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

Profiling basics

Profiling is a dynamic analysis that instruments code to understand how it runs. This information is extracted and compiled in a way that can be used to get a better knowledge of a particular behavior based on a real case, as the code is running as usual. This information can be used to improve the code.

Certain static analysis tools, as opposed to dynamic, can provide insight into aspects of the code. For example, they can be used to detect if certain code is dead code, meaning it's not called anywhere in the whole code. Or, they can detect some bugs, like the usage of variables that haven't been defined before, like when having a typo. But they don't work with the specifics of code that's actually being run. Profiling will bring specific data based on the use case instrumented and will return much more information on the flow of the code.

The normal application of profiling is to improve the performance of the code under analysis...