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Advanced Python Programming

You're reading from   Advanced Python Programming Build high performance, concurrent, and multi-threaded apps with Python using proven design patterns

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Product type Course
Published in Feb 2019
Publisher Packt
ISBN-13 9781838551216
Length 672 pages
Edition 1st Edition
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Authors (3):
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Quan Nguyen Quan Nguyen
Author Profile Icon Quan Nguyen
Quan Nguyen
Sakis Kasampalis Sakis Kasampalis
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Sakis Kasampalis
Dr. Gabriele Lanaro Dr. Gabriele Lanaro
Author Profile Icon Dr. Gabriele Lanaro
Dr. Gabriele Lanaro
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Table of Contents (41) Chapters Close

Title Page
Copyright
About Packt
Contributors
Preface
Benchmarking and Profiling Pure Python Optimizations FREE CHAPTER Fast Array Operations with NumPy and Pandas C Performance with Cython Exploring Compilers Implementing Concurrency Parallel Processing Advanced Introduction to Concurrent and Parallel Programming Amdahl's Law Working with Threads in Python Using the with Statement in Threads Concurrent Web Requests Working with Processes in Python Reduction Operators in Processes Concurrent Image Processing Introduction to Asynchronous Programming Implementing Asynchronous Programming in Python Building Communication Channels with asyncio Deadlocks Starvation Race Conditions The Global Interpreter Lock The Factory Pattern The Builder Pattern Other Creational Patterns The Adapter Pattern The Decorator Pattern The Bridge Pattern The Facade Pattern Other Structural Patterns The Chain of Responsibility Pattern The Command Pattern The Observer Pattern 1. Appendix 2. Other Books You May Enjoy Index

An introduction to the Global Interpreter Lock


The GIL is quite popular in the Python concurrent programming community. Designed as a lock that will only allow one thread to access and control the Python interpreter at any given time, the GIL in Python is often known as the infamous GIL that prevents multithreaded programs from reaching their fully optimized speed. In this section, we will discuss the concept behind the GIL, and its goals: why it was designed and implemented, and how it affected multithreaded programming in Python.

An analysis of memory management in Python

Before we jump into the specifics of the GIL and its effects, let's consider the problems that Python core developers encountered during the early days of Python, and that gave rise to a need for the GIL. Specifically, there is a significant difference between Python programming and programming in other popular languages, in terms of managing objects in the memory space.

For example, in the programming language C++, a variable...

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