AsyncIO, await, and async - Concurrency in Python

Transcribed Jun 14, 2026 Watch on YouTube ↗

Intermediate 5 min read For: Python developers familiar with basic programming concepts who want to learn asynchronous programming.

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AI Summary

Async IO in Python uses the async and await keywords to enable concurrent execution of tasks that involve waiting, such as I/O operations, HTTP requests, and database calls. By allowing tasks to pause and resume, async IO improves efficiency and reduces overall execution time.

Chapters

1 Introduction to Async IO 0:02 2 Synchronous vs Asynchronous 0:40 3 Subroutines and Coroutines 1:34 4 Concurrency and Parallelism 2:54 5 Python Example: Synchronous Breakfast 3:30 6 Converting to Async with async/await 4:50 7 Using asyncio.gather and create_task 6:00 8 Conclusion and Best Practices 8:03

[0:02]

Async IO Introduction

Async IO solves lag in communications, like chatting on Jupiter's moon, by allowing tasks to wait without blocking.

[0:14]

Async Syntax in Python

The async and await keywords in Python enable managing a collection of laggy tasks efficiently.

[0:40]

Synchronous vs Asynchronous

Synchronous code runs tasks one after another; asynchronous code allows tasks to overlap in time.

[1:34]

Subroutines vs Coroutines

Subroutines run to completion without pausing; coroutines can be paused and resumed, maintaining state between pauses.

[2:54]

Concurrency and Parallelism

Concurrency overlaps start and stop times of coroutines; parallelism uses multiple threads executing simultaneously.

[3:30]

Python Example Setup

Create two functions: brew_coffee (3 seconds) and toast_bagel (2 seconds) to simulate concurrent breakfast preparation.

[4:50]

Synchronous Execution

Synchronous code takes 5+ seconds to complete both tasks sequentially.

[5:09]

Converting to Coroutines

Add async keyword to functions and await keyword before awaitable commands like asyncio.sleep.

[6:00]

Using asyncio.gather

asyncio.gather groups coroutines for concurrent execution; returns results in the same order as arguments.

[7:43]

Using asyncio.create_task

Alternative to gather: create tasks with asyncio.create_task and await each individually.

Async IO in Python allows efficient concurrent execution of I/O-bound tasks, reducing total time by overlapping waiting periods. The async and await keywords, along with asyncio module functions like gather and create_task, provide powerful tools for concurrency.

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Tutorial Checklist

1 3:30 Define two functions: brew_coffee() and toast_bagel() with time-consuming operations (e.g., time.sleep).

2 4:50 Run synchronously to see sequential execution time.

3 5:09 Add async keyword before def to make functions coroutines.

4 5:38 Replace time.sleep with asyncio.sleep and add await keyword before it.

5 6:00 Use asyncio.gather(coroutine1, coroutine2) to run coroutines concurrently.

6 6:30 Await the gather result to get return values.

7 7:00 Declare main function as async and call asyncio.run(main()).

8 7:43 Alternative: use asyncio.create_task for each coroutine and await them individually.

Study Flashcards (10)

What is the difference between synchronous and asynchronous programming?

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Synchronous: tasks run one after another. Asynchronous: tasks can start, process, and finish in overlapping time periods.