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Python Architecture Patterns

Python Architecture Patterns

By : Jaime Buelta
4.6 (22)
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Python Architecture Patterns

Python Architecture Patterns

4.6 (22)
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)
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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

It is quite common that written code doesn't behave perfectly after being tested with real data. Other than bugs, we can find the problem that the performance of the code is not adequate. Perhaps some requests are taking too much time, or perhaps the usage of memory is too high.

In those cases, it's difficult to know exactly what the key elements are, that are taking the most time or memory. While it's possible to try to follow the logic, normally once the code is released, the bottlenecks will be at points that are almost impossible to know beforehand.

To get information on what exactly is going on and follow the code flow, we can use profilers to dynamically analyze the code and better understand how the code is executed, in particular, where most time is spent. This can lead to adjustments and improvements affecting the most significant elements of the code, driven by data, instead of vague speculation.

In this chapter, we'll cover the...

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Python Architecture Patterns
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