Python Concurrency with Asyncio

Learn how to speed up slow Python code with concurrent programming and the cutting-edge asyncio library.

Use coroutines and tasks alongside async/await syntax to run code concurrently

Build web APIs and make concurrency web requests with aiohttp

Run thousands of SQL queries concurrently

Create a map-reduce job that can process gigabytes of data concurrently

Use threading with asyncio to mix blocking code with asyncio code

Python is flexible, versatile, and easy to learn. It can also be very slow compared to lower-level languages. Python Concurrency with asyncio teaches you how to boost Python's performance by applying a variety of concurrency techniques. You'll learn how the complex-but-powerful asyncio library can achieve concurrency with just a single thread and use asyncio's APIs to run multiple web requests and database queries simultaneously. The book covers using asyncio with the entire Python concurrency landscape, including multiprocessing and multithreading.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology

It’s easy to overload standard Python and watch your programs slow to a crawl. Th e asyncio library was built to solve these problems by making it easy to divide and schedule tasks. It seamlessly handles multiple operations concurrently, leading to apps that are lightning fast and scalable.

About the book

Python Concurrency with asyncio introduces asynchronous, parallel, and concurrent programming through hands-on Python examples. Hard-to-grok concurrency topics are broken down into simple flowcharts that make it easy to see how your tasks are running. You’ll learn how to overcome the limitations of Python using asyncio to speed up slow web servers and microservices. You’ll even combine asyncio with traditional multiprocessing techniques for huge improvements to performance.

What's inside

Build web APIs and make concurrency web requests with aiohttp

Run thousands of SQL queries concurrently

Create a map-reduce job that can process gigabytes of data concurrently

Use threading with asyncio to mix blocking code with asyncio code

About the reader

For intermediate Python programmers. No previous experience of concurrency required.

About the author

Matthew Fowler has over 15 years of software engineering experience in roles from architect to engineering director.

Table of Contents

1 Getting to know asyncio

2 asyncio basics

3 A first asyncio application

4 Concurrent web requests

5 Non-blocking database drivers

6 Handling CPU-bound work

7 Handling blocking work with threads

8 Streams

9 Web applications

10 Microservices

11 Synchronization

12 Asynchronous queues

13 Managing subprocesses

14 Advanced asyncio