Programming

After reading the technicalwriting.dev post on embeddings I thought to myself - this seems like something that I can implement quite quickly and would serve as a good starting point for some hands on experience with machine learning. I want to start small, so something as simple as filling the related posts section of this blog seems like an ideal candidate to get my hands dirty. This blog is made with Hugo and uses the related content feature which provides a list of related posts based on the tags & keywords you use. While I have no quarrels with the mechanism, I thought this would be a good place to try and experiment with embeddings. ...

About cache stampedes I often end up in situations where I need to cache this or that. Often, these values are cached for a period of time. You’re probably familiar with the pattern. You try to get a value from cache, if you succeed, you return it to the caller and call it a day. If the value is not there, you fetch it(most likely from the database) or compute it and the put it in the cache. In most cases, this works great. However, if the key you’re using for your cache entry gets accessed frequently and the operation to compute the data takes a while you’ll end up in a situation where multiple parallel requests will simultaneously get a cache miss. All of these requests will independently load the from source and store the value in cache. This results in wasted resources and can even lead to a denial of service. ...

SQLC has become my go-to tool for interacting with databases in Go. It gives you full control over your queries since you end up writing SQL yourself. It then generates models and type safe code to interact with those queries. I won’t go over the basics here, if you feel like it you can try their interactive playground. Dynamic queries Frequently I end up needing to filter the data by a set of fields in the database. This set of fields is often determined by the caller, be it via REST API or other means. This means that the code I’m writing has to support dynamic queries, where we query by a subset of fields. ...

I’ve seen many discussions about whether Go should add enum support to the language. I’m not going to bother arguing for or against but instead show how to make due with what we have in the language now. A very short enum intro Enumerated types, or enums, represent finite sets of named values. They are usually introduced to signal that variables can only take one of the predefined values for the enum. For example, we could have an enum called Colors with members Red, Green, Blue. Usually, the members are represented as integer values, starting from zero. In this case, Red would correspond to 0, Green to 1 and Blue to 2 with Red, Green and Blue being the names of corresponding members. They help simplify the code as they are self-documenting and explicitly list all possible values for the given type. In many languages enums will also return compile errors if you’ll try to assign to an invalid value. However, since enums do not exist in Go, we do not have such guarantees. ...

I was working on a project that uses PostgreSQL and as part of my task I needed to write a migration. The migrations are written as plain SQL and applied using the migrate library. The migration itself was not that complex, some rows needed to be updated where a column matched one of the values in a list. In my rush, I’ve opted for a rather simple query that went something like this: ...

I’ve written in the past that this blog is a playground for me to try various tools and play with code around it. Jenkins has been my choice as the CI for it since the start, mostly since it was something I’m used to. However, I’ve also stated that running it on an old laptop with no real backups is a recipe for disaster. I have since rectified the issue by hiding the laptop under a box in a closet but that meant moving away from Jenkins to something that’s lighter and more portable. The choice is the self-hosted enterprise edition of Drone. ...

I’ve recently had my birthday and as a gift decided to give myself a NUC. The intention here is that I could replace the old laptop running Jenkins and Grafana with something that’s a bit more silent and in a smaller form factor. Note that I’m not affiliated with any of the hardware manufacturers in any way shape or form. The planned change The laptop I’ve been using for Jenkins has become rather critical for this blog and a few side projects I’ve got. This meant that I first needed to find a way to migrate everything to a new machine that would be coming. I’ve then focused on rewriting my infra to code with the help of Ansible®. Even though I’m not a super big fan of it I’ve managed to write everything as an Ansible playbook. I did, however, skip the Jenkins and moved towards Drone CI instead. With this complete, I needed to figure out what type of a machine I might want to go for. ...

I’ve written about the infrastructure behind this blog in a previous post and the major issue with it currently is the old laptop I have at home that’s running the Jenkins instance. I really have no way of backing it all up. I’ve asked around a bit and the reddit thread I’ve made pointed me towards moving it all towards Ansible. Since people are using Ansible at work I thought it would be appropriate for me to try it as well. ...

Go comes with great tools for profiling out of the box. It’s one of the features I’ve come to love. I’m not going to go into detail about pprof, but if you need a primer, Julia Evans has a great post about it on her blog. Instead, I’ll try and show a real world example of the optimizations you can make using pprof. I’ll use pprof on mouthful, a gin based server, to see what I could do to make it just a bit faster. It turns out I can detect a rather stupid mistake I’ve made. ...

There are quite a few components to this blog now. There’s an AWS S3 bucket the blog gets served from, there’s a backend service responsible for the mouthful comments, there’s another instance of the service for the mouthful demo page, with another S3 bucket to serve the page for it. There’s also a yet unused service living in the same “cluster” as these two(It’ll get added to this blog soon™). There’s quite a few private github repositories that in unison with a Jenkins instance make this all work. Oh, and everything is running under Docker, with ECS handling all the orchestration for that. For the data stores, I use dynamodb, mysql and sqlite. There’s also some miscellaneous bits and bobs, like the grafana instance that plots response times and uptimes of the APIs. I’ve tried to keep as much as I can inside AWS but due to cost constraints I had to do some workarounds. So how does it all work then? ...