dependencies and the dependencies are solved based on the declared types. So, it's not possible to declare more than one "component" that provides a certain type.

Routes are declared in a single place, using functions declared in other places (instead of using decorators that can be placed right on top of the function that handles the endpoint). This is closer to how Django does it than to how Flask (and Starlette) does it. It separates in the code things that are relatively tightly coupled....

        ******@****.***ck {
          finished = this.finished
          flowControlError = remainingByteCount + readBuffer.size > maxByteCount
        }

        // If the peer sends more data than we can handle, discard it and close the connection.
        if (flowControlError) {
          source.skip(remainingByteCount)
          closeLater(ErrorCode.FLOW_CONTROL_ERROR)
          return
        }

* Memory
* Previous steps before starting

## Memory

If you run **a single process per container** you will have a more or less well-defined, stable, and limited amount of memory consumed by each of those containers (more than one if they are replicated).

This means that you can send only the data that you want to update, leaving the rest intact.

!!! note
    `PATCH` is less commonly used and known than `PUT`.

    And many teams use only `PUT`, even for partial updates.

    You are **free** to use them however you want, **FastAPI** doesn't impose any restrictions.

But in many cases, you will want to run several worker processes at the same time.

### Multiple Processes - Workers

## Uvicorn with Workers

Uvicorn also has an option to start and run several **worker processes**.

Nevertheless, as of now, Uvicorn's capabilities for handling worker processes are more limited than Gunicorn's. So, if you want to have a process manager at this level (at the Python level), then it might be better to try with Gunicorn as the process manager.

In any case, you would run it like this:

<div class="termy">

            }
            val goBackSteps = fullFqName.pathSegments().size - selectedFqName.pathSegments().size
            check(goBackSteps > 0) {
                "Selected FqName ($selectedFqName) should be smaller than the whole FqName ($fullFqName)"
            }
            return fullSymbolsResolved.mapNotNullTo(mutableSetOf()) { findParentSymbol(it, goBackSteps, selectedFqName) }
        }
    }

    /**

    'ignore:starlette.middleware.wsgi is deprecated and will be removed in a future release\..*:DeprecationWarning:starlette',
    # TODO: remove after upgrading HTTPX to a version newer than 0.23.0
    # Including PR: https://github.com/encode/httpx/pull/2309
    "ignore:'cgi' is deprecated:DeprecationWarning",
    # For passlib

    Because of that, versions of FastAPI previous to 0.99.0 still used versions of OpenAPI lower than 3.1.0.

### Pydantic and FastAPI `examples`

When you add `examples` inside a Pydantic model, using `schema_extra` or `Field(examples=["something"])` that example is added to the **JSON Schema** for that Pydantic model.

If you declare both a return type and a `response_model`, the `response_model` will take priority and be used by FastAPI.

This way you can add correct type annotations to your functions even when you are returning a type different than the response model, to be used by the editor and tools like mypy. And still you can have FastAPI do the data validation, documentation, etc. using the `response_model`.