When deploying applications you will probably want to have some **replication of processes** to take advantage of **multiple cores** and to be able to handle more requests.

As you saw in the previous chapter about [Deployment Concepts](concepts.md){.internal-link target=_blank}, there are multiple strategies you can use.

```Python hl_lines="3"
{!../../docs_src/configure_swagger_ui/tutorial002.py!}
```

Essa configuração alteraria o tema de cores de destaque de sintaxe:

<img src="/img/tutorial/extending-openapi/image04.png">

## Alterar parâmetros de UI padrão do Swagger

streaming compression due to its stability and performance.

This algorithm is specifically optimized for machine generated content.
Write throughput is typically at least 500MB/s per CPU core,
and scales with the number of available CPU cores.
Decompression speed is typically at least 1GB/s.

This means that in cases where raw IO is below these numbers
compression will not only reduce disk usage but also help increase system throughput.

    /**
     * ProjectBuilder parallelism.
     *
     * @since 4.0.0
     */
    @Config(type = "java.lang.Integer", defaultValue = "cores/2 + 1")
    public static final String MAVEN_MODEL_BUILDER_PARALLELISM = "maven.modelBuilder.parallelism";

    /**
     * User property for enabling/disabling the consumer POM feature.
     *
     * @since 4.0.0

`max_sleep` to a *lower* value and setting `max_io` to a *higher* value would make heal go faster.

Each node is responsible of healing its local drives; Each drive will have multiple heal workers which is the quarter of the number of CPU cores of the node or the quarter of the configured nr_requests of the drive (https://www.kernel.org/doc/Documentation/block/queue-sysfs.txt). It is also possible to provide a custom number of workers by using this command: `mc admin config set alias/ heal...

Por exemplo:

* **Processamento de áudio** ou **imagem**
* **Visão do Computador**: uma imagem é composta por milhões de pixels, cada pixel tem 3 valores (cores, processamento que normalmente exige alguma computação em todos esses pixels ao mesmo tempo)

### Multiple Processes - Workers

If you have more clients than what a single process can handle (for example if the virtual machine is not too big) and you have **multiple cores** in the server's CPU, then you could have **multiple processes** running with the same application at the same time, and distribute all the requests among them.

process** (e.g. a Uvicorn process running your FastAPI application). They would all be **identical containers**, running the same thing, but each with its own process, memory, etc. That way you would take advantage of **parallelization** in **different cores** of the CPU, or even in **different machines**.

And the distributed container system with the **load balancer** would **distribute the requests** to each one of the containers with your app **in turns**. So, each request could be handled...

# Additional Status Codes

By default, **FastAPI** will return the responses using a `JSONResponse`, putting the content you return from your *path operation* inside of that `JSONResponse`.

It will use the default status code or the one you set in your *path operation*.

## Additional status codes

## Mais informações

Para mais informações <abbr title="Cross-Origin Resource Sharing">CORS</abbr>, acesse <a href="https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS" class="external-link" target="_blank">Mozilla CORS documentation</a>.

/// note | "Detalhes técnicos"

Você também pode usar `from starlette.middleware.cors import CORSMiddleware`.