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Thank you so much for being here! We really appreciate it, and we're super excited to share our presentation with all of you.
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Before we start, we just wanted to express how inspired we were to be here with all of you and to hear your stories.
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We're also really inspired by all the amazing speakers.
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Are there any speakers in the room? Let's give them a round of applause!
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We've learned a ton from all of you and really enjoyed being here.
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So thank you for coming to our talk! Welcome to "A Brewer's Guide to Filtering Out Complexity and Churn" or, as we like to call it, the coffee machine talk.
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The goal of this talk is to show you how to remove the bitterness caused by complexity and churn in your application.
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We have three key objectives over the next 30 minutes.
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We want to show you how complexity sneaks into your application, how you can manage and notify it, and then how to remove it permanently.
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First, let us introduce ourselves. Hello again, my name is Alan, and I use he/him pronouns. I've been using Ruby for about a dozen years and I'm originally from Seattle, so there's coffee in my veins! My favorite is a New Orleans cold brew with chicory.
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Hi everyone, I'm Fito. I also use he/him pronouns and have 12 years of experience in Ruby. I'm from South America, but I'm coming from the San Francisco Bay Area this time.
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I have to say, if there's one thing I love as much as Ruby, it's coffee! My favorite is a Ghirardelli dark chocolate mocha.
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Alan and I work together at a place you might not expect, given that this is a conference for Ruby developers. We've actually heard this a few times during the conference—we work for Cisco Meraki, which is the largest Rails shop you've never heard of.
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We've been friends for years and have worked together at three different companies over the last nine years. Along the way, we've seen a wide variety of codebases. Plus, we spend a lot of weekends writing code and drinking coffee.
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Alan, you grew up around coffee, didn't you? Yeah, I did! Back when I was a kid, nothing fascinated me more than these mechanical coffee machines they had in my dad's office.
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You drop in a coin, listen for the clink, make your selection, and then all of a sudden, the machine springs to life, hissing, clicking, and whirring. At the end of the ballet, there's this one last sound—the glorious, aromatic black liquid splashing into the cup. Ah, c'est magnifique!
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These days, I'm a little bit more fascinated by the complexity we find in software. We both are. Like the coffee machine, there are all kinds of hidden complexity in software, but it doesn't always start out that way, does it?
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Show of hands, how many of you have worked on a Greenfield application? Awesome! How did that feel? Great! Now, how many of you have worked on a legacy application? Even more of you! How did that feel? Yeah, that's been our experience too.
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You know, Greenfield development feels great—it feels fast, and there's not any existing code getting in your way. But legacy code always feels harder to work with.
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Why is that? We believe it has to do with software complexity. We think there's a threshold that a particular application will hit at some point, after which you only have two choices.
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You can either live with the complexity as it grows and development slows down, under the impression that it will somehow magically disappear, or you can pause temporarily and reflect on the design of your software.
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Then, you can reorganize it and speed up development again. We've seen organizations go both directions. When you take the path of living with the complexity, engineers often end up frustrated.
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In our experience, they sometimes even begin to blame Ruby for the problem and look for alternative solutions. I don't know if there are any Rust fans here, but we have Rust enthusiasts in our office who constantly try to push our Ruby code in that direction.
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It’s not Ruby's fault though—it’s the complexity that we ourselves added to the codebase. What we're going to do today is show you how to take that second path and remove the complexity from your system, so you can fall back in love with Ruby.
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We'll build a little coffee machine application, and this application will have several features. We'll add them one by one, go back, look at how complexity sneaked into the application, and review each commit. Finally, we'll reorganize the code to demonstrate how to eliminate that complexity.
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Are we ready? All right, let's get started! Who wants to build a coffee machine?
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Fito: Let's do it!
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Alan: Great! So, how does complexity sneak into software? The answer, of course, is one commit at a time.
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Now, I'm going to go through these slides pretty fast just to show you the shape of the code as it grows, so don't worry about trying to understand exactly what's on the screen.
