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

Generally speaking, the cycle for debugging problems has the following steps:

  1. Detecting the problem. A new problem or defect is discovered
  2. Analyzing and assigning priority to this problem, to be sure that we spend time on meaningful problems and focus on the most important ones
  3. Investigating what exactly causes the problem. Ideally, this should end with a way of replicating the problem in a local environment
  4. Replicating the problem locally, and getting into the specific details on why it happens
  5. Fixing the problem

As you can see, the general strategy is to first locate and understand the problem, so we can then properly debug and fix it.

In this chapter, we'll cover the following topics to see effective techniques on how to work through all those phases:

  • Detecting and processing defects
  • Investigation in production
  • Understanding the problem in production
  • Local debugging
  • Python introspection...