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.

{* ../../docs_src/configure_swagger_ui/tutorial002_py39.py hl[3] *}

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 { #change-default-swagger-ui-parameters }

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.

`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...

![hdfs-configs](https://github.com/minio/minio/blob/master/docs/bigdata/images/image2.png?raw=true "hdfs advanced configs")

Navigate to **Custom core-site** to configure MinIO parameters for `_s3a_` connector

![s3a-config](https://github.com/minio/minio/blob/master/docs/bigdata/images/image5.png?raw=true "custom core-site")

```
sudo pip install yq
alias kv-pairify='yq ".configuration[]" | jq ".[]" | jq -r ".name + \"=\" + .value"'
```

#### Configuration Migration

Map Solr configuration to Fess:

| Solr Feature | Fess Equivalent |
|--------------|----------------|
| Solr Core | Fess doesn't use cores; all documents in one index |
| Schema Fields | Define in crawl configuration or use default fields |
| RequestHandlers | Use Fess JSON API with parameters |
| Facet Fields | Configure in Fess search settings |

Por exemplo:

* **Processamento de áudio** ou **imagem**
* **Visão Computacional**: uma imagem é composta por milhões de pixels, cada pixel tem 3 valores / cores, processar isso normalmente exige alguma computação em todos esses pixels ao mesmo tempo

### Multiple Processes - Workers { #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...

  - [Known Issues and Important Steps before Upgrading](#known-issues-and-important-steps-before-upgrading)
      - [ThirdPartyResource](#thirdpartyresource)
      - [kubectl](#kubectl)
      - [kubernetes Core Known Issues](#kubernetes-core-known-issues)
      - [Docker runtime Known Issues](#docker-runtime-known-issues)
      - [Rkt runtime Known Issues](#rkt-runtime-known-issues)
  - [Provider-specific Notes](#provider-specific-notes)