In some cases, for pure data validation and processing, you can get performance improvements of **20x** or more. This means 2,000% or more. 🤯

As, by default, singular values are interpreted as query parameters, you don't have to explicitly add a `Query`, you can just do:

```Python
q: Union[str, None] = None
```

Or in Python 3.10 and above:

```Python
q: str | None = None
```

For example:

=== "Python 3.10+"

    ```Python hl_lines="27"

RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt

COPY ./app /code/app

CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "80"]

# If running behind a proxy like Nginx or Traefik add --proxy-headers
# CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "80", "--proxy-headers"]
```

</details>

## 컨테이너란

    * Hear when I make announcements or release new tools (although I use Twitter more often 🤷‍♂).
* Read what I write (or follow me) on <a href="https://dev.to/tiangolo" class="external-link" target="_blank">**Dev.to**</a> or <a href="https://medium.com/@tiangolo" class="external-link" target="_blank">**Medium**</a>.

### Short

It has sensible **defaults** for everything, with optional configurations everywhere. All the parameters can be fine-tuned to do what you need and to define the API you need.

But by default, it all **"just works"**.

### Validation

* Validation for most (or all?) Python **data types**, including:

    But **FastAPI** will know that it has to solve `query_extractor` first, to pass the results of that to `query_or_cookie_extractor` while calling it.

```mermaid
graph TB

query_extractor(["query_extractor"])
query_or_cookie_extractor(["query_or_cookie_extractor"])

read_query["/items/"]

```
http://127.0.0.1:8000/items/foo?short=1
```

or

```
http://127.0.0.1:8000/items/foo?short=True
```

or

```
http://127.0.0.1:8000/items/foo?short=true
```

or

```
http://127.0.0.1:8000/items/foo?short=on
```

or

```
http://127.0.0.1:8000/items/foo?short=yes
```

{
    'username': 'john',
    'password': 'secret',
    'email': '******@****.***',
    'full_name': None,
}
```

#### Unwrapping a `dict`

If we take a `dict` like `user_dict` and pass it to a function (or class) with `**user_dict`, Python will "unwrap" it. It will pass the keys and values of the `user_dict` directly as key-value arguments.

So, continuing with the `user_dict` from above, writing:

```Python
UserInDB(**user_dict)

# Advanced Dependencies

## Parameterized dependencies

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.

# Dependencies in path operation decorators

In some cases you don't really need the return value of a dependency inside your *path operation function*.

Or the dependency doesn't return a value.

But you still need it to be executed/solved.

For those cases, instead of declaring a *path operation function* parameter with `Depends`, you can add a `list` of `dependencies` to the *path operation decorator*.