### FastAPI Data Filtering

Now, for FastAPI, it will see the return type and make sure that what you return includes **only** the fields that are declared in the type.

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.

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

You can also combine `dataclasses` with other type annotations to make nested data structures.

In some cases, you might still have to use Pydantic's version of `dataclasses`. For example, if you have errors with the automatically generated API documentation.

And then you can also have a path `/users/{user_id}` to get data about a specific user by some user ID.

Because *path operations* are evaluated in order, you need to make sure that the path for `/users/me` is declared before the one for `/users/{user_id}`:

```Python hl_lines="6  11"
{!../../../docs_src/path_params/tutorial003.py!}
```

These examples run the server program (e.g Uvicorn), starting **a single process**, listening on all the IPs (`0.0.0.0`) on a predefined port (e.g. `80`).

This is the basic idea. But you will probably want to take care of some additional things, like:

* Security - HTTPS
* Running on startup
* Restarts
* Replication (the number of processes running)
* Memory
* Previous steps before starting

This schema definition includes your API paths, the possible parameters they take, etc.

#### Data "schema"

The term "schema" might also refer to the shape of some data, like a JSON content.

In that case, it would mean the JSON attributes, and data types they have, etc.

It requires a little bit more verbose configurations. And as it is based on WSGI (instead of ASGI), it is not designed to take advantage of the high performance provided by tools like Uvicorn, Starlette and Sanic.

* Decompressing gzip-compressed request bodies.
* Automatically logging all request bodies.

## Handling custom request body encodings

Let's see how to make use of a custom `Request` subclass to decompress gzip requests.

And an `APIRoute` subclass to use that custom request class.

### Create a custom `GzipRequest` class

!!! tip

    ```Python hl_lines="7  48  55-56  59-60  69-75"
    {!> ../../../docs_src/security/tutorial004.py!}
    ```

!!! note
    If you check the new (fake) database `fake_users_db`, you will see how the hashed password looks like now: `"$2b$12$EixZaYVK1fsbw1ZfbX3OXePaWxn96p36WQoeG6Lruj3vjPGga31lW"`.

## Handle JWT tokens

Import the modules installed.

I'll give you more concrete examples in the next chapters.

## Restarts

Similar to making sure your application is run on startup, you probably also want to make sure it is **restarted** after failures.

### We Make Mistakes

We, as humans, make **mistakes**, all the time. Software almost *always* has **bugs** hidden in different places. 🐛