Thinking About Innovation

Drawing a Blank

It’s a brisk autumn morning. You just woke up and you lay in bed waiting for your brain to load the essential bits of information required to start your day. Suddenly, you remember. You just finished a big project and nothing new will be lined up until sometime tomorrow. Today is a free day and you get to work on whatever you want. You get out of bed and start getting ready with a growing sense of anticipation. The sun is shining, there’s an invigorating chill in the air, and a flood of ideas begins to fill your mind. Anything is possible.

You’re one of the first people to show up in the office this morning. You sit at your desk staring at the blinking cursor of your text editor. What to many options. “I could learn a new front end framework. Or I could write that one Rails app I’ve been thinking about in my spare time. Maybe I should learn Haskell! Or I could try to untangle that horrible piece of code that’s been nagging at me.” Then, the first email of the day arrives in your inbox. It looks important and you figure you’d better respond to it so that it isn’t hanging over your head the rest of the day.

A few hours later it’s time for lunch and you realize you’ve spent the majority of the time fending off emails and trying to figure out where to get started. Half of your cherished free day is gone and you’ve got nothing substantial to show for it. Anything was possible, but now it seems like nothing is possible.

The Raw Materials of Innovation

Why is innovation so difficult? On the one hand, many of us have endless lists of ideas we’d love to spend time pursuing. We go about our day-to-day jobs yearning for a moment of freedom when we could forget about our responsibilities and try something totally new. And yet, when that moment presents itself to us, we feel unprepared or lost. The things which we thought would have motivated us seem to lose their appeal. Our interest fades and we hop from one idea to the next, never really getting anything done.

I recently attended a workshop on innovation and came away with some useful ideas for making innovation happen. One of the most valuable things I came away with is a more concrete definition of what innovation actually is.

So what is innovation? What makes something innovative? The common belief (or at least my prior belief) is that innovation is a novel idea - doing something that hasn’t been done before. So, if we want a group of people to be innovative, we should get together in a room and have a brainstorm of cool ideas. The more unique the idea or the approach, the more innovative.

The speakers at the workshop I attended provided a more useful definition. An innovation is the fulfilling of a customer’s need with a technology in a novel way. So, there are two essential components to innovation: customer needs and technologies. Innovative ideas result from the combination of needs and technologies in new ways.

At a previous job, I spent a couple of years working on a project which, in the end, had no real customer. We solved many technical challenges with this project and I’d even say that many of the ideas in the system we designed were quite novel. But, according to the definition above, we weren’t truly innovating because we weren’t solving a need. We were just building technology.

Customer needs and technologies are the raw materials for generating innovative ideas. This may help us better understand why it can be so difficult for us on that glorious free day to get beyond a blinking cursor in an empty text editor. Perhaps we lack the necessary building materials. Think about the times when you’ve had the best ideas, been the most engaged with your work, and been the most productive. For me, these times occur when there is a clear need and I’ve got (or can learn) the technical know-how to meet it.

So, the answer to moving forward is not to have a better brainstorm session, it’s to acquire more information. Are you the person who knows all sorts of programming languages and frameworks but can’t think of any good use for them? You need to spend some time finding a real customer problem that you can match up with some of this technology. On the other hand, maybe you’ve got a list of complaints from customers and don’t know where to begin. In this case, you may need a better view of the technology landscape.

Finding the Need

It probably goes without saying that finding real customer needs takes work. Most software companies have a product team whose mission is to understand and evaluate the importance of customer needs. In such cases, it is crucial for product folks and engineers to work together as a team. In general, product will supply the customer needs and engineers will supply technology. Together, they generate innovative solutions by evaluating which technologies are the best fit for the needs.

The degree to which a team is able to innovate is dependent upon how deep a team can go with the customer need. The deeper the team goes, the wider the solution space and the more technologies there are that can be applied to the problem - hence, more opportunity for innovative ideas. Any product manager will tell you that customers love to provide solutions and rarely begin by talking about the underlying need. By asking the right questions, the team can gain access to the deeper problem. The customer may say he needs a better horse, but what he really needs is a faster form of transportation. Or maybe what the customer really needs to spend less time traveling to and from work. The pool of available solutions increases dramatically based on how the problem is framed.

The speakers emphasized that the best opportunities for innovation come from needs that customers don’t realize they have. These are the needs that have likely never been addressed by any combination of technologies. According to the speakers, a customer will never realize there is a need unless they perceive some alternative. So just asking customers about their needs is not sufficient because you are likely to wind up with a list of problems for which solutions already exist.

One remedy for this is actually quite simple: go live life with the customer. The longer you sit with a customer as they use the product, the more opportunities you will have to see the hidden needs. If you find that the customer is using the product in an unexpected way, this could also indicate a hidden need.

Finding the Technology

So you’ve lived life with a customer, you’ve found a high impact need, now what? The speakers described an interesting approach to finding the right technology for a truly innovative solution. First, think about the core problem that must be solved and ask yourself “what other industry has faced a similar challenge?” Try to think far outside of your own industry. Then, go talk to folks in that industry and see what they’ve done to solve the problem. See if you can take their solution and apply it within your own problem domain.

The speakers gave several fun examples of this approach. When NASA began designing their first space suit, they had difficulty attaching the fabric and metal portions of the suit. Apparently, this is a pretty challenging thing to do, especially when someone’s life depends on the strength of the fabric/metal interface. NASA looked outside the space and military industries for a solution. They found an unlikely partner in the Playtex corporation, a popular producer of women’s undergarments. The problem of attaching fabric to metal is common in the design of bras and NASA was able to leverage Playtex’s experience in this domain to overcome their design challenges. At the time, NASA was embarrassed to admit that a portion of their space suit was developed by a bra company, so they contracted out to another company with the understanding that the real work would be sub-contracted to Playtex.

When looking to other industries for technologies, it’s wise to ask the people you talk to, “who has a similar problem, but even worse?” This can lead you people who have already solved the problem in really creative ways. The medical division of the 3M corporation was looking for a way to help surgeons prevent incisions from getting infected. They asked surgeons, “who might have an even harder time with infections than you?” This led them to investigate veterinarians, battlefield medics, and surgeons who operate on patients whose immune system has been compromised by chemotherapy. The key innovation for 3M’s product ultimately came from these discussions.

Similarly, the development of anti-lock brakes in cars came from an industry with a more extreme version of the problem. In this case, it was the airplane industry. Almost everything about this industry is more extreme. The stakes are higher, the speeds are faster, and icy runways cannot be salted because salt can damage aircraft. This made it a great candidate for solving a similar problem with automobiles.

When Should We Innovate?

How deep should a team dive into the customer’s needs? Indeed, opportunity for innovation increases, but so does the uncertainty for the project. With a large solution space, the team could spend ages finding and evaluating available technologies. And there is no guarantee that any of these technologies will pan out in the end. At the same time, there is the potential for immense gains. What if the team discovers a solution that eliminates an entire class of problems for the customer? Or, what if they find a solution that allows them to move ten times as fast? These could be game-changers for an entire industry.

One answer is that it depends on the business context. Maybe there is a short window of opportunity to make some key sales. Providing a feature within this timeframe will allow the company to get the sales while they are still available. The product manager may decide that these sales are more valuable than the potential for innovation and therefore focus the team on the immediate need. This may make sense given the context.

I think there are two dangers to avoid when approaching a project in this manner. First, it’s possible for a team to jump to a solution too quickly under pressure from the context. They may overlook a very simple solution to a deeper need and add unnecessary complexity to the product. This danger can be somewhat mitigated by at least timeboxing a brief exploration of the solution space.

The second danger occurs when the context forces the team to approach every problem at a surface level. History is replete with examples of organizations that failed because they got locked into addressing customer needs at a single level. I’m thinking specifically of Blockbuster and Kodak right now. These companies were once immensely successful, but they continued to solve problems the same way. They were ultimately destroyed by innovations which met customer needs at a deeper level.

Innovating Within an Organization

It’s clear that a company must innovate in order to survive long-term. In the initial startup phase of a company there isn’t much to lose and everything looks like an opportunity. Entrepreneurship and innovation are center stage. The problem is that companies become increasingly risk-averse as they grow. There is no immediate reason to change the way things are done when a company is enjoying a period of great success - if it ain’t broke, don’t fix it.

The speakers at the workshop talked about the value of a storm in these cases. A storm is some event that causes the leadership in a large organization to question the future. Examples include a competitor success or a new technological breakthrough. These events provide the motivation to accept more risk and make changes. In my view, a reliance on storms does not seem like a great way to run a business. Storms are unpredictable and may leave too little time for an organization to adjust. On the other hand, you might try to generate artificial storms, but this will eventually erode trust within the business.

Innovation is uncertain because it involves questions like, “is this problem even solvable?” An innovative solution could take many years to implement. The key to innovation within a larger organization is the recognition that uncertainty is not the same as risk. Uncertainty comes from our inability to predict the future. Risk is the impact of this uncertainty to the business. If you can find ways to mitigate risk, then you can still innovate even in the largest companies.

How do you mitigate risk? The same way you would do anything else with an agile mindset: start small and work in small increments. Don’t ask for a big budget or a large team right off the bat. Instead, find ways to show progress at each phase with the minimal resources at your disposal. As you demonstrate progress you may find that the organization is more willing to invest in your idea.

