There's also the chance that for some reason you have a **spike** of usage of your API. Maybe it went viral, or maybe some other services or bots start using it. And you might want to have extra resources to be safe in those cases.

Es besteht auch die Möglichkeit, dass es aus irgendeinem Grund zu **Spitzen** in der Nutzung Ihrer API kommt. Vielleicht ist diese viral gegangen, oder vielleicht haben andere Dienste oder Bots damit begonnen, sie zu nutzen. Und vielleicht möchten Sie in solchen Fällen über zusätzliche Ressourcen verfügen, um auf der sicheren Seite zu sein.

This way, you can get the best of both worlds: type annotations with **tooling support** and **data filtering**.

## See it in the docs

When you see the automatic docs, you can check that the input model and output model will both have their own JSON Schema:

<img src="/img/tutorial/response-model/image01.png">

And both models will be used for the interactive API documentation:

Esse é o caso da maioria das aplicações web.

Geralmente são muitos usuários, e seu servidor está esperando pelas suas conexões não tão boas para enviar as requisições.

E então esperando novamente pelas respostas voltarem.

Essa "espera" é medida em microssegundos, e ainda assim, somando tudo, é um monte de espera no final.

# Concurrence et les mots-clés async et await

Cette page vise à fournir des détails sur la syntaxe `async def` pour les *fonctions de chemins* et quelques rappels sur le code asynchrone, la concurrence et le parallélisme.

## Vous êtes pressés ?

<abbr title="'too long; didn't read' en anglais, ou 'trop long ; j'ai pas lu'"><strong>TL;DR :</strong></abbr>

Si vous utilisez des bibliothèques tierces qui nécessitent d'être appelées avec `await`, telles que :

=== "Python 3.10+"

    ```Python
    q: Annotated[str | None] = None
    ```

=== "Python 3.8+"

    ```Python
    q: Annotated[Union[str, None]] = None
    ```

Both of those versions mean the same thing, `q` is a parameter that can be a `str` or `None`, and by default, it is `None`.

Now let's jump to the fun stuff. 🎉

## Add `Query` to `Annotated` in the `q` parameter

### Concurrency and Burgers

This idea of **asynchronous** code described above is also sometimes called **"concurrency"**. It is different from **"parallelism"**.

**Concurrency** and **parallelism** both relate to "different things happening more or less at the same time".

But the details between *concurrency* and *parallelism* are quite different.

To see the difference, imagine the following story about burgers:

We want to get the `current_user` *only* if this user is active.

So, we create an additional dependency `get_current_active_user` that in turn uses `get_current_user` as a dependency.

Both of these dependencies will just return an HTTP error if the user doesn't exist, or if is inactive.

So, in our endpoint, we will only get a user if the user exists, was correctly authenticated, and is active:

    These Pydantic models define more or less a "schema" (a valid data shape).

    So this will help us avoiding confusion while using both.

### Create the Pydantic *models* / schemas

Create all the same Pydantic models as in the SQLAlchemy tutorial:

```Python hl_lines="16-18  21-22  25-30  34-35  38-39  42-48"

You will see the JSON response as:

```JSON
{"item_id": 5, "q": "somequery"}
```

You already created an API that:

* Receives HTTP requests in the _paths_ `/` and `/items/{item_id}`.
* Both _paths_ take `GET` operations (also known as HTTP _methods_).
* The _path_ `/items/{item_id}` has a _path parameter_ `item_id` that should be an `int`.