Sebastián Ramírez <******@****.***> 1688142182 +0200

There's also the chance that for some reason you have a **spike** of usage of your API. Maybe it went viral, or maybe some other services or bots start using it. And you might want to have extra resources to be safe in those cases.

    as a dependency. All other OAuth2 classes inherit from it and customize it for
    each OAuth2 flow.

    You normally would not create a new class inheriting from it but use one of the
    existing subclasses, and maybe compose them if you want to support multiple flows.

    Read more about it in the
    [FastAPI docs for Security](https://fastapi.tiangolo.com/tutorial/security/).
    """

    def __init__(
        self,

INHERIT: ../en/mkdocs.maybe-insiders.yml
site_name: FastAPI
site_description: FastAPI framework, high performance, easy to learn, fast to code, ready for production
site_url: https://fastapi.tiangolo.com/
theme:
  name: material
  custom_dir: ../en/overrides
  palette:
  - media: '(prefers-color-scheme: light)'
    scheme: default
    primary: teal
    accent: amber
    toggle:
      icon: material/lightbulb
      name: Switch to dark mode

!!! tip
    The `shutdown` would happen when you are **stopping** the application.

    Maybe you need to start a new version, or you just got tired of running it. 🤷

### Lifespan function

The first thing to notice, is that we are defining an async function with `yield`. This is very similar to Dependencies with `yield`.

Probably the main use case for this is if you already have some autogenerated client code/SDKs and you don't want to update all the autogenerated client code/SDKs yet, you probably will want to do it at some point, but maybe not right now.

In that case, you can disable this feature in **FastAPI**, with the parameter `separate_input_output_schemas=False`.

!!! info

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.

need to specify hard memory limits. But if you are **using a lot of memory** (for example with **machine learning** models), you should check how much memory you are consuming and adjust the **number of containers** that runs in **each machine** (and maybe add more machines to your cluster).

If you run **multiple processes per container** (for example with the official Docker image) you will have to make sure that the number of processes started doesn't **consume more memory** than what is...