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Mastering Go – Third Edition - Third Edition

By : Mihalis Tsoukalos

4.6 (16)

Mastering Go – Third Edition

4.6 (16)

By: Mihalis Tsoukalos

Overview of this book

Mastering Go is the essential guide to putting Go to work on real production systems. This freshly updated third edition includes topics like creating RESTful servers and clients, understanding Go generics, and developing gRPC servers and clients. Mastering Go was written for programmers who want to explore the capabilities of Go in practice. As you work your way through the chapters, you’ll gain confidence and a deep understanding of advanced Go concepts, including concurrency and the operation of the Go Garbage Collector, using Go with Docker, writing powerful command-line utilities, working with JavaScript Object Notation (JSON) data, and interacting with databases. You’ll also improve your understanding of Go internals to optimize Go code and use data types and data structures in new and unexpected ways. This essential Go programming book will also take you through the nuances and idioms of Go with exercises and resources to fully embed your newly acquired knowledge. With the help of Mastering Go, you’ll become an expert Go programmer by building Go systems and implementing advanced Go techniques in your projects.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

A Quick Introduction to Go

A Quick Introduction to Go

Introducing Go

Hello World!

Running Go code

Important characteristics of Go

Developing the which(1) utility in Go

Logging information

Overview of Go generics

Developing a basic phone book application

Exercises

Summary

Additional resources

Free Chapter

Basic Go Data Types

Basic Go Data Types

The error data type

Numeric data types

Non-numeric data types

Go constants

Grouping similar data

Pointers

Generating random numbers

Updating the phone book application

Exercises

Summary

Additional resources

Composite Data Types

Composite Data Types

Maps

Structures

Regular expressions and pattern matching

Improving the phone book application

Exercises

Summary

Additional resources

Reflection and Interfaces

Reflection and Interfaces

Reflection

Type methods

Interfaces

Working with two different CSV file formats

Object-oriented programming in Go

Updating the phone book application

Exercises

Summary

Additional resources

Go Packages and Functions

Go Packages and Functions

Go packages

Functions

Developing your own packages

Using GitHub to store Go packages

A package for working with a database

Modules

Creating better packages

Generating documentation

GitLab Runners and Go

GitHub Actions and Go

Versioning utilities

Exercises

Summary

Additional resources

Telling a UNIX System What to Do

Telling a UNIX System What to Do

stdin, stdout, and stderr

UNIX processes

Handling UNIX signals

File I/O

Reading text files

Writing to a file

Working with JSON

Working with XML

Working with YAML

The viper package

The cobra package

Finding cycles in a UNIX file system

New to Go 1.16

Updating the phone book application

Exercises

Summary

Additional resources

Go Concurrency

Go Concurrency

Processes, threads, and goroutines

The Go scheduler

Goroutines

Channels

Race conditions

The select keyword

Timing out a goroutine

Go channels revisited

Shared memory and shared variables

Closured variables and the go statement

The context package

The semaphore package

Exercises

Summary

Additional resources

Building Web Services

Building Web Services

The net/http package

Creating a web server

Updating the phone book application

Exposing metrics to Prometheus

Developing web clients

Creating file servers

Timing out HTTP connections

Exercises

Summary

Additional resources

Working with TCP/IP and WebSocket

Working with TCP/IP and WebSocket

TCP/IP

The net package

Developing a TCP client

Developing a TCP server

Developing a UDP client

Developing a UDP server

Developing concurrent TCP servers

Working with UNIX domain sockets

Creating a WebSocket server

Creating a WebSocket client

Exercises

Summary

Additional resources

Working with REST APIs

Working with REST APIs

An introduction to REST

Developing RESTful servers and clients

Creating a functional RESTful server

Creating a RESTful client

Uploading and downloading binary files

Using Swagger for REST API documentation

Exercises

Summary

Additional resources

Code Testing and Profiling

Code Testing and Profiling

Optimizing code

Benchmarking code

Profiling code

The go tool trace utility

Testing Go code

Testing an HTTP server with a database backend

Fuzzing

Cross-compilation

Using go:generate

Creating example functions

Exercises

Summary

Additional resources

Working with gRPC

Working with gRPC

Introduction to gRPC

Defining an interface definition language file

Developing a gRPC server

Developing a gRPC client

Exercises

Summary

Additional resources

Go Generics

Go Generics

Introducing generics

Constraints

Defining new data types with generics

Interfaces versus generics

Reflection versus generics

Exercises

Summary

Additional resources

Other Books You May Enjoy

Other Books You May Enjoy

Index

Index

Appendix A – Go Garbage Collector

Appendix A – Go Garbage Collector

Heap and stack

Garbage collection

Additional resources

Big O complexity

The computational complexity of an algorithm is usually denoted using the popular Big O notation. The Big O notation is used for expressing the worst-case scenario for the order of growth of an algorithm. It shows how the performance of an algorithm changes as the size of the data it processes grows.

O(1) means constant time complexity, which does not depend on the amount of data at hand. O(n) means that the execution time is proportional to n (linear time)—you cannot process data without accessing it, so O(n) is considered good. O(n²) (quadratic time) means that the execution time is proportional to n². O(n!) (factorial time) means that the execution time of the algorithm is directly proportional to the factorial of n. Simply put, if you have to process 100 values of some kind, then the O(n) algorithm will do about 100 operations, O(n²) is going to perform about 10,000 operations, and the algorithm with the O(n!) complexity 10¹⁵⁸ operations!

Now that...

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