DCI: The King of the Single Responsibility Principle

The single responsibility principle (SRP) is one of the most widely followed ideals in object oriented programming. For decades, developers have been striving to ensure their classes take on just enough, but not too much, responsibility. A valiant effort and by far one of the best ways to produce maintainable code.

SRP is hard, though. Of all the SOLID design principles, it is the most difficult to embrace. Due to the abstract nature if its definition, based purely on example instead of directive process, it's hard to concretize. More specifically, it's difficult to define "responsibility", in general or in context. There are some rules-of-thumb to help, like reasons for change, but even these are enigmatic and hard to apply.

Simply put, SRP says a class should be comprised of just one responsibility, and only a single reason should force modification.

Let's take a look at the following class which clearly has three responsibilities and therefore breaks SRP:

class User < ActiveRecord::Base
  def check_in(location); ... end
  def solr_search(term); ... end
  def request_friendship(other_user); ... end
end

This class would require churn for a variety of reasons, some of which include:

1. The algorithm for checking in changes.
2. The fields used to search SOLR are renamed.
3. Additional information needs to be stored when requesting a friendship.

So, based on the rules of SRP, this class needs to be broken out into three different classes, each with their own responsibility. Awesome, we're done, right? This is often the extent of discussions around SRP, because it's extremely difficult to provide solutions beyond contrived, minute examples. Theoretically, SRP is very easy to follow. In practice, it's much more opaque. It's too pie in the sky for my taste; like most OOP principles, I think SRP should be more of a guideline than a hard-and-fast rule.

The real difficulty of SRP surfaces when your project grows beyond 100 lines of code. SRP is easy if you're satisfied with single method classes or decide to think about responsibility exclusively in terms of methods. In my opinion, neither of these are suitable options.

DCI provides more robust guidelines for following SRP, but we need to redefine responsibility. That's the focus of this article.

Identity and Responsibility

OK, let's try to elucidate responsibility, but first, let's talk about object orientation.

The word "object" can be defined as a resource that contains state, behavior and identity. Its state is the value of the class's attributes. Its behavior is the actions it can perform. And its identity is...well...it's object id? It feels strange to narrow the definition of identity into a mere number. I certainly don't identify myself by my social security number. My identity is derived from my name, the things I enjoy doing, and potentially my environment. More importantly, my identity is always changing.

While building a class, when was the last time you thought about the forthcoming object ids of the instances of that class? I hope never. We don't program classes with identity in mind, yet if we're trying to model the world, it's an intrinsic component. Identity means nothing to us while building classes, yet everything to us in the real world.

Therefore, it's appropriate to say that the mental model of the programmer is to map identity to state and behavior, rather than to object id. Object id is a quality of uniqueness.

Identity is closely related to responsibility. As expressed above, I don't identify by my social security number, but by my state and behavior. When we attempt to find the appropriate location for a method definition, we look at the responsibility of the prospective classes. "Does this method fit into this class's single responsibility?" If we consider that identity should truly be a representation of an object's state and behavior, we can deduce that identity is a derivative of responsibility.

An example of this observation is polymorphism; probably the most predominant and powerful object-oriented technique. When we consider the possible duck-typed objects in a scenario, we don't think, "this object will work if its object id is within the following set..." We think "this object will work if it responds to the right methods." We rarely care about the object id. What's important is the true identity of an object: the methods it responds to.

DCI Roles

Object ids mean nothing to programmers. Defining identity by the memory location of an object is very rarely a means for writing effective software. Computers care about memory locations, not programmers. Programmers care about state and behavior, hence, the reason we build classes.

SRP is about acutely defining state and behavior, thus, identity. In DCI, we define state through data objects and behavior through roles. Generally speaking, behavior changes state so the primary focus is on behavior. If we ask, "what can an object do?", we can subsequently ask, "how does its state change?"

