Sure, it might not be obvious why the world needs yet another bot framework, but I’m working on one. The idea is to make a scalable, intuitive, and feature-rich framework that runs on the latest Ruby. Long-running tasks shouldn’t be a problem and the framework should make things like caching a breeze. Permissions are a first-class concept, as are features like threaded replies, reactions, blocks (for rich-text responses), and support for more than just chat input. Enter Waylon: a bot framework built with these concepts (and more) in mind.
Made it to Las Vegas for the Sin City Ruby conference. I’m stoked to be here and excited to see what cool stuff everyone is working on.
Most of the time, challenges I tackle with Ruby solve code-related problems. This includes things like web services, scripts, chatbots, etc., all of which are virtual in some way. Recently though, I decided to solve a puzzle in the real world.
My son was trying to put together a wooden puzzle. It requires having four colors (blue, green, red, and yellow) each present on all four sides of a rectangle. These colors are on wooden cubes that can be rotated. So four cubes, each with six sides to rotate around. It turns out that this means there are a lot of possible combinations of these cubes, most of which won’t work. By my math, there are six sides that can face up (a “
y” axis), multiplied by four possible rotations around that axis (
6 * 4 = 24) for each cube. Raised to the power of the four cubes (
24^4 = 331776). After about 30 minutes of hearing his frustration, I told him “I bet I can solve this puzzle with some code” but he was skeptical.
I took that as a challenge to both my coding skills and his respect for me as his dad, so I obliged. After finishing, I figured it would be worth sharing.
I recently solved a problem I’ve always wanted to solve: writing a library that can unscramble words. I know, this is a pretty geeky and pointless endeavor. Geeky, though, is what I do.
Originally, the library focused on exact matches. Given some string, I could find all words that can be formed with those letters. After accomplishing my goal, I decided to extend the project to finding the longest word possible. I noticed that this sounded a lot like finding solutions for the game Scrabble. While the library is still a work-in-progress, I thought it might be fun to share my progress. This is the story of how I wrote a library and simple web service for solving Scrabble with Crystal.
Occasionally, Kubernetes workloads require specialized nodes. Sometimes it’s for machine learning, or to save money through burstable node types, or maybe just to lock certain Pods to a dedicated subset of nodes. Thankfully, Kubernetes offers a few useful mechanisms to inform the scheduler how we’d like our workloads distributed: node-based or pod-based affinity rules along with taints and tolerations. I’ll go over how to use these briefly, but I use these frequently at work for numerous reasons. Recently, I realized something interesting about how Persistent Volume Claims (PVCs) work with dynamically provisioned storage like EBS volumes (meaning volumes that are created automatically by Kubernetes based on a StorageClass, rather than referencing existing volumes). The default behavior of a StorageClass is to immediately create a volume as soon as the PVC is created. This can have some consequences when trying to guide how Pods are scheduled.
Recently, I wrote an AWS Lambda function at work in Ruby but I didn’t have a handy tool for creating a project skeleton like
bundle gem does. That means nothing bootstrapped my testing for me. While copy+pasting code into
pry proved that my simple function worked, that wasn’t quite good enough. What I really wanted was the ability to use RSpec with my Lambda code. After a cursory search of the Internet for some examples, I was left disappointed with how little I found. So, I rolled up my sleeves and figured it out.
I’m a huge fan of Let’s Encrypt and what they’ve done to secure the Internet. They’ve made safe communication free and open. Through their ACME protocol (and subsequent ACMEv2 protocol), they have changed PKI and the way we look at automating certificate provisioning for good. All that said, Let’s Encrypt only really helps for public-facing services; for internal domains and services, I want a solution that doesn’t require creating public records. I also don’t want to reinvent all the tooling; I want to be able to use ACMEv2 (through things like cert-manager on Kubernetes or certbot for EC2). Enter Bullion, an unassuming Ruby ACMEv2 Certificate Authority built specifically for internal domains.
A common problem posed in coding competitions and undergraduate computer science classes is implementing an algorithm for finding the shortest path through a series of interconnected nodes. It can be phrased in many ways, mostly because graph theory (which is the domain of this particular problem) applies to so many areas of life. Things like driving directions, routing packets in a network, shipping logistics, and so many NP problems are examples of the same problem. It turns out that finding the shortest/lowest cost path through a graph is difficult. While Edsger Dijkstra wrote his famous algorithm to solve this problem back in 1956, there aren’t many libraries to actually use it, at least not for Ruby and in a practical sense. Sure, there are plenty of academic demonstrations
but I couldn’t find anything that I would want to depend on as a library (it turns out that rgl is pretty great). This inspired me to write connected, which can be used to search weighted, directed, or undirected graphs.
In Ruby, especially while writing low-level protocols, you may have encountered
String#unpack(). If you’ve ever experimented with them, they may seem mysterious; they take a cryptic string as a parameter (maybe something like
w*) and seem to return gibberish (or, conversely, convert gibberish into something humans can understand). I’m not going to go into tons of detail, nor am I going to cover every format that these methods accept, but I’ll cover a few that I’ve used.
What led me to writing this post was actually some recent experimenting that I’ve been doing with Crystal. I’m trying my hand at writing a simple BER parser/encoder as a part of my journey to writing an LDAP library for Crystal. The lack of an LDAP library is honestly the biggest reason I haven’t used Crystal for more things. Since BER is a binary method of encoding, the Ruby LDAP BER code uses a ton of
String#unpack(). Unfortunately, Crystal doesn’t have analogous methods for its
Array class or
String struct so I’ve had to write my own.
Here, I’ll describe a few of the formats supported by
#pack and write some compatible examples in Crystal.
Any developer worth their salt knows that Redis is great for caching. As an in-memory cache, it gets the job done. You certainly don’t have to take my word for it; the major sponsors of Redis (redislabs) wrote a white paper to explain it. What isn’t quite as widely known is that Redis has some other uses worth considering. I’ll list the ones I’m aware of (and have used) which are all available with open-source Redis.