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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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The with statement in concurrent programming

Obviously, opening and closing external files does not resemble concurrency very much. However, we mentioned earlier that the with statement, as a context manager, is not only used to manage file descriptors, but most resources in general. And if you actually found managing lock objects from the threading.Lock() class similar to managing external files while going through Chapter 2, Amdahl's Law, then this is where the comparison between the two comes in handy.

As a refresher, locks are mechanisms in concurrent and parallel programming that are typically used to synchronize threads in a multithreaded application (that is, to prevent more than one thread from accessing the critical session simultaneously). However, as we will discuss again in Chapter 12, Starvation, locks are also a common source of deadlock, during which a thread...

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Mastering Concurrency in Python
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