We still haven't really defined responsibility. As a human being, my responsibilities change on a regular basis. At one point, I'm responsible for writing an email. At another, I'm responsible for mentoring a developer. Rarely, although occasionally, am I responsible for doing both. Enter role-based programming.

We can reprogram the example class above using DCI data objects and roles:

# Data
class User < ActiveRecord::Base; end

# Roles
module Customer
  def check_in(location); ... end
end

module Searcher
  def solr_search(term); ... end
end

module SocialButterfly
  def request_friendship(other_user); ... end
end

Now, each role has a single responsibility. If we define responsibility by a role played by a data object, it becomes obvious where new methods should go and when you're breaking responsibility. Let's give it a shot.

Say we want to add functionality that allows users to accept a requested friendship. First ask the question in terms of business objectives, "As a social butterfly, can I accept a friendship?" By converting our expectations into English, we can then easily map business rules to code. It would clearly be wrong if we ask, "As a searcher, can I accept a friendship?" Therefore, #accept_friendship should belong in SocialButterfly:

module SocialButterfly
  def request_friendship(other_user); ... end
  def accept_friendship(other_user); ... end
end

By defining responsibility as a role, we can converge on contextual identity, the true essence of object orientation. While building a role, we are building identity, a crucial part of a programmer's mental model. Roles are the inverse abstraction of classes. While classes focus on abstracting away identity, roles focus on highlighting it. It's no wonder it's so difficult to define responsibility when we're programming classes, not object.

The King of SRP

It's hard to define responsibility. It's even harder to program for it. As an artifact, the responsibility of a class often ends up being either too narrowly or too broadly defined. Define responsibility too narrowly, and it's daunting to wrap your head around 1000 classes. Define responsibility too broadly, and it's arduous to maintain and refactor.

By defining responsibility as a role, we have a clear notion of behavioral locality. We can ask questions like, "as a customer, can I add an item to my cart?" If the answer is yes and we've appropriately named our roles, the method belongs in that role. This gives us a means for defining responsibility, and we can refactor accordingly.

Roles won't alleviate potential clutter, but they can give us a structure for defining responsibility. With DCI, we can talk about responsibility in terms of directive process instead of contrived examples.

First, understand the business objectives of the system and subsequently understand the roles. Understand the roles and subsequently understand the responsibilities.

Posted by Mike Pack on 03/12/2013 at 08:28AM

Tags: srp, solid, oop, dci


DCI: The King of the Open/Closed Principle

The open/closed principle (OCP) is a fundamental "run of thumb" in object-oriented languages. It has hands in proper inheritance, polymorphism, and encapsulation amongst other core properties of object-oriented programming.

The open/closed principle says that we should refine classes to the point at which we eliminate churn. In other words, the less times we need to open a file for modification, the better. With DCI, we can compose objects while still following OCP.

Extension is Inheritance

Wikipedia's definition of inheritance:

Inheritance is a way to reuse code of existing objects, or to establish a subtype from an existing object, or both, depending upon programming language support.

By using #extend to modify objects at runtime, we are both reusing code from data objects while also forming a new subtype of the data object.

A typical DCI context might look like this:

app/use_cases/customer_purchases_book.rb

customer = User.find(1) # customer is a data object
customer.extend(Customer) # inject the Customer role
customer.purchase(book) # invoke Customer#purchase

After calling #extend, the user object can be used as both a data object and a purchasing customer. The #purchase method likely uses attributes of the user object to create joins between him and his book. We're reusing code from its former self.

Similarly, the new object is now a subtype of its former self. That is, the Customer version of user can be used polymorphically in place of the data object itself.

The Open/Closed Principle (OCP)

The open/closed principle is often discussed in the context of inheritance; we use inheritance to adhere to the "closed" aspect of OCP. In order to follow OCP, a class can be open for extension, but closed for modification. Let's look at how the principle could be applied with classical inheritance to reimplement the above scenario.

