Concurrent Programming with Asyncio(Python Core in Action 20)
Explore Python's concurrency methods in our latest lesson. Learn how Asyncio improves efficiency over multithreading for I/O-bound tasks, and dive into practical code examples.
Welcome to the "Python Core in Action" Series
In the last class, we learned about one way to implement concurrency in Python—multithreading.
Today, we'll explore another way to implement Python concurrency—Asyncio. Unlike the previous section on coroutines, this lesson focuses more on understanding the underlying principles.
From the last class, we learned that when handling I/O operations, multithreading significantly improves efficiency compared to single-threaded programs.
You might wonder, why use Asyncio if multithreading works so well? While multithreading has many advantages and is widely used, it also has some limitations:
For example, multithreaded operations are prone to interruptions, which can lead to race conditions.
Also, switching between threads incurs overhead, and there's a limit to how many threads you can create. If your I/O operations are heavy, multithreading may not be efficient enough.
Asyncio was designed to solve these problems.
