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.

    ```

=== "Python 3.8+"

    ```Python hl_lines="14"
    {!> ../../../docs_src/body_nested_models/tutorial002.py!}
    ```

## Set-Typen

Aber dann denken wir darüber nach und stellen fest, dass sich die Tags nicht wiederholen sollen, es sollen eindeutige Strings sein.

Python hat einen Datentyp speziell für Mengen eindeutiger Dinge: das <abbr title="Menge">`set`</abbr>.

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.

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).

Dann könnten Sie dieses Python-Programm aufrufen:

<div class="termy">

```console
// Hier legen wir die Umgebungsvariable noch nicht fest
$ python main.py

// Da wir die Umgebungsvariable nicht festgelegt haben, erhalten wir den Standardwert

Hello World from Python

// Aber wenn wir zuerst eine Umgebungsvariable erstellen

something()
```

oder

```Python
something(some_argument, some_keyword_argument="foo")
```

dann ist das ein „Callable“ (ein „Aufrufbares“).

## Klassen als Abhängigkeiten

Möglicherweise stellen Sie fest, dass Sie zum Erstellen einer Instanz einer Python-Klasse die gleiche Syntax verwenden.

Zum Beispiel:

```Python
class Cat:
    def __init__(self, name: str):
        self.name = name

* **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

This idea of **asynchronous** code described above is also sometimes called **"concurrency"**. It is different from **"parallelism"**.

**Concurrency** and **parallelism** both relate to "different things happening more or less at the same time".

But the details between *concurrency* and *parallelism* are quite different.

To see the difference, imagine the following story about burgers:

### Concurrent Burgers

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