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The code is made up anyway, and we're skipping tests for the sake of time. In reality, we will not be doing this without tests, right?
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Let's get started! Here's the first commit in our coffee machine application.
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At this point, the coffee machine does one and only one thing: it serves coffee. First, it dispenses a cup, heats the water, prepares the grounds, dispenses the water, and finally disposes of the grounds.
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This works great, but it turns out not everyone likes coffee. To increase our sales, we're going to add tea.
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Here, we've added a conditional to determine whether to serve coffee or tea, and in the process, we added some duplication.
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The dispense cup, heat water, and dispense water steps are all duplicated between the two beverages.
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Now that we have both coffee and tea in production, we're getting some feedback. Turns out the most requested feature is to add sweetener.
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So let's go ahead and do that! Okay, so we’ve added the sweetener just after dispensing the hot water. Of course, not everyone likes sugar, so we're going to make it an optional ingredient.
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We push this out, customers love it, and now they want cream. Let's give it to them! Since we already have a pattern for optional ingredients, let's dispense cream right after dispensing the sugar.
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Some folks don’t like coffee or tea, so let’s offer them something else—like cocoa! Here we follow the existing pattern and add cocoa to the main if-statement, but there's no need to add sugar or milk since cocoa is already sweet and creamy.
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Finally, who doesn’t like whipped cream on cocoa? Heck, I even like it on my coffee! So, let's add it. Whipped cream is an optional ingredient that no one wants on their tea, so let's add it after the other optional ingredients and exclude it when the customer is requesting tea.
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Here we are, seven commits into this codebase, and we've already got nine conditionals in one method. At this point, it’s still relatively simple to understand and work with if you're the only one working on it.
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However, procedural code like this does not scale well for a larger team, and one of the problems is that future developers will just keep adding more conditionals with each new feature, causing the complexity to skyrocket.
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Our little coffee machine has been so successful that we've just been purchased by a big national soup chain, and they want us to add soup to our coffee machines. That's going to add a lot of complexity!
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Let's pause here and evaluate where we are before we try adding more features. Alan, can you walk us through it?
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Sure thing! So far, we've reached an inflection point in the life of our little application. But how can we tell what indicates that it's time to pause and rethink things?
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The first hint is that we had to keep reducing the font size to get everything to fit on one slide. The second hint is that method length is actually a good indicator of complexity.
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Even without measuring it, Sandy Metz has a rule about it. Sandy is the author of 'Practical Object-Oriented Development in Ruby.' If you haven't seen her talks, you should definitely check those out.
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Sandy has a rule about method length: it can't be longer than five lines of code. This might seem really restrictive, but trust us; it’s a very important rule. In addition to method length, we also look at method complexity.
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This is a quantitative measurement of how difficult it is to understand a piece of code. Our preferred metric is called assignments, branches, and conditionals.
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There are other metrics like cyclomatic complexity, but this one is our preferred metric. The higher the number, the harder it is to understand.
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We use a gem called Ruby Flog to measure this for us. It also includes some Ruby-specific metrics, so it’s a great tool for those who use Ruby. But how do we know what to do with it?
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What’s a good score? What's a bad score for a method? Well, way back in 2008, a gentleman named Jake Scruggs, who wrote the Metric Fu gem, produced a list on his blog.
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To this day, it’s the only list we've found that outlines good scores. We've been using these scores for years, and they truly do help drive you toward simpler software.
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How do we think our little coffee machine fared? Let’s go back through it one commit at a time and look at what the complexity scores were.
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Our first commit weighs in with a complexity score of 5.5. According to Jake, that’s awesome! The churn number there, by the way, is the number of commits that have been made to this file, which becomes important later.
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Adding the conditional and duplicated code shoots the complexity up to 14.6. We’re not in that awesome zone anymore, but we're still in the good enough zone, so we’ll keep going.
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After we removed that duplication, the complexity dropped significantly down to 10.9. This may seem like a good thing, but it's actually where things started to go wrong.
