Book Image

Advanced Python Programming

By : Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis
Book Image

Advanced Python Programming

By: Dr. Gabriele Lanaro, Quan Nguyen, Sakis Kasampalis

Overview of this book

This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing. By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems. This Learning Path includes content from the following Packt products: • Python High Performance - Second Edition by Gabriele Lanaro • Mastering Concurrency in Python by Quan Nguyen • Mastering Python Design Patterns by Sakis Kasampalis
Table of Contents (41 chapters)
Title Page
Copyright
About Packt
Contributors
Preface
Index

concurrent.futures as a solution for blocking tasks


In this section, we will be considering another way to implement threading/multiprocessing: the concurrent.futures module, which is designed to be a high-level interface for implementing asynchronous tasks. Specifically, the concurrent.futures module works seamlessly with the asyncio module, and, in addition, it provides an abstract class called Executor, which contains the skeleton of the two main classes that implement asynchronous threading and multiprocessing, respectively (as suggested by their names): ThreadPoolExecutor and ProcessPoolExecutor.

Changes in the framework

Before we jump into the API from concurrent.futures, let's discuss the theoretical basics of asynchronous threading/multiprocessing, and how it plays into the framework of the asynchronous programming that asyncio provides.

As a reminder, we have three major elements in our ecosystem of asynchronous programming: the event loop, the coroutines, and their corresponding futures...