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Learning Concurrency in Python

Learning Concurrency in Python

By : Forbes
3.3 (3)
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Learning Concurrency in Python

Learning Concurrency in Python

3.3 (3)
By: Forbes

Overview of this book

Python is a very high level, general purpose language that is utilized heavily in fields such as data science and research, as well as being one of the top choices for general purpose programming for programmers around the world. It features a wide number of powerful, high and low-level libraries and frameworks that complement its delightful syntax and enable Python programmers to create. This book introduces some of the most popular libraries and frameworks and goes in-depth into how you can leverage these libraries for your own high-concurrent, highly-performant Python programs. We'll cover the fundamental concepts of concurrency needed to be able to write your own concurrent and parallel software systems in Python. The book will guide you down the path to mastering Python concurrency, giving you all the necessary hardware and theoretical knowledge. We'll cover concepts such as debugging and exception handling as well as some of the most popular libraries and frameworks that allow you to create event-driven and reactive systems. By the end of the book, you'll have learned the techniques to write incredibly efficient concurrent systems that follow best practices.
Table of Contents (13 chapters)
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Standard data structures


Some of Python's traditional data structure features provide various degrees of thread safety by default. However, in most cases, we will have to define some form of a locking mechanism for controlling access to these data structures in order to guarantee thread safety.

Sets

During my time working with communication between multiple threads in Python, I discovered that one excellent solution to using sets in a thread-safe manner is to actually extend the set class, and to implement my own locking mechanism around the actions that I wish to perform.

Extending the class

If you are used to working in Python then extending the class should be a somewhat simple operation. We define a LockedSet class object, which inherits from our traditional Python set class. Within the constructor for this class, we create a lock object, which we'll use in subsequent functions in order to allow for thread-safe interactions.

Below our constructor, we define the add, remove, and contains functions...

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Learning Concurrency in Python
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