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Notice how we've intermingled the two algorithms: you can no longer really see what it takes to steep tea or brew coffee; you have to read the whole thing and parse it.
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This also sets a precedent for how future developers will extend this code by adding more conditional logic. From Flog's perspective, the math says that the complexity went down, but there is a valuable lesson here.
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There is no magic metric that will tell you exactly what's going on with your code. There are tools that can inform you and help with your decision-making, but they don't replace the need to use your intuition about these things.
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Pay attention to how hard it feels to add software as you’re going, and if it starts to feel like it’s slowing down, that’s a good time to pause and reflect.
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Next, we added sweetener, which brought the complexity up to 13.5. Then we added cream, and it went up to 16.0. Adding cocoa pushed us over the good enough line to 21.3.
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Finally, adding whipped cream increased the complexity to 25.5. If you look at the trend line here, you can see it's starting to curve upward, which is a good sign that it's time to pause and reflect.
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Plus, we crossed Jake's threshold of 20 for good enough, so there you go! These are three ways to recognize when it's time to take a pause and reflect:
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First, look at the method length. If you can’t see it all on one screen, it’s probably too complex. Sandy’s rule again is five lines of code.
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Second, consider method complexity. Use a tool like Flog to produce a score; anything under 20 means you’re good to go.
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Finally, use your intuition. How do you feel about the software you’re adding?
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That’s how we knew we reached this inflection point, and it was time to start thinking about reorganizing this method—something Fito is about to do right now.
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Fito: This sounds good! So we broke Sandy’s rule, crossed over Jake’s good enough line, and intermixed three algorithms—all of which sounds pretty dire. But is it possible to turn it around? Yes, absolutely! Let’s do it!
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Here’s the method as we left it a moment ago. Now, while ‘drying’ code early can lead us in the wrong direction, let’s start by undrying this code to see if there are any missing abstractions hiding in plain sight.
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We call this practice 'rehydration,' and it looks like this. Now, I know what you're thinking—this obviously increases duplication. But that’s what we need to do in order to find these missing abstractions.
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Now we can clearly see each recipe, and since there's no overlap in the algorithms anymore, we can safely extract each one into separate polymorphic classes.
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As you can see, we moved each recipe into its own class: one for coffee, one for tea, and one for cocoa. This structure has several benefits.
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Each algorithm is now separate from the others, meaning that if you ever need to modify one of them—let's say to fix a bug or extend it—there's a much lower risk of introducing a regression in another one.
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Plus, the main method is now much simpler. However, you might have noticed that there’s duplication between the classes. Methods like dispense cup, heat water, and dispense water are present in every class.
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Now, we actually want that duplication because it makes understanding the complete algorithms for each beverage much easier since you only need to open one of these files to look at them.
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We do not want to eliminate this duplication; rather, we want to ensure that there is only one implementation of each of these methods.
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That’s what's meant by 'don’t repeat yourself.' It’s perfectly okay to call a method multiple times, but it’s preferable to implement that method only once.
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Ruby provides multiple options for doing this. We could include a module, use composition, or introduce inheritance to define the methods in a base class.
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In this case, because we’re using polymorphic classes and are unlikely to need these methods elsewhere in the application, we'll probably go with inheritance.
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Now we’re almost done! There are just two remaining problems here. First, the main method is not open-closed, meaning that you'll have to modify the code to extend it.
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Second, it has multiple responsibilities. Let's take a look at those responsibilities.
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Its one true responsibility should be to prepare beverages, but right now it's also selecting which class to instantiate. That's the job of a factory, so let’s introduce one.
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Okay, here we pulled class instantiation out into a factory class. Its only job is to choose which class to build based on the selected drink.
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Now, the main method only has the remaining responsibility to prepare beverages, and it is now open-closed. This means we’ll never have to modify the main method's code again when we want to extend the functionality.
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But you might have noticed that the open-closed problem just moved to the factory. We introduced another issue: the build method in the factory might return nil, which will cause the coffee machine’s main method to throw an undefined method error.
