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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

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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