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

Adding logs while developing

Any test runner will capture logs and display it as part of the trace while running tests.

pytest, which we introduced in Chapter 10, Testing and TDD, will display logs as part of the result of a failing test.

This is a good opportunity to check that the expected logs are being generated while the feature is still in development phase, especially if it's done in a TDD process where the failing tests and errors are produced routinely as part of the process, as we saw in Chapter 10, Testing and TDD. Any test that checks an error should also add a corresponding log and, while developing the feature, check that they are being produced.

You can explicitly add to the test a check to validate that the log is being generated by using a tool like pytest-catchlog (https://pypi.org/project/pytest-catchlog/).

Typically, though, we just take a bit of care and incorporate the practice of checking while using TDD practices as...