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

Debugging with breakpoints

In other situations, it's better to stop the execution of the code and take a look at the current status. Given that Python is a dynamic language, it means that, if we stop the execution of the script and enter the interpreter, we can run any kind of code and see its results.

This is exactly what is done through the usage of the breakpoint function.

breakpoint is a relatively new addition to Python, available since Python 3.7. Previously, it was necessary to import the module pdb, typically in this way in a single line:

import pdb; pdb.set_trace()

Other than the ease of usage, breakpoint has some other advantages that we will see.

When the interpreter finds a breakpoint call, it stops and opens an interactive interpreter. From this interactive interpreter, the current status of the code can be examined and any investigation can take place, simply executing the code. This makes it possible to understand interactively...