Specifically, to see Uvicorn, Starlette and FastAPI compared together (among many other tools).

The simpler the problem solved by the tool, the better performance it will get. And most of the benchmarks don't test the additional features provided by the tool.

The hierarchy is like:

* **Uvicorn**: an ASGI server
    * **Starlette**: (uses Uvicorn) a web microframework

You can read more about this in the [Advanced User Guide](../advanced/index.md){.internal-link target=_blank}.

## Details

The first `"/static"` refers to the sub-path this "sub-application" will be "mounted" on. So, any path that starts with `"/static"` will be handled by it.

The `directory="static"` refers to the name of the directory that contains your static files.

The `name="static"` gives it a name that can be used internally by **FastAPI**.

You can also use `dataclasses` in the `response_model` parameter:

```Python hl_lines="1  7-13  19"
{!../../../docs_src/dataclasses/tutorial002.py!}
```

The dataclass will be automatically converted to a Pydantic dataclass.

This way, its schema will show up in the API docs user interface:

<img src="/img/tutorial/dataclasses/image01.png">

## Dataclasses in Nested Data Structures

Let's create a dependency `get_current_user`.

Remember that dependencies can have sub-dependencies?

`get_current_user` will have a dependency with the same `oauth2_scheme` we created before.

The same as we were doing before in the *path operation* directly, our new dependency `get_current_user` will receive a `token` as a `str` from the sub-dependency `oauth2_scheme`:

=== "Python 3.10+"

    ```Python hl_lines="25"

# How To - Recipes

Here you will see different recipes or "how to" guides for **several topics**.

Most of these ideas would be more or less **independent**, and in most cases you should only need to study them if they apply directly to **your project**.

If something seems interesting and useful to your project, go ahead and check it, but otherwise, you might probably just skip them.

!!! tip

    <div class="termy">

    ```console
    $ pip install "uvicorn[standard]"

    ---> 100%
    ```

    </div>

    !!! tip
        By adding the `standard`, Uvicorn will install and use some recommended extra dependencies.

        That including `uvloop`, the high-performance drop-in replacement for `asyncio`, that provides the big concurrency performance boost.

## Run it

After that, you just need to install `pytest`:

<div class="termy">

```console
$ pip install pytest

---> 100%
```

</div>

It will detect the files and tests automatically, execute them, and report the results back to you.

Run the tests with:

<div class="termy">

```console
$ pytest

🚥 👆 ❎ 🏎 ✳, 👆 💪 ❎ 🛠️ ⏮️:

<div class="termy">

```console
$ typer --install-completion

zsh completion installed in /home/user/.bashrc.
Completion will take effect once you restart the terminal.
```

</div>

### 📱 &amp; 🩺 🎏 🕰

🚥 👆 🏃 🖼 ⏮️, ✅:

<div class="termy">

```console
$ uvicorn tutorial001:app --reload

* OS上で**実行**している特定のプログラム
    * これはファイルやコードを指すのではなく、OSによって**実行**され、管理されているものを指します。
* どんなプログラムやコードも、それが**実行されているときにだけ機能**します。つまり、**プロセスとして実行されているときだけ**です。
* プロセスは、ユーザーにあるいはOSによって、 **終了**（あるいは "kill"）させることができます。その時点で、プロセスは実行/実行されることを停止し、それ以降は**何もできなくなります**。
* コンピュータで実行されている各アプリケーションは、実行中のプログラムや各ウィンドウなど、その背後にいくつかのプロセスを持っています。そして通常、コンピュータが起動している間、**多くのプロセスが**同時に実行されています。
* **同じプログラム**の**複数のプロセス**が同時に実行されていることがあります。

### Server Memory

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.