Joël has a fascinating discovery! He learned a new nuance around working with dependency graphs. Stephanie just finished playing a 100-hour video game on Nintendo Switch: a Japanese role-playing game called Octopath Traveler II. On the work front, she is struggling with a lot of churn in acceptance criteria and ideas about how features should work. How do these get documented? What happens when they change? What happens when people lose this context over time? Strangler Fig Pattern Octopath Traveler 2 Empowering other departments Transcript: JOËL: You're the one who controls the pacing here. STEPHANIE: Oh, I am. Okay, great. Hello and welcome to another episode of The Bike Shed, a weekly podcast from your friends at thoughtbot about developing great software. I'm Stephanie Minn. JOËL: And I'm Joël Quenneville. And together, we're here to share a bit of what we've learned along the way. STEPHANIE: So, Joël, what's new in your world? JOËL: So long-time Bike Shed listeners will know that I'm a huge fan of dependency graphs for modeling all sorts of problems and particularly when trying to figure out how to work in an iterative fashion where you can do a bunch of small chunks of work that are independent, that can be shipped one at a time without having your software be in a breaking state in all of these intermediate steps. And I recently made a really exciting discovery, or I learned a new nuance around working with dependency graphs. So the idea is that if you have a series of entities that have dependencies on each other, so maybe you're trying to build, let's say, some kind of object model or maybe a series of database tables that will reference each other, that kind of thing, if you draw a dependency graph where each bubble on your graph points to other bubbles that it depends on, that means that it can't be created without those other things already existing. Then, in order to create all of those entities for the first time, let's say they're database tables, you need to work your way from kind of the outside in. You start with any bubbles on your graph that have no arrows going out from them. That means they have no dependencies. They can be safely built on their own, and then you kind of work your way backwards up the arrows. And that's how I've sort of thought about working with dependency graphs for a long time. Recently, I've been doing some work that involves deleting entities in such a graph. So, again, let's say we're talking about database tables. What I came to realize is that deleting works in the opposite order. So, if you have a table that have other tables that depend on it, but it doesn't depend on anything, that's the first one you want to create. But it's also the last one you want to delete. So, when you're deleting, you want to start with the table that maybe has dependencies on other tables, but no other tables depend on it. It is going to be kind of like the root node of your dependency graph. So I guess the short guideline here is when you're creating, work from the bottom up or work from the leaves inward, and when you're deleting, work from the top-down or work from the root outward or roots because a graph can have multiple roots; it's not a tree. STEPHANIE: That is interesting. I'm wondering, did you have a mental model for managing deleting of dependencies prior? JOËL: No. I've always worked with creating new things. And I went into this task thinking that deleting would be just like creating and then was like, wait a minute, that doesn't work. And then, you know, a few cycles later, realized, oh, wait, deleting is the opposite of creating when you're navigating the graph. And, all of a sudden, I feel like I've got a much clearer mental model or just another way of thinking about how to work with something like this. STEPHANIE: Cool. That actually got me thinking about a case where you might have a circular dependency. Is that something you've