Meetings can take up a lot of time and cost companies a fair amount of money. They often distract from real work, and fill up our day. When we have too many of them, it is difficult to remain enthusiastic about participating. Here, I’ll provide my thoughts on meetings. I’ll make the case for why we should seek to reduce the amount of time we spend them, and why we should make the time we do spend in them as productive as possible.
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.
I recently ran into an interesting issue with my home Kubernetes environment that runs my blog. As I mentioned in a previous post, I run my blog on k3s and I use cert-manager to manage my SSL certificates provided by Let’s Encrypt. Let’s say that I’ve temporarily changed my Internet provider and along with it, my router. This router does not appear to support NAT Loopback. The cert-manager documentation acknowledges the issue but doesn’t provide much of a solution. Cert-manager couldn’t renew my blog’s certificate because its self-check kept failing. I managed to solve the issue through a fairly simple CoreDNS change. Let’s take a look.
One of my favorite questions to ask (or be asked) during an interview is the classic “how does the Internet work?” question. It usually goes something like this:
You open your favorite web browser, type in “www.mysite.com”, and hit return. Almost like magic, a fully-rendered web page shows up on your screen ready for you to view. Tell me, with as much detail as you can muster, what just happened.
The reason I like this question so much is that it isn’t just academic; it is a peek behind the curtains at what this person knows. It reveals what they’ve dug into in the past, learned in school, or dealt with while troubleshooting. The details show how much time they’ve spent demystifying the world (at least related to our working environment). Thinking about this made me realize how really fleshing out a solid answer to this would make a great blog post, so here we are.
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.
The world is pretty crazy right now. COVID-19 has caused the closure of countless businesses and slowed down in-person business for most others (though there is a glimmer of hope as small business and schools are looking at reopening in some respect). Many, many people are without a job or facing some kind of cutback. This isn’t just a US tragedy either; the economic impacts are being felt around the world.
So the question is, why? Why has the world essentially ground to a halt? People get sick all the time, right?
Continue reading Rationality and COVID-19
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.