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 6

  1. Relational databases are a better fit for transactional workloads and for performing complex queries. They can scale horizontally using mechanisms such as data sharding but at the cost of requiring additional coordination for executing queries. On the other hand, NoSQL databases are best suited for crunching massive volumes of denormalized data. By design, NoSQL databases can efficiently scale horizontally (even across data centers), with many NoSQL offerings promising a linear increase in query performance as more nodes are added to the cluster. The main caveat of NoSQL databases is that they can only satisfy two facets of the CAP (consistency, availability, and partition tolerance) theorem.

A relational database would be a great fit for systems that perform a large volume of concurrent transactions, such as the ones you would expect to find in a bank. On the other...