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Mastering Concurrency in Python

Mastering Concurrency in Python

By : Quan Nguyen
1 (1)
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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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Building on simple data structures

Throughout this chapter, we have worked with a number of simple, concurrent data structures, such as counters and networks. For this reason, we were able to truly get to the bottom of the problems that we encountered in the concurrent programs that utilize these data structures, and were able perform in-depth analyses of how to improve their structures and design.

As you work on more complex concurrent data structures in your work and projects, you will see that their designs and structures, and the problems that accompany them, are, in fact, fundamentally similar to those that we saw in the data structures we analyzed. By truly understanding the underlying architecture of the data structures, as well as the root of problems that can occur in the programs that use them, you can build on this knowledge and design data structures that are more...

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