Allow context for tasks that block the event loop

先阅读 uvloop/cbhandles.pyx 的第 268-273 行，并在 PYTHONASYNCIODEBUG=1 下使用 server.py 中的 FastAPI 复现，对比其 debug slow-log 行为与 asyncio 的实现。该 issue 没有定义可接受的输出或实现，因此在编码之前，请先明确所需的上下文和堆栈跟踪行为。

Hi all,

This is probably more of a discussion than an actual issue and I fully appreciate that this could be more of a Python asyncio question versus anything specific to uvloop's implementation (I've also started a discussion there), but I figured I'd ask here first because uvloop is wonderful and you all clearly have a deep understanding of creating an event loop implementation in Python.

My issue is this: when debug mode is enabled for the Python event loop, is it somehow possible to get slow logs to tell us more about the context the task is running in?

The motivation is that in many asyncio server frameworks like FastAPI, it is critical to not block the event loop but using asyncio's debug mode provides output that's not helpful.

Consider the following FastAPI server:

# server.py
# run with PYTHONASYNCIODEBUG=1 uvicorn server:app --reload --port 8000 --host 0.0.0.0
import time

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
async def root():
    time.sleep(2)  # intentionally block event loop for 2 seconds
    return {"message": "Hello World"}

If you curl http://127.0.0.1:8000/, you'll see something along the lines of:

Executing <Task finished name='Task-4' coro=<RequestResponseCycle.run_asgi() done, defined at /Users/mike.sukmanowsky/code/z/z/.venv/lib/python3.13/site-packages/uvicorn/protocols/http/h11_impl.py:401> result=None created at /Users/mike.sukmanowsky/code/z/z/.venv/lib/python3.13/site-packages/uvicorn/protocols/http/h11_impl.py:250> took 2.006 seconds

This is consistent with what uvloop and other event loop implementations do here.

But from this output I cannot determine:

1. What endpoint this occurred on
2. The full stack trace of my user-defined code that might point me to the culprit that's blocking the loop

For ASGI frameworks like FastAPI, blocking the event loop effectively means a death for concurrency.

I'm unaware of any way to make outputs more useful here, but I'm very much hoping either 1) I've missed an obvious way to do this or 2) this inspires some discussion that could lead to changes that'll eventually help all Python asyncio users better troubleshoot issues like this.