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.

* **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.

But still, FastAPI got quite some inspiration from Requests.

**Requests** is a library to *interact* with APIs (as a client), while **FastAPI** is a library to *build* APIs (as a server).

They are, more or less, at opposite ends, complementing each other.

Requests has a very simple and intuitive design, it's very easy to use, with sensible defaults. But at the same time, it's very powerful and customizable.

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).

  to fetch the OpenAPI may be slower. ([#118808](https://github.com/kubernetes/kubernetes/pull/118808), [@Jefftree](https://github.com/Jefftree))
- Shrank the `OpenAPI v2` spec by more than 50%, especially for less CPU resource consumption. ([#118204](https://github.com/kubernetes/kubernetes/pull/118204), [@sttts](https://github.com/sttts))

- When scheduling a mixture of pods using ResourceClaims and others which don't, scheduling a pod with ResourceClaims impacts scheduling latency less. ([#121876](https://github.com/kubernetes/kubernetes/pull/121876), [@pohly](https://github.com/pohly)) [SIG API Machinery, Node, Scheduling and Testing]

### Feature

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.