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We can solve that by introducing a null object pattern. As you can see, the factory now returns a null beverage by default. So if we were to get a beverage type that we don’t support, it will just return the null beverage.
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This class is just a simple beverage class with a prepare method that does nothing. Regarding the second problem with the factory, it’s still not open-closed.
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This means every time we want to add a new beverage, we’ll have to modify the factory class and probably the tests that go with it.
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We can solve that by using a different kind of factory. Here we’ve replaced the conditional-based factory with one that's based on a convention and a bit of metaprogramming.
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The advantage of this approach is that it’s mostly open-closed. If we want to add a class name that doesn’t follow our convention, we’ll need to modify the convention, which would violate the open-closed principle.
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For this reason, and because metaprogramming tends to obscure what’s happening, we actually consider this kind of factory an anti-pattern.
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There are alternative approaches that are fully open-closed, and we would love to discuss those offline. We really enjoy that topic, so come chat with us later.
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For the sake of this talk, let’s stick with our convention-based factory that’s mostly open-closed.
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Now let’s take a look at where we are with complexity. Here’s that graph Alan was showing us.
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Here’s where we left off after adding whipped cream. The next thing we did was hydrate the code, and look at that: it’s much more complex than the dry version!
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But remember, the dry solution was hiding the fact that there were missing abstractions. So what we did next was pull the algorithms out into their own polymorphic classes, and this dropped the complexity significantly.
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Finally, we extracted the factory object, and now the complexity is lower than it’s ever been! We are now well back in the awesome range.
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In the main method we'll never need to change it again! Now let’s take a look at all the other classes we introduced in the process of refactoring.
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There are six in total. The coffee machine has now settled down to a very low complexity of 3.9. The factory is next at 6.2.
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Then there are the three beverages that are a little bit more complex but well within the good enough zone, so there’s nothing more to do with them. The same goes for the null beverage—it has zero complexity.
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Now, there was a reason we did all this, right? We needed to add soup to our coffee machine without making it more complex.
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So here’s where we left off. Let’s add some soup! There you go!
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Adding soup did not require us to change any of the existing files; it just works. As long as the soup class is loading into memory, it will be available for the factory to instantiate.
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In the future, adding a lower beverage will be as simple as just adding another class and the corresponding tests.
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That's it! That’s a look at a very small greenfield application.
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I know what you're thinking: your applications are obviously a lot bigger and a lot more complex. So how will you know where to start when you get back to work?
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I’m glad you asked! So far, we’ve really only talked about method complexity and how monitoring it as the method changes over time will help prevent complexity from sneaking into your application and becoming painful.
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But you can also use complexity and churn together—remember that number we mentioned earlier? Churn is the measurement of how many times that code has been changed.
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We like to think of complexity as representing the amount of pain you'll experience the next time you work on that code, while churn represents how often you're inflicting that pain on yourself.
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If there's something with high complexity and high churn, you're really just beating yourself up. Let's look at how to use churn and complexity to evaluate your entire codebase.
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If you saw Ernesto's talk yesterday, this is going to look familiar—but we think it bears repeating; it’s very important. Like Dr. Tannenbaum said, you need to hear things two to four times.
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So here we go! Find the areas in your code that need the most attention. You can plot file complexity and churn on a graph like this using a tool like Ruby Critic or Code Climate.
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This kind of churn versus complexity chart was first proposed by Michael Feathers, the author of 'Working Effectively with Legacy Code.' Yet another book you should go read.
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We use this kind of chart to locate areas of our code base that need intervention. To understand the graph, let’s divide it into quadrants and look at each one.
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The lower left quadrant is the pain-free zone. These files are easy to change and easy to understand. In a healthy application, the majority of your files should live here.
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The upper right quadrant is the painful zone. These files are hard to understand, hard to change, and prone to regressions. Being aware that these files are in this quadrant will help you make decisions about where to add new code.
