Python’s asyncio library enables you to write concurrent code using the async and await keywords. The core building blocks of async I/O in Python are awaitable objects—most often coroutines—that an event loop schedules and executes asynchronously. This programming model lets you efficiently manage multiple I/O-bound tasks within a single thread of execution.In this tutorial, you’ll learn how Python asyncio works, how to define and run coroutines, and when to use asynchronous programming for better performance in applications that perform I/O-bound tasks.By the end of this tutorial, you’ll understand that:Python’s asyncio provides a framework for writing single-threaded concurrent code using coroutines, event loops, and non-blocking I/O operations.For I/O-bound tasks, async I/O can often outperform multithreading—especially when managing a large number of concurrent tasks—because it avoids the overhead of thread management.You should use asyncio when your application spends significant time waiting on I/O operations, such as network requests or file access, and you want to run many of these tasks concurrently without creating extra threads or processes.Through hands-on examples, you’ll gain the practical skills to write efficient Python code using asyncio that scales gracefully with increasing I/O demands.Get Your Code: Click here to download the free sample code that you’ll use to learn about async I/O in Python. Take the Quiz: Test your knowledge with our interactive “Python's asyncio: A Hands-On Walkthrough” quiz. You’ll receive a score upon completion to help you track your learning progress: Interactive Quiz Python's asyncio: A Hands-On Walkthrough Test your knowledge of `asyncio` concurrency with this quiz that covers coroutines, event loops, and efficient I/O-bound task management. A First Look at Async I/OBefore exploring asyncio, it’s worth taking a moment to compare async I/O with other concurrency models to see how it fits into Python’s broader, sometimes dizzying, landscape. Here are some essential concepts to start with:Parallelism consists of executing multiple operations at the same time.Multiprocessing is a means of achieving parallelism that entails spreading tasks over a computer’s central processing unit (CPU) cores. Multiprocessing is well-suited for CPU-bound tasks, such as tightly bound for loops and mathematical computations.Concurrency is a slightly broader term than parallelism, suggesting that multiple tasks have the ability to run in an overlapping manner. Concurrency doesn’t necessarily imply parallelism.Threading is a concurrent execution model in which multiple threads take turns executing tasks. A single process can contain multiple threads. Python’s relationship with threading is complicated due to the global interpreter lock (GIL), but that’s beyond the scope of this tutorial.Threading is good for I/O-bound tasks. An I/O-bound job is dominated by a lot of waiting on input/output (I/O) to complete, while a CPU-bound task is characterized by the computer’s cores continually working hard from start to finish.The Python standard library has offered longstanding support for these models through its multiprocessing, concurrent.futures, and threading packages.Now it’s time to add a new member to the mix. In recent years, a separate model has been more comprehensively built into CPython: asynchronous I/O, commonly called async I/O. This model is enabled through the standard library’s asyncio package and the async and await keywords.Note: Async I/O isn’t a new concept. It exists in—or is being built into—other languages such as Go, C#, and Rust.The asyncio package is billed by the Python documentation as a library to write concurrent code. However, async I/O isn’t threading or multiprocessing. It’s not built on top of either of these.Async I/O is a single-threaded, single-process technique that uses cooperative multitasking. Async I/O gives a feeling of concurrency despite using a single thread in a single process. Coroutines—or coro for short—are a central feature of async I/O and can be scheduled concurrently, but they’re not inherently concurrent.To reiterate, async I/O is a model of concurrent programming, but it’s not parallelism. It’s more closely aligned with threading than with multiprocessing, but it’s different from both and is a standalone member of the concurrency ecosystem.That leaves one more term. What does it mean for something to be asynchronous? This isn’t a rigorous definition, but for the purposes of this tutorial, you can think of two key properties:Asynchronous routines can pause their execution while waiting for a result and allow other routines to run in the meantime.Asynchronous code facilitates the concurrent execution of tasks by coordinating asynchronous routines.Here’s a diagram that puts it all together. The white terms represent concepts, and the green terms represent the ways they’re implemented:Diagram Comparing Concurrency and Parallelism in Python (Threading, Async I/O, Multiprocessing)For a thorough exploration of threading versus multiprocessing versus async I/O, pause here and check out the Speed Up Your Python Program With Concurrency tutorial. For now, you’ll focus on async I/O.Async I/O ExplainedAsync I/O may seem counterintuitive and paradoxical at first. How does something that facilitates concurrent code use a single thread in a single CPU core? Miguel Grinberg’s PyCon talk explains everything quite beautifully:Chess master Judit Polgár hosts a chess exhibition in which she plays multiple amateur players. She has two ways of conducting the exhibition: synchronously and asynchronously.Assumptions:24 opponentsJudit makes each chess move in 5 secondsOpponents each take 55 seconds to make a moveGames average 30 pair-moves (60 moves total)Synchronous version: Judit plays one game at a time, never two at the same time, until the game is complete. Each game takes (55 + 5) * 30 == 1800 seconds, or 30 minutes. The entire exhibition takes 24 * 30 == 720 minutes, or 12 hours.Asynchronous version: Judit moves from table to table, making one move at each table. She leaves the table and lets the opponent make their next move during the wait time. One move on all 24 games takes Judit 24 * 5 == 120 seconds, or 2 minutes. The entire exhibition is now cut down to 120 * 30 == 3600 seconds, or just 1 hour. 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