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Mastering Concurrency in Python

Mastering Concurrency in Python

By : Quan Nguyen
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Mastering Concurrency in Python

Mastering Concurrency in Python

1 (1)
By: Quan Nguyen

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
Table of Contents (22 chapters)
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Simulating race conditions in Python

Before we discuss a solution that we can implement to solve the problem of race conditions, let's try to simulate the problem in Python. If you have already downloaded the code for this book from the GitHub page, go ahead and navigate to the Chapter14 folder. Let's take a look at the Chapter14/example1.py file—specifically, the update() function, as follows:

# Chapter14/example1.py

import random
import time

def update():
global counter

current_counter = counter # reading in shared resource
time.sleep(random.randint(0, 1)) # simulating heavy calculations
counter = current_counter + 1 # updating shared resource

The goal of the preceding update() function is to increment a global variable called counter, and it is to be called by a separate thread in our script. Inside the function, we are interacting with a shared resource...

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