<img src="https://fastapi.tiangolo.com/img/async/concurrent-burgers/concurrent-burgers-04.png" alt="illustration">

Mientras esperas, vas con esa persona 😍 y eliges una mesa, se sientan y hablan durante un rato largo (ya que las hamburguesas son muy impresionantes y necesitan un rato para prepararse ✨🍔✨).

from fastapi.testclient import TestClient

from docs_src.advanced_middleware.tutorial003 import app


@app.get("/large")
async def large():
    return PlainTextResponse("x" * 4000, status_code=200)


client = TestClient(app)


def test_middleware():
    response = client.get("/large", headers={"accept-encoding": "gzip"})
    assert response.status_code == 200, response.text
    assert response.text == "x" * 4000

And multiple processes normally **don't share any memory**. This means that each running process has its own things, variables, and memory. And if you are consuming a large amount of memory in your code, **each process** will consume an equivalent amount of memory.

* Fix broken link in docs about OAuth 2.0 with scopes. PR [#275](https://github.com/tiangolo/fastapi/pull/275) by [@dmontagu](https://github.com/dmontagu).

* Refactor param extraction using Pydantic `Field`:
    * Large refactor, improvement, and simplification of param extraction from *path operations*.

# See https://pre-commit.com/hooks.html for more hooks
default_language_version:
    python: python3.10
repos:
-   repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v4.4.0
    hooks:
    -   id: check-added-large-files
    -   id: check-toml
    -   id: check-yaml
        args:
        -   --unsafe
    -   id: end-of-file-fixer
    -   id: trailing-whitespace
-   repo: https://github.com/charliermarsh/ruff-pre-commit

The result of calling it is something that can be encoded with the Python standard <a href="https://docs.python.org/3/library/json.html#json.dumps" class="external-link" target="_blank">`json.dumps()`</a>.

It doesn't return a large `str` containing the data in JSON format (as a string). It returns a Python standard data structure (e.g. a `dict`) with values and sub-values that are all compatible with JSON.

!!! note

* It uses a "spooled" file:
    * A file stored in memory up to a maximum size limit, and after passing this limit it will be stored in disk.
* This means that it will work well for large files like images, videos, large binaries, etc. without consuming all the memory.
* You can get metadata from the uploaded file.

  og:image:type: "{{ image.type }}"
  og:image:width: "{{ image.width }}"
  og:image:height: "{{ image.height }}"
  og:url: "{{ page.canonical_url }}"

  # Twitter
  twitter:card: summary_large_image
  twitter.title: *page_title_with_site_name
  twitter:description: *page_description
  twitter:image: "{{ image.url }}"

# -----------------------------------------------------------------------------

This will be especially useful when you use it in a **large code base** where you use **the same dependencies** over and over again in **many *path operations***.

## To `async` or not to `async`

from fastapi import FastAPI
from fastapi.responses import StreamingResponse

some_file_path = "large-video-file.mp4"
app = FastAPI()


@app.get("/")
def main():
    def iterfile():  # (1)
        with open(some_file_path, mode="rb") as file_like:  # (2)
            yield from file_like  # (3)