-schema.sql)

The "data" directory is for data loading like this:
/- - - - - - - - - - - - - - - - - - - -
playsql
  |-data
     |-common
     |  |-xls
     |     |-10-master.xls
     |     |-defaultValueMap.dataprop

    drainCancelQueue(eventSource)
  }

  override fun onEvent(
    eventSource: EventSource,
    id: String?,
    type: String?,
    data: String,
  ) {
    get().log("[ES] onEvent", Platform.INFO, null)
    events.add(Event(id, type, data))
    drainCancelQueue(eventSource)
  }

  override fun onClosed(eventSource: EventSource) {
    get().log("[ES] onClosed", Platform.INFO, null)

### Remotely Inspecting backend data