And be compatible with all of them at the same time.

Create a utility function to hash a password coming from the user.

And another utility to verify if a received password matches the hash stored.

And another one to authenticate and return a user.

=== "Python 3.10+"

!!! tip
    You could adopt ideas from the section about SQLAlchemy ORM ([SQL (Relational) Databases](../tutorial/sql-databases.md){.internal-link target=_blank}), like using utility functions to perform operations in the database, independent of your **FastAPI** code.

### Other utility functions

Any other utility function that you call directly can be created with normal `def` or `async def` and FastAPI won't affect the way you call it.

This is in contrast to the functions that FastAPI calls for you: *path operation functions* and dependencies.

Because it's a Python exception, you don't `return` it, you `raise` it.

Par exemple, vous pouvez déclarer une réponse avec un code HTTP `404` qui utilise un modèle Pydantic et a une `description` personnalisée.

Et une réponse avec un code HTTP `200` qui utilise votre `response_model`, mais inclut un `example` personnalisé :

```Python hl_lines="20-31"
{!../../../docs_src/additional_responses/tutorial003.py!}
```

Dans les versions précédentes de Python, vous auriez utilisé des *threads* ou <a href="https://www.gevent.org/" class="external-link" target="_blank">Gevent</a>.  Mais le code aurait été bien plus difficile à comprendre, débugger, et concevoir.

    * It's now possible to use the low level utility `iterate_in_threadpool` from `starlette.concurrency` (for advanced scenarios).
    * PR [#243](https://github.com/tiangolo/fastapi/pull/243).

Import `models` (the SQLAlchemy models) and `schemas` (the Pydantic *models* / schemas).

Create utility functions to:

* Read a single user by ID and by email.
* Read multiple users.
* Read multiple items.

```Python hl_lines="1  3  6-7  10-11  14-15  27-28"
{!../../../docs_src/sql_databases/sql_app/crud.py!}

That means that any value read in Python from an environment variable will be a `str`, and any conversion to a different type or validation has to be done in code.

## Pydantic `Settings`

Fortunately, Pydantic provides a great utility to handle these settings coming from environment variables with <a href="https://docs.pydantic.dev/latest/usage/pydantic_settings/" class="external-link" target="_blank">Pydantic: Settings management</a>.

## Resource Utilization

Your server(s) is (are) a **resource**, you can consume or **utilize**, with your programs, the computation time on the CPUs, and the RAM memory available.