* Configure any required permissions and roles using dependencies.
* Never store plaintext passwords, only password hashes.
* Implement and use well-known cryptographic tools, like Passlib and JWT tokens, etc.
* Add more granular permission controls with OAuth2 scopes where needed.
* ...etc.

This is just a **quick tutorial / refresher** about Python type hints. It covers only the minimum necessary to use them with **FastAPI**... which is actually very little.

**FastAPI** is all based on these type hints, they give it many advantages and benefits.

But even if you never use **FastAPI**, you would benefit from learning a bit about them.

!!! note

seja executado pelo mesmo processo (por exemplo, você não precisa compartilhar memória, variáveis, etc), você pode se beneficiar do uso de outras ferramentas maiores, como <a href="http://www.celeryproject.org/" class="external-link" target="_blank"> Celery </a>.

Eles tendem a exigir configurações mais complexas, um gerenciador de fila de mensagens/tarefas, como RabbitMQ ou Redis, mas permitem que você execute tarefas em segundo plano em vários processos e, especialmente, em vários servidores....

unbedingt vom selben Prozess ausgeführt werden müssen (z. B. müssen Sie Speicher, Variablen, usw. nicht gemeinsam nutzen), könnte die Verwendung anderer größerer Tools wie z. B. <a href="https://docs.celeryq.dev" class="external-link" target="_blank">Celery</a> von Vorteil sein.

Sie erfordern in der Regel komplexere Konfigurationen und einen Nachrichten-/Job-Queue-Manager wie RabbitMQ oder Redis, ermöglichen Ihnen jedoch die Ausführung von Hintergrundtasks in mehreren Prozessen und insbesondere...

from fastapi.testclient import TestClient

from docs_src.query_params.tutorial005 import app

client = TestClient(app)


def test_foo_needy_very():
    response = client.get("/items/foo?needy=very")
    assert response.status_code == 200
    assert response.json() == {"item_id": "foo", "needy": "very"}


def test_foo_no_needy():
    response = client.get("/items/foo")
    assert response.status_code == 422

If you have an API that does a comparable amount of computations every time and you have a lot of clients, then the **CPU utilization** will probably *also be stable* (instead of constantly going up and down quickly).

### Examples of Replication Tools and Strategies

Mas uma vez que você tenha concluído, você poderá testar tudo como se parecesse _online_.

Para fazer isso, primeiro construa toda a documentação:

<div class="termy">

```console
// Use o comando "build-all", isso leverá um tempinho
$ python ./scripts/docs.py build-all

Updating es
Updating en
Building docs for: en
Building docs for: es
Successfully built docs for: es
Copying en index.md to README.md
```

@needs_py310
@needs_pydanticv2
def test_post_body_example(client: TestClient):
    response = client.put(
        "/items/5",
        json={
            "name": "Foo",
            "description": "A very nice Item",
            "price": 35.4,
            "tax": 3.2,
        },
    )
    assert response.status_code == 200


@needs_py310
@needs_pydanticv2
def test_openapi_schema(client: TestClient):

    price: float
    tax: Union[float, None] = None

    class Config:
        schema_extra = {
            "examples": [
                {
                    "name": "Foo",
                    "description": "A very nice Item",
                    "price": 35.4,
                    "tax": 3.2,
                }
            ]
        }


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item):

    price: float
    tax: float | None = None

    model_config = {
        "json_schema_extra": {
            "examples": [
                {
                    "name": "Foo",
                    "description": "A very nice Item",
                    "price": 35.4,
                    "tax": 3.2,
                }
            ]
        }
    }


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item):