The authors of Accelerate dedicate a whole chapter to software architecture and how it affects development performance. One thing that often comes up is designing applications to be “loosely coupled”. The goal is for your architecture to support the ability of teams to get their work done—from design through to deployment—without requiring high-bandwidth communication between teams. Accelerate If you haven’t read Accelerate yet, I highly recommend it.
Did you ever hear about a project where changes were tested on customers that you don’t like or countries that are not profitable? Or even worse – did you work on such project? It’s not enough to say that it’s just not fair and not professional. It’s also hard to develop anything new because you are afraid to make any change in your codebase. In 2019 HackerRank Developer Skills Report Professional growth & learning was marked as the most critical factor during looking for a new job.
I’ve seen a lot of complicated code in my life. Pretty often, the reason of that complexity was application logic coupled with database logic. Keeping logic of your application along with your database logic makes your application much more complex, hard to test, and maintain. There is already a proven and simple pattern that solves these issues. The pattern that allows you to separate your application logic from database logic. It allows you to make your code simpler and easier to add new functionalities.
When I started working in Go, the community was not looking positively on techniques like DDD (Domain-Driven Design) and Clean Architecture. I heard multiple times: “Don’t do Java in Golang!”, “I’ve seen that in Java, please don’t!". These times, I already had almost 10 years of experience in PHP and Python. I’ve seen too many bad things already there. I remember all these “Eight-thousanders” (methods with +8k lines of code 😉) and applications that nobody wanted to maintain.
In case you’re here for the first time, this post is the next in our Business Applications in Go series. Previously, we introduced Wild Workouts, our example application built in a modern way with some subtle anti-patterns. We added them on purpose to present common pitfalls and tips on how to avoid them. In this post, we begin refactoring of Wild Workouts. Previous articles will give you more context, but reading them isn’t necessary to understand this one.
Welcome in the third and last article covering how to build “Too Modern Go application”. But don’t worry. It doesn’t mean that we are done with showing you how to build applications that are easy to develop, maintain, and fun to work with in the long term. It’s actually just the beginning of a bigger series! We intentionally built the current version of the application to make it hard to maintain and develop in the future.
Welcome in the third article form the series covering how to build business-oriented applications in Go! In this series, we want to show you how to build applications that are easy to develop, maintain, and fun to work in the long term. In this week’s article I will describe how you can build robust, internal communication between your services using gRPC. I will also cover some extra configuration required to set up authentication and TLS for the Cloud Run.
In the previous post, Robert introduced Wild Workouts, our example serverless application. Every week or two, we will release new articles related to this project, focusing on creating business-oriented applications in Go. In this post, I continue where Robert left off and describe the infrastructure setup. We picked Google Cloud Platform (GCP) as the provider of all infrastructure parts of the project. We use Cloud Run for running Go services, Firestore as the database, Cloud Build as CI/CD, and Firebase for web hosting and authentication.
Welcome to the first article from the series covering how to build business-oriented applications in Go! In this series, we want to show you how to build applications that are easy to develop, maintain, and fun to work with in the long term. The idea of this series is to not focus too much on infrastructure and implementation details. But we need to have some base on which we can build later.
If you compare MySQL or PostgreSQL with Kafka or RabbitMQ, at first, it seems they are entirely different software. And usually, that’s true, as you would use them for quite different tasks. What they have in common is processing streams of data, and they specialize in specific ways of doing it. While Kafka and RabbitMQ are popular examples of Pub/Subs (also known as message queues or stream processing platforms), I’d like to share some patterns for using SQL databases as Pub/Subs as well.
© Three Dots Labs Cookies Policy