* **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 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(...)`.

</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": "",

So, the frontend (that runs in the browser) would try to reach `/openapi.json` and wouldn't be able to get the OpenAPI schema.

Because we have a proxy with a path prefix of `/api/v1` for our app, the frontend needs to fetch the OpenAPI schema at `/api/v1/openapi.json`.

```mermaid
graph LR

browser("Browser")

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.

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.

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.

I only speak a few languages (and not very well 😅). So, the reviewers are the ones that have the [**power to approve translations**](contributing.md#translations){.internal-link target=_blank} of the documentation. Without them, there wouldn't be documentation in several other languages.

{% if people %}
<div class="user-list user-list-center">
{% for user in people.top_translations_reviewers[:50] %}

                """
                The bytes to write to the file.
                """
            ),
        ],
    ) -> None:
        """
        Write some bytes to the file.

        You normally wouldn't use this from a file you read in a request.

        To be awaitable, compatible with async, this is run in threadpool.
        """
        return await super().write(data)

    async def read(
        self,

---

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!}
```