from fastapi import APIRouter, Body

router = APIRouter()


@router.post("/compute")
def compute(a: int = Body(), b: str = Body()):

                    },
                    "summary": "Compute",
                    "operationId": "compute_a_compute_post",
                    "requestBody": {
                        "content": {
                            "application/json": {
                                "schema": {
                                    "$ref": "#/components/schemas/Body_compute_a_compute_post"
                                }

from fastapi import APIRouter, Body

router = APIRouter()


@router.post("/compute/")
def compute(a: int = Body(), b: str = Body()):

* Each application that you have running on your computer has some process behind it, each running program, each window, etc. And there are normally many processes running **at the same time** while a computer is on.
* There can be **multiple processes** of the **same program** running at the same time.

from .utils import needs_pydanticv2


@pytest.fixture(name="client")
def get_client():
    app = FastAPI()

    from pydantic import BaseModel, computed_field

    class Rectangle(BaseModel):
        width: int
        length: int

        @computed_field
        @property
        def area(self) -> int:
            return self.width * self.length

    @app.get("/")
    def read_root() -> Rectangle:

## Server Machine and Server Program

There's a small detail about names to keep in mind. 💡

The word "**server**" is commonly used to refer to both the remote/cloud computer (the physical or virtual machine) and also the program that is running on that machine (e.g. Uvicorn).

Just keep in mind that when you read "server" in general, it could refer to one of those two things.

```hl_lines="4"
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
```

Diese Zeile zeigt die URL, unter der Ihre Anwendung auf Ihrem lokalen Computer bereitgestellt wird.

### Testen Sie es

Öffnen Sie Ihren Browser unter <a href="http://127.0.0.1:8000" class="external-link" target="_blank">http://127.0.0.1:8000.</a>

Sie werden folgende JSON-Response sehen:

Asynchronous code just means that the language 💬 has a way to tell the computer / program 🤖 that at some point in the code, it 🤖 will have to wait for *something else* to finish somewhere else. Let's say that *something else* is called "slow-file" 📝.

So, during that time, the computer can go and do some other work, while "slow-file" 📝 finishes.

Mais en prêtant attention aux tests de performance et aux comparaisons, il faut tenir compte de ce qu'il suit.

## Tests de performance et rapidité

Lorsque vous vérifiez les tests de performance, il est commun de voir plusieurs outils de différents types comparés comme équivalents.

* 🔧 Update sponsors badges. PR [#5943](https://github.com/tiangolo/fastapi/pull/5943) by [@tiangolo](https://github.com/tiangolo).
* ✨ Compute FastAPI Experts including GitHub Discussions. PR [#5941](https://github.com/tiangolo/fastapi/pull/5941) by [@tiangolo](https://github.com/tiangolo).