For example, it doesn't receive `datetime` objects, as those are not compatible with JSON.

So, a `datetime` object would have to be converted to a `str` containing the data in <a href="https://en.wikipedia.org/wiki/ISO_8601" class="external-link" target="_blank">ISO format</a>.

The same way, this database wouldn't receive a Pydantic model (an object with attributes), only a `dict`.

You can use `jsonable_encoder` for that.

* **Uvicorn**:
    * 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.

The history of **FastAPI** is in great part the history of its predecessors.

As said in the section [Alternatives](alternatives.md){.internal-link target=_blank}:

<blockquote markdown="1">

**FastAPI** wouldn't exist if not for the previous work of others.

There have been many tools created before that have helped inspire its creation.

The way you use it is very simple. For example, to do a `GET` request, you would write:

```Python
response = requests.get("http://example.com/some/url")
```

The FastAPI counterpart API *path operation* could look like:

```Python hl_lines="1"
@app.get("/some/url")
def read_url():
    return {"message": "Hello World"}
```

See the similarities in `requests.get(...)` and `@app.get(...)`.

#### A "professional" attack

Of course, the attackers would not try all this by hand, they would write a program to do it, possibly with thousands or millions of tests per second. And would get just one extra correct letter at a time.

But doing that, in some minutes or hours the attackers would have guessed the correct username and password, with the "help" of our application, just using the time taken to answer.

This Manager Process would probably be the one listening on the **port** in the IP. And it would transmit all the communication to the worker processes.

Those worker processes would be the ones running your application, they would perform the main computations to receive a **request** and return a **response**, and they would load anything you put in variables in RAM.

---

But for this example, we'll use a very simple HTML document with some JavaScript, all inside a long string.

This, of course, is not optimal and you wouldn't use it for production.

In production you would have one of the options above.

But it's the simplest way to focus on the server-side of WebSockets and have a working example:

```Python hl_lines="2  6-38  41-43"
{!../../../docs_src/websockets/tutorial001.py!}
```

You could have turns as in the burgers example, first the living room, then the kitchen, but as you are not waiting 🕙 for anything, just cleaning and cleaning, the turns wouldn't affect anything.

It would take the same amount of time to finish with or without turns (concurrency) and you would have done the same amount of work.

And it was thoroughly tested at the design phase, before any implementation, to ensure it would work with all the editors.

There were even some changes to Pydantic itself to support this.

The previous screenshots were taken with <a href="https://code.visualstudio.com" class="external-link" target="_blank">Visual Studio Code</a>.

</div>

#### Generate Client Code

To generate the client code you can use the command line application `openapi-ts` that would now be installed.

Because it is installed in the local project, you probably wouldn't be able to call that command directly, but you would put it on your `package.json` file.

It could look like this:

```JSON  hl_lines="7"
{
  "name": "frontend-app",
  "version": "1.0.0",
  "description": "",