book

Python Concurrency with asyncio

by Matthew Fowler

February 2022

Intermediate to advanced

376 pages

10h 54m

English

Manning Publications

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prefaceacknowledgmentsabout this bookWho should read this book?How this book is organized: A road mapAbout the codeliveBook discussion forumabout the authorabout the cover illustration
1.1 What is asyncio?1.2 What is I/O-bound and what is CPU-bound?1.3 Understanding concurrency, parallelism, and multitasking1.3.1 Concurrency1.3.2 Parallelism1.3.3 The difference between concurrency and parallelism1.3.4 What is multitasking?1.3.5 The benefits of cooperative multitasking1.4 Understanding processes, threads, multithreading, and multiprocessing1.4.1 Process1.4.2 Thread1.5 Understanding the global interpreter lock1.5.1 Is the GIL ever released?1.5.2 asyncio and the GIL1.6 How single-threaded concurrency works1.6.1 What is a socket?1.7 How an event loop worksSummary
2.1 Introducing coroutines2.1.1 Creating coroutines with the async keyword2.1.2 Pausing execution with the await keyword2.2 Introducing long-running coroutines with sleep2.3 Running concurrently with tasks2.3.1 The basics of creating tasks2.3.2 Running multiple tasks concurrently2.4 Canceling tasks and setting timeouts2.4.1 Canceling tasks2.4.2 Setting a timeout and canceling with wait_for2.5 Tasks, coroutines, futures, and awaitables2.5.1 Introducing futures2.5.2 The relationship between futures, tasks, and coroutines2.6 Measuring coroutine execution time with decorators2.7 The pitfalls of coroutines and tasks2.7.1 Running CPU-bound code2.7.2 Running blocking APIs2.8 Accessing and manually managing the event loop2.8.1 Creating an event loop manually2.8.2 Accessing the event loop2.9 Using debug mode2.9.1 Using asyncio.run2.9.2 Using command-line arguments2.9.3 Using environment variablesSummary
3.1 Working with blocking sockets3.2 Connecting to a server with Telnet3.2.1 Reading and writing data to and from a socket3.2.2 Allowing multiple connections and the dangers of blocking3.3 Working with non-blocking sockets3.4 Using the selectors module to build a socket event loop3.5 An echo server on the asyncio event loop3.5.1 Event loop coroutines for sockets3.5.2 Designing an asyncio echo server3.5.3 Handling errors in tasks3.6 Shutting down gracefully3.6.1 Listening for signals3.6.2 Waiting for pending tasks to finishSummary
4.1 Introducing aiohttp4.2 Asynchronous context managers4.2.1 Making a web request with aiohttp4.2.2 Setting timeouts with aiohttp4.3 Running tasks concurrently, revisited4.4 Running requests concurrently with gather4.4.1 Handling exceptions with gather4.5 Processing requests as they complete4.5.1 Timeouts with as_completed4.6 Finer-grained control with wait4.6.1 Waiting for all tasks to complete4.6.2 Watching for exceptions4.6.3 Processing results as they complete4.6.4 Handling timeouts4.6.5 Why wrap everything in a task?Summary

5.1 Introducing asyncpg5.2 Connecting to a Postgres database5.3 Defining a database schema5.4 Executing queries with asyncpg5.5 Executing queries concurrently with connection pools5.5.1 Inserting random SKUs into the product database5.5.2 Creating a connection pool to run queries concurrently5.6 Managing transactions with asyncpg5.6.1 Nested transactions5.6.2 Manually managing transactions5.7 Asynchronous generators and streaming result sets5.7.1 Introducing asynchronous generators5.7.2 Using asynchronous generators with a streaming cursorSummary
6.1 Introducing the multiprocessing library6.2 Using process pools6.2.1 Using asynchronous results6.3 Using process pool executors with asyncio6.3.1 Introducing process pool executors6.3.2 Process pool executors with the asyncio event loop6.4 Solving a problem with MapReduce using asyncio6.4.1 A simple MapReduce example6.4.2 The Google Books Ngram dataset6.4.3 Mapping and reducing with asyncio6.5 Shared data and locks6.5.1 Sharing data and race conditions6.5.2 Synchronizing with locks6.5.3 Sharing data with process pools6.6 Multiple processes, multiple event loopsSummary
7.1 Introducing the threading module7.2 Using threads with asyncio7.2.1 Introducing the requests library7.2.2 Introducing thread pool executors7.2.3 Thread pool executors with asyncio7.2.4 Default executors7.3 Locks, shared data, and deadlocks7.3.1 Reentrant locks7.3.2 Deadlocks7.4 Event loops in separate threads7.4.1 Introducing Tkinter7.4.2 Building a responsive UI with asyncio and threads7.5 Using threads for CPU-bound work7.5.1 Multithreading with hashlib7.5.2 Multithreading with NumPySummary
8.1 Introducing streams8.2 Transports and protocols8.3 Stream readers and stream writers8.4 Non-blocking command-line input8.4.1 Terminal raw mode and the read coroutine8.5 Creating servers8.6 Creating a chat server and clientSummary
9.1 Creating a REST API with aiohttp9.1.1 What is REST?9.1.2 aiohttp server basics9.1.3 Connecting to a database and returning results9.1.4 Comparing aiohttp with Flask9.2 The asynchronous server gateway interface9.2.1 How does ASGI compare to WSGI?9.3 ASGI with Starlette9.3.1 A REST endpoint with Starlette9.3.2 WebSockets with Starlette9.4 Django asynchronous views9.4.1 Running blocking work in an asynchronous view9.4.2 Using async code in synchronous viewsSummary
10.1 Why microservices?10.1.1 Complexity of code10.1.2 Scalability10.1.3 Team and stack independence10.1.4 How can asyncio help?10.2 Introducing the backend-for-frontend pattern10.3 Implementing the product listing API10.3.1 User favorite service10.3.2 Implementing the base services10.3.3 Implementing the backend-for-frontend service10.3.4 Retrying failed requests10.3.5 The circuit breaker patternSummary
11.1 Understanding single-threaded concurrency bugs11.2 Locks11.3 Limiting concurrency with semaphores11.3.1 Bounded semaphores11.4 Notifying tasks with events11.5 ConditionsSummary
12.1 Asynchronous queue basics12.1.1 Queues in web applications12.1.2 A web crawler queue12.2 Priority queues12.3 LIFO queuesSummary
13.1 Creating a subprocess13.1.1 Controlling standard output13.1.2 Running subprocesses concurrently13.2 Communicating with subprocessesSummary
14.1 APIs with coroutines and functions14.2 Context variables14.3 Forcing an event loop iteration14.4 Using different event loop implementations14.5 Creating a custom event loop14.5.1 Coroutines and generators14.5.2 Generator-based coroutines are deprecated14.5.3 Custom awaitables14.5.4 Using sockets with futures14.5.5 A task implementation14.5.6 Implementing an event loop14.5.7 Implementing a server with a custom event loopSummary

Overview

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.

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.

Quotes
A clear and practical guide to using asyncio to solve real-world concurrency problems.
- Didier Garcia, Software Sushi

Well organized and written…effectively guides the reader through a variety of impactful concurrency techniques.
- Dan Sheikh, BCG Digital Ventures

If you want to efficiently take your Python skills to the next level, look no further. This is the book for you.
- Eli Mayost, IBM

Provides a lot of insights into one of the trickier aspects of Python programming, namely asyncio.
- Peter White, Charles Sturt University