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

Advanced pytest

While we've described the basic functionalities for pytest, we barely scratched the surface in terms of the number of possibilities that it presents to help generate testing code.

Pytest is a big and comprehensive tool. It is worth learning how to use it. Here, we will only scratch the surface. Be sure to check the official documentation at https://docs.pytest.org/.

Without being exhaustive, we will see some useful possibilities of the tool.

Grouping tests

Sometimes it is useful to group tests together so they are related to specific things, like modules, or to run them in unison. The simplest way of grouping tests together is to join them into a single class.

For example, going back to the test examples before, we could structure tests into two classes, as we see in test_group_classes.py.

from tdd_example import parameter_tdd
class TestEdgesCases():
    def test_negative(self):
        assert parameter_tdd(-1) == 0
    def...