Mastering Concurrency in Python: Create faster programs using concurrency, asynchronous, multithreading, and parallel programming

Mastering Concurrency in Python: Create faster programs using concurrency, asynchronous, multithreading, and parallel programming
Mastering Concurrency in Python: Create faster programs using concurrency, asynchronous, multithreading, and parallel programming by Quan Nguyen
English | 2018 | ISBN: 1789343052 | 446 Pages | True PDF, EPUB | 48 MB

Immerse yourself in the world of Python concurrency and tackle the most complex concurrent programming problems
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
What you will learn

  • Explore the concepts of concurrency in programming
  • Explore the core syntax and features that enable concurrency in Python
  • Understand the correct way to implement concurrency
  • Abstract methods to keep the data consistent in your program
  • Analyze problems commonly faced in concurrent programming
  • Use application scaffolding to design highly-scalable programs