Und dann können sie es noch einmal versuchen, wohl wissend, dass es wahrscheinlich eher etwas mit `stanleyjobsox` als mit `johndoe` zu tun hat.

#### Ein „professioneller“ Angriff

Und Sie können auch Validierungen für Zahlen deklarieren:

* `gt`: `g`reater `t`han – größer als
* `ge`: `g`reater than or `e`qual – größer oder gleich
* `lt`: `l`ess `t`han – kleiner als
* `le`: `l`ess than or `e`qual – kleiner oder gleich

!!! info
    `Query`, `Path`, und andere Klassen, die Sie später kennenlernen, sind Unterklassen einer allgemeinen `Param`-Klasse.

So we create a new `dict`, that contains the key-value pairs from `note.dict()` with:

```Python
{**note.dict()}
```

`**note.dict()` "unpacks" the key value pairs directly, so, `{**note.dict()}` would be, more or less, a copy of `note.dict()`.

And then, we extend that copy `dict`, adding another key-value pair: `"id": last_record_id`:

```Python
{**note.dict(), "id": last_record_id}
```

An interesting detail is that the percentage of the **CPU used** by each process can **vary** a lot over time, but the **memory (RAM)** normally stays more or less **stable**.

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).

Pydantic version 2 has the **core** re-written in **Rust** and includes a lot of improvements and features, for example:

* Improved **correctness** in corner cases.
* **Safer** types.
* Better **performance** and **less energy** consumption.
* Better **extensibility**.
* etc.

...all this while keeping the **same Python API**. In most of the cases, for simple models, you can simply upgrade the Pydantic version and get all the benefits. 🚀

# SQL (Relational) Databases

!!! info
    These docs are about to be updated. 🎉

    The current version assumes Pydantic v1, and SQLAlchemy versions less than 2.0.

    The new docs will include Pydantic v2 and will use <a href="https://sqlmodel.tiangolo.com/" class="external-link" target="_blank">SQLModel</a> (which is also based on SQLAlchemy) once it is updated to use Pydantic v2 as well.

**FastAPI** doesn't require you to use a SQL (relational) database.

* **Intuitive**: Great editor support. <abbr title="also known as auto-complete, autocompletion, IntelliSense">Completion</abbr> everywhere. Less time debugging.
* **Easy**: Designed to be easy to use and learn. Less time reading docs.
* **Short**: Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs.

        security_scopes=security_scopes,
    )


def get_parameterless_sub_dependant(*, depends: params.Depends, path: str) -> Dependant:
    assert callable(
        depends.dependency
    ), "A parameter-less dependency must have a callable dependency"
    return get_sub_dependant(depends=depends, dependency=depends.dependency, path=path)


def get_sub_dependant(
    *,
    depends: params.Depends,

    lt: Annotated[
        Optional[float],
        Doc(
            """
            Less than. If set, value must be less than this. Only applicable to numbers.
            """
        ),
    ] = None,
    le: Annotated[
        Optional[float],
        Doc(
            """
            Less than or equal. If set, value must be less than or equal to this.
            Only applicable to numbers.
            """
        ),

* **Uvicorn**:
    * Will have the best performance, as it doesn't have much extra code apart from the server itself.
    * You wouldn't write an application in Uvicorn directly. That would mean that your code would have to include more or less, at least, all the code provided by Starlette (or **FastAPI**). And if you did that, your final application would have the same overhead as having used a framework and minimizing your app code and bugs.