Coroutines have become an integral part of Python’s toolkit for writing concurrent and asynchronous code. In this blog post, we’ll dive deep into what coroutines are, their benefits, and how they differ from traditional threads and processes.
Coroutines are a generalization of subroutines (or functions) used for cooperative multitasking. They allow functions to have multiple entry and exit points, enabling them to “pause” and “resume” execution.
- Definition: Coroutines resemble regular functions but are defined using the
async def my_coroutine():
- Pause and Resume: The
awaitkeyword allows coroutines to be paused, giving a chance for other coroutines to run. Once the awaited task completes, the coroutine resumes from where it paused.
async def another_coroutine():
3.** Execution**: Coroutines aren’t invoked directly. They need to be “scheduled” using an event loop, like the one from asyncio.
- Tasks: To run multiple coroutines concurrently, encapsulate them in Task objects.
task1 = asyncio.create_task(coroutine1())
Coroutines shine for I/O-bound tasks. They allow for efficient concurrency without requiring threads or processes. This is particularly useful when waiting for one operation, like a network request, without blocking other operations.
Before the modern async/await syntax (introduced in Python 3.5), coroutines were constructed using generator functions and the yield keyword. Although async/await is now more prevalent, generator-based coroutines remain relevant in specific contexts.
It’s crucial to differentiate between coroutines, threads, and processes:
- Threads and processes are managed by the OS and can run simultaneously on multi-core CPUs.
- Coroutines provide cooperative multitasking, where one coroutine runs at a time but can yield control, allowing others to run. The lightweight context switching between coroutines makes them highly efficient for I/O-bound tasks.