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Learning Concurrency in Python

You're reading from   Learning Concurrency in Python Build highly efficient, robust, and concurrent applications

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787285378
Length 360 pages
Edition 1st Edition
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Author (1):
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Elliot Forbes Elliot Forbes
Author Profile Icon Elliot Forbes
Elliot Forbes
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Table of Contents (13) Chapters Close

Preface 1. Speed It Up! FREE CHAPTER 2. Parallelize It 3. Life of a Thread 4. Synchronization between Threads 5. Communication between Threads 6. Debug and Benchmark 7. Executors and Pools 8. Multiprocessing 9. Event-Driven Programming 10. Reactive Programming 11. Using the GPU 12. Choosing a Solution

Multiprocessing managers


Within the multiprocessing module, we have the Manager class; this class can be utilized as a means of controlling Python objects, and providing thread and process safety within your Python applications.

We can initialize a new Manager like this:

Import multiprocessing
myManager = multiprocessing.Manager()

With this manager object, we can then start to share things such as lists and dicts across multiple processes, and typically, it's the first port of call you'd go to if you needed to implement your own form of communication.

Namespaces

Managers come with this concept of namespaces; these namespaces feature no public methods that we can call, but they are useful in the sense that they have writable attributes.

In situations where you need a quick and dirty method for sharing several attributes across multiple processes, this is the prime candidate for doing so.

Example

In this example, we look at how to utilize namespaces in order to share some data across both a main process...

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