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

Exploring job queues in Go

Job queues are frequently used to process units of work in a computer system. They are often used to schedule both synchronous and asynchronous functions. While working with larger datasets, there can be data structures and algorithms that take quite a bit of time to process. Either the system is processing a very large segment of data, the algorithm that is being applied to the dataset is very complex, or there's a combination of the two. Being able to add these jobs to a job queue and perform them in a different order or at different times can be very helpful to maintain the stability of a system and give an end user a better experience. Job queues are also frequently used for asynchronous jobs since the time when the job completes isn't as impactful for the end user. The job system can also prioritize the jobs in a priority queue if one...