Book Image

Mastering Concurrency in Python

By : Quan Nguyen
Book Image

Mastering Concurrency in Python

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)

Summary

In this chapter, we studied the theoretical differences between lock-based and mutex-free data structures: a lock-based data structure uses a locking mechanism to protect the integrity of its data, while a mutex-free one does not. We analyzed the problem of race conditions that can occur in poorly-designed data structures, and looked at how to address it in both situations.

In our example of the concurrent lock-based counter data structure, we considered the design of approximate counters, as well as the improved scalability that the design can offer. In our analysis of the concurrent network data structure, we studied the RCU technique, which isolates reading instructions from updating instructions, with the goal of maintaining the integrity of the concurrent data structure.

In the next chapter, we will look at another set of advanced concepts in Python concurrent programming...