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Mastering Concurrency in Python

You're reading from   Mastering Concurrency in Python Create faster programs using concurrency, asynchronous, multithreading, and parallel programming

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781789343052
Length 446 pages
Edition 1st Edition
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Author (1):
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Quan Nguyen Quan Nguyen
Author Profile Icon Quan Nguyen
Quan Nguyen
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Table of Contents (22) Chapters Close

Preface 1. Advanced Introduction to Concurrent and Parallel Programming FREE CHAPTER 2. Amdahl's Law 3. Working with Threads in Python 4. Using the with Statement in Threads 5. Concurrent Web Requests 6. Working with Processes in Python 7. Reduction Operators in Processes 8. Concurrent Image Processing 9. Introduction to Asynchronous Programming 10. Implementing Asynchronous Programming in Python 11. Building Communication Channels with asyncio 12. Deadlocks 13. Starvation 14. Race Conditions 15. The Global Interpreter Lock 16. Designing Lock-Based and Mutex-Free Concurrent Data Structures 17. Memory Models and Operations on Atomic Types 18. Building a Server from Scratch 19. Testing, Debugging, and Scheduling Concurrent Applications 20. Assessments 21. Other Books You May Enjoy

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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