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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

Generating metrics with Prometheus

Prometheus is a popular metrics system that is well supported and easy to use. We will use it as an example during the chapter to show how to collect metrics and how it interconnects with other tools to display metrics.

As we saw before, Prometheus uses the pulling approach to metrics generation. That means that any system that produces metrics will run its own internal Prometheus client that keeps track of metrics.

For web services, this can be added as an extra endpoint that serves the metrics. This is the approach taken by the django-prometheus module, which will automatically collect a lot of common metrics for a Django web service.

We will build up from the Django application code presented in Chapter 6, Web Server Structures, to present a working application. Check the code in GitHub at https://github.com/PacktPublishing/Python-Architecture-Patterns/tree/main/chapter_13_metrics/microposts.

Preparing the environment...

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