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Mastering Go

Mastering Go

By : Mihalis Tsoukalos
4.8 (27)
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Mastering Go

Mastering Go

4.8 (27)
By: Mihalis Tsoukalos

Overview of this book

Mastering Go, now in its fourth edition, remains the go-to resource for real-world Go development. This comprehensive guide delves into advanced Go concepts, including RESTful servers, and Go memory management. This edition brings new chapters on Go Generics and fuzzy Testing, and an enriched exploration of efficiency and performance. As you work your way through the chapters, you will gain confidence and a deep understanding of advanced Go topics, including concurrency and the operation of the Garbage Collector, using Go with Docker, writing powerful command-line utilities, working with JavaScript Object Notation (JSON) data, and interacting with databases. You will be engaged in real-world exercises, build network servers, and develop robust command-line utilities. With in-depth chapters on RESTful services, the WebSocket protocol, and Go internals, you are going to master Go's nuances, optimization, and observability. You will also elevate your skills in efficiency, performance, and advanced testing. With the help of Mastering Go, you will become an expert Go programmer by building Go systems and implementing advanced Go techniques in your projects.
Table of Contents (19 chapters)
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16
Other Books You May Enjoy
17
Index

Updating the statistics application

In this section, we are going to improve the functionality and the operation of the statistics application. When there is no valid input, we are going to populate the statistics application with ten random values, which is pretty handy when you want to put lots of data in an application for testing purposes—you can change the number of random values to fit your needs. However, keep in mind that this takes place when all user input is invalid.

I have randomly generated data in the past in order to put sample data into Kafka topics, RabbitMQ queues and MySQL tables.

Additionally, we are going to normalize the data. Officially, this is called z-normalization and is helpful for allowing sequences of values to be compared more accurately. We are going to use normalization in forthcoming chapters.

The function for the normalization of the data is implemented as follows:

func normalize(data []float64, mean float64, stdDev...

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