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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 pools


When working with multiple processes within our Python applications, we have the option to leverage the very versatile Pool class that lives within the multiprocessing module.

The Pool implementation allows us to succinctly spin up a number of child processes within our programs, and then delegate tasks for the workers in these pools to pick up.

The difference between concurrent.futures.ProcessPoolExecutor and Pool

We covered concurrent.futures.ProcessPoolExecutors in Chapter 7, Executors and Pools, so, what is the need for another implementation of a process pool?

The multiprocessing.Pool implementation of process pools utilizes an almost identical implementation in order to provide parallel processing capabilities. However, the aim of the concurrent.futures module was to provide a simpler interface to work with when creating process pools. This simpler interface is easy for programmers to immediately start working with both Thread and process pools. However, with this...

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