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Python Architecture Patterns

Python Architecture Patterns

By : Jaime Buelta
4.6 (22)
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Python Architecture Patterns

Python Architecture Patterns

4.6 (22)
By: Jaime Buelta

Overview of this book

Developing large-scale systems that continuously grow in scale and complexity requires a thorough understanding of how software projects should be implemented. Software developers, architects, and technical management teams rely on high-level software design patterns such as microservices architecture, event-driven architecture, and the strategic patterns prescribed by domain-driven design (DDD) to make their work easier. This book covers these proven architecture design patterns with a forward-looking approach to help Python developers manage application complexity—and get the most value out of their test suites. Starting with the initial stages of design, you will learn about the main blocks and mental flow to use at the start of a project. The book covers various architectural patterns like microservices, web services, and event-driven structures and how to choose the one best suited to your project. Establishing a foundation of required concepts, you will progress into development, debugging, and testing to produce high-quality code that is ready for deployment. You will learn about ongoing operations on how to continue the task after the system is deployed to end users, as the software development lifecycle is never finished. By the end of this Python book, you will have developed "architectural thinking": a different way of approaching software design, including making changes to ongoing systems.
Table of Contents (23 chapters)
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2
Part I: Design
6
Part II: Architectural Patterns
12
Part III: Implementation
15
Part IV: Ongoing operations
21
Other Books You May Enjoy
22
Index

Celery

Celery is the most popular task queue created in Python. It allows us to create new tasks easily and can handle the creation of the events that trigger new tasks.

Celery requires to work to set up a broker, which will be used as a queue to handle the messages.

In Celery parlance, the broker is the message queue, while the backend is reserved for interacting with a storage system to return information.

The code that creates the message will add it to the broker, and the broker will pass it to one of the connected workers. When everything happens with Python code, where the celery package can be installed, it's simple to operate. We'll see later how to operate it in other cases.

Celery can use multiple systems as brokers. The most popular are Redis and RabbitMQ.

In our examples, we will use Redis as it can be used for the broker and the backend, and it's widely available in cloud systems. It's also quite scalable and...

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Python Architecture Patterns
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