Book Image

Learning Concurrency in Python

By : Elliot Forbes
Book Image

Learning Concurrency in Python

By: Elliot Forbes

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.
Table of Contents (20 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

Debugging asyncio programs


Thankfully, when it comes to debugging asyncio-based applications, we have a couple of options to consider. The writers of the asyncio module have very kindly provided a debug mode, which is quite powerful and can really aid us in our debugging adventures without the overhead of modifying the system's code base too dramatically.

Debug mode

Turning on this debug mode within your asyncio-based programs is relatively simply and requires just a call to this function:

loop.set_debug(True)

Let's take a look at a fully fledged example of this and how it differs from your standard logging. In this example, we'll create a very simple event loop and submit some simple tasks to the event loop:

import asyncio
import logging
import time

logging.basicConfig(level=logging.DEBUG)

async def myWorker():
logging.info("My Worker Coroutine Hit")
time.sleep(1)

async def main():
logging.debug("My Main Function Hit")
await asyncio.wait([myWorker()])

loop = asyncio.get_event_loop()
loop...