The 3M corporation is often cited as a company with a culture of innovation. Employees within 3M are allowed to work on whatever they want as long as they fulfill their day-to-day responsibilities reasonably well. It’s an environment that allows time for curiosity and exploration. The Post-It note was invented by a chemist at 3M who stumbled upon a unique adhesive while performing some experiments as part of a side-project. He knew his technology could be useful for something and he spent the next decade trying to figure out what that useful thing was.

Innovating at AppFolio

So how does all of this connect with work at AppFolio? Our second company value is: Great, innovative products are key to a great business.

I see several ways we strive to embody this value. First, product and UX spend an insane amount of time with our customers, talking to them, watching them, and deciphering their needs. They visit customer offices, they hang out with customers at meetups, they talk on the phone with customers all day, and they give customers demos of prototypes and new features.

As an organization, we are learning more and more how to drill down to the deep customer needs and make those the focus of our engineering efforts. We generally work without strict deadlines and this gives engineers a bit more freedom to ask questions and explore.

AppFolio provides engineers with a few regular opportunities to innovate. Every other Friday is Future Focused Friday. This is a day when engineers are free from their day-to-day responsibilities. They may use this day of work however they wish, without any accountability. Some engineers spend the day reading a book to deepen their understanding of a particular technology. Other engineers have projects that they are working on. For example, one engineer built a web app for sharing appreciations of other employees with the company. Still other engineers work on improving some part of our infrastructure or product. One engineer recently implemented an optimization in our continuous integration system which cut the runtime of our test suite in half.

Future Focused Friday is a zero-risk day. It frees engineers to try something crazy, like programing a drone to perform a property inspection or create a wireless submetering device. Because this day is already set aside, it costs the company nothing extra. In fact, these experiments may prove to be future streams of revenue for the business.

AppFolio hosts two hack days each year. Hack Day is similar to Future Focused Friday in that engineers can work on whatever they want for the day, but Hack Day tends to emphasize team projects and includes members of the company outside of engineering. A lot of cross-pollination between various parts of the company occurs on Hack Day. It’s also just plain fun. There are costumes, funny presentations, and beer. This gives the day a feeling of lighthearted playfulness and creativity.

We’ve recently introduced a new event to promote innovation called Shark Week. During this week, teams present innovative ideas they think will create value for our customers to a panel of judges. The judges select the project they think will have most value and the corresponding team has one month to implement it.

These are some basic structures and events that aim to promote innovation here at AppFolio. But just providing events doesn’t automatically make innovation happen. It takes an atmosphere of playfulness, curiosity, exploration, and, as I argue in this article, intense focus on customer needs and technology.

It's a Wiring Problem

IMG_2616 I once had a mentor who liked to say, “If you can reduce it to a wiring problem, you’re half-way there.” It took me a while to really understand what this means and why it is useful advice for a software engineer. In this post, I’m going to do my best to explain it and provide you with some practical applications. I hope you find it helpful!

When we aren’t at work, we move around in a world of physical objects. Many of these objects are quite complex: airplanes, iPhones, refrigerators, and Ikea furniture. Some of these are composed of hundreds of thousands, or even millions of intricate parts (Ikea furniture), all delicately assembled in a very precise configuration. It’s easy to forget how complex some of these objects are because they present such a simple physical interface. To the consumer, the iPhone is just a solid flat rectangle.

Even easier to forget are the complex processes used to fabricate these objects. How many different vendors, machines, and stages are involved in the creation of an iPhone? It would take us a long time to count. When I was a kid, I loved the episode of Mr. Roger’s Neighborhood where they went on a field trip to the crayon factory. It amazed me that the creation of something as simple as a wax cylinder could require so much behind-the-scenes infrastructure.

Ok, so what does this have to do with my mentor’s favorite phrase and your work as a software engineer? As developers, we spend the majority of our time modeling the world around us in software. We model physical objects, processes, and relationships to name a few. We strive to break these things down into independent concepts that we then assemble together within a software system.

These systems can become just as complex (and perhaps more so) than the hardware configuration of an iPhone. If you’ve done your job well, then you can walk over to a whiteboard, draw out a whole network of software components, and show how these components relate to one another. Component A sends a message to component B which forwards it along to these three other components, C1, C2, and C3. These components perform callbacks on component B when they have finished their work, and so forth.


Great! You understand how the software system will perform it’s job once the components have been arranged properly. But how were these components arranged in the first place? How did component A ever come to know about component B? How did C1, C2, and C3 come to know about the callbacks for B? The answer is that something had to wire them together prior to the interactions you drew on the whiteboard. You drew half of your problem on the whiteboard, the other half is wiring.

Now maybe my point is clear. Just as with a complex physical object, the components in a complex running software system must be meticulously wired together. And the machinery for performing this wiring can often be just as complex as the system itself. Whenever you are solving a problem using software, remember that you are actually dealing with two separate subproblems: modeling the domain and wiring the components. Now you might be thinking, “That’s not true. I’ve worked on complex software projects and didn’t have to think much about wiring.” I’m wondering whether the code for the project looked something like this:

class Pen
  def initialize
    @ink_cartridge = :blue)


class InkCartridge
  def initialize(ink_color:)
    @ink =
    @ball_point =


class BallPoint

If so, then I’d say that the wiring aspect of the system is still there, but it’s somewhat hidden. In this example, the problem of assembling a ballpoint pen and the problem of using a ballpoint pen are intermingled. The pen is creating and storing a reference to the ink cartridge, the ink cartridge is creating the ballpoint, and so on.

In the physical world, I doubt you could find a pen that created and assembled itself. But, I see this type of design all the time in the software world; and it causes trouble, as the system’s design evolves, because it attempts to address two very different problems with a single solution. Your thinking, as a developer, is muddled as you write the software because you are constantly trying to solve two distinct problems at the same time.

To make matters worse, the single solution you devise binds the two problems together, making them inseparable. Now what happens if the requirements for how the components are arranged changes? You begin to compromise the integrity of your model to meet the demands of the wiring problem.

Instead of trying to solve both problems simultaneously, what if you focus on one problem at a time? You will need to solve both problems regardless of how you approach the design, but at least if they are separated, you will have the chance to give each problem due thought. First, focus on the primary goal -- how to model your problem domain in software. Once you can confidently draw a diagram of control flow through your components for various use-cases, it’s time to turn your attention to the secondary problem of wiring these components together.

So what tools do you have at your disposal for solving the wiring problem? Well, if we are attempting to address our two problems separately then the code for solving each of them should be separate. I typically refer to the code used to wire components together as a factory. Factories can take several forms, each with its own strengths and weaknesses. In the following sections, I will describe three forms of factories.

Class Factory Methods

Let’s go back to our ballpoint pen example and use a factory to pull out the wiring code. The code fragment below shows one way we might do this.

class Pen
  def self.create

  def initialize(ink_cartridge)
    @ink_cartridge = ink_cartridge


In this example, we’ve pulled the creation of the ink-cartridge out of the pen initializer and have added a new class method, create(). This method instantiates the ink-cartridge, injects it into the pen’s initializer, and returns the new pen instance. The pen initializer knows nothing about the ink-cartridge it is given. It just assigns the ink-cartridge to a member variable for use within the pen instance.

There are a few advantages with this simple refactor. First, it gives us options for how pens might be created in the future. For example, we might add several creation methods as the system evolves:

  • create_ballpoint_pen()
  • create_whiteboard_pen()
  • create_ipad_stylus()

Some have argued that software architecture is an exercise in deferring decisions and keeping your options open. If this is true, then our refactor supports good architecture because it has opened up a world of options while still keeping pen creation simple for the common cases. Users can take advantage of our factory methods, or they are free to write their own wiring code outside the scope of the pen class.

A second, related advantage is that this method gives us greater freedom to mock the ink-cartridge in test code. In your test setup, just create the mock object and pass it directly into the initializer. I recognize that there are mocking libraries in Ruby which allow you to stub class initializers so that they return mocks, but I find the injection technique much easier to understand, especially when you’ve got a test involving several pens.

The strength of this technique compared with other factory approaches is simplicity. It decouples the wiring from the main problem with very little extra effort. At the same time, it keeps the factory close to the class that it creates (it’s in the same file). Readers will have little trouble understanding how pens are created just by looking at the pen class.

This approach does have at least one potential downside -- it makes pen class directly dependent upon the ink-cartridge class. The two classes cannot be separated even though instances of the pen class don’t care what type of ink-cartridge they refer to. If you wanted to turn your pen class into a library, you would have to include the ink-cartridge implementation as well. This may or may not make sense depending on your context.

Factory Modules

Now consider the case where there are more than a few possible pen configurations. Rather than overloading your class with fifty different class factory methods, you might consider extracting this code into a factory module. You could take this further by grouping similar configurations in separate modules. For example:

module WhiteboardPenFactory
  def create_thin_expo_marker

  def create_thick_expo_marker

module PermanentPenFactory
  def create_standard_sharpie

  def create_twin_tip_sharpie

module BallpointPenFactory
  def create_standard_pen

  def create_erasible_pen

The strength of this approach is in the complete separation of wiring code from the problem domain. The pen class knows nothing in particular about the components of which it is composed and will not need to change when new pen variants are added to the system.

Similarly, the factory modules aren’t required to generate instances of the pen class. There may be, in fact, several pen classes with completely different implementations. Users of the factory modules needn’t be concerned with the specific type of pen that pops out of the factories, as long as each implementation adheres to a common interface.

