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Python Architecture Patterns

Python Architecture Patterns

By : Jaime Buelta
4.6 (22)
close
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

Memory profiling

Sometimes, applications use too much memory. The worst-case scenario is that they use more and more memory as time goes by, normally due to what's called a memory leak, maintaining memory that is no longer used, due to some mistake in the coding. Other problems can also include the fact that the usage of memory may be improved, as it's a limited resource.

To profile memory and analyze what the objects are that use the memory, we need first to create some example code. We will generate enough Leonardo numbers.

Leonardo numbers are numbers that follow a sequence defined as the following:

  • The first Leonardo number is one
  • The second Leonardo number is also one
  • Any other Leonardo number is the two previous Leonardo numbers plus one

Leonardo numbers are similar to Fibonacci numbers. They are actually related to them. We use them instead of Fibonacci to show more variety. Numbers are fun!

We present the first 35 Leonardo...

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