Book Image

Hands-On High Performance with Go

By : Bob Strecansky
Book Image

Hands-On High Performance with Go

By: Bob Strecansky

Overview of this book

Go is an easy-to-write language that is popular among developers thanks to its features such as concurrency, portability, and ability to reduce complexity. This Golang book will teach you how to construct idiomatic Go code that is reusable and highly performant. Starting with an introduction to performance concepts, you’ll understand the ideology behind Go’s performance. You’ll then learn how to effectively implement Go data structures and algorithms along with exploring data manipulation and organization to write programs for scalable software. This book covers channels and goroutines for parallelism and concurrency to write high-performance code for distributed systems. As you advance, you’ll learn how to manage memory effectively. You’ll explore the compute unified device architecture (CUDA) application programming interface (API), use containers to build Go code, and work with the Go build cache for quicker compilation. You’ll also get to grips with profiling and tracing Go code for detecting bottlenecks in your system. Finally, you’ll evaluate clusters and job queues for performance optimization and monitor the application for performance regression. By the end of this Go programming book, you’ll be able to improve existing code and fulfill customer requirements by writing efficient programs.
Table of Contents (20 chapters)
Section 1: Learning about Performance in Go
Section 2: Applying Performance Concepts in Go
Section 3: Deploying, Monitoring, and Iterating on Go Programs with Performance in Mind

Clustering in Go

Clustering is a methodology that you can use in order to search for consistent groups of data within a given dataset. Using comparison techniques, we can look for groups of items within the dataset that contain similar characteristics. These individual datapoints are then divided into clusters. Clustering is commonly used in order to solve multi-objective problems.

There are two general classifications of clustering, both of which have distinct subclassifications:

  • Hard clustering: The datapoints within the dataset are either explicitly a part of a cluster or not explicitly part of a cluster. Hard clustering can be further classified as follows:
    • Strict partitioning: An object can belong to exactly one cluster.
    • Strict partitioning with outliers: Strict partitioning, which also includes a concept that objects can be classified as outliers (meaning they belong...