}
            ]
        }
    )


@needs_py310
def test_post_with_only_name_price(client: TestClient):
    response = client.post("/items/", json={"name": "Foo", "price": "twenty"})
    assert response.status_code == 422
    assert response.json() == IsDict(
        {
            "detail": [
                {
                    "type": "float_parsing",

                }
            ]
        }
    )


def test_post_with_only_name_price(client: TestClient):
    response = client.post("/items/", json={"name": "Foo", "price": "twenty"})
    assert response.status_code == 422
    assert response.json() == IsDict(
        {
            "detail": [
                {
                    "type": "float_parsing",

* Wsparcie dla **WebSocket**.
* <abbr title='Zadania wykonywane w tle, bez zatrzymywania żądań, w tym samym procesie. Po angielsku: In-process background tasks'>Zadania w tle</abbr>.
* Eventy startup i shutdown.
* Klient testowy zbudowany na bazie biblioteki `requests`.
* **CORS**, GZip, pliki statyczne, streamy.
* Obsługa **sesji i ciasteczek**.
* 100% pokrycie testami.
* 100% adnotacji typów.

Instead of that, by being an "asynchronous" system, once finished, the task can wait in line a little bit (some microseconds) for the computer / program to finish whatever it went to do, and then come back to take the results and continue working with them.

In this case, it would be better to get **one extra server** and run some processes on it so that they all have **enough RAM and CPU time**.

There's also the chance that for some reason you have a **spike** of usage of your API. Maybe it went viral, or maybe some other services or bots start using it. And you might want to have extra resources to be safe in those cases.

The important thing is that by using standard Python types, in a single place (instead of adding more classes, decorators, etc), **FastAPI** will do a lot of the work for you.

!!! info