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

Clusters and Job Queues

Clustering and job queues in Go are good ways to get distributed systems to work synchronously and deliver a consistent message. Distributed computing is difficult and it becomes very important to watch for potential performance optimizations within both clustering and job queues.

In this chapter, we will learn about the following topics:

  • Clustering with hierarchical and centroid algorithms
  • Goroutines as queues
  • Buffered channels as job queues
  • Implementing third-party queuing systems (Kafka and RabbitMQ)

Learning about different clustering systems can help you identify large groups of data and how to accurately classify them in your datasets. Learning about queueing systems will help you move large amounts of information from your data structures into specific queueing mechanisms in order to pass large amounts of data to different systems in real time...