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Mastering Concurrency in Python

You're reading from   Mastering Concurrency in Python Create faster programs using concurrency, asynchronous, multithreading, and parallel programming

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789343052
Length 446 pages
Edition 1st Edition
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Concepts
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Author (1):
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Quan Nguyen Quan Nguyen
Author Profile Icon Quan Nguyen
Quan Nguyen
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Table of Contents (22) Chapters Close

Preface 1. Advanced Introduction to Concurrent and Parallel Programming FREE CHAPTER 2. Amdahl's Law 3. Working with Threads in Python 4. Using the with Statement in Threads 5. Concurrent Web Requests 6. Working with Processes in Python 7. Reduction Operators in Processes 8. Concurrent Image Processing 9. Introduction to Asynchronous Programming 10. Implementing Asynchronous Programming in Python 11. Building Communication Channels with asyncio 12. Deadlocks 13. Starvation 14. Race Conditions 15. The Global Interpreter Lock 16. Designing Lock-Based and Mutex-Free Concurrent Data Structures 17. Memory Models and Operations on Atomic Types 18. Building a Server from Scratch 19. Testing, Debugging, and Scheduling Concurrent Applications 20. Assessments 21. Other Books You May Enjoy

An overview of the threading module

There are a lot of choices when it comes to implementing multithreaded programs in Python. One of the most common ways to work with threads in Python is through the threading module. Before we dive into the module's usage and its syntax, first, let's explore the thread model, which was previously the main thread-based development module in Python.

The thread module in Python 2

Before the threading module became popular, the primary thread-based development module was thread. If you are using an older version of Python 2, it is possible to use the module as it is. However, according to the module documentation page, the thread module was, in fact, renamed _thread in Python 3.

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