Python Concurrency with asyncio
- Length: 376 pages
- Edition: 1
- Language: English
- Publisher: Manning
- Publication Date: 2022-03-15
- ISBN-10: 1617298662
- ISBN-13: 9781617298660
- Sales Rank: #1421244 (See Top 100 Books)
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.
Python Concurrency with asyncio contents preface acknowledgments about this book Who should read this book? How this book is organized: A road map About the code liveBook discussion forum about the author about the cover illustration 1 Getting to know asyncio 1.1 What is asyncio? 1.2 What is I/O-bound and what is CPU-bound? 1.3 Understanding concurrency, parallelism, and multitasking 1.3.1 Concurrency 1.3.2 Parallelism 1.3.3 The difference between concurrency and parallelism 1.3.4 What is multitasking? 1.3.5 The benefits of cooperative multitasking 1.4 Understanding processes, threads, multithreading, and multiprocessing 1.4.1 Process 1.4.2 Thread 1.5 Understanding the global interpreter lock 1.5.1 Is the GIL ever released? 1.5.2 asyncio and the GIL 1.6 How single-threaded concurrency works 1.6.1 What is a socket? 1.7 How an event loop works Summary 2 asyncio basics 2.1 Introducing coroutines 2.1.1 Creating coroutines with the async keyword 2.1.2 Pausing execution with the await keyword 2.2 Introducing long-running coroutines with sleep 2.3 Running concurrently with tasks 2.3.1 The basics of creating tasks 2.3.2 Running multiple tasks concurrently 2.4 Canceling tasks and setting timeouts 2.4.1 Canceling tasks 2.4.2 Setting a timeout and canceling with wait_for 2.5 Tasks, coroutines, futures, and awaitables 2.5.1 Introducing futures 2.5.2 The relationship between futures, tasks, and coroutines 2.6 Measuring coroutine execution time with decorators 2.7 The pitfalls of coroutines and tasks 2.7.1 Running CPU-bound code 2.7.2 Running blocking APIs 2.8 Accessing and manually managing the event loop 2.8.1 Creating an event loop manually 2.8.2 Accessing the event loop 2.9 Using debug mode 2.9.1 Using asyncio.run 2.9.2 Using command-line arguments 2.9.3 Using environment variables Summary 3 A first asyncio application 3.1 Working with blocking sockets 3.2 Connecting to a server with Telnet 3.2.1 Reading and writing data to and from a socket 3.2.2 Allowing multiple connections and the dangers of blocking 3.3 Working with non-blocking sockets 3.4 Using the selectors module to build a socket event loop 3.5 An echo server on the asyncio event loop 3.5.1 Event loop coroutines for sockets 3.5.2 Designing an asyncio echo server 3.5.3 Handling errors in tasks 3.6 Shutting down gracefully 3.6.1 Listening for signals 3.6.2 Waiting for pending tasks to finish Summary 4 Concurrent web requests 4.1 Introducing aiohttp 4.2 Asynchronous context managers 4.2.1 Making a web request with aiohttp 4.2.2 Setting timeouts with aiohttp 4.3 Running tasks concurrently, revisited 4.4 Running requests concurrently with gather 4.4.1 Handling exceptions with gather 4.5 Processing requests as they complete 4.5.1 Timeouts with as_completed 4.6 Finer-grained control with wait 4.6.1 Waiting for all tasks to complete 4.6.2 Watching for exceptions 4.6.3 Processing results as they complete 4.6.4 Handling timeouts 4.6.5 Why wrap everything in a task? Summary 5 Non-blocking database drivers 5.1 Introducing asyncpg 5.2 Connecting to a Postgres database 5.3 Defining a database schema 5.4 Executing queries with asyncpg 5.5 Executing queries concurrently with connection pools 5.5.1 Inserting random SKUs into the product database 5.5.2 Creating a connection pool to run queries concurrently 5.6 Managing transactions with asyncpg 5.6.1 Nested transactions 5.6.2 Manually managing transactions 5.7 Asynchronous generators and streaming