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Mastering Concurrency in Python

Mastering Concurrency in Python

By : Quan Nguyen
1 (1)
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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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Chapter 19

What is APScheduler? Why isn't it a scheduling service?

APScheduler is an external Python library that supports scheduling Python code to be executed later. APScheduler is not, in itself, a scheduling service that has a built-in GUI or command-line interface. It is still a Python library that has to be imported and utilized inside existing applications. However, APScheduler comes with numerous functionalities that can be leveraged in order to build an actual scheduling service.

What are the main scheduling functionalities of APScheduler?

It offers three different scheduling mechanisms: cron-style scheduling, interval-based execution, and delayed execution. Furthermore, APScheduler allows for storing the jobs to be executed in various backend systems, and working with common Python concurrency frameworks, such as AsyncIO, Gevent, Tornado, and Twisted. Finally, APScheduler...

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