We have a dumb data object:

class User
  # A dumb data object
end

To abide by the "closed" aspect of OCP, we define a subtype of the User class; we do not modify the class itself:

class Customer < User
  def purchase(book)
    # Update the system to record purchase
  end
end

Somewhere else in our codebase, we tell a user to purchase his book:

customer = Customer.new
customer.purchase(book)

This is great, we've accomplished OCP by ensuring that any customer related aspects of a User are neatly tucked away in the Customer class. In order to change the behavior of a user, we formed a new class while leaving the User class alone.

This is the guts of the open/closed principle. We want to structure our classes in such a way as to ensure they never need to change. Guaranteeing the classes don't change is also a function of the method bodies.

DCI and OCP

Following OCP without incorporating the Data, Context and Interaction architecture has proven to lead to looser coupling, stronger encapsulation, and higher cohesion. Just apply it and your world will be rainbows and ponies!

Wrong. While OCP has absolutely helped in producing higher quality code, it's just another lofty object oriented principle. It's very difficult to adhere to all principles, and some may be entirely inappropriate in various scenarios. The SOLID principles (of which OCP is one) are a great frame-of-reference when discussing software design, however, heeding to them 100% of the time is frankly, impossible.

I often find it very difficult to ensure the first iteration of my core software, test suite, and ancillary code meet the qualifications of the SOLID principles. Not because I don't understand or refuse to apply them, but because I'm human and I'm working with frequently-varying business rules. Principles in general end up being this pie-in-the-sky goal; I prefer to just write software.

One of the reasons I love DCI so much is because it forces you to work in an orthogonal way. It breaks the cemented programming models we've seen for over 20 years. Models which, in my opinion, do not lend themselves towards these principles. DCI acts much like a lighthouse: guiding you towards proper object orientation.

DCI enables you to automatically apply many best practice principles in object oriented programming. The open/closed principle is one.

Closed for Modification

The whole point of DCI is to decouple what changes from what remains constant. In DCI, our data objects are strictly persistence related, and as such, do not change frequently. The way in which we use data objects is often what changes.

So, when we build out a data object...

app/models/user.rb

class User < ActiveRecord::Base
  # A dumb data object
end

...it's closed for business.

DCI tells us that if we want to add behavior to this class, we should be doing so within a role. A deliberate effect of this is that our class remains closed. OCP is telling us to optimize our classes so that we never need to modify them. This aspect of OCP is baked into the core of DCI.

Open for Extension

The name says everything. The best way to accomplish DCI in Ruby is to use #extend. We seek to inject roles into objects at runtime to accomplish our behavioral needs. Let's create our Customer role:

app/roles/customer.rb

module Customer
  def purchase(book)
    # Update the system to record purchase
  end
end

We would then join our data and roles within a context:

app/use_cases/customer_purchases_book.rb

class CustomerPurchasesBook
  def initialize(user, book)
    @customer, @book = user, book
    @customer.extend(Customer)
  end

  def call
    @customer.purchase(@book)
  end
end

The open/closed principle states that a class should be open for extension. Within the above context, we extend our user object with the Customer role. Our DCI code adheres to this rule.

OCP talks a lot about extension of classes via inheritance. Demonstrations of OCP are usually forged with classes, instead of objects. In the above paragraph, I say that classes are open for extension, but the user object is extended. When we define a class, it's simply a container in which methods live. That container then becomes part of an object's lookup hierarchy. So, behaviorally speaking, there's no semantic difference between composing an object from scratch with DCI and creating an instance of a class.

In the customer example above, we use #extend as a means of composing the customer object to include its necessary behavior. We do this in lieu of classical inheritance. As I mentioned earlier in this article, extension is inheritance.

The Silver Bullet

By applying DCI, you are ever-so-nicely nudged into following OCP. DCI is a paradigm shift, but it's coated with reward. By simply working in objects and extending them at runtime, you are guided towards many well-respected, object-oriented principles. The strong emphasis DCI puts on decoupling static classes from dynamic behavior means that your classes remain closed for modification.

