Book Image

Python Architecture Patterns

By : Jaime Buelta
Book Image

Python Architecture Patterns

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)
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

Pipelines

The flow of events doesn't have to be contained in a single system. The receiving end of the system can produce its own events, directed to other systems. Events will cascade into multiple systems, generating a process.

This is a similar situation to the one presented previously, but in this case it's a more deliberate process aimed at creating specific data pipelines where the flow between systems is triggered and processed.

A possible example of this is a system to rescale videos into different sizes and formats. When a video is uploaded into the system, it needs to be converted into multiple versions to be used in different situations. A thumbnail should also be created to display the first frame of the video before playing it.

We will do this in three steps. First, a queue will receive the event to start the processing. This will trigger two events in two different queues to process the resize and the thumbnail generation independently. This will...