Python asyncio.as_completed() Function: Usage and Examples

asyncio.

Understanding the Concept

asyncio.as_completed makes task results available as operations finish rather than preserving input order. It is useful for early partial processing.

The interface differs slightly by Python version; await returned awaitables as shown.

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 square(number):
    await asyncio.sleep(0.001 * number)
    return number * number

async def main():
    tasks = [asyncio.create_task(square(n)) for n in (3, 1, 2)]
    for finished in asyncio.as_completed(tasks):
        print(await finished)

asyncio.run(main())

Walkthrough: What Each Part Does

The central operation is asyncio.as_completed(tasks). Read its actual arguments: those values determine what is tested, logged, measured or scheduled. Then locate the line that inspects its result or handles a failure.
The imports make asyncio available. These are standard-library building blocks unless the example explicitly names an external tool.
The definition square, main creates reusable behavior. Its body runs when called, awaited or instantiated; defining it alone does not run the body of an ordinary function.
Each await is a suspension point. When the awaited operation is not ready, the loop can schedule other tasks instead of blocking the entire thread.
The loop repeats one well-defined unit of work. Check termination, ordering and whether an item may be processed more than once.

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

1
4
9

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

Assuming results from as_completed correspond to the input order or leaving remaining work unhandled on failure.

The interface differs slightly by Python version; await returned awaitables as shown.

Practice Exercise

Attach an identifier to each result and explain why the first result may not belong to the first submitted task.

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

Assuming results from as_completed correspond to the input order or leaving remaining work unhandled on failure.
Calling a blocking function inside the event loop, or creating tasks without awaiting, cancelling or owning them.

Key Takeaways

asyncio.
The interface differs slightly by Python version; await returned awaitables as shown.
Practice: Attach an identifier to each result and explain why the first result may not belong to the first submitted task.