Explore software engineering methodologies, techniques, and best practices in Go programming to build easy-to-maintain software that can effortlessly scale on demand
▶What You Will Learn
⦁Understand different stages of the software development life cycle and the role of a software engineer
⦁Create APIs using gRPC and leverage the middleware offered by the gRPC ecosystem
⦁Discover various approaches to managing package dependencies for your projects
⦁Build an end-to-end project from scratch and explore different strategies for scaling it
⦁Develop a graph processing system and extend it to run in a distributed manner
⦁Deploy Go services on Kubernetes and monitor their health using Prometheus
▶Key Features
⦁Apply best practices to produce lean, testable, and maintainable Go code and avoid the accumulation of technical debt
⦁Explore Go's built-in support for concurrency and message passing to build high-performance applications
⦁Scale your Go programs across machines and manage their life cycle using Kubernetes
▶Who This Book Is For
This Golang programming book is for developers and software engineers looking to use Go to design and build scalable distributed systems effectively. Knowledge of Go programming and basic networking principles is required.
▶What this book covers
⦁ Chapter 1, A Bird's-Eye View of Software Engineering, explains the difference between software engineering and programming and outlines the different types of engineering roles that you may encounter in small, medium, and large organizations. What's more, the chapter summarizes the basic software design life cycle models that every software engineer (SWE) should be aware of.
⦁ Chapter 2, Best Practices for Writing Clean and Maintainable Go Code, explains how the SOLID design principles can be applied to Go projects and provides useful tips for organizing your Go code in packages and writing code that is easy to maintain and test.
⦁ Chapter 3, Dependency Management, highlights the importance of versioning Go packages and discusses tools and strategies for vendoring your project dependencies.
⦁ Chapter 4, The Art of Testing, advocates the use of primitives such as stubs, mocks, spies, and fake objects for writing comprehensive unit tests for your code. Furthermore, the chapter enumerates the pros and cons of different types of tests (for example, black- versus white-box, integration versus functional) and concludes with an interesting discussion on advanced testing techniques such as smoke testing and chaos testing.
⦁ Chapter 5, The Links 'R' Us project, introduces the hands-on project that we will be building from scratch in the following chapters.
⦁ Chapter 6, Building a Persistence Layer, focuses on the design and implementation of the data access layer for two of the Links 'R' Us project components: the link graph and the text indexer.
⦁ Chapter 7, Data-Processing Pipelines, explores the basic principles behind data-processing pipelines and implements a framework for constructing generic, concurrent-safe, and reusable pipelines using Go primitives such as channels, contexts, and go-routines. The framework is then used to develop the crawler component for the Links 'R' Us project.
⦁ Chapter 8, Graph-Based Data Processing, explains the theory behind the Bulk Synchronous Parallel (BSP) model of computation and implements, from scratch, a framework for executing parallel algorithms against graphs. As a proof of concept, we will be using this framework to investigate parallel versions of popular graph-based algorithms (namely, shortest path and graph coloring) with our efforts culminating in the complete implementation of the PageRank algorithm, a critical component of the Links 'R' Us project.
⦁ Chapter 9, Communicating with the Outside World, outlines the key differences between RESTful and gRPC-based APIs with respect to subjects such as routing, security, and versioning. In this chapter, we will also define gRPC APIs for making the link graph and text indexer data stores for the Links 'R' Us project accessible over the network.
⦁ Chapter 10, Building, Packaging, and Deploying Software, enumerates the best practices for dockerizing your Go applications and optimizing their size. In addition, the chapter explores the anatomy of a Kubernetes cluster and enumerates the essential list of Kubernetes resources that we can use. As a proof of concept, we will be creating a monolithic version of the Links 'R' Us project and will deploy it to a Kubernetes cluster that you will spin up on your local machine.
⦁ Chapter 11, Splitting Monoliths into Microservices, explains the SOA pattern and discusses some common anti-patterns that you should be aware of and pitfalls that you want to avoid when switching from a monolithic design to microservices. To put the ideas from this chapter to the test, we will be breaking down the monolithic version of the Links 'R' Us project into microservices and deploying them to Kubernetes.
⦁ Chapter 12, Building Distributed Graph-Processing Systems, combines the knowledge from the previous chapters to create a distributed version of the graph-based data processing framework, which can be used for massive graphs that do not fit in memory (out-of-core processing).
⦁ Chapter 13, Metrics Collection and Visualization, enumerates the most popular solutions for collecting and indexing metrics from applications with a focus on Prometheus. After discussing approaches to instrumenting your Go code to capture and export Prometheus metrics, we will delve into the use of tools such as Grafana for metrics visualization, and Alert manager for setting up alerts based on the aggregated values of collected metrics.
⦁ Chapter 14, Epilogue, provides suggestions for furthering your understanding of the material by extending the hands-on project that we have built throughout the chapters of the book.