result sets 5.7.1 Introducing asynchronous generators 5.7.2 Using asynchronous generators with a streaming cursor Summary 6 Handling CPU-bound work 6.1 Introducing the multiprocessing library 6.2 Using process pools 6.2.1 Using asynchronous results 6.3 Using process pool executors with asyncio 6.3.1 Introducing process pool executors 6.3.2 Process pool executors with the asyncio event loop 6.4 Solving a problem with MapReduce using asyncio 6.4.1 A simple MapReduce example 6.4.2 The Google Books Ngram dataset 6.4.3 Mapping and reducing with asyncio 6.5 Shared data and locks 6.5.1 Sharing data and race conditions 6.5.2 Synchronizing with locks 6.5.3 Sharing data with process pools 6.6 Multiple processes, multiple event loops Summary 7 Handling blocking work with threads 7.1 Introducing the threading module 7.2 Using threads with asyncio 7.2.1 Introducing the requests library 7.2.2 Introducing thread pool executors 7.2.3 Thread pool executors with asyncio 7.2.4 Default executors 7.3 Locks, shared data, and deadlocks 7.3.1 Reentrant locks 7.3.2 Deadlocks 7.4 Event loops in separate threads 7.4.1 Introducing Tkinter 7.4.2 Building a responsive UI with asyncio and threads 7.5 Using threads for CPU-bound work 7.5.1 Multithreading with hashlib 7.5.2 Multithreading with NumPy Summary 8 Streams 8.1 Introducing streams 8.2 Transports and protocols 8.3 Stream readers and stream writers 8.4 Non-blocking command-line input 8.4.1 Terminal raw mode and the read coroutine 8.5 Creating servers 8.6 Creating a chat server and client Summary 9 Web applications 9.1 Creating a REST API with aiohttp 9.1.1 What is REST? 9.1.2 aiohttp server basics 9.1.3 Connecting to a database and returning results 9.1.4 Comparing aiohttp with Flask 9.2 The asynchronous server gateway interface 9.2.1 How does ASGI compare to WSGI? 9.3 ASGI with Starlette 9.3.1 A REST endpoint with Starlette 9.3.2 WebSockets with Starlette 9.4 Django asynchronous views 9.4.1 Running blocking work in an asynchronous view 9.4.2 Using async code in synchronous views Summary 10 Microservices 10.1 Why microservices? 10.1.1 Complexity of code 10.1.2 Scalability 10.1.3 Team and stack independence 10.1.4 How can asyncio help? 10.2 Introducing the backend-for-frontend pattern 10.3 Implementing the product listing API 10.3.1 User favorite service 10.3.2 Implementing the base services 10.3.3 Implementing the backend-for-frontend service 10.3.4 Retrying failed requests 10.3.5 The circuit breaker pattern Summary 11 Synchronization 11.1 Understanding single-threaded concurrency bugs 11.2 Locks 11.3 Limiting concurrency with semaphores 11.3.1 Bounded semaphores 11.4 Notifying tasks with events 11.5 Conditions Summary 12 Asynchronous queues 12.1 Asynchronous queue basics 12.1.1 Queues in web applications 12.1.2 A web crawler queue 12.2 Priority queues 12.3 LIFO queues Summary 13 Managing subprocesses 13.1 Creating a subprocess 13.1.1 Controlling standard output 13.1.2 Running subprocesses concurrently 13.2 Communicating with subprocesses Summary 14 Advanced asyncio 14.1 APIs with coroutines and functions 14.2 Context variables 14.3 Forcing an event loop iteration 14.4 Using different event loop implementations 14.5 Creating a custom event loop 14.5.1 Coroutines and generators 14.5.2 Generator-based coroutines are deprecated 14.5.3 Custom awaitables 14.5.4 Using sockets with futures 14.5.5 A task implementation 14.5.6 Implementing an event loop 14.5.7 Implementing a server with a custom event loop Summary index A B C D E F G H I J K L M N O P Q R S T U V W
Donate to keep this site alive
1. Disable the AdBlock plugin. Otherwise, you may not get any links.
2. Solve the CAPTCHA.
3. Click download link.
4. Lead to download server to download.