The factory module approach does come at a cost. Because the wiring code is totally separated from the domain, readers will likely have a harder time understanding where and how pens are created, especially if our factory modules are spread across a large codebase.

Factory Classes

Sometimes wiring is not as straightforward as calling the appropriate factory method. The first factor that can complicate wireup is the need to maintain state across several invocations of a factory. In our pen example, what could we do if each pen needed a unique serial number? We might achieve this by maintaining a counter to generate successive serial numbers, passing each serial number as a parameter to the factory method.

However, this approach is cumbersome for the user, who shouldn’t have to care about the details of how the serial numbers are generated. Instead, these details should be hidden within the factory itself. Why not write a factory class that can maintain the counter state internally? The factory would store the counter as a member variable and increment it whenever it created a new pen. This process is transparent to the user of the factory class.


Another reason you might create a factory class is if you have a system that needs to create components but doesn’t care about the specific type of component created. Imagine we are modeling a school classroom. The classroom must provide one writing implement for every student who walks through the door.

When we create the classroom, we can inject a ballpoint pen factory into its initializer. The classroom calls create_pen() on the factory instance each time a student enters the classroom. Once again, this approach helps us keep our options open for future extension. For example, we might decide that students will write on whiteboards. In this case, we simply inject a whiteboard pen factory into the classroom. The classroom itself doesn’t need to change in order to support the new behavior.


This approach is probably the most flexible of the wiring options we’ve discussed. It allows the developer to fully separate wiring code from domain code, maintain wiring state, and inject the wiring system into other components. On the other hand, this is also the heaviest approach. If you create a factory class for every class, then you wind up doubling the number of classes in your system. This places a great burden on someone else who is trying to make sense of your system. If you find yourself creating factory classes for your factory classes, you’ve definitely gone too far with this approach.

Sometimes you need the ability to maintain state and inject a factory, but creating a whole new class just feels overkill. In these cases, lambdas fit the bill quite well. They carry context along with them, they can be passed around just like any other object, and they can act as factories. But be forewarned, your lambda can get pretty unwieldy if you have a significant amount of wiring code. If your code starts getting hairy with large lambda definitions, you’re probably best served by extracting it into a class.

I’ve found that it works well to start with the class factory method and refactor to meet the demands of new requirements as the system evolves. Refactoring is generally not too difficult because you’ve already got your wiring code and your domain code separated. It’s usually just a matter of moving the wiring code around into the right location.

Concluding Thoughts

If you’ve done any reading about software engineering, you’ve likely come across the SOLID acronym which attempts to summarize some of the basic principles of software design. In my view, the stuff we’re discussing here is all about SOLID. For example, the separation of wiring and domain code is a direct application of the single responsibility principle. Our example of injecting a pen factory into a classroom is an application of the Liskov substitution principle (classrooms may use any sort of factory that produces a compatible pen), the dependency inversion principle (the pen factory is injected into the classroom), and the open-closed principle (the behavior of the classroom can be extended without modifying the class by injecting a different kind of factory).

I waited until the end of the article to mention these connections to SOLID because I believe that learning the principles from the top down can sometimes leave a person with only a vague sense of their value and how they might be practically applied. This article represents how I personally came to understand and internalize most of the principles of SOLID.

My aim in this article was to emphasize the need for separation between wiring and domain code and provide some simple tools for achieving this separation. In general, my software design approach can be summarized as follows:

  1. Model the domain draw diagrams showing how the components interact.
  2. Ask how the components get wired up in the first place and draw more diagrams.

When thinking about which wiring tool to reach for, I usually reach for the simplest one that will satisfy my current needs and plan on refactoring later when requirements change. I’ve found this approach to yield flexible designs that are able to cope well with changing requirements. As with anything, it’s possible to go overboard with this stuff, so if you feel like the code is working against you, it might be time to stop and re-evaluate. This topic is closely related to the topic of object composition. If you are interested in learning more about that, take a look at my previous post. Thanks for reading!

A Composition Regarding Inheritance

Observations from an Object Oriented Interview

I've had the opportunity to interview a number of candidates for software engineering positions on my team over the past few years. In most of these interviews, I presented the candidate with the same design question: “Let’s say you were designing a traffic simulation. In this simulation, you’ve got a car. It can start, stop, move forward, and move in reverse. Yes, it’s a really boring simulation.” I then asked the candidate to draw this design on a whiteboard. In every interview, I got something that looked like the following picture:

Ok, so far, so good. The candidate thinks of a car as a component in a software system that has capabilities matching my requirements. Then I said, “let’s make this simulation more interesting by including two cars: A and B. Car A and B are similar, but the way car A starts and stops is different from car B. Please update your diagram to show how you would accommodate this requirement while minimizing the amount of repeated code.” In almost every case (and especially when the candidate was fresh out of school) I got the same reaction: a confident smile appeared on the candidate’s face, as if to say, “I’ve done this a million times...textbook object oriented programming.” The candidate returned to the whiteboard and drew this:


“Okay, next task: I want to introduce a new car, say car C. Car C starts like car A, stops like car B, and has its own way of moving forward. Please update the diagram to accommodate this requirement while minimizing the amount of duplicated code.” Long pause. Eyebrows furrowed. At this point, the solutions people gave begin to diverge. C++ folks began struggling with multiple/virtual inheritance, Ruby folks started talking about mixins, and Java folks were stuck. Regardless of the language, the diagram ended up with some more lines to indicate inheritance relationships. I simply proceeded to ask the candidate to add new cars with different combinations of functionality. So for example, I asked the candidate to add car D which moves forward the same way as C, starts like car A, and stops like car B. I continued this process until the whiteboard diagram was an incomprehensible mass of circles and arrows.

My goal in this exercise was not to torture the candidate or to see if they “knew object oriented programming,” but to watch their thought process as they realized that their design approach was brittle and needed some rethinking. At some point the exasperated candidate stopped and said, “there must be a better way,” and this was generally the beginning of a fun and constructive design discussion.

The next question I asked in such interviews was, “can you think of a way to redraw this diagram without any inheritance or mixin relationships.” Many people really struggled with this -- they thought that I was asking a trick question. “Here’s a hint, what if you had a class called Ignition? How would you use it in this picture to remove some of these other class relationships.” This was usually the moment of epiphany for the candidate. The vocabulary of our discussion rapidly expanded from talking about the methods of the Car class to new concepts like Brake, Accelerator, and Transmission. Pretty soon our diagram looked like this:


Pretty straight forward. This design approach is called class composition. Many of the candidates said, “wow, that solution should have been obvious to me.” Maybe so, but I must admit that I had their very same approach to design when I first began working at a software company. To some, the software requirements described in the exercise may seem pathological, but this has not been my experience. I feel that the exercise is actually a pretty good picture of the way some of the first software projects that I worked on developed after I graduated. I encountered many of the same frustrations that the candidates I interviewed did, just on a larger scale.

Those who are familiar with Ruby will point out that mixins are another way to meet the requirements of the system described above. For example, we might create Startable, Stoppable, and Movable mixins that could be included in any combination within our different car classes. But, in the context of the interview exercise, I could still push my requirements further. “Okay, let’s say we want a new car X that starts like car A on hot summer days, but starts like car B on cold winter days. Please update your diagram to accommodate this requirement while minimizing the amount of repeated code.” This is trivial with a composition-based design. Our car X just updates its ignition reference from IgnitionA to IgnitionB on cold days. This is more difficult to achieve with Ruby mixins since, again, we declare an explicit class relationship before runtime. Or how about another requirement, “let’s say that the start sequence for car Y begins with the sequence for car A and ends with the sequence for car B.” Again, the mixin relationship represents a design commitment we make up-front that reduces our options later in the game. Traditional inheritance and mixins are very similar in this respect.

Comparing Composition and Inheritance as Design Tools

Why is it that many of us have a tendency to approach design problems using inheritance rather than composition? One reason could be the way object oriented programming is presented in school and in textbooks. Whenever I asked interview candidates why they chose inheritance over composition, the general response was that inheritance describes an is-a relationship and composition describes a has-a relationship. They initially felt that the interview exercise was best suited to an is-a relationship, so that’s what they went with. Indeed, this is exactly how I learned to think about object oriented programming. The problem with this view is that the classification is subjective. You can almost always think of a way to convert an is-a relationship into a has-a relationship and vice-versa, just like I described in my example above.

The is-a/has-a paradigm can give one the impression that composition and inheritance are two similar tools on equal footing that can be used interchangeably to achieve a similar goal. In reality, these tools have very different strengths and are therefore suited to different problems. It is wise, then, to consider these strengths in choosing which tool to apply in a particular situation.

I've already alluded to some of composition’s strengths in the examples above. These include a greater opportunity to defer design commitments and a more natural way to talk about and model the problem space. The first is due to the fact that composed relationships are based on object references, not class references. In the car example above, we refactored our design such that a car is composed of several internal components. In this scheme we have the option of changing the references to these components at any time, including runtime. That is to say, we could design a system in which a car could be reassembled with totally different components all while traveling 80 mph down the highway. That’s pretty powerful. With inheritance, we would either need to stop our software and modify the inheritance hierarchy or, if we were working in Ruby, engage in some metaprogramming wizardry. The point is, with composition we minimize early design commitments and maximize the likelihood we'll be able to accommodate unexpected requirements.

