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.

    assert response.status_code == 422
    assert response.json() == IsDict(
        {
            "detail": [
                {
                    "type": "less_than",
                    "loc": ["path", "item_id"],
                    "msg": "Input should be less than 3",
                    "input": "42",
                    "ctx": {"lt": 3.0},
                }
            ]
        }
    ) | IsDict(

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.

Then, for the request in the next tab, your app will wait for one second less, and so on.

This means that it will end up finishing some of the last tabs' requests earlier than some of the previous ones.

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).

Now the client will get the same *HTTP 500 Internal Server Error* response, but the server will have our custom `InternalError` in the logs. 😎

## Execution of dependencies with `yield`

The sequence of execution is more or less like this diagram. Time flows from top to bottom. And each column is one of the parts interacting or executing code.

```mermaid
sequenceDiagram

participant client as Client
participant handler as Exception handler

* **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.

### `UJSONResponse`

An alternative JSON response using <a href="https://github.com/ultrajson/ultrajson" class="external-link" target="_blank">`ujson`</a>.

!!! warning
    `ujson` is less careful than Python's built-in implementation in how it handles some edge-cases.

```Python hl_lines="2  7"
{!../../../docs_src/custom_response/tutorial001.py!}
```

!!! tip

# 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.