DCI contexts are naturally built for OCP. Use cases rarely change. If a user is buying a book, the use case of that purchase remains relatively constant. Since contexts act as simple glue between data and roles, if a use case changes, it's likely to be a new context. In this regard, contexts remain closed for modification.

DCI won't help you properly construct your roles, but it does guide you in the right direction. Since roles are actor-based, their methods tend to be use case specific. This means that role methods don't need to accomodate for drastic variations. If variation increases, I tend to reach for service objects to abstract that complexity.

There is no silver bullet to following object-oriented principles. We're always making tradeoffs. Managing complexity is inherently complex. DCI can help you cope by ensuring your objects remain open for extension, yet closed for modification.

Posted by Mike Pack on 12/18/2012 at 08:14AM

Tags: ocp, solid, oop, dci


The First Step to Applying Design Patterns: Don't

Design patterns are awesome. The more we build software with them in mind, the better off we'll be as a community. They can help us elegantly construct solutions which can be readily discussed with peers. They're common solutions to common problems. They're not just common solutions, however. They're battle tested, proven, performant and generally considered "the best" solution. Design patterns are the apotheosis, the epitome, of solution.

In this article, I'll look at varying levels of design pattern application, starting from worse to better, and ultimately landing on what I would consider the utopia of software engineering. The ideas in this article are largely derived from what I've observed, devoted and reasoned about.

Working from Scratch

Personally, one of the most compelling exercises in software engineering is exploration. Just like in any other engineering field, the problem set expands indefinitely, and thusly, our solution set. As businesses strive to keep a competitive edge, engineers must continue to solve problems which are both new and challenging. Through the process of solving new problems, we manage to come up with some not-so-pleasant-to-work-with solutions. Doing so is natural and healthy and is just about the only way we can continue to improve, especially when first learning. In fact, we're going to create one of those not-so-good solutions right now.

Take, for example, a simple arithmetic problem:

1 + 1 = 2

Imagine modern programming languages didn't have a + operator. Knowing the result is 2, how would you prove the Left Hand Side (1 + 1)? Well, let's briefly explore one option. For all intents and purposes, the following could be written in pseudocodde. It's not the running code that matters, it's the exploration process which invokes an active mind.

Let's stick to Ruby idioms and define a class, with a method, +:

class Fixnum
  def +(other)
    if self.value == 1 and other.value == 1
      2
    end
  end
end

Ruby's + oprator aids in this process, but let's call the + method directly:

1.+(1) #=> should == 2

Nothing tricky going on here. What if we want to evaluate 1 + 2? The most obvious thing is to add some conditional branching to our + method:

class Fixnum
  def +(other)
    if self.value == 1 and other.value == 1
      2
    elsif self.value == 1 and other.value == 2
      3
    end
  end
end

This is where our solution starts to fall apart. While this would work with a minimal set of operands, as our set grows, our conditional logic grows linearly, if not exponentially. At this point in the exploration process, we probably want to reconsider our solution. It's easy to recognize this first iteration is heading down the wrong path. Given some background in computer science, you might try refactoring this solution to use binary instead.

Feel free to skip the following code, it's not the destination that matters, but the journey by which we got there. Here's our final binary addition code:

class BinaryPlus
  def initialize(first, second)
    @first, @second = first, second
    # to_s accepts a base to convert to. In this case, base 2.
    @first_bin  = @first.to_s(2)
    @second_bin = @second.to_s(2)
    normalize
  end

  def +
    carry = '0'
    result_bin = ''

    @max_size.times do |i|
      # We want to work in reverse, from the rightmost bit
      index = @max_size - i - 1
      first_bit, second_bit = @first_bin[index], @second_bin[index]

      if first_bit == '1' and second_bit == '1'
        result_bin << carry
        carry = '1'
      else
        if first_bit == '1' or second_bit == '1'
          if carry == '1'
            result_bin << '0'
            # carry remains 1
          else
            result_bin << '1'
            carry = '0'
          end
        else
          result_bin << carry
          carry = '0'
        end
      end
    end

