- What is a process? What are the core differences between a process and a thread?
- What is multiprocessing? What are the core differences between multiprocessing and multithreading?
- What are the API options provided by the multiprocessing module?
- What are the core differences between the Process class and the Pool class from the multiprocessing module?
- What are the options to determine the current process in a Python program?
- What are daemon processes? What are their purposes in terms of waiting for processes in a multiprocessing program?
- How do you terminate a process? Why is it sometimes acceptable to terminate processes?
- What is one of the ways to facilitate interprocess communication in Python?
Mastering Concurrency in Python
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Mastering Concurrency in Python
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Overview of this book
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
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Current Title:
Mastering Concurrency in Python
Table of Contents (22 chapters)
Preface
Free Chapter
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
Designing Lock-Based and Mutex-Free Concurrent Data Structures
Memory Models and Operations on Atomic Types
Building a Server from Scratch
Testing, Debugging, and Scheduling Concurrent Applications
Assessments
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