Book Image

Mastering Concurrency in Python

By : Quan Nguyen
Book Image

Mastering Concurrency in Python

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)

Scheduling with APScheduler

APScheduler (short for Advanced Python Scheduler) is an external Python library that supports the scheduling of Python code to be executed later, either once or periodically. This library gives us high-level options to dynamically add/remove jobs to/from the job list so they can be scheduled and executed, as well as to decide how to distribute those jobs to different threads and processes.

Some might think of Celery (http://www.celeryproject.org/) as the go-to scheduling tool for Python. However, while Celery is a distributed task queue with basic scheduling capabilities, APScheduler is quite the opposite: a scheduler with basic task queuing options and advanced scheduling functionalities. Additionally, users of both tools have reported that APScheduler is easier to set up and implement.

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