* If you are comparing Uvicorn, compare it against Daphne, Hypercorn, uWSGI, etc. Application servers.
* **Starlette**:
    * Will have the next best performance, after Uvicorn. In fact, Starlette uses Uvicorn to run. So, it probably can only get "slower" than Uvicorn by having to execute more code.
    * But it provides you the tools to build simple web applications, with routing based on paths, etc.

Wir werden sie auch „**Operationen**“ nennen.

#### Definieren eines *Pfadoperation-Dekorators*

```Python hl_lines="6"
{!../../../docs_src/first_steps/tutorial001.py!}
```

Das `@app.get("/")` sagt **FastAPI**, dass die Funktion direkt darunter für die Bearbeitung von Anfragen zuständig ist, die an:

 * den Pfad `/`
 * unter der Verwendung der <abbr title="eine HTTP GET Methode"><code>get</code>-Operation</abbr> gehen

Many of the tutorials have blocks of code.

In most of the cases, these blocks of code are actual complete applications that can be run as is.

In fact, those blocks of code are not written inside the Markdown, they are Python files in the `./docs_src/` directory.

And those Python files are included/injected in the documentation when generating the site.

### Docs for tests

For now, you just need to know that `Annotated` exists, and that it's standard Python. 😎

Later you will see how **powerful** it can be.

!!! tip
    The fact that this is **standard Python** means that you will still get the **best possible developer experience** in your editor, with the tools you use to analyze and refactor your code, etc. ✨

**FastAPI** sait quoi faire dans chaque cas et comment réutiliser le même objet, afin que tous les paramètres de type `BackgroundTasks` soient fusionnés et que les tâches soient exécutées en arrière-plan :

```Python hl_lines="13  15  22  25"

* resources
* providers
* services
* injectables
* components

## **FastAPI** plug-ins

Integrations and "plug-ins" can be built using the **Dependency Injection** system. But in fact, there is actually **no need to create "plug-ins"**, as by using dependencies it's possible to declare an infinite number of integrations and interactions that become available to your *path operation functions*.

<img src="/img/tutorial/security/image11.png">

Wenn Sie keinen Scope auswählen, werden Sie „authentifiziert“, aber wenn Sie versuchen, auf `/users/me/` oder `/users/me/items/` zuzugreifen, wird eine Fehlermeldung angezeigt, die sagt, dass Sie nicht über genügend Berechtigungen verfügen. Sie können aber auf `/status/` zugreifen.

But you can return a `JSONResponse` directly from your *path operations*.

It might be useful, for example, to return custom headers or cookies.

## Return a `Response`

In fact, you can return any `Response` or any sub-class of it.

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

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

But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for **CPU bound** workloads like those in Machine Learning systems.

That, plus the simple fact that Python is the main language for **Data Science**, Machine Learning and especially Deep Learning, make FastAPI a very good match for Data Science / Machine Learning web APIs and applications (among many others).

You could do that with `openapi_extra`:

```Python hl_lines="20-37  39-40"
{!../../../docs_src/path_operation_advanced_configuration/tutorial006.py!}
```