PyNEng

Backwards incompatible and other major changes Convert send_command to use read_timeout. Convert send_command_timing to use read_timeout and last_read Add type hints and mypy support Relocate exce...

Learn how to speed up slow Python code with concurrent programming and the cutting-edge asyncio library.</b> Use coroutines and tasks alongside async/await syntax to run code concurrently</li> Build web APIs and make concurrency web requests with aiohttp</li> Run thousands of SQL queries concurrently</li> Create a map-reduce job that can process gigabytes of data concurrently</li> Use threading with asyncio to mix blocking code with asyncio code</li> </ul> Python is flexible, versatile, and easy to learn. It can also be very slow compared to lower-level languages. Python Concurrency with asyncio</i> 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.

Interested in learning async Python using a real-world example? Concurrency can be hard. Getting started with asyncio may feel intimidating if you are starting off by reading the language reference documentation. In this webinar, Jeremy introduces the Python asyncio keywords async/await and how to use asyncio.as_completed to tackle a common network engineering use case of locating an end host on a large network.