The other strength is somewhat less tangible in a discussion like this, but in my view no less important. Composition is the way we generally think about how systems work in the physical world. It therefore lends itself nicely to the development of clear vocabulary for talking about a problem. If I asked you to tell me how a blender works, you would probably begin by identifying the key components: blade, motor, power cord, switch, carafe, and lid. Each component in the system has a specific job and works in concert with other components in the system. For a given blender use-case, you could draw me a diagram of how the parts interact with one another -- the motor turns the blade, the lid locks into the carafe, the switch opens the flow of electricity through the motor. On the other hand, you could consider an inheritance-based approach. You might begin by describing a blender as a type of kitchen appliance. Appliances consume electricity to perform work, where the work performed depends on the specific type of appliance. In the case of a blender, the work performed is the rotation of a blade to chop or liquify food. While this is a reasonable definition for a blender, it tells us little about what a blender is from an implementation point of view. Based on this description, it would be more difficult to draw a diagram of what happens internally when someone presses the “on” switch.

Our blender discussion brings us back around to the is-a/has-a distinction, but perhaps with the opportunity for a deeper understanding. The composition-based design is better suited for describing how our blender accomplishes its job whereas the inheritance-based design is better suited for defining what a blender is. Do you see the difference? The definition of a blender can apply to a wide range of blender implementations. Silver Bullet and VitaMix are both blenders but they have different implementations. Our view of a blender as the composition of many parts represents the description of a specific blender implementation. Inheritance therefore finds it’s strength in describing the relationships between class interfaces apart from implementation. Composition by its very nature is an implementation detail and finds it’s strength in modeling the implementation of a system. This observation has major implications for software design in explicitly typed languages like C++ and Java. In these languages, we can define a hierarchy of pure interfaces (that is, classes with method definitions but no corresponding implementation). We can then refer to objects at different levels of abstraction in various contexts by moving up or down the interface inheritance chain. The classes which implement these interfaces, however, can follow a different structure and can be changed without modification to the interfaces.

I won't get into a detailed discussion of interface-based design here -- it’s a topic that deserves it’s own discussion. I expect that most of the people reading this article are Rubyists. Ruby does not provide an explicit interface concept so the benefits of inheritance for interface-based design are diminished. In the Ruby realm I most often see inheritance applied as a method of code reuse. While code reuse is certainly achievable with inheritance, I hope our discussion so far has provided some convincing evidence that this is not what it excels at -- just consider the frustrated interview candidate from the beginning of this article. Composition for code reuse is more flexible than inheritance and typically comes at little cost.

Okay, so what are the strengths of inheritance in a language like Ruby? I view inheritance in Ruby as a tool of convenience; one that can reduce boilerplate code in cases where several classes share the same method signatures. Consider again the traffic simulation design in my interview question. Each type of car in this example delegates its methods directly to the internal components. This leaves us with a bunch of classes that repeat the same delegation pattern in each method, just with different types of components. This boilerplate code can be removed with the creation of a base class which operates on components provided by subclasses. Notice that we’ve used composition and inheritance together to achieve a flexible and convenient design.

composition-regarding-inheritance (2)

That said, I think there is an even better approach that does not use inheritance if our design does not require a separate class for each type of car. In this case, we can implement a single car class and a car factory that builds cars. The car factory creates different configurations of components depending on what behavior we want in our car and injects them during creation of the car object. See the figure below:

composition-regarding-inheritance (3)

What about the role of mixins in Ruby? Again, in my view mixins provide a measure of convenience, but do not represent a tool that is well suited as the foundation of a system’s design. The Enumerable mixin is a good example to consider. The Enumerable module in Ruby requires that classes which include it implement the each method (and optionally the <=> method). It uses these methods to provide a rich set of traversal, sorting, and searching methods in the including class. Ruby mixes the Enumerable methods into several basic data types including arrays, hashes, and ranges. Working with these types is quite pleasant since they allow all of these methods to be called directly on the objects themselves and allow for several method calls to be chained together. Most people would probably see the Enumerable module as a great example of code-reuse, but I disagree. The same degree of code-reuse can be achieved through simple class methods within a module. Here’s what the call to any? would look like if Enumerable were written in this way:

Enumerable::any?(numbers) { |number| number > 10 }

The main benefit of the mixin approach is ease-of-use. It is more natural for a developer to call the methods directly on the objects, especially when several method calls need to be chained together. Calling class methods in a module is still doable, but would be quite cumbersome in such cases. So the advantages we see in the Enumerable mixin example focus on syntactic niceties. They help developers read and write code (and this is certainly important!), but they do not significantly contribute to a developer’s conceptual understanding of a system, which I believe should be considered as a first priority when designing software.

Concluding Thoughts

The longer I work with object oriented programming languages, the more clearly I see a priority in tools these languages have to offer. In my opinion, composition is the bread and butter of design tools. It’s the tool I reach for first when I’m trying to model the solution for a problem. Doing this allows me to develop meaningful vocabulary for talking about each aspect of the solution. I find that I can draw diagrams showing clear interactions between components and this allows me to verify my understanding of the solution before I spend time implementing it. Once I have implemented my solution using composition, I sometimes notice repeated or excessive boilerplate code. In such cases, I will turn to my tools of convenience: inheritance and modules (domain specific languages and metaprogramming also fall into this category for me). These tools are the icing on the cake. The system can be understood without them, but they can make using or extending the system more pleasant.

When I first learned about object oriented programming I read a book that made this recommendation: “prefer composition; use inheritance judiciously.” This bit of wisdom stood out to me mainly because I didn’t really understand why I should follow it. I’m sure the book provided some justification regarding flexibility, but not in a way that I could grasp at the time. It was only after several years of pain from ignoring this advice that I began to see the basis for it. My hope in writing this article is that others would be provoked to careful thought regarding the application of inheritance in object oriented languages and perhaps avoid some of the same frustration I encountered. Thanks for reading!

My Experience With Mob Programming

At Appfolio we often have guest speakers from the software community come in and present topics at our internal tech talks. Three months ago we had guest speaker Woody Zuill here to describe a practice he and his team use called Mob Programming.

We already encourage our developers to pair program due to the numerous advantages we find that it provides. These advantages include having two sets of eyes on a problem and having mentorship between more and less senior developers. Mob programming takes the idea of pair programming to its extreme. The basic concept is to have everyone on the team take part in a collaborative coding session, with one person as the driver and everyone else as navigators.

When Woody began talking to us, he showed us a time-lapse video of his team during their typical work day, with large text over the screen pitching the concept of mob programming. The letters at one point read “Using this technique the team has achieved 10x productivity”. Needless to say, I was a bit skeptical. The rest of the presentation dove into the details of how Woody and his team use mob programming, but the basic concept was easy to understand. The question is, can having your entire team (usually 5-7 people in Woody’s video) focused on the same task using the same screen, keyboard, mouse, and editor actually be better than more traditional methods?

After the talk, my team discussed what we liked and disliked about the idea of mob programming. Since at Appfolio teams can adopt and practice new techniques without any sort of approval, we decided that we’d give mob programming a shot. If it worked then we’d have a valuable new tool. If not then no real harm done besides possibly being a few commits behind. With that in mind, we hijacked a conference room and got to work.

The first feature we tried the new technique out on was very UI heavy. Having the whole team (three developers and one QA engineer) present helped out not only in visual design decisions, but also in remembering which CSS styles to use where. Not to mention, asking product managers or UX specialists to come join the mob for questions and group conversation was very easy.

We finished the feature in considerably less time than we thought it would take, yet still had a very high quality, well tested piece of software. Having other developers around kept us accountable for having good test coverage, using good design patterns, and refactoring any nasty code we’re currently touching.

We’ve since worked on many other features using the technique and found our experience to, for the most part, be more of the same. Recently, it has been especially useful having so many eyes to look at some of the more critical changes we have been working on, such as enhancements to the payments platform. Despite our success, it doesn’t mean that there aren’t downsides or that mob programming works for everyone.

How can we evaluate whether or not a software development technique is better than any other? As far as I can tell, there are at least four things we can try to measure.

1) How productive is the team? (i.e. how much gets done per unit of time)

2) How high quality is the product? (not only customer facing quality but also code quality)

3) How much are team members learning? (do less experienced team members get mentoring)

4) How happy are the individuals on the team? (do you go home happier at the end of the day)

Unsurprisingly, the team did not achieve 10x productivity. In fact, we found our productivity to be almost the same as it was before. We’re not sure how Woody’s team measured their productivity increase, or what their workflow looked like before, but it seems that our findings were not the same here. Is it worth mob programming solely based on productivity boosts? Your mileage may vary, but as far as we’re concerned it’s a resounding no.

Is the product higher quality? Is there better test coverage? Is the code idiomatic and does it follow best practices? Are the chances of a bug crawling into the product minimized? From our experience this is the most emphatic yes of all the concerns listed above. Not only does having everyone together increase accountability and awareness, but mistakes that may be made by more junior developers are more likely to be caught. Furthermore, when our QA engineer was in the mob, he gained a much better sense of how to go about testing the feature as thoroughly as possible.

How much learning is going on? This question doesn’t really have a straightforward answer. Yes, more senior developers get to spread expertise to the less senior ones, and that is enormously useful, but that’s not all that is involved in learning. Personally, I know that sometimes I just need some time to myself to dive into unfamiliar code and figure out what is going on. When the mob is working, people not at the keyboard can’t pause time and open a rails console to experiment, and they can’t go down the rabbit hole of some large stack of functions to find out what’s really going on. And while those at the keyboard may physically be able to do so, they probably shouldn’t if they want the other team members to stay engaged.

