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Throughout a career as a software developer, you encounter many patterns. Some appear just often enough to remember that they exist, but you still need to look them up every time. I've discovered that writing things down helps me remember them more easily. This particular pattern is very useful for my current project. So, it's time to write it down and hopefully commit it to memory properly this time. Although this post is specific to PostgreSQL, I'm sure other databases have the necessary features to achieve the same results efficiently.

One of the things I love with the Scala ecosystem is the tooling. One of the tools I believe we should have used more is Scalafix. It's an awesome tool that can be used for all from linting to automatic rewrites. In the blog post we-love-scala3 we have written some rules to help adaption of the new Scala 3 syntax. Now let's explore why we ended up with rewrites that did not compile.

I have a confession to make; I find Scala 3 awesome and I love it! I am a lazy developer; I love a good codebase, and I absolutely hate doing boring chores. Manual labor is not for me. Let's automate it ;-). The scala team and the community have enabled us to automate the migration and in this post we will take it one step further.

A while back, I wrote Careful with That Lock,Eugene about an idea for how to check if a database migration is likely to disturb production. That post came about after having an inspiring chat with a colleague about the advantages of transactional migration scripts and the ability to check the postgres system catalog views before committing a transaction.

It is rewarding to work on software that people care about and use all around the clock. This constant usage means we can't simply take the system offline for maintenance without upsetting users. Therefore, techniques that allow us to update the software seamlessly without downtime or compromising service quality are incredibly valuable.

If you're developing a transactional application backed by postgres, there's a pretty cool trick you can use to check if you're missing indexes that could potentially cause serious performance issues or even outages. In particular, I mean foreign keys where the referencing side of the constraint does not have an index. The idea is very simple, we can select all of the columns that take part in a foreign key, then remove the ones that take part in a complete index, and the remainder should be the empty set, or possibly match a known allowlist. I think this is a valuable addition to the test cases for your database migrations, or if you can't easily do that, maybe in your CI/CD pipeline.

I worked for a few years in the intersection between data science and software engineering. On the whole, it was a really enjoyable time and I'd like to have the chance to do so again at some point. One of the least enjoyable experiences from that time was to deal with big CSV exports. Unfortunately, this file format is still very common in the data science space. It is easy to understand why -- it seems to be ubiquitous, present everywhere, it's human-readable, it's less verbose than options like JSON and XML, it's super easy to produce from almost any tool. What's not to like?