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  • Book Overview & Buying Mastering Concurrency in Python
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Mastering Concurrency in Python

Mastering Concurrency in Python

By : Quan Nguyen
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Mastering Concurrency in Python

Mastering Concurrency in Python

1 (1)
By: Quan Nguyen

Overview of this book

Python is one of the most popular programming languages, with numerous libraries and frameworks that facilitate high-performance computing. Concurrency and parallelism in Python are essential when it comes to multiprocessing and multithreading; they behave differently, but their common aim is to reduce the execution time. This book serves as a comprehensive introduction to various advanced concepts in concurrent engineering and programming. Mastering Concurrency in Python starts by introducing the concepts and principles in concurrency, right from Amdahl's Law to multithreading programming, followed by elucidating multiprocessing programming, web scraping, and asynchronous I/O, together with common problems that engineers and programmers face in concurrent programming. Next, the book covers a number of advanced concepts in Python concurrency and how they interact with the Python ecosystem, including the Global Interpreter Lock (GIL). Finally, you'll learn how to solve real-world concurrency problems through examples. By the end of the book, you will have gained extensive theoretical knowledge of concurrency and the ways in which concurrency is supported by the Python language
Table of Contents (22 chapters)
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Summary

More often than not, low-level network programming involves the manipulation and handling of sockets (defined as theoretical endpoints within the nodes of a specific computer network, responsible for receiving or sending data from the nodes that they are in). The architecture of server-side communication consists of multiple steps involving socket handling, such as bind, listen, accept, read, and write. The socket module provides an intuitive API that facilitates these steps.

To create a non-blocking server with the socket module, asynchronous generators need to be implemented, in order for the execution flow to switch between tasks and data. This process also involves using callbacks that can be run by the execution flow at a later time. These two elements allow for the server to read and handle the data coming in from multiple clients at the same time, allowing the server...

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Mastering Concurrency in Python
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