Do we go home happier at the end of the day? It’s hard to tell really. I do enjoy getting to interact with all of my team members all day, but it’s honestly a little draining sometimes. Being a fairly introverted person myself, the constant interaction is more of an energy drain than a lot of my previous work at the company, despite possibly being more fun. By the end of the work day I often find myself anxious to get home and recharge with a good book or a show on Netflix.

What other pitfalls might exist? For one, we’ve found that having three developers seems to be the magic number, at least for us. Any more and we’d experience a decrease in productivity without an equally valuable increase in quality, learning, or happiness. Most of our teams have four developers, so this might explain why we are the only team that I am aware of who has tried mob programming and stuck with it. On that note, is it still a mob with only three developers? (Lately our QA engineer seems to get caught up in other work much of the time, despite spending a lot of time in the mob initially) In addition, keeping everyone engaged is important. If one or two people dominate the conversation, it’s pretty easy for others to completely lose focus or get bored. If you find yourself losing focus often, try to be the driver more.

If you do decide to try mob programming, here are a few suggestions that we found work for us:

1) Keep track of time. We have timer cubes that allow whoever is at the keyboard to pick how long they want to drive. Once the timer goes off, switch to a new person.

2) Get a big screen. We started out using large television screens in conference rooms and after getting kicked out of enough rooms we decided to just swipe a nice 50 inch LED screen and put it in our team’s normal work area.

3) Try to merge everyones’ preferred bash/git aliases together to minimize “command not found” errors at the terminal. On the same note, try to have everyones’ editor of choice available. We have two VIM users and one RubyMine user on the team, but we’re all comfortable with both after working together.

So that’s about it. We plan on sticking with mob programming on our team for the short term. However, none of the other teams are still using it. Will we keep using it? Who knows. Yes for the short term, but probably not forever, especially considering how often we rotate teams to spread knowledge. Should you use mob programming? It’s hard to say for sure, but I definitely recommend giving it a try.

Ruby Mixins & ActiveSupport::Concern

A few people have asked: what is the dealio with ActiveSupport::Concern? My answer: it encapsulates a few common patterns for building modules intended for mixins. Before understanding why ActiveSupport::Concern is useful, we first need to understand Ruby mixins.

Here we go...!

First, the Ruby object model:

As you can see mixins are "virtual classes" that have been injected in a class's or module's ancestor chain. That is, this:

module MyMod

class Base

class Child < Base
  include MyMod

# irb> Child.ancestors
#  => [Child, MyMod, Base, Object, Kernel, BasicObject]

results in the same ancestor chain as this:

class Base

class MyMod < Base

class Child < MyMod

# irb> Child.ancestors
#  => [Child, MyMod, Base, Object, Kernel, BasicObject]

Great? Great.

Modules can also be used to extend objects, for example:

my_obj =
my_obj.extend MyMod

# irb> my_obj.singleton_class.ancestors
#  => [MyMod, Object, Kernel, BasicObject]

In Ruby, every object has a singleton class. Object#extend does what Module#include does but on an object's singleton class. That is, the following is equivalent to the above:

my_obj =
my_obj.singleton_class.class_eval do
  include MyMod

# irb> my_obj.singleton_class.ancestors
#  => [MyMod, Object, Kernel, BasicObject]

This is how "static" or "class" methods work in Ruby. Actually, there's no such thing as static/class methods in Ruby. Rather, there are methods on a class's singleton class. For example, w.r.t. the ancestors chain, the following are equivalent:

class MyClass
  extend MyMod

# irb> MyClass.singleton_class.ancestors
#  => [MyMod, Class, Module, Object, Kernel, BasicObject]

class MyClass
  class << self
    include MyMod

# irb> MyClass.singleton_class.ancestors
#  => [MyMod, Class, Module, Object, Kernel, BasicObject]

Classes are just objects "acting" as "classes".

Back to mixins...

Ruby provides some hooks for modules when they are being mixed into classes/modules:

For example:

module MyMod
  def self.included(target)
    puts "included into #{target}"

  def self.extended(target)
    puts "extended into #{target}"

class MyClass
  include MyMod
# irb>
# included into MyClass

class MyClass2
  extend MyMod
# irb>
# extended into MyClass2

# irb> MyClass.ancestors
# => [MyClass, MyMod, Object, Kernel, BasicObject]
# irb> MyClass.singleton_class.ancestors
# => [Class, Module, Object, Kernel, BasicObject]

# irb> MyClass2.ancestors
# => [MyClass2, Object, Kernel, BasicObject]
# irb> MyClass2.singleton_class.ancestors
# => [MyMod, Class, Module, Object, Kernel, BasicObject]

Great? Great.

Back to ActiveSupport::Concern...

Over time it became a common pattern in the Ruby worldz to create modules intended for use as mixins like this:

module MyMod
  def self.included(target)
    target.send(:include, InstanceMethods)
    target.extend ClassMethods
    target.class_eval do

  module InstanceMethods
    def an_instance_method

  module ClassMethods
    def a_class_method
      puts "a_class_method called"

class MyClass
  include MyMod
# irb> end
# a_class_method called

# irb> MyClass.ancestors
#  => [MyClass, MyMod::InstanceMethods, MyMod, Object, Kernel, BasicObject]
# irb> MyClass.singleton_class.ancestors
#  => [MyMod::ClassMethods, Class, Module, Object, Kernel, BasicObject]

As you can see, this single module is adding instance methods, "class" methods, and acting directly on the target class (calling a_class_method() in this case).

ActiveSupport::Concern encapsulates this pattern. Here's the same module rewritten to use ActiveSupport::Concern:

module MyMod
  extend ActiveSupport::Concern

  included do

  def an_instance_method

  module ClassMethods
    def a_class_method
      puts "a_class_method called"

You'll notice the nested InstanceMethods is removed and an_instance_method() is defined directly on the module. This is because this is standard Ruby; given the ancestors of MyClass ([MyClass, MyMod::InstanceMethods, MyMod, Object, Kernel]) there's no need for a MyMod::InstanceMethods since methods on MyMod are already in the chain.

So far ActiveSupport::Concern has taken away some of the boilerplate code used in the pattern: no need to define an included hook, no need to extend the target class with ClassMethods, no need to class_eval on the target class.

The last thing that ActiveSupport::Concern does is what I call lazy evaluation. What's that?

Back to Ruby mixins...


module MyModA

module MyModB
  include MyModA

class MyClass
  include MyModB

# irb> MyClass.ancestors
#  => [MyClass, MyModB, MyModA, Object, Kernel, BasicObject]

Let's say MyModA wanted to do something special when included into the target class, say:

module MyModA
  def self.included(target)
    target.class_eval do
      has_many :squirrels

When MyModA is included in MyModB, the code in the included() hook will run, and if has_many() is not defined on MyModB things will break:

irb :050 > module MyModB
irb :051?>   include MyModA
irb :052?> end
NoMethodError: undefined method `has_many' for MyModB:Module
 from (irb):46:in `included'
 from (irb):45:in `class_eval'
 from (irb):45:in `included'
 from (irb):51:in `include'

ActiveSupport::Concern skirts around this issue by delaying all the included hooks from running until a module is included into a non-ActiveSupport::Concern. Redefining the above using ActiveSupport::Concern:

module MyModA
  extend ActiveSupport::Concern

  included do
    has_many :squirrels

module MyModB
  extend ActiveSupport::Concern
  include MyModA

class MyClass
  def self.has_many(*args)
    puts "has_many(#{args.inspect}) called"

  include MyModB
# irb>
# has_many([:squirrels]) called

# irb> MyClass.ancestors
#  => [MyClass, MyModB, MyModA, Object, Kernel, BasicObject]
# irb> MyClass.singleton_class.ancestors
#  => [Class, Module, Object, Kernel, BasicObject]

Great? Great.

But why is ActiveSupport::Concern called "Concern"? The name Concern comes from AOP ( Concerns in AOP encapsulate a "cohesive area of functionality". Mixins act as Concerns when they provide cohesive chunks of functionality to the target class. Turns out using mixins in this fashion is a very common practice.

ActiveSupport::Concern provides the mechanics to encapsulate a cohesive chunk of functionality into a mixin that can extend the behavior of the target class by annotating the class' ancestor chain, annotating the class' singleton class' ancestor chain, and directly manipulating the target class through the included() hook.


Is every mixin a Concern? No. Is every ActiveSupport::Concern a Concern? No.

While I've used ActiveSupport::Concern to build actual Concerns, I've also used it to avoid writing out the boilerplate code mentioned above. If I just need to share some instance methods and nothing else, then I'll use a bare module.

Modules, mixins and ActiveSupport::Concern are just tools in your toolbox to accomplish the task at hand. It's up to you to know how the tools work and when to use them.

I hope that helps somebody.

Discovery of a MySQL bug

Recently, one of our MySQL instances crashed. We responded to the alert, but by the time we logged into the server, the MySQL instance has already restarted and recovered. So, we started to investigate the cause of the crash. Our MySQL data volume is nfs-mounted on a NetApp filer in order to allow for reliable crash-recovery and data replication. Our monitoring system showed a spike of near-Gb-line-rate output from the MySQL server to the NetApp filer and it quickly became apparent that MySQL crashed because it ran out of disk space. However, the MySQL disk usage did not change. Instead, the space was used by the NetApp snapshots, each on the order of 20GBs. Snapshots, much like source control, keep track of changes made to files allowing you to view past versions. The NetApp is configured to automatically create snapshots and garbage collect old ones. However, as the rate of change grows, the size of snapshots grows as well. The writing of GBs of data caused enormous snapshots to be created. This meant that whatever wrote those massive files in a short time, also deleted them in that same short time.

