Book Image

Software Architecture with Python

By : Anand Balachandran Pillai
Book Image

Software Architecture with Python

By: Anand Balachandran Pillai

Overview of this book

This book starts by explaining how Python fits into an application's architecture. As you move along, you will get to grips with architecturally significant demands and how to determine them. Later, you’ll gain a complete understanding of the different architectural quality requirements for building a product that satisfies business needs, such as maintainability/reusability, testability, scalability, performance, usability, and security. You will also use various techniques such as incorporating DevOps, continuous integration, and more to make your application robust. You will discover when and when not to use object orientation in your applications, and design scalable applications. The focus is on building the business logic based on the business process documentation, and understanding which frameworks to use and when to use them. The book also covers some important patterns that should be taken into account while solving design problems, as well as those in relatively new domains such as the Cloud. By the end of this book, you will have understood the ins and outs of Python so that you can make critical design decisions that not just live up to but also surpassyour clients’ expectations.
Table of Contents (18 chapters)
Software Architecture with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Index

Celery – a distributed task queue


Celery is a distributed task queue written in Python, which works using distributed messages. Each execution unit in celery is called a task. A task can be executed concurrently on one or more servers using processes called workers. By default, celery achieves this using multiprocessing, but it can also use other backend such as gevent, for example.

Tasks can be executed synchronously or asynchronously with results available in the future, like objects. Also, task results can be stored in storage backend such as Redis, databases, or in files.

Celery differs from message queues in that the basic unit in celery is an executable task—a callable in Python—rather than just a message.

Celery, however, can be made to work with message queues. In fact, the default broker for passing messages in celery is RabbitMQ, the popular implementation of AMQP. Celery can also work with Redis as the broker backend.

Since Celery takes a task, and scales it over multiple workers...