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

Log limitations

Logs are very useful to understand what's happening in a running system, but they have certain limitations that are important to understand:

  • Logs are only as good as their messages. A good, descriptive message is critical in making logs useful. Reviewing the log messages with a critical eye, and correcting them when needed, is important to save precious time on production problems.
  • Have an appropriate number of logs. Too many logs can confuse a flow, and too few may not include enough information to allow us to understand the problem. Large numbers of logs also create problems with storage.
  • Logs should work as an indication of the context of the problem, but likely won't pinpoint it. Trying to generate specific logs that fully explain a bug will be an impossible task. Instead, focus on showing the general flow and surrounding context of the action, so it can be replicated locally and debugged. For example, for a request, make sure...