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Learning Concurrency in Python
Learning Concurrency in Python

Learning Concurrency in Python: Build highly efficient, robust, and concurrent applications

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Learning Concurrency in Python

Parallelize It

Concurrency and parallelism are two concepts that are commonly confused. The reality, though, is that they are quite different, and if you designed software to be concurrent when instead you needed parallel execution, then you could be seriously impacting your software's true performance potential.

Due to this, it's vital to know exactly what the two concepts mean so that you can understand the differences. Through knowing these differences, you'll be putting yourself at a distinct advantage when it comes to designing your own high performance software in Python.

In this chapter, we'll be covering the following topics:

  • What is concurrency and what are the major bottlenecks that impact our applications
  • What is parallelism and how does this differ from concurrency
  • The different styles of computer system architecture and how we can utilize these...

Understanding concurrency

Concurrency is, essentially, the practice of doing multiple things at the same time, but not, specifically, in parallel. It can help us to improve the perceived performance of our applications, and it can also improve the speed at which our applications run.

The best way to think of how concurrency works is to imagine one person working on multiple tasks and quickly switching between these tasks. Imagine this one person working concurrently on a program, and, at the same time, dealing with support requests. This person would focus primarily on the writing of their program, and quickly context switch to fixing a bug or dealing with a support issue should there be one. Once they complete the support task, they could switch context again, back to writing their program really quickly.

However, in computing, there are typically two performance bottlenecks...

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...

Understanding parallelism

In the first chapter, we covered a bit about Python's multiprocessing capabilities, and how we could use this to take advantage of more of the processing cores in our hardware. But what do we mean when we say that our programs are running in parallel?

Parallelism is the art of executing two or more actions simultaneously as opposed to concurrency in which you make progress on two or more things at the same time. This is an important distinction, and in order to achieve true parallelism, we'll need multiple processors on which to run our code at the same time.

A good analogy for parallel processing is to think of a queue for Coke. If you have, say, two queues of 20 people, all waiting to use a coke machine so that they can get through the rest of the day with a bit of a sugar rush, well, this would be an example of concurrency. Now say you were...

How do they work on a CPU?

Understanding the differences outlined in the previous section between both concurrency and parallelism is essential, but it's also very important to understand more about the systems that your software will be running on. Having an appreciation of the different architecture styles as well as the low-level mechanics helps you make the most informed decisions in your software design.

Single-core CPUs

Single-core processors will only ever execute one thread at any given time as that is all they are capable of. However, in order to ensure that we don't see our applications hanging and being unresponsive, these processors rapidly switch between multiple threads of execution many thousands of...

Understanding concurrency


Concurrency is, essentially, the practice of doing multiple things at the same time, but not, specifically, in parallel. It can help us to improve the perceived performance of our applications, and it can also improve the speed at which our applications run.

The best way to think of how concurrency works is to imagine one person working on multiple tasks and quickly switching between these tasks. Imagine this one person working concurrently on a program, and, at the same time, dealing with support requests. This person would focus primarily on the writing of their program, and quickly context switch to fixing a bug or dealing with a support issue should there be one. Once they complete the support task, they could switch context again, back to writing their program really quickly.

However, in computing, there are typically two performance bottlenecks that we have to watch out for and guard against when writing our programs. It's important to know the differences between...

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...

Understanding parallelism


In the first chapter, we covered a bit about Python's multiprocessing capabilities, and how we could use this to take advantage of more of the processing cores in our hardware. But what do we mean when we say that our programs are running in parallel?

Parallelism is the art of executing two or more actions simultaneously as opposed to concurrency in which you make progress on two or more things at the same time. This is an important distinction, and in order to achieve true parallelism, we'll need multiple processors on which to run our code at the same time.

A good analogy for parallel processing is to think of a queue for Coke. If you have, say, two queues of 20 people, all waiting to use a coke machine so that they can get through the rest of the day with a bit of a sugar rush, well, this would be an example of concurrency. Now say you were to introduce a second coke machine into the mix--this would then be an example of something happening in parallel. This is...

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Key benefits

  • Build highly efficient, robust, and concurrent applications
  • Work through practical examples that will help you address the challenges of writing concurrent code
  • Improve the overall speed of execution in multiprocessor and multicore systems and keep them highly available

Description

Python is a very high level, general purpose language that is utilized heavily in fields such as data science and research, as well as being one of the top choices for general purpose programming for programmers around the world. It features a wide number of powerful, high and low-level libraries and frameworks that complement its delightful syntax and enable Python programmers to create. This book introduces some of the most popular libraries and frameworks and goes in-depth into how you can leverage these libraries for your own high-concurrent, highly-performant Python programs. We'll cover the fundamental concepts of concurrency needed to be able to write your own concurrent and parallel software systems in Python. The book will guide you down the path to mastering Python concurrency, giving you all the necessary hardware and theoretical knowledge. We'll cover concepts such as debugging and exception handling as well as some of the most popular libraries and frameworks that allow you to create event-driven and reactive systems. By the end of the book, you'll have learned the techniques to write incredibly efficient concurrent systems that follow best practices.

Who is this book for?

This book is for Python developers who would like to get started with concurrent programming. Readers are expected to have a working knowledge of the Python language, as this book will build on these fundamentals concepts.

What you will learn

  • Explore the concept of threading and multiprocessing in Python
  • Understand concurrency with threads
  • Manage exceptions in child threads
  • Handle the hardest part in a concurrent system — shared resources
  • Build concurrent systems with Communicating Sequential Processes (CSP)
  • Maintain all concurrent systems and master them
  • Apply reactive programming to build concurrent systems
  • Use GPU to solve specific problems
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Table of Contents

12 Chapters
Speed It Up! Chevron down icon Chevron up icon
Parallelize It Chevron down icon Chevron up icon
Life of a Thread Chevron down icon Chevron up icon
Synchronization between Threads Chevron down icon Chevron up icon
Communication between Threads Chevron down icon Chevron up icon
Debug and Benchmark Chevron down icon Chevron up icon
Executors and Pools Chevron down icon Chevron up icon
Multiprocessing Chevron down icon Chevron up icon
Event-Driven Programming Chevron down icon Chevron up icon
Reactive Programming Chevron down icon Chevron up icon
Using the GPU Chevron down icon Chevron up icon
Choosing a Solution Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.3
(3 Ratings)
5 star 33.3%
4 star 33.3%
3 star 0%
2 star 0%
1 star 33.3%
Kat Simon Aug 18, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Always wondered how all this stuff works.Great compilation of topics and examples.
Amazon Verified review Amazon
Brent Wagenseller Aug 28, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
I am about halfway done with the book now. While there are some errors (as pointed out in other reviews), it has many useful pieces of information. I was glad to see a decent mix of theory with practical applications.If you need to learn about Python threading / multiprocessing, this is a great starter book that will help you get off the ground.
Amazon Verified review Amazon
高柱錫 Jul 12, 2020
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
There are many minor errors in sample codes.
Amazon Verified review Amazon
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