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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

Summary

In this chapter, we have seen more event-driven systems with a variety of advanced and complex architectures that can be designed. We have presented some of the flexibility and power that event-driven design can bring to a design, but also the challenges attached to event-driven design.

We started by presenting common systems such as logs and metrics as event-driven systems, as they are, and considered how looking at them in this way allows us to create alerting and feedback systems that can be used to control the source of the events.

We also presented an example with Celery of a more complex pipeline, including the usage of multiple queues and shared storage to generate multiple coordinated tasks, such as resizing a video and extracting a thumbnail.

We presented the idea of a bus, a shared access point for all events in the system, and looked at how we can generate more complex systems where events are delivered to multiple systems and cascade into complex actions...

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