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You probably don’t want to add more code to these classes. In fact, if you have to touch them, it’s better to extract code rather than adding more to them. This will create simpler classes with a churn score of one.
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In the lower right quadrant are the low complexity files that change frequently. It's possible these files are actually configuration data masquerading as Ruby code.
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For example, there might be some JSON hiding in a .rb file. These files are better served as data files that your Ruby code can read in when needed.
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Finally, in the upper left are the high complexity files that rarely change. These are what Sandy Metz refers to as 'omega messes.' An omega mess has a big, scary algorithm in it that really never needs to change.
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Sandy’s advice? Don’t change it. Leave it alone! It’s not causing you any continued pain because there’s no churn, and it's so complex that changing it would likely break it.
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These quadrants are helpful for thinking about this, but in reality, things look more like this. The red line represents the pain threshold. Anything in the pink zone is resistant to change and prone to regression.
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This file here needs the most attention; it’s super complex and being modified all the time. Extracting hidden abstractions from this class will improve the complexity of your entire application.
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But there's no need to tackle it all at once. Try to improve the code gradually as you touch the file. You may not want to start with that particular file—it’s both super complex and changing all the time.
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Starting with a simpler file will give you a chance to practice these techniques we’ve taught you without the pressure of working on your most complex code.
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So that's it! That’s the story of our little coffee machine application: how complexity snuck in, how we recognized it, and how we removed it.
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Let’s wrap up with a few takeaways and some homework. Here are three things we'd like you to take away.
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First, complexity will sneak into your application, and it happens one commit at a time. So, be vigilant! Pay particular attention to conditionals that you’re introducing into your code; they are often signs of abstractions trying to escape.
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One of our friends, Josh Clayton, says: 'Don't make your code so dry that it chafes!'
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Second, you can recognize complexity before it becomes painful. Look at method length, consider complexity, and trust your intuition. If methods are longer than five lines, if your Flog score crosses 20, or if you feel like things are getting slower, it’s probably time to pause and reflect.
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Third, it's possible to back away from this complexity. Leverage polymorphism and factories to enable you to add new features without changing existing code.
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Rehydrate your code by reintroducing some duplication to find those missing abstractions that can be pulled out as polymorphic classes.
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Those are the three things we want you to take away. Now, here’s some homework.
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First, find out what your average method complexity is using the Flog gem. I'm not going to tell you how to do that; you’ll have to figure it out for yourself—it’s easy!
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Second, find out which file has the most churn in your application by using the churn gem—also easy. Finally, identify which class needs the most attention—this is the file with the highest churn and the highest complexity.
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We bet you already know which class this is in your application, but confirm it. I see a lot of nodding heads; that’s awesome!
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Please let us know what you find out! Here’s how to reach us. Feel free to tweet or reach out via the Slack channel we created for this talk.
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We’ll answer questions there for as long as the Slack channel remains open. Also, if you want to walk through the coffee machine application, you can find it on GitHub.
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Plus, you can check out other personal projects we have there, including a VS Code extension that shows you your Flog score.
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The extension displays the average method complexity for the currently selected text, the method where the cursor is located, or for the entire file.
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Just one last thing before we go: as we mentioned, we’re from Cisco Meraki. We are the largest Rails shop you’ve never heard of; we don’t build coffee machines, but we do build internet machines for coffee lovers.
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Both Starbucks and Peet's Coffee are customers of ours. They use our software and hardware to connect their stores and customers to the internet.
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We have a very old, 15-year-old Rails monolith with over a million and a quarter lines of code. It’s old, yes, super complex, yes, and it handles billions of requests today, supporting a multi-billion dollar business.
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If you're interested in solving problems related to software complexity or large-scale Rails implementations, come talk to us! We’d love to chat with other passionate Rubyists who want to make a change.
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That's it for our presentation! Thank you so much for coming! Here’s a list of our references and influences. Feel free to take a picture.
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If you have any questions, come find us! Thank you!
