Just keep in mind that when you read "server" in general, it could refer to one of those two things.

When referring to the remote machine, it's common to call it **server**, but also **machine**, **VM** (virtual machine), **node**. Those all refer to some type of remote machine, normally running Linux, where you run programs.

## Install the Server Program

    * Will have the best performance, as it doesn't have much extra code apart from the server itself.
    * You wouldn't write an application in Uvicorn directly. That would mean that your code would have to include more or less, at least, all the code provided by Starlette (or **FastAPI**). And if you did that, your final application would have the same overhead as having used a framework and minimizing your app code and bugs.

## Instale o FastAPI

O primeiro passo é instalar o FastAPI.

Para o tutorial, você deve querer instalá-lo com todas as dependências e recursos opicionais.

<div class="termy">

```console
$ pip install "fastapi[all]"

---> 100%
```

</div>

...isso também inclui o `uvicorn`, que você pode usar como o servidor que rodará seu código.

!!! note "Nota"
    Você também pode instalar parte por parte.

    **FastAPI** provides the same `starlette.testclient` as `fastapi.testclient` just as a convenience for you, the developer. But it comes directly from Starlette.

!!! tip
    If you want to call `async` functions in your tests apart from sending requests to your FastAPI application (e.g. asynchronous database functions), have a look at the [Async Tests](../advanced/async-tests.md){.internal-link target=_blank} in the advanced tutorial.

* Create a `metadata` object.
* Create a table `notes` using the `metadata` object.

```Python hl_lines="4  14  16-22"
{!../../../docs_src/async_sql_databases/tutorial001.py!}
```

!!! tip
    Notice that all this code is pure SQLAlchemy Core.

    `databases` is not doing anything here yet.

## Import and set up `databases`

* Import `databases`.
* Create a `DATABASE_URL`.
* Create a `database` object.

You will see the automatic API docs for the sub-application, including only its own _path operations_, all under the correct sub-path prefix `/subapi`:

<img src="/img/tutorial/sub-applications/image02.png">

    # TODO: this should be part of some pydantic optional extra dependencies
    # # Settings management
    # "pydantic-settings >=2.0.0",
    # # Extra Pydantic data types
    # "pydantic-extra-types >=2.0.0",
]

all = [
    "fastapi-cli >=0.0.2",
    # # For the test client
    "httpx >=0.23.0",
    # For templates
    "jinja2 >=2.11.2",
    # For forms and file uploads
    "python-multipart >=0.0.7",

### Short

It has sensible **defaults** for everything, with optional configurations everywhere. All the parameters can be fine-tuned to do what you need and to define the API you need.

But by default, it all **"just works"**.

### Validation

* Validation for most (or all?) Python **data types**, including:
    * JSON objects (`dict`).
    * JSON array (`list`) defining item types.

# OpenAPI Webhooks

There are cases where you want to tell your API **users** that your app could call *their* app (sending a request) with some data, normally to **notify** of some type of **event**.

This means that instead of the normal process of your users sending requests to your API, it's **your API** (or your app) that could **send requests to their system** (to their API, their app).

This is normally called a **webhook**.

## Webhooks steps

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

and then we call:

```Python
user_dict = user_in.dict()
```

we now have a `dict` with the data in the variable `user_dict` (it's a `dict` instead of a Pydantic model object).

And if we call:

```Python
print(user_dict)
```

we would get a Python `dict` with:

```Python
{