Additional metrics in our monitoring system pointed us towards MySQL's internal temporary tables. MySQL uses these internal temporary tables to satisfy certain queries. When an internal temporary table becomes too big for memory, MySQL flushes it to disk. Once the query finishes, the internal temporary table is deleted. So, in this case, MySQL was processing a query and needed a 20GB internal temporary table!? Naturally, we need to find out what query is causing this and on which data set (each customer is stored in a separate database using the multi-tenancy principle).

Based on mytop and the MySQL slow query log, we were able to single out one suspicious query. It was a query executed by Solr's data import handler during delta reindex. The application knows which models and which attributes are stored in Solr. Whenever any of those models and attributes change, the application sends a request to Solr asking it to delta reindex. We have an extension in Solr which waits 60 seconds before actually delta reindexing, which effectively coalesces multiple delta reindex requests into a single delta reindex and lightening the load on the MySQL

It turned out that this suspicious query was changed at the beginning of December, but, due to the Christmas / New Year, was only in production for a couple of weeks. The change was rather unusual as it added a join from table A to table X to table B to a query that was already joining from table A to table Y to table B. This was a deliberate change allowing us to migrate from table X to table Y over a few development cycles and the fact that we now have a double join (and a cartesian product) from table A to table B was discussed at development time and was deemed okay. We assumed less than 50 rows resulting from the join of table A to table B, and so in the worse case we'd end up with 2,500 (50 x 50) rows because of the double join.

We found the internal temporary table that MySQL flushed to disk in one of the NetApp's snapshots. We then matched the data in that file to a particular data set. In this data set, the join from table A to table B had 500 rows, which would produce 250,000 (500 x 500) rows because of the double join. Clearly 250,000 rows is significantly larger than the expected 2,500 rows, but still nothing that MySQL should not be able to handle.

We manually explained and executed this query and we saw no issues whatsoever. Hmmm??? Perhaps this query was not the cause after all?

Nevertheless, we proceeded to copy the data set to our staging servers and see whether we can reproduce the issue there. Based on the suspicious delta reindex query, we knew what to edit in the UI in order to trigger that query. We made the edit and waited. The application asked Solr to delta reindex. Solr waited 60 seconds and started to delta reindex. After few seconds, Solr started to execute our suspicious query and after another 30 seconds, MySQL dumped 20GB internal temporary table to disk. Bingo!

So again we tried to manually explain and execute that query and again we saw no issues whatsoever. Wat!?

We speculated there must be some difference between Solr running the query and us running the query manually. We enabled the MySQL full query log and repeated the edit in the UI. This is what Solr executed,

711723 Query /* mysql-connector-java-5.1.21 ( Revision: ${bzr.revision-id} ) */SELECT @@session.auto_increment_increment
711723 Query SET NAMES utf8mb4
711723 Query SET character_set_results = NULL
711723 Query SET autocommit=1
711723 Query SET sql_mode='STRICT_TRANS_TABLES'
711723 Query SET autocommit=0
711723 Query <delta reindex query>

We quickly focused on SET NAMES utf8mb4. Running the delta reindex query from MySQL client by itself did not cause the problem, but running the delta reindex query after SET NAMES utf8mb4 caused the 20GB file. In fact, we tried other encodings, including utf8, and all of them seem to cause the 20GB file. The only one that worked seemed to be latin1.

This led us to realize that by default our MySQL client uses latin1 whereas our application and Solr use utf8 and utf8mb4, respectively. So in theory, if Solr used latin1 encoding it would avoid the 20GB file. We quickly reconfigured Solr to use latin1 and sure enough, it worked.

Our attention now turned to understanding why that particular delta reindex query on that particular data set with utf8mb4 encoding was causing MySQL to write 20GB file. It turns out MySQL has a bug when a GROUP_CONCAT(DISTINCT ...) is too big to fit in memory and requires MySQL to flush to disk. In our case, the bug is triggered because utf8 / utf8mb4 use multiple bytes per character where as latin1 uses one byte per character. You can read more details about this bug here.

Don't make your users wait for GC

One of the weaknesses of Ruby is garbage collection. Although some improvements have been made (tunable GC settings, lazy sweep, bitmap marking), it is still a simple stop-the-world GC. This can be problematic especially for large applications. We have a rather large application running on Passenger 3 using REE 1.8.7 with tuned GC. As the application grew, the GC performance has been degrading until we were faced with the following situation where GC averages 130 ms per request.

It was time to address our obvious GC problem.

We could have continued to tune the GC and optimize memory usage to reduce the impact of GC on response times. But why? Why have the GC impact the response time at all? Instead, we decided to trigger GC after one request finishes but before the next request starts. This is not a new idea. It is actually generically referred to as out of band work (OOBW). It has been implemented by Unicorn and discussed here. However, it is not supported by Passenger. So we decided to add OOBW support to Passenger.

Our patch allows the application to respond with an additional header, namely X-Passenger-OOB-Work. When Passenger sees this header, it will stop sending new requests to that application process and tell that application process to perform the out of band work. The application registers oob_work callback for the work it wants done using Passenger's event mechanism. When the work is done, the application process resumes handling of normal requests.

All the application code can be packaged in an initializer,

PhusionPassenger.on_event(:oob_work) do
 t0 =
 GC.start "Out-Of-Bound GC finished in #{ - t0} sec"

class RequestOOBWork
 def initialize(app, frequency)
   @app = app
   @frequency = frequency
   @request_count = 0

 def call(env)
   @request_count += 1
   status, headers, body =
   if @request_count % @frequency == 0
     headers['X-Passenger-Request-OOB-Work'] = 'true'
   [status, headers, body]

Rails.application.config.middleware.use RequestOOBWork, 5

After experimentation, we decided to trigger GC after every 5 requests. If you set the parameter too high, then GC will continue to occur in the middle of requests. If you set the parameter too low, then application processes will be busy performing their out-of-band GC causing Passenger to spawn more workers.

With these changes in place, our graphs show 10x improvement in GC average per request,

The patch we implemented and deployed to production is for Passenger 3. It is available here.

Passenger 4 is currently under active development. In fact, beta 1 has already been released. We've ported our patch to Passenger 4. It is merged and expected to be included in beta 2.

CSS Architecture

To many Web developers, being good at CSS means you can take a visual mock-up and replicate it perfectly in code. You don't use tables, and you pride yourself on using as few images as possible. If you're really good, you use the latest and greatest techniques like media queries, transitions and transforms. While all this is certainly true of good CSS developers, there's an entirely separate side to CSS that rarely gets mentioned when assessing one's skill. Interestingly, we don't usually make this oversight with other languages. A Rails developer isn't considered good just because his code works to spec. This is considered baseline. Of course it must work to spec; its merit is based on other things: Is the code readable? Is it easy to change or extend? Is it decoupled from other parts of the application? Will it scale?

These questions are natural when assessing other parts of the code base, and CSS shouldn't be any different. Today's web applications are larger than ever, and a poorly thought-out CSS architecture can cripple development. It's time to evaluate CSS the same way we evaluate every other part of the application. It cannot be an afterthought or written off as merely the "designer's" problem.

The Goals of Good CSS Architecture

In the CSS community, a general consensus of best practices is very difficult to come by. Judging purely by the comments on Hacker News and the reaction of developers to the release of CSS Lint, it's clear that many people disagree over even the basic things CSS authors should and shouldn't do.

So instead of laying out an argument for my own set of best practices, I think we should start by defining our goals. If we can agree upon the goals, hopefully we can start to spot bad CSS not because it breaks our preconceived notions of what's good but because it actually hinders the development process.

I believe the goals of good CSS architecture shouldn't be that different from the goals of all good software development. I want my CSS to be predictable, reusable, maintainable, and scalable.


Predictable CSS means your rules behave as you'd expect. When you add or update a rule, it shouldn't affect parts of your site that you didn't intend. On small sites that rarely change, this isn't as important, but on large sites with tens or hundreds of pages, predictable CSS is a must.


CSS rules should be abstract and decoupled enough that you can build new components quickly from existing parts without having to recode patterns and problems you've already solved.


When new components and features need to be added, updated or rearranged on your site, doing so shouldn't require refactoring existing CSS. Adding component X to the page shouldn't break component Y by its mere presence.


As your site grows in size and complexity it usually requires more developers to maintain. Scalable CSS means it can be easily managed by a single person or a large engineering team. It also means your site's CSS architecture is easily approachable without requiring an enormous learning curve. Just because you're the only developer touching the CSS today doesn't mean that will always be the case.

Common Bad Practices

Before we look at ways to achieve the goals of good CSS architecture, I think it can be helpful to look at common practices that get in the way of our goals. It's often only through repeated mistakes that we can begin to embrace an alternate path.

The following examples are all generalizations of code I've actually written, and, while technically valid, each has lead to disaster and headache. Dispite my best intentions and the promise that this time would be different, these patterns consistently got me into trouble.

