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 12

  1. In a leader-follower configuration, the nodes hold an election and elect a leader for the cluster. All reads and writes go through the cluster leader, while the other nodes monitor the leader and automatically hold a new election if the leader becomes unavailable. As the name implies, in a multi-master configuration, the cluster has several master nodes and each of the master nodes can serve both read and write requests. The master nodes implement some form of distributed consensus algorithm (Raft, Paxos, and so on) to ensure that they always share the same view of the cluster's state.
  1. When implementing the checkpoint strategy, workers are periodically asked by the master to persist their current state to durable storage. If this operation succeeds, a new checkpoint is created. If a worker crashes or becomes unavailable, the master will request for the remaining...