    # Is there still a carry hangin' around?
    result_bin << '1' if carry == '1'

    result_bin.reverse.to_i(2)
  end

  private

  def normalize
    # We want both binary numbers to have the same length
    @max_size   = @first_bin.size < @second_bin.size ? @second_bin.size : @first_bin.size
    @first_bin  = @first_bin.rjust(@max_size, '0')
    @second_bin = @second_bin.rjust(@max_size, '0')
  end
end

For which we would call with:

BinaryPlus.new(3, 4).+ #=> should == 7

At this point, we've managed to weave our way through a forest of solutions to land on one that doesn't require us to change the code to accommodate new operands. Aside from increasing the maintainability of the code, going through this process has likely taught us a few things about doing basic arithmetic in Ruby:

  • The underlying principles are not as simple as the syntax leads us to believe.
  • Considering potential operands is likely something we should do before writing code (TDD).
  • Ruby has built-in methods for base conversion.
  • (The list goes on depending on the explorer.)

This process is both fruitful and enlightening. It's one of beauty and purity. Only by actually solving a problem can we truly say we've conquered it. This is the sensation I seek every day. That of utter accomplishment. This is software engineering, and only through time can be become better at finding maintainable solutions.

There's a catch. I'm not the best problem solver in the world, and neither are you. Individually, we simply can't grasp the vast landscape of problems, much less solve them all enough times that we can confidently say we have the best solution. Collectively, we all strive for the best solutions and combine our results. It's called the Gang of Four, not the Gang of One.

Aside: For those interested in the actual source of Fixnum#+, check out the Ruby source. Also, more on binary arithmetic.

Applying Design Patterns

We live in a beautiful age where all problems are already solved. We can thank Leonard Euler, Carl Guass and Isaac Newton for advanced mathematics and forming the foundation of computer science. We can thank Ewald Christian von Kleist, Benjamin Franklin and Alessandro Volta for their work in electricity so we can program on the airplane. We can thank Alan Turing and Donald Knuth for modern computer science and Dennis Ritchie for C. We can thank Matz for Ruby. And we can thank the Gang of Four for design patterns.

Design patterns help us do one thing really well: think and speak in the abstract. Given a problem with input i1, i2 and i3, design patterns can help us elegantly solve such a problem by correct association of i1, i2 and i3. They're generic solutions to generic problems. By its very definition, a (software) pattern is a theme of recurring solutions. The primary benefit of using patterns is we can circumvent a large degree of work. We no longer have to reinvent the "undo" button, the command pattern has already been discussed and documented.

It's very easy to apply design patterns. All you have to do is know they exist. If I know the factory pattern exists and it's a tried-and-true technique of generating objects, all I have to do is research the pattern and follow the steps. With the resources available to us today, we can read about common problems and their resolutions with a single google. There's really no excuse for not applying design patterns at work every day. They can drastically simplify code, increase modularity, increase legibility, decrease duplication, improve translation to English, and the list goes on.

If I had to draw a conclusion right now, I would say "use design patterns." But I don't, so I would rather say something a little more robust.

Design patterns can be extremely helpful in crafting beautiful code, but the way in which they're applied often determines their usefulness. Applying a design pattern in the wrong scenario can push you into a corner, ultimately leading to more disarray than would have been present if it weren't for the design pattern. I'm going to pick on the singleton pattern a bit.

Singletons get a bad rep. In my opinion, rightfully so. Let's look at a situation where "applying a design pattern" can be discouraging.

We only have one file system, right? Naively, I'm thinking, "I know the singleton pattern, that would be a great fit here!" Let's create a file system singleton that writes some text to /dev/null:

require 'singleton'

class DevNullSingleton
  include Singleton

  def write(text)
    File.open('/dev/null', 'w') do |file|
      file.write text
    end
  end
end

We can use the singleton by referencing its instance:

DevNullSingleton.instance.write('Something to dev null')

Realistically, this has the same semantics as setting a global constant if we didn't want to use Ruby's singleton library:

DEV_NULL = DevNullSingleton.new
# ... later in the code ...
DEV_NULL.write('Something to dev null')

So, now our application grows, and we need another file system writer that outputs to /tmp. We're posed with a few options.