Modifying Components Based On Who Their Parents Are

In almost every site on the web there will be a particular visual element that looks exactly the same with each occurrence, except one. And when faced with this one-off situation almost every new CSS developer (and even experienced ones) handles it the same way. You figure out some unique parent for this one particular occurrence (or you create one), and you write a new rule to handle it.

.widget {
  background: yellow;
  border: 1px solid black;
  color: black;
  width: 50%;

#sidebar .widget {
  width: 200px;

body.homepage .widget {
  background: white;

At first this might seem like fairly harmless code, but let's examine it based on the goals established above.

First, the widget in the examle is not predictable. A developer who's made several of these widgets will expect it to look a certain way, yet when she uses it in the sidebar or on the homepage, it will look different, despite the markup being exactly the same.

It's also not very reusable or scalable. What happens when the way it looks on the homepage is requested on some other page? New rules will have to be added.

Lastly, it's not easily maintainable because if the widget were to get redesigned, it would have to get updated in several places in the CSS, and unlike the above example, the rules that commit this particular anti-pattern rarely appear right next to each other.

Imagine if this type of coding were done in any other language. You're essentially making a class definition, and then in another part of the code you're reaching into that class definition and changing it for a particular use case. This directly violates the open/closed principle of software development:

Software entities (classes, modules, functions, etc.) should be open for extension, but closed for modification.

Later in this article we'll look at how to modify components without relying on parent selectors.

Overly Complicated Selectors

Occasionally an article will make its way around the Internet showcasing the power of CSS selectors and proclaiming that you can style an entire site without using any classes or IDs.

While technically true, the more I develop with CSS, the more I stay away from complex selectors. The more complicated a selector the more coupled it is to the HTML. Relying on HTML tags and combinators keeps your HTML squeaky clean, but it makes your CSS gross and dirty.

#main-nav ul li ul li div { }
#content article h1:first-child { }
#sidebar > div > h3 + p { }

All of the above examples make logical sense. The first is probably styling a dropdown menu, the second says that the article's main heading should look different than all other h1 elements, and the last example is likely adding some extra spacing for the first paragraph in the sidebar sections.

If this HTML were never going to change, an argument could be made for its merits, but how realistic is it to assume the HTML will never change? Overly complicated selectors can be impressive and they can rid the HTML of all presentational hooks, but they rarely help us achieve our goals for good CSS architecture.

These examples above are not reusable at all. Since the selector is pointing to a very particular place in the markup, how could another component with a different HTML structure reuse those styles? Taking the first selector (the dropdown) as an example, what if a similar looking dropdown were needed on a different page and it wasn't inside of the #main-nav element? You'd have to recreate the entire style.

These selectors are also very unpredictable if the HTML needs to change. Imagine that a devloper wanted to change the div in the third example to the HTML5 section tag, the whole rule would break.

Finally, since these selectors only work when the HTML remains constant, they're by definition not maintainable or scalable.

In large applictaions you have to make trade-offs and compromises. The fragility of complex selectors are rarely worth the price in the name of keeping your HTML "clean".

Overly Generic Class Names

When creating reusable design components, it's very common to scope (as it were) the component's sub-elements inside the component's class name. For example:

<div class="widget">
  <h3 class="title">...</h3>
  <div class="contents">
    Lorem ipsum dolor sit amet, consectetur adipiscing elit.
    In condimentum justo et est dapibus sit amet euismod ligula ornare.
    Vivamus elementum accumsan dignissim.
    <button class="action">Click Me!</button>

.widget {}
.widget .title {}
.widget .contents {}
.widget .action {}

The idea is that the .title, .contents, and .action sub-element classes can be safely styled without having to worry about those styles spilling over to any other elements styled with the same classes. This is true, but it doesn't prevent the styling of classes with those same names from spilling into this component.

On a large project it's very likely that a class name like .title would get used in another context or even by itself. If that happened, the widget's title would suddenly look very different than intended.

Overly generic class names lead to very unpredictable CSS.

Making a Rule Do Too Much

Sometimes you make a visual component that needs to be 20 pixels from the top left corner of a section in your site:

.widget {
  position: absolute;
  top: 20px;
  left: 20px;
  background-color: red;
  font-size: 1.5em;
  text-transform: uppercase;

Then down the road you need to use this exact same component in a different location. The above CSS won't work because it's not reusable in different contexts.

The problem is that you're making this one selector do too much. You're defining the look and feel as well as the layout and position within the same rule. The look and feel is reusable but the layout and position is not. And since they're used together, the whole rule is compromised.

While this may seem harmless at first, it often leads to copying and pasting from less CSS-savvy developers. If a new team member wants something to look like a particular component, say an .infobox, they'll probably start by trying that class. But if that doesn't work because it positions that new infobox in an undesired way, what are they likely to do? In my experience, most new developers will not break the rule up into its reusable parts. Instead they'll simply copy and paste the lines of code needed for this particular instance into a new selector, unnecessarily duplicating code.

The Cause

All of the above bad practices share one similarity, they place far too much of the styling burden on the CSS.

That may seem like strange statement. After all, it is a stylesheet; shouldn't it bear most (if not all) of the styling burden? Isn't that what we want?

The simple answer to this question is "yes", but, as usual, things aren't always so simple. Separating content from presentation is a good thing, but just because your CSS is separate from your HTML doesn't mean your content is separate from your presentation. Put another way, striping all presentational code from your HTML doesn't fulfill the goal if your CSS requires an intimate knowledge of your HTML structure in order to work.

Furthermore, HTML is rarely just content; it's almost always structure too. And often that structure consists of container elements with no purpose other than to allow the CSS to isolate a certain group of elements. Even without presentational classes, this is still clearly presentation mixed into the HTML. But is it necessarily mixing presentation with content?

I believe, given the current state of HTML and CSS, it's necessary and often wise to have the HTML and CSS work together as a presentational layer. The content layer can still be abstracted away via templates and partials.

The Solution

If your HTML and your CSS are going to work together to form the presentation layer of a web application, they need to do so in a way that promotes all of the principles of good CSS architecture.

The best approach that I've found is for the CSS include as little HTML structure as possible. The CSS should define how a set of visual elements look and (in order to minimize coupling with the HTML) those elements should look as they're defined regardless of where they appear in the HTML. If a certain component needs to look different in a different scenario, it should be called something different and it's the HTML's responsibility to call it that.

As an example, the CSS might define a button component via the .button class. If the HTML wants a particular element to look like a button, it should use that class. If there's a situation were the button needs to look different (perhaps larger and full-width), then the CSS needs to define that look as well with a new class, and the HTML can include that new class to employ the new look.

The CSS defines what your components look like, and the HTML assigns those looks to the elements on the page. The less the CSS needs to know about the HTML structure the better.

A huge benefit of declaring exactly what you want in the HTML is it allows other developers to look at the markup and know exactly what the element is supposed to look like. The intent is obvious. Without this practice it's impossible to tell if the look of an element is intentional or accidental, and this leads to confusion on the team.

A common objection to putting a lot of classes in the markup is the extra effort required to do so. A single CSS rule could target a thousand instances of a particular component. Is it really worth writing that classes a thousand times just to have it explicitly declared in the markup?

While this concern is clearly valid, it can be misleading. The implication is that either you use a parent selector in the CSS or you write that HTML class 1000 times by hand, but there are obviously other alternatives. View level abstractions in Rails or other frameworks can go a long way toward keeping the visual look explicitly declared in the HTML without having to write the same class over and over again.

Best Practices

After making the above mistakes over and over again, and paying the consequences later on down the road, I've come up with the following bits of advice. While by no means comprehensive, my experience has shown that sticking to these principles will help you better achieve the goals of good CSS architecture.

Be intentional

The best way to make sure your selectors don't style unwanted elements is to not give them the opportunity. A selector like #main-nav ul li ul li div could very easily end up applying to unwanted elements as your markup changes down the road. A style like .subnav, on the other hand, will have absolutely no chance of accidentally applying to an unwanted element. Applying classes directly to the elements you want to style is the best way to keep your CSS predictable.

/* Grenade */
#main-nav ul li ul { }

/* Sniper Rifle */
.subnav { }

Given the two examples above, think of the first one like a grenade and the second like a sniper rifle. The grenade might work just fine today, but you never know when an innocent civilian could move inside the blast radius.

Separate your concerns

I've already mentioned that a well organized component layer can help loosen the coupling of HTML structure in the CSS. In addition to that, your CSS components themselves should be modular. Components should know how to style themselves and do that job well, but they should not be responsible for their layout or positioning nor should they make too many assumptions about how they'll be spaced in relation to surrounding elements.

In general, components should define how they look, but not their layout or position. Be careful when you see properties like background, color, and font in the same rule as position, width, height, and margin.

Layout and position should be handled by either a separate layout class or a separate container element. (Remember that to effectively separate content from presentation it's often essential to separate content from its container.)

Namespace your classes

We already examined why parent selectors aren't 100% effective at encapsulation and preventing style cross-contamination. A much better approach is applying namespaces to the classes themselves. If an element is a member of a visual component, every one of its sub-element classes should use the component's base class name as a namespace.

/* High risk of style cross-contamination */
.widget { }
.widget .title { }

/* Low risk of style cross-contamination */
.widget { }
.widget-title { }

Namespacing your classes keeps your components self-contained and modular. It minimizes the likelihood that an existing class will conflict, and it lowers the specificity required to style child elements.

