├── schemas.py
    └── tests
        ├── __init__.py
        └── test_sql_app.py
```

## Create the new database session

First, we create a new database session with the new database.

We'll use an in-memory database that persists during the tests instead of the local file `sql_app.db`.

But the rest of the session code is more or less the same, we just copy it.

```Python hl_lines="8-13"

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

* 403: `status.HTTP_403_FORBIDDEN`
* etc.

It can be convenient to quickly access HTTP (and WebSocket) status codes in your app, using autocompletion for the name without having to remember the integer status codes by memory.

Read more about it in the [FastAPI docs about Response Status Code](https://fastapi.tiangolo.com/tutorial/response-status-code/).

## Example

```python
from fastapi import FastAPI, status

app = FastAPI()

# Server Workers - Gunicorn with Uvicorn

Let's check back those deployment concepts from before:

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

Up to this point, with all the tutorials in the docs, you have probably been running a **server program** like Uvicorn, running a **single process**.

Específicamente, para ver Uvicorn, Starlette y FastAPI comparadas entre sí (entre muchas otras herramientas).

Cuanto más sencillo sea el problema resuelto por la herramienta, mejor rendimiento obtendrá. Y la mayoría de los benchmarks no prueban las funciones adicionales proporcionadas por la herramienta.

La jerarquía sería:

* **Uvicorn**: como servidor ASGI

```

!!! tip
    The app above is a minimal and simple example to demonstrate how to handle and broadcast messages to several WebSocket connections.

    But keep in mind that, as everything is handled in memory, in a single list, it will only work while the process is running, and will only work with a single process.

## Caveat

If you need to perform heavy background computation and you don't necessarily need it to be run by the same process (for example, you don't need to share memory, variables, etc), you might benefit from using other bigger tools like <a href="https://docs.celeryq.dev" class="external-link" target="_blank">Celery</a>.

And then, right after the `yield`, we unload the model. This code will be executed **after** the application **finishes handling requests**, right before the *shutdown*. This could, for example, release resources like memory or a GPU.

!!! tip
    The `shutdown` would happen when you are **stopping** the application.

    Maybe you need to start a new version, or you just got tired of running it. 🤷

### Lifespan function

<img src="/img/tutorial/path-params/image03.png">

### Trabalhando com os *enumeration* do Python

O valor do *parâmetro da rota* será um *membro de enumeration*.

#### Compare *membros de enumeration*

Você pode comparar eles com o *membro de enumeration* no enum `ModelName` que você criou:

```Python hl_lines="17"
{!../../../docs_src/path_params/tutorial005.py!}
```

!!! tip
    La versión "MINOR" es el número en el medio, por ejemplo, en `0.2.3`, la <abbr title="versión menor">"MINOR" version</abbr> es `2`.

## Actualizando las versiones de FastAPI

Para esto es recomendable primero añadir tests a tu aplicación.