Book Image

Building Microservices with Go

By : Nic Jackson
Book Image

Building Microservices with Go

By: Nic Jackson

Overview of this book

Microservice architecture is sweeping the world as the de facto pattern to build web-based applications. Golang is a language particularly well suited to building them. Its strong community, encouragement of idiomatic style, and statically-linked binary artifacts make integrating it with other technologies and managing microservices at scale consistent and intuitive. This book will teach you the common patterns and practices, showing you how to apply these using the Go programming language. It will teach you the fundamental concepts of architectural design and RESTful communication, and show you patterns that provide manageable code that is supportable in development and at scale in production. We will provide you with examples on how to put these concepts and patterns into practice with Go. Whether you are planning a new application or working in an existing monolith, this book will explain and illustrate with practical examples how teams of all sizes can start solving problems with microservices. It will help you understand Docker and Docker-Compose and how it can be used to isolate microservice dependencies and build environments. We finish off by showing you various techniques to monitor, test, and secure your microservices. By the end, you will know the benefits of system resilience of a microservice and the advantages of Go stack.
Table of Contents (18 chapters)
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

Metrics


In my opinion, metrics are the most useful form of logging for day-to-day operations. Metrics are useful because we have simple numeric data. We can plot this onto a time-series dashboard and quite quickly set up alerting from the output because the data is incredibly cheap to process and collect.

No matter what you are storing, the superior efficiency of metrics is that you are storing numeric data in a time-series database using a unique key as an identifier. Numeric data allows the computation and comparison of the data to be incredibly efficient. It also allows the data store to reduce the resolution of the data as time progresses, enabling you to have granular data when you need it most at the right time and retain historical reference data without requiring petabytes of data storage.

Types of data best represented by metrics

This is quite simple: it is the data that is meaningful when expressed by simple numbers, such as request timings and counts. How granular you want to be...