Learning Concurrency in Python

By : Forbes

3.3 (3)

Buy this Book

Learning Concurrency in Python

3.3 (3)

By: Forbes

Buy this Book

Overview of this book

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.

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Free Chapter

Speed It Up!

History of concurrency

Threads and multithreading

Processes

Multiprocessing

Reactive programming

GPU programming

The limitations of Python

Concurrent image download

Improving number crunching with multiprocessing

Summary

Parallelize It

Understanding concurrency

I/O bottlenecks

Understanding parallelism

How do they work on a CPU?

System architecture styles

Computer memory architecture styles

Summary

Life of a Thread

Threads in Python

Handling threads in Python

How does the operating system handle threads

Multithreading models

Summary

Synchronization between Threads

Synchronization between threads

Shared resources and data races

Summary

Communication between Threads

Standard data structures

Defining your own thread-safe communication structures

Summary

Debug and Benchmark

Testing strategies

Debugging

Benchmarking

Profiling

Summary

Executors and Pools

Concurrent futures

Future objects

ProcessPoolExecutor

Improving our crawler

concurrent.futures in Python 2.7

Summary

Multiprocessing

Working around the GIL

The life of a process

Multiprocessing pools

Communication between processes

Multiprocessing managers

Communicating sequential processes

Summary

Event-Driven Programming

Event-driven programming

Asyncio

Debugging asyncio programs

Twisted

Gevent

Summary

Reactive Programming

Basic reactive programming

ReactiveX, or RX

PyFunctional

Summary

Using the GPU

Introduction to GPUs

Why use the GPU?

CUDA

PyCUDA

Numba

Accelerate

Theano

PyOpenCL

Summary

Choosing a Solution

Libraries not covered in this book

Designing your systems

Summary

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Shared resources and data races

One of the major things we need to guard against when implementing concurrency in your applications is race conditions. These race conditions can cripple our applications, and cause bugs that are hard to debug and even harder to fix. In order to prevent these issues, we need to both understand how these race conditions occur and how we can guard against them using the synchronization primitives we'll be covering in this chapter.

Understanding synchronization and the basic primitives that are available to you is vital if you are to create thread-safe, high-performance programs in Python. Thankfully, we have numerous different synchronization primitives available to us in the threading Python module that can help us in a number of different concurrent situations.

In this section, I'll be giving you a brief overview of all of the synchronization...