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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

ProcessPoolExecutor


ProcessPoolExecutors can be used and created in much the same way as your standard ThreadPoolExecutors. It subclasses the Executor class the same way the ThreadPoolExecutor class does, and thus, features many of the same methods within it.

Creating a ProcessPoolExecutor

The process for creating a ProcessPoolExecutor is almost identical to that of the ThreadPoolExecutor except for the fact that we have to specify that we've imported that class from the concurrent.futures module, and that we also instantiate our executor object like this:

executor = ProcessPoolExecutor(max_workers=3)

Example

The following example features a very simple full example of how you can instantiate your own ProcessPoolExecutor and submit a couple of tasks into this pool. It should be noted that our task function here isn't that computationally expensive, so we may not see the full benefit of using multiple processes, and it could, in fact, be significantly slower than your typical single-threaded process...

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