When you check the benchmarks, it is common to see several tools of different types compared as equivalent.

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

            """
        ),
    ] = None,
    alias_priority: Annotated[
        Union[int, None],
        Doc(
            """
            Priority of the alias. This affects whether an alias generator is used.
            """
        ),
    ] = _Unset,
    # TODO: update when deprecating Pydantic v1, import these types
    # validation_alias: str | AliasPath | AliasChoices | None

* If you translate pages, add a single pull request per page translated. That will make it much easier for others to review it.

* To check the 2-letter code for the language you want to translate, you can use the table <a href="https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes" class="external-link" target="_blank">List of ISO 639-1 codes</a>.

#### Existing language

```console
$ hypercorn main:app --worker-class trio
```

</div>

And that will start Hypercorn with your app using Trio as the backend.

Now you can use Trio internally in your app. Or even better, you can use AnyIO, to keep your code compatible with both Trio and asyncio. 🎉

## Deployment Concepts

But I'll show you how to improve that next. 🤓

## Custom Operation IDs and Better Method Names

You can **modify** the way these operation IDs are **generated** to make them simpler and have **simpler method names** in the clients.

In this case you will have to ensure that each operation ID is **unique** in some other way.

First let's see how editors, mypy and other tools would see this.

`BaseUser` has the base fields. Then `UserIn` inherits from `BaseUser` and adds the `password` field, so, it will include all the fields from both models.

We annotate the function return type as `BaseUser`, but we are actually returning a `UserIn` instance.

    Then APIStar stopped to exist as a server and Starlette was created, and was a new better foundation for such a system. That was the final inspiration to build **FastAPI**.

    I consider **FastAPI** a "spiritual successor" to APIStar, while improving and increasing the features, typing system, and other parts, based on the learnings from all these previous tools.

## Used by **FastAPI**

# Reference - Code API

Here's the reference or code API, the classes, functions, parameters, attributes, and
all the FastAPI parts you can use in your applications.

If you want to **learn FastAPI** you are much better off reading the

If you want to secure your API, there are several better things you can do, for example:

* Make sure you have well defined Pydantic models for your request bodies and responses.
* Configure any required permissions and roles using dependencies.

In that case, it could be better to have only 2 servers and use a higher percentage of their resources (CPU, memory, disk, network bandwidth, etc).