For this blog post, I’m trying something different. This is a jupyter notebook that I’m using to study some data, and just dumping my brain out in text. If I can easily export this to a format that works with hugo, this might become a common occurrence. For this one, I’m leaving the code in. There isn’t that much of it, but I think it’s fun to show how much visualization per line of code you can get with seaborn and pandas....
Database servers are usually long-lived, and important parts of the infrastructure that we build on. We rarely set them up from scratch, because we have to take such good care of them over time. I think this causes a lot of people to think that setting up a database server is some mysteriously difficult ordeal. To be clear, that’s actually true, if you need high availability and a solid recovery point objective....
I’ve been busy working on a documentation site for eugene, and I think it’s starting to look pretty good. I wanted to write down some of my thoughts around the process so far, and some of the things I’ve learned. It’s just been a few days since I ported my blog to hugo, so since I was already feeling like I was up to speed on that, I decided I’d try using it for the eugene documentation too....
I’ve been using pelican for my blog for a while now, and I don’t really have anything negative to say about it. But for a while, I’ve been wanting a more minimal theme. I ended up on the front page of hacker news a couple of times, and the old theme had my face on all the pages, which made me feel a bit uncomfortable. I was looking at some other themes around the web, and I found PaperMod which I absolutely loved....
I have been working quite a bit on picking up dangerous migration patterns in migration scripts over at the eugene repository lately. A major feature I’ve added is syntax tree analysis, so that we can pick up some patterns without having to run the SQL scripts. This isn’t quite as precise as running the scripts, but it’s a lot faster and can catch quite a few common mistakes. So let’s take a look at how it works!...
In my current project, we’re working on a large-ish code base that is written in Scala and uses cats effect as an effect system in large parts of the code base. If you’re not familiar with what an effect system is, I think the most important detail is that it’s a tool that gives you certain superpowers if you promise to be honest about it when your code does things that can be considered “effectful”, such as interacting with the network or reading files....
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. Over the past few weeks, I’ve been experimenting with this idea to test if I can use it to build valuable safety checks for DDL migrations....
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. Most projects I’ve worked on use a relational database for persistence, and have some sort of migration tool like flyway or liquibase to make changes to the database schema....
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 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....