name: Mint Tests

on:
  pull_request:
    branches:
      - master

# This ensures that previous jobs for the PR are canceled when the PR is
# updated.
concurrency:
  group: ${{ github.workflow }}-${{ github.head_ref }}
  cancel-in-progress: true

permissions:
  contents: read

jobs:
  mint-test:
    runs-on: mint
    timeout-minutes: 120
    steps:

export JOB_NAME="$4"
export MINT_MODE="full"

docker system prune -f || true
docker volume prune -f || true
docker volume rm $(docker volume ls -f dangling=true) || true

## change working directory
cd .github/workflows/mint

docker-compose -f minio-${MODE}.yaml up -d
sleep 1m

docker system prune -f || true
docker volume prune -f || true
docker volume rm $(docker volume ls -q -f dangling=true) || true

Carlos Amedee <******@****.***> 1715265901 -0400

But when checking benchmarks and comparisons you should keep the following in mind.

## Benchmarks and speed

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).

ed){var p1=axis.c2p(c1[axis.direction]),p2=axis.c2p(c2[axis.direction]);r[name]={from:Math.min(p1,p2),to:Math.max(p1,p2)}}});return r}function triggerSelectedEvent(){var r=getSelection();plot.getPlaceholder().trigger("plotselected",[r]);if(r.xaxis&&r.yaxis)plot.getPlaceholder().trigger("selected",[{x1:r.xaxis.from,y1:r.yaxis.from,x2:r.xaxis.to,y2:r.yaxis.to}])}function clamp(min,value,max){return value<min?min:value>max?max:value}function setSelectionPos(pos,e){var o=plot.getOptions();var offset...

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.

!!! info
    Keep in mind that dataclasses can't do everything Pydantic models can do.

    So, you might still need to use Pydantic models.

<img src="/img/tutorial/websockets/image02.png">

And your **FastAPI** application with WebSockets will respond back:

<img src="/img/tutorial/websockets/image03.png">

You can send (and receive) many messages:

<img src="/img/tutorial/websockets/image04.png">

And all of them will use the same WebSocket connection.

## Using `Depends` and others

## Future

By this point, it's already clear that **FastAPI** with its ideas is being useful for many people.

It is being chosen over previous alternatives for suiting many use cases better.

Many developers and teams already depend on **FastAPI** for their projects (including me and my team).

But still, there are many improvements and features to come.

**FastAPI** has a great future ahead.

```Python hl_lines="18  22"
{!../../../docs_src/additional_responses/tutorial001.py!}
```

!!! note
    Keep in mind that you have to return the `JSONResponse` directly.

!!! info
    The `model` key is not part of OpenAPI.

    **FastAPI** will take the Pydantic model from there, generate the `JSON Schema`, and put it in the correct place.

// and against which we measure optimized parsers.
//
// parseIPSlow understands the following forms of IP addresses:
//   - Regular IPv4: 1.2.3.4
//   - IPv4 with many leading zeros: 0000001.0000002.0000003.0000004
//   - Regular IPv6: 1111:2222:3333:4444:5555:6666:7777:8888
//   - IPv6 with many leading zeros: 00000001:0000002:0000003:0000004:0000005:0000006:0000007:0000008
//   - IPv6 with zero blocks elided: 1111:2222::7777:8888