Blog Posts

Cohesion is part of the low coupling, high cohesion principle that’s supposed to keep your code maintainable. While low coupling means few dependencies, high cohesion roughly translates to single responsibility. Highly cohesive code (a module or a function) is focused on a single purpose. Low cohesion means it does many unrelated things. I’ve written about coupling in the previous article on anti-patterns.

Note Hello! Please give Krzysztof a warm welcome in the first guest post on our blog. 🎉 We’ve been working with Krzysztof for the past two years, and we’re excited to share his work here. Miłosz & Robert We all struggle with software architecture diagrams, don’t we? Have you ever wondered why? If you ask yourself that question, why maintenance of up-to-date and detailed software architecture diagrams is so painful, you will come up with a long list of valid answers.

Enums are a crucial part of web applications. Go doesn’t support them out of the box, but there are ways to emulate them. Many obvious solutions are far from ideal. Here are some ideas we use that make enums safer by design. iota Go lets you enumerate things with iota. const ( Guest = iota Member Moderator Admin ) Full source: github.

At one point in my career, I was no longer excited about the software I was building. My favorite part of the job were low-level details and complex algorithms. After switching to user-facing applications, they were mostly gone. It seemed programming was about moving data from one place to another using existing libraries and tools. What I’ve learned so far about software wasn’t that useful anymore.

A couple of years ago, I worked in a SaaS company that suffered from probably all possible issues with software development. Code was so complex that adding simples changes could take months. All tasks and the scope of the project were defined by the project manager alone. Developers didn’t understand what problem they were solving. Without an understanding the customer’s expectations, many implemented functionalities were useless.

This post is a direct follow-up to Microservices test architecture where I’ve introduced new kinds of tests to our example project. Wild Workouts uses Google Cloud Build as CI/CD platform. It’s configured in a continuous deployment manner, meaning the changes land on production as soon as the pipeline passes. If you consider our current setup, it’s both brave and naive. We have no tests running there that could save us from obvious mistakes (the not-so-obvious mistakes can rarely be caught by tests, anyway).

Thanks to the tests and code review, you can make your project bug-free. Right? Well… actually, probably not. That would be too easy. 😉 These techniques lower the chance of bugs, but they can’t eliminate them entirely. But does it mean we need to live with the risk of bugs until the end of our lives? Over one year ago, I found a pretty interesting PR in the harbor project.

Do you know the rare feeling when you develop a new application from scratch and can cover all lines with proper tests? I said “rare” because most of the time, you will work with software with a long history, multiple contributors, and not so obvious testing approach. Even if the code uses good patterns, the test suite doesn’t always follow.

In the previous articles, we introduced techniques like DDD Lite, CQRS, and Clean (Hexagonal) Architecture. Even if using them alone is beneficial, they work the best together. Like Power Rangers. Unfortunately, it is not easy to use them together in a real project. In this article, I will show you how to connect DDD Lite, CQRS, and Clean Architecture in the most pragmatic and efficient way.

It’s highly likely you know at least one service that: has one big, unmaintainable model that is hard to understand and change, or where work in parallel on new features is limited, or can’t be scaled optimally. But often, bad things come in threes. It’s not uncommon to see services with all these problems. What is an idea that comes to mind first for solving these issues?