Immerse yourself in the world of Python concurrency and tackle the most complex concurrent programming problems
Key Features
- Explore the core syntaxes, language features and modern patterns of concurrency in Python
- Understand how to use concurrency to keep data consistent and applications responsive
- Utilize application scaffolding to design highly-scalable programs
Book Description
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
Who this book is for
This book is for developers who wish to build high-performance applications and learn about signle-core, multicore programming or distributed concurrency. Some experience with Python programming language is assumed.
Table of Contents
- Concurrent and Parallel Programming - An Advanced Introduction
- Amdahl's Law
- Working with Threads in Python
- Using the 'with' Statement in Threads
- Concurrent Web Scraping
- Working with Processes in Python
- The Reduction Operation in Processes
- Concurrent Image Processing
- Introduction to Asynchronous I/O
- Asyncio: Pros and Cons
- TCP with Asyncio
- Deadlock
- Starvation
- Race Conditions
- The Global Interpreter Lock
- Designing Lock-Free and Lock-Based Concurrent Data Structures
- Memory Models and Operations on Atomic Types
- Building a Server from Scratch
- Testing, Debugging, and Scheduling Concurrent Applications
Quan Nguyen is a Python enthusiast and data scientist. He is currently a data analysis engineer at Micron Technology, Inc. With a strong background in mathematics and statistics, Quan is interested in the fields of scientific computing and machine learning. With data analysis being his focus, Quan also enjoys incorporating technology automation into everyday tasks through programming.
Quan's passion for Python programming has led him to be heavily involved in the Python community. He started as a primary contributor for the book Python for Scientists and Engineers and various open source projects on GitHub. Quan is also a writer for the Python Software Foundation and an occasional content contributor for DataScience (part of Oracle)
