Book Image

Learning Python Networking - Second Edition

By : José Manuel Ortega, Dr. M. O. Faruque Sarker, Sam Washington
Book Image

Learning Python Networking - Second Edition

By: José Manuel Ortega, Dr. M. O. Faruque Sarker, Sam Washington

Overview of this book

Network programming has always been a demanding task. With full-featured and well-documented libraries all the way up the stack, Python makes network programming the enjoyable experience it should be. Starting with a walk through of today's major networking protocols, through this book, you'll learn how to employ Python for network programming, how to request and retrieve web resources, and how to extract data in major formats over the web. You will utilize Python for emailing using different protocols, and you'll interact with remote systems and IP and DNS networking. You will cover the connection of networking devices and configuration using Python 3.7, along with cloud-based network management tasks using Python. As the book progresses, socket programming will be covered, followed by how to design servers, and the pros and cons of multithreaded and event-driven architectures. You'll develop practical clientside applications, including web API clients, email clients, SSH, and FTP. These applications will also be implemented through existing web application frameworks.
Table of Contents (19 chapters)
Free Chapter
1
Section 1: Introduction to Network and HTTP Programming
4
Section 2: Interacting with APIs, Web Scraping, and Server Scripting
9
Section 3: IP Address Manipulation and Network Automation
13
Section 4: Sockets and Server Programming

Building asynchronous network applications with Celery

In this section, you will learn about building asynchronous network applications with the Celery framework.

Celery architecture

Celery is an efficient and scalable way to execute tasks asynchronously and acts as a distributor of high-level tasks where tasks are queued and run concurrently using various paradigms such as multiprocessing or gevent.

An efficient and scalable way to perform tasks asynchronously is to use a queue library as Celery. With this library, you define workers that are processes for executing the heavy tasks. An interesting aspect of this solution is that there can be many workers (even in different servers) executing the tasks.

The architecture of...