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

Investigation in production

Once we are aware that we have a problem in production, we need to understand what is happening and what the key elements that produce it are.

It's very important to remark on the importance of being able to replicate a problem. If that's the case, tests can be done to produce the error and follow the consequences.

The most important tools when analyzing why a particular problem is produced are the observability tools. That's why it is important to do preparation work in advance to be sure to be able to find problems when required.

We talked in previous chapters about logs and metrics. When debugging, metrics are normally not relevant, except to show the relative importance of a bug. Checking an increase in returned errors can be important to detect that there's an error, but detecting what error will require more precise information.

Do not underestimate metrics, though. They can help quickly determine what...