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

Summary

In this chapter, we described what metrics are and how they compare with logs. We described how metrics are useful to analyze the general state of the system, while logs describe specific tasks, being more difficult to describe the aggregated situation.

We enumerated different kinds of metrics that can be produced and described Prometheus, a common metrics system that uses the pull approach on how to capture metrics.

We set an example of how to generate metrics automatically in Django by installing and configuring the django-prometheus module, and how to start a Prometheus server that scrapes the generated metrics.

Keep in mind that you can also generate your own custom metrics, not having to only rely on the ones in an external module. Check the Prometheus client to see how, for example, for Python: https://github.com/prometheus/client_python.

Next, we described how to query metrics in Prometheus, introducing PromQL, and showed some common examples...