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

Distributed relational databases

As we've discussed before, relational databases weren't designed with scalability in mind. They are great for enforcing strong data assurances, including ACID transactions, but their preferred way of operating is through a single server.

This can impose limitations in terms of how big an application can be using relational databases.

It is worth noting that a database server can grow vertically, which means using better hardware. Increasing the capacity of a server or replacing it with a bigger one is an easier solution for high demand than applying some of these techniques, but there's a limit. In any case, please double-check that the expected size is big enough. These days, there are servers in cloud providers that reach 1 terabyte of RAM or more. That's enough to cover a huge number of cases.

Note that these techniques are useful to grow a system after it is up and running, and can be added to most usages...