Extend components with modifier classes

When an existing component needs to look slightly different in a certain context, create a modifier class to extend it.

/* Bad */
.widget { }
#sidebar .widget { }

/* Good */
.widget { }
.widget-sidebar { }

We've already seen the downsides of modifying components based on one of their parent elements, but to reiterate: A modifier class can be used anywhere. Location based overrides can only be used in a specific location. Modifier classes can also be reused as many times as you need. Lastly, modifier classes express the intention of the developer very clearly right in the HTML. Location based classes, on the other hand, are completely invisible to a developer only looking at the HTML, greatly increasing the probability that it will be overlooked.

Organize Your CSS Into a Logical Structure

Jonathan Snook, in his excellent book SMACSS, argues for organizing your CSS rules into four separate categories: base, layout, modules, and state. Base consists of reset rules and element defaults. Layout is for positioning of site-wide elements as well as generic layout helpers like grid systems. Modules are reusable visual elements, and state refers to styling that can be toggled on or off via JavaScript.

In the SMACSS system, modules (which are equivalent to what I call components) comprise the vast majority of all the CSS rules, so I often find it necessary to break them down even further into abstract templates.

Components are standalone visual elements. Templates, on the other hand, are building blocks. Templates don't stand on their own and rarely describe look and feel. Instead, they're single, repeatable patterns that can be put together to form a component.

To provide a concrete example, a component might be a modal dialog box. The modal might have the site's signature background gradient in the header, it might have a drop shadow around it, it might have a close button in the top right corner, and it might be positioned fixed and centered vertically and horizontally. Each of these four patterns might be used again and again all over the site, so you wouldn't want to have to recode those patterns each time. As such they're all templates, and together they comprise the modal component.

I typically don't use template classes in the HTML unless I have a good reason. Instead I use a preprocessor to include the template styles in the component definition. I'll discuss this and my rational for doing so in more detail later.

Use Classes For Styling And Styling Only

Anyone who has worked on a large project has come across an HTML element with a class whose purpose was completely unknown. You want to remove it, but you're hesitant because it may have some purpose that you're not aware of. As this happens again and again, over time, your HTML become filled with classes that serve no purpose just because team members are afraid to delete them.

The problem is that classes are generally given too many responsibilities in front-end web development. They style HTML elements, they act as JavaScript hooks, they're added to the HTML for feature detections, they're used in automated tests, etc.

This is a problem. When classes are used by too many parts of the application, it becomes very scary to remove them from your HTML.

However, with an established convention, this problem can be completely avoided. When you see a class in the HTML, you should be able to tell instantly what its purpose is. My recommendation is to give all non-styled classes a prefix. I use .js- for JavaScript and I use .supports- for Modernizr classes. All classes without a prefix are for styling and styling only.

This makes finding unused classes and removing them from the HTML as easy as searching the stylesheets directory. You can even automate this process in JavaScript by cross referencing the classes in the HTML with the classes in the document.styleSheets object. Classes that aren't in document.styleSheets can be safely removed.

In general, just as it's a best practice to separate your content from your presentation, it's also important to separate your presentation from your functionality. Using styled classes as JavaScript hooks deeply couples your CSS and JavaScript in a way that can make it hard or impossible to update the look of certain elements without breaking functionality.

Name your classes with a logical structure

These days most people write CSS with hyphens as word separators. But hyphens alone are usually not enough to distinguish between different types of classes.

Nicolas Gallagher recently wrote about his solution to this problem which I have also adopted (with slight changes) with great success. To illustrate the need for a naming convention consider the following:

/* A component */
.button-group { }

/* A component modifier (modifying .button) */
.button-primary { }

/* A component sub-object (lives within .button) */
.button-icon { }

/* Is this a component class or a layout class? */
.header { }

From looking at the above classes, it's impossible to tell what type of rule they apply to. This not only increases confusion during development, but it also makes it harder to test your CSS and HTML in an automated way. A structured naming convention allows you to look at a class name and know exactly what its relationship is to other classes and where it should appear in the HTML — making naming easier and testing possible where it previously was not.

/* Templates Rules (using Sass placeholders) */

/* Component Rules */

/* Layout Rules */

/* State Rules */

/* Non-styled JavaScript Hooks */

The first example redone:

/* A component */
.button-group { }

/* A component modifier (modifying .button) */
.button--primary { }

/* A component sub-object (lives within .button) */
.button__icon { }

/* A layout class */
.l-header { }


Maintaining an effective and well-organized CSS architecture can be very difficult, especially on large teams. A few bad rules here and there can snowball into an unmanageable mess. Once your application's CSS has entered into the realm of specificity wars and !important trumps, it can be next to impossible to recover without starting over. The key is to avoid those problems from the beginning.

Fortunately, there are tools that can make controlling your site's CSS architecture much easier.


These days it's impossible to talk about CSS tools without mentioning preprocessors, so this article won't be any different. But before I praise their usefulness, I should offer a few words of caution.

Preprocessors help you write CSS faster, not better. Ultimately it gets turned into plain CSS, and the same rules should apply. If a preprocessor lets you write your CSS faster then it also lets you write bad CSS faster, so it's important to understand good CSS architecture before thinking a preprocessor will solve your problems.

Many of the so-called "features" of preprocessors can actually be very bad for CSS architecture. The following are some of the "features" I avoid at all costs (and though the general ideas apply to all preprocessor languages, these guidelines apply specifically to Sass).

  • Never nest rules purely for code organization. Only nest when the outputted CSS is what you want.
  • Never use a mixin if you're not passing an argument. Mixins without arguments are much better used as templates which can be extended.
  • Never use @extend on a selector that isn't a single class. It doesn't make sense from a design perspective and it bloats the compiled CSS.
  • Never use @extend for UI components in component modifier rules because you lose the inheritance chain (more on this in a bit).

The best parts of preprocessors are functions like @extend and %placeholder. Both allow you to easily manage CSS abstraction without adding bloat to your CSS or a huge list of base classes in your HTML that can be very hard to manage.

@extend should be used with care though because sometime you want those classes in your HTML. For example, when you first learn about @extend it might be tempting to use it with all of your modifier classes like so:

.button {
  /* button styles */

/* Bad */
.button--primary {
  @extend .button;
  /* modification styles */

The problem with doing this is you lose the inheritance chain in the HTML. Now it's very difficult to select all button instances with JavaScript.

As a general rule, I never extend UI components or anything that I might want to know the type of later. This is what templates are for and another way to help distinguish between templates and components. A template is something you wouldn't ever need to target in your application logic, and therefore can be safely extended with a preprocessor.

Here's how it might look using the modal example referenced above:

.modal {
  @extend %dialog;
  @extend %drop-shadow;
  @extend %statically-centered;
  /* other modal styles */

.modal__close {
  @extend %dialog__close;
  /* other close button styles */

.modal__header {
  @extend %background-gradient;
  /* other modal header styles */

CSS Lint

Nicole Sullivan and Nicholas Zakas created CSS Lint as a code quality tool to help developers detect bad practices in their CSS. Their site describes it as such:

CSS Lint points out problems with your CSS code. It does basic syntax checking as well as applying a set of rules to the code that look for problematic patterns or signs of inefficiency. The [rules] are all pluggable, so you can easily write your own or omit ones you don't want.

While the general ruleset may not be perfect for most projects, the best feature of CSS Lint is its ability to be customized exactly how you want it. This means you can pick and choose the rules you want from their default list as well as write your own.

A tool like CSS Lint is essential for any large team to ensure at least a baseline of consistency and convention compliance. And like I hinted at previously, one of the great reasons for conventions is they allow for tools like CSS Lint to easily identify anything that breaks them.

Based on the conventions I've proposed above, it becomes very easy to write rules to detect particular antipatterns. Here are a few suggestions that I use:

  • Don't allow IDs in your selectors.
  • Don't use non-semantic type selectors (e.g. DIV, SPAN) in any multi-part rule.
  • Don't use more than 2 combinators in a selector.
  • Don't allow any class names that begin with "js-".
  • Warn if frequently using layout and positioning for non "l-" prefixed rules.
  • Warn if a class defined by itself is later redefined as a child of something else.

These are obviously just suggestions, but they're intended to get you thinking about how to enforce the standards you want on your projects.

HTML Inspector

Earlier I suggested that it would be easy to search your HTML classes and all linked stylesheets and warn if a class was used in the HTML but not defined in any stylesheet. I'm currently developing a tool called the HTML Inspector to make this process easier.

HTML Inspector traverses your HTML and (much like CSS Lint) allows you to write your own rules that throw errors and warnings when some convention is being broken. I currently use the following rules:

  • Warn if the same ID is used more than once on a page.
  • Don't use any classes that aren't mentioned in any stylesheet or pass a whitelist (like "js-" prefixed classes).
  • Modifer classes shouldn't be used without their base class.
  • Sub-object classes shouldn't be used when no ancestor contains the base class.
  • Plain old DIV or SPAN elements, without classes attached, should not be used in the HTML.


CSS isn't just visual design. Don't throw out programming best practices just because you're writing CSS. Concepts like OOP, DRY, the open/closed principle, separation of concerns, etc. still apply to CSS.

The bottom line is that whatever you do to organize your code, make sure you judge your methods by whether or not they actually help make your development easier and more maintainable in the long term.

About the Author

Philip Walton is a Front End Software Engineer at AppFolio. For more of this thoughts you can read his blog or follow him on Twitter: @philwalton