Python asyncio.Debug Mode: Diagnose Slow Callbacks

Asyncio debug mode provides warnings about slow callbacks, unawaited coroutines and misuse of loop APIs.

Understanding the Concept

Asyncio debug mode provides warnings about slow callbacks, unawaited coroutines and misuse of loop APIs. It is useful in development but can add diagnostic overhead.

Debug mode helps detect misuse but does not replace correctness tests.

How the event loop makes progress

When a coroutine reaches await, it may suspend so another ready Task can execute. This scheduling is cooperative: blocking operations that do not yield hold up every coroutine sharing the event loop. Assign ownership of background tasks, put timeouts around operations that may wait, and release resources on cancellation.

An async function does not automatically use several CPU cores. Use async-compatible I/O for waiting tasks and executors or separate processes for blocking or CPU-heavy work when appropriate.

Worked Example

Read the code from the first line to the last. Identify which values are inputs, which operation is being demonstrated and what should happen before running it.

import asyncio

async def main():
    loop = asyncio.get_running_loop()
    print(loop.get_debug())

with asyncio.Runner(debug=True) as runner:
    runner.run(main())

Walkthrough: What Each Part Does

The imports make asyncio available. These are standard-library building blocks unless the example explicitly names an external tool.
The definition main creates reusable behavior. Its body runs when called, awaited or instantiated; defining it alone does not run the body of an ordinary function.
The with or async with scope controls lifetime. Setup takes place on entry, and cleanup runs on exit even when an exception interrupts the body.
The coordination call waits for a result or a completion condition. Predict what would happen if the worker failed or never became ready.
The top-level entry point starts event-loop execution and prints a result after the awaited operation is complete.

This demonstration focuses on one practical behavior. Before changing several lines, vary one input or one configuration choice and check whether the result changes for the reason you expected.

Example Output

True

The output above was captured by running the example in the available Python environment. The order of concurrent events, durations and platform-dependent values can legitimately differ on your computer.

Important Behavior and Edge Cases

Enabling debug output in production without assessing log volume or accidentally revealing sensitive diagnostic context.

Debug mode helps detect misuse but does not replace correctness tests.

Practice Exercise

Run a small coroutine with and without debug=True and inspect warnings.

Write down the result you expect before running your changed code. Check at least one normal case and one boundary or failure case. If your example starts workers or opens resources, verify how they are cleaned up.

Further reading: official documentation and API reference.

Common Beginner Mistakes

Enabling debug output in production without assessing log volume or accidentally revealing sensitive diagnostic context.
Calling a blocking function inside the event loop, or creating tasks without awaiting, cancelling or owning them.

Key Takeaways

Asyncio debug mode provides warnings about slow callbacks, unawaited coroutines and misuse of loop APIs.
Debug mode helps detect misuse but does not replace correctness tests.
Practice: Run a small coroutine with and without debug=True and inspect warnings.