Book Image

Microservice Patterns and Best Practices

By : Vinicius Feitosa Pacheco
Book Image

Microservice Patterns and Best Practices

By: Vinicius Feitosa Pacheco

Overview of this book

Microservices are a hot trend in the development world right now. Many enterprises have adopted this approach to achieve agility and the continuous delivery of applications to gain a competitive advantage. This book will take you through different design patterns at different stages of the microservice application development along with their best practices. Microservice Patterns and Best Practices starts with the learning of microservices key concepts and showing how to make the right choices while designing microservices. You will then move onto internal microservices application patterns, such as caching strategy, asynchronism, CQRS and event sourcing, circuit breaker, and bulkheads. As you progress, you'll learn the design patterns of microservices. The book will guide you on where to use the perfect design pattern at the application development stage and how to break monolithic application into microservices. You will also be taken through the best practices and patterns involved while testing, securing, and deploying your microservice application. At the end of the book, you will easily be able to create interoperable microservices, which are testable and prepared for optimum performance.
Table of Contents (20 chapters)
Title Page
Dedication
Packt Upsell
Contributors
Preface
Index

Storage distribution


Saving data is always something very important in any kind of an application; microservices are no different. The main point is that with a distributed application, there is more flexibility to distribute our data.

There are good practices for treating storage in microservices. Obviously, patterns as CQRS are very useful, but are not always sufficient when it comes to performance. The regionalization and depreciation of data are very useful for the health of the application.

Observing the process run for the creation of containers, we can see that not only for the container of the application itself, in the case of UsersService, that there are also specific containers for the data storage layer for both, as a cache for the database itself.

Depreciating data

In the era of scientific computing, where data analysis is so important, deleting data is something that sounds absurd. Yes, it's absurd. Similarly, a database with 1 million data rows generates increasingly slow queries...