Keeping Track of Asyncio Loops in Python

When working with asyncio in Python, it is often useful to know what event loops are currently running. Here are some tips for detecting and keeping track of active asyncio loops in your code.

The get_running_loop() Method

The easiest way to get the current running loop is to call asyncio.get_running_loop(). This will return the loop that is currently executing:

import asyncio

loop = asyncio.get_running_loop()
print(loop)

If no loop is running, it will raise an exception. So this is best used inside coroutines and async context managers where a loop is guaranteed to be running.

The all_tasks() Method

You can also iterate through the tasks scheduled on the loop using loop.all_tasks(). This returns a set-like object containing all the Task objects that have been scheduled and not yet finished:

for task in asyncio.all_tasks():
    print(task)

This allows you to inspect what tasks are currently running or pending execution.

Task Locals

Sometimes you want to track what loop a task is running on outside of the task itself. A handy way is to use contextvars to store the loop in a task local:

current_loop = contextvars.ContextVar('current_loop') 

# Get running loop
loop = asyncio.get_running_loop()  

# Set on task local
current_loop.set(loop)

Now the running loop will be available through the context variable current_loop anywhere downstream in the task.

By using these approaches of getting the running loop, iterating tasks, and tracking with context vars, you can monitor what asyncio loops are running and what tasks are scheduled on them. This helps with debugging, introspection, and application management.