Book Image

Hands-On Software Engineering with Golang

By : Achilleas Anagnostopoulos
Book Image

Hands-On Software Engineering with Golang

By: Achilleas Anagnostopoulos

Overview of this book

Over the last few years, Go has become one of the favorite languages for building scalable and distributed systems. Its opinionated design and built-in concurrency features make it easy for engineers to author code that efficiently utilizes all available CPU cores. This Golang book distills industry best practices for writing lean Go code that is easy to test and maintain, and helps you to explore its practical implementation by creating a multi-tier application called Links ‘R’ Us from scratch. You’ll be guided through all the steps involved in designing, implementing, testing, deploying, and scaling an application. Starting with a monolithic architecture, you’ll iteratively transform the project into a service-oriented architecture (SOA) that supports the efficient out-of-core processing of large link graphs. You’ll learn about various cutting-edge and advanced software engineering techniques such as building extensible data processing pipelines, designing APIs using gRPC, and running distributed graph processing algorithms at scale. Finally, you’ll learn how to compile and package your Go services using Docker and automate their deployment to a Kubernetes cluster. By the end of this book, you’ll know how to think like a professional software developer or engineer and write lean and efficient Go code.
Table of Contents (21 chapters)
1
Section 1: Software Engineering and the Software Development Life Cycle
3
Section 2: Best Practices for Maintainable and Testable Go Code
7
Section 3: Designing and Building a Multi-Tier System from Scratch
14
Section 4: Scaling Out to Handle a Growing Number of Users
18
Epilogue

Chapter 7

  1. The Go interface{} type conveys no useful information about the underlying type. If we use it for representing an argument to a function or a method, we effectively bypass the compiler's ability to statically check the function/method arguments at compile-time and instead have to manually check whether the input can be safely cast into a supported known type.
  2. Instead of running the compute-intensive stages locally, we can migrate them to a remote machine with enough computing resources. The respective local stages can then be replaced with a proxy that transmits the local payload data to the remote machine via a remote procedure call (RPC), waits for the results, and pushes them to the next local stage. The following diagram outlines the proposed solution:

  1. Each processor function must satisfy the Processor interface, whose definition is as follows: