Book Image

Advanced Python Programming - Second Edition

By : Quan Nguyen
Book Image

Advanced Python Programming - Second Edition

By: Quan Nguyen

Overview of this book

Python's powerful capabilities for implementing robust and efficient programs make it one of the most sought-after programming languages. In this book, you'll explore the tools that allow you to improve performance and take your Python programs to the next level. This book starts by examining the built-in as well as external libraries that streamline tasks in the development cycle, such as benchmarking, profiling, and optimizing. You'll then get to grips with using specialized tools such as dedicated libraries and compilers to increase your performance at number-crunching tasks, including training machine learning models. The book covers concurrency, a major solution to making programs more efficient and scalable, and various concurrent programming techniques such as multithreading, multiprocessing, and asynchronous programming. You'll also understand the common problems that cause undesirable behavior in concurrent programs. Finally, you'll work with a wide range of design patterns, including creational, structural, and behavioral patterns that enable you to tackle complex design and architecture challenges, making your programs more robust and maintainable. By the end of the book, you'll be exposed to a wide range of advanced functionalities in Python and be equipped with the practical knowledge needed to apply them to your use cases.
Table of Contents (32 chapters)
1
Section 1: Python-Native and Specialized Optimization
8
Section 2: Concurrency and Parallelism
18
Section 3: Design Patterns in Python

Summary

A race condition occurs when two or more threads/processes access and alter a shared resource simultaneously, resulting in mishandled and corrupted data. Race conditions also have significant implications in real-life applications, such as security, operating systems, and networking.

In this chapter, we learned how to isolate the execution of different threads/processes to tackle many forms of race conditions. We have examined how to use locks to turn a shared resource into a critical section to protect the integrity of its data. Additionally, we have discussed a number of practical disadvantages when it comes to using locks.

In the next chapter, we will consider one of the biggest problems in Python concurrent programming: the infamous Global Interpreter Lock (GIL). You will learn about the basic idea behind the GIL, its purposes, and how to effectively work with it in concurrent Python applications.