Blog: Classier Twitter threads

I really like Spring Boot because it’s super-powerful, but like with any other power tool, you have to be careful not to lose a finger or even a whole arm.

One such tool is Spring’s @MockBean, which allows you to easily replace a service (bean) in Spring’s application context. This is really useful because having to think about all the places where a service is used and how to fully replace it in the context in order to mock it is a huge pain, and sometimes it can even be impossible.

But is it worth the price?

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One of the things nobody loves about AWS is billing. Mainly because the more your application is cloud-native, the more unpredictable your bill will become. All that autoscaling and serverless saves you a lot of money, but if there is a massive spike in your app usage, there will also be a spike in your AWS bill. That’s usually a good thing because if you’ve done it right, the opposite is also true, and low traffic means lower costs.

Either way, the more you use AWS, the less useful the default monthly bill becomes. Maybe you have several environments (production, staging, dev, …), or perhaps you have multiple applications. The aggregated view simply becomes a problem, and you start to wonder how much each of those applications costs separately. Then you discover AWS cost allocation tags and cost explorer that can group by service or tags, which also lasts a while before you become unhappy again.

The problem is always the same - the data is not granular enough. Can we somehow get more granular data?

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All CI pipelines would finish in a few minutes in an ideal world, but sadly we don’t live in a perfect world. Sometimes your pipelines take 15 minutes or more, and reducing that time would be pretty hard. That alone can be rather unpleasant, but the real problem begins if you’re trying to ensure that you verify all your commits correctly. First, you have to wait for the CI to pass on a rebased merge request, then you merge the merge request, then you wait for the CI to pass on the merge commit in main, and only then a conditional job that runs only on main would deploy the application.

If you sum up all that waiting, you’ll realize that a pipeline that takes 15 minutes means a full CI cycle of a bugfix will take you at least 30 minutes to deploy - potentially 30 minutes of downtime on production.

In this article, I’ll show you one of the possible optimizations you can do to cut that time in half without losing any safety. A significant benefit of this approach is that you can implement it without re-architecting your workflow and CI from the ground up because it’s only an incremental improvement.

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I often find myself copying AWS access keys from IAM Users to Gitlab’s ENV if I’m automating a deployment for a project or something else. Most of the time, it’s a set-and-forget process, but sometimes you have to go back and investigate, and sometimes you wonder - where did these come from? What are their actual permissions? Who else is using them? Where can I change their permissions? How can I completely delete this user?

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In modern software development, it’s unimaginable that you don’t have some CI/CD build server hooked into your version control, so I probably don’t have to introduce Gitlab CI/CD.

But how do you do that effectively, to save money and time?

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