**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

You go with your crush to get fast food, you stand in line while the cashier takes the orders from the people in front of you. 😍

Being able to use asynchronous functions in your tests could be useful, for example, when you're querying your database asynchronously. Imagine you want to test sending requests to your FastAPI application and then verify that your backend successfully wrote the correct data in the database, while using an async database library.

Let's look at how we can make that work.

    ...
```

But by using the `secrets.compare_digest()` it will be secure against a type of attacks called "timing attacks".

### Timing Attacks

But what's a "timing attack"?

Let's imagine some attackers are trying to guess the username and password.

And they send a request with a username `johndoe` and a password `love123`.

Then the Python code in your application would be equivalent to something like:

## An app with callbacks

Let's see all this with an example.

Imagine you develop an app that allows creating invoices.

These invoices will have an `id`, `title` (optional), `customer`, and `total`.

The user of your API (an external developer) will create an invoice in your API with a POST request.

Then your API will (let's imagine):

* Send the invoice to some customer of the external developer.
* Collect the money.

For example, if you need to store it in a database.

For that, **FastAPI** provides a `jsonable_encoder()` function.

## Using the `jsonable_encoder`

Let's imagine that you have a database `fake_db` that only receives JSON compatible data.

For example, it doesn't receive `datetime` objects, as those are not compatible with JSON.

!!! info "`@décorateur` Info"
    Cette syntaxe `@something` en Python est appelée un "décorateur".

    Vous la mettez au dessus d'une fonction. Comme un joli chapeau décoratif (j'imagine que ce terme vient de là 🤷🏻‍♂).

    Un "décorateur" prend la fonction en dessous et en fait quelque chose.

All the dependencies we have seen are a fixed function or class.

But there could be cases where you want to be able to set parameters on the dependency, without having to declare many different functions or classes.

Let's imagine that we want to have a dependency that checks if the query parameter `q` contains some fixed content.

But we want to be able to parameterize that fixed content.

## A "callable" instance

        Prefer to use the `Annotated` version if possible.

    ```Python hl_lines="13"
    {!> ../../../docs_src/query_params_str_validations/tutorial008.py!}
    ```

## Alias parameters

Imagine that you want the parameter to be `item-query`.

Like in:

```
http://127.0.0.1:8000/items/?item-query=foobaritems
```

But `item-query` is not a valid Python variable name.

But in some cases you need to return a different status code than the default.

## Use case

For example, imagine that you want to return an HTTP status code of "OK" `200` by default.

But if the data didn't exist, you want to create it, and return an HTTP status code of "CREATED" `201`.

is.'

  `I quite agree with you,' said the Duchess; `and the moral of
that is--"Be what you would seem to be"--or if you'd like it put
more simply--"Never imagine yourself not to be otherwise than
what it might appear to others that what you were or might have
been was not otherwise than what you had been would have appeared
to them to be otherwise."'