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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

I/O bottlenecks


I/O bottlenecks, or I/O bottlenecks for short, are bottlenecks where your computer spends more time waiting on various inputs and outputs than it does on processing the information.

You'll typically find this type of bottleneck when you are working with an I/O heavy application. We could take your standard web browser as an example of a heavy I/O application. In a browser, we typically spend a significantly longer amount of time waiting for network requests to finish for things such as style sheets, scripts, or HTML pages to load as opposed to rendering this on the screen.

If the rate at which data is requested is slower than the rate at which it is consumed, then you have an I/O bottleneck.

One of the main ways to improve the speed of these applications is to either improve the speed of the underlying I/O by buying more expensive and faster hardware, or to improve the way in which we handle these I/O requests.

A great example of a program bound by I/O bottlenecks would be a...

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