Without a data validation system, you would have to do all the checks by hand, in code.

These features are what Marshmallow was built to provide. It is a great library, and I have used it a lot before.

But it was created before there existed Python type hints. So, to define every <abbr title="the definition of how data should be formed">schema</abbr> you need to use specific utils and classes provided by Marshmallow.

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).

=== "Python 3.9+"

    You can use the same builtin types as generics (with square brackets and types inside):

    * `list`
    * `tuple`
    * `set`
    * `dict`

In almost any framework handling the security becomes a rather complex subject quite quickly.

Many packages that simplify it a lot have to make many compromises with the data model, database, and available features. And some of these packages that simplify things too much actually have security flaws underneath.

---

In many cases you might already know the answer for those questions. 🤓

If you are helping a lot of people with their questions, you will become an official [FastAPI Expert](fastapi-people.md#fastapi-experts){.internal-link target=_blank}. 🎉

* `$`: ends there, doesn't have any more characters after `fixedquery`.

If you feel lost with all these **"regular expression"** ideas, don't worry. They are a hard topic for many people. You can still do a lot of stuff without needing regular expressions yet.

But whenever you need them and go and learn them, know that you can already use them directly in **FastAPI**.

### Pydantic v1 `regex` instead of `pattern`

So, to balance that out, imagine the following short story:

> You have to clean a big, dirty house.

*Yep, that's the whole story*.

---

There's no waiting 🕙 anywhere, just a lot of work to be done, on multiple places of the house.

So, if your application consumes a lot of memory (for example with machine learning models), and your server has a lot of CPU cores **but little memory**, then your container could end up trying to use more memory than what is available, and degrading performance a lot (or even crashing). 🚨

### Create a `Dockerfile`

    ```Python hl_lines="11"
    {!> ../../../docs_src/dependencies/tutorial001.py!}
    ```

But then we get a `dict` in the parameter `commons` of the *path operation function*.

And we know that editors can't provide a lot of support (like completion) for `dict`s, because they can't know their keys and value types.

We can do better...

## What makes a dependency

Up to now you have seen dependencies declared as functions.

Then we can test that it is used.

## Reading a `.env` file

If you have many settings that possibly change a lot, maybe in different environments, it might be useful to put them on a file and then read them from it as if they were environment variables.