We can rename our singleton and allow the #write method to accept a path. The API looks like this:

FileSystemSingleton.instance.write('/dev/null', 'Something to dev null')
FileSystemSingleton.instance.write('/tmp', 'Something to tmp')

This is bad. If we want to write numerous things to /dev/null, we have a large degree of duplication:

FileSystemSingleton.instance.write('/dev/null', 'Something to dev null')
FileSystemSingleton.instance.write('/dev/null', 'Something ele to dev null')
FileSystemSingleton.instance.write('/dev/null', 'Another thing to dev null')

Alternatively, we can create a new singleton class that writes to /tmp:

class TmpSingleton
  include Singleton

  def write(text)
    # ...
  end
end

But now, every time we want to write to a different location on the file system, we need to create a new singleton class. Not great, either.

Probably, the better option is to break ties with the singleton and start instantiating classes normally:

class FileSystem
  def initialize(path)
    @path = path
  end

  def write(text)
    File.open(@path, 'w') do |file|
      file.write text
    end
  end
end

Now, when we want to write multiple times to /dev/null, we instantiate only once and use it as we would any other class:

dev_null = FileSystem.new('/dev/null')
dev_null.write('Something to dev null')
dev_null.write('Something ele to dev null')
dev_null.write('Another thing to dev null')

Here's My Gripe

I don't have anything against the singleton pattern, per se. I have issues with the process by which it's applied. In a number of cases, I've seen design patterns applied in the following steps:

  1. Read up on design patterns.
  2. Think in terms of design patterns.
  3. Apply design patterns.

It's really awesome to read as much as possible, but things start to fall apart around Step 2. If design patterns become the only lens by which you see your software, you'll inevitably end up pigeonholed like the singleton situation above.

Don't name your designs after patterns. This often happens because early in the design process you say, "I can use a singleton here!" So you go about defining classes as you're elaborating the design. Early, it makes sense to name something "FileSystemSingleton" so you can follow the design as it's being built. It acts as a form of documentation. However, it does that, and only that. "FileSystemSingleton" is no more descriptive or expressive than "FileSystem." In fact, it just adds noise. If you name something "BubbleSortStrategy" to denote the strategy pattern, but later compositionally apply subsequent "strategies", is it still technically a strategy? Is it a component of an overall strategy? Drop the "Strategy" and just call it "BubbleSort." That way, no matter whether your design is in fact the strategy pattern, a derivation thereof, or something completely different, it doesn't add clutter or confusion.

Don't design around patterns. Although it would be nice, we can't trust design patterns as the correct solution. For a majority of patterns, I would speculate that only a small amount of problems fit directly in the mold. In the above example, the singleton is not what we ultimately needed. If we hadn't been thinking "singleton, singleton, singleton" early in the design process, we probably wouldn't have ended up with that design. If we had taken a TDD approach to building out the file system writer, we would have likely just ended up with a normal Ruby class, no singletons involved. As software grows and changes, don't get pigeonholed by a design pattern.

Learning from Design Patterns

In the previous section on Applying Design Patterns, I said that all problems have been solved. This is, of course, not true. One of my primary fuel sources is solving problems that neither I've solved nor have I seen solved. That's not to say they haven't been solved, however. My problems are not unique snowflakes. The difference between normal problems and problems which can be readily solved with design patterns is a matter of exposure. We're not exposed to problems for which we've never seen, and therefore we do not readily have a solution. We must compose our own.

When I encounter new problems, I never think in terms of design patterns. I often think in terms of domain. My utopic engineering process consists of a boundless array of knowledge from which I comprise my own solution. I don't rely on one tool, methodology, or process to drive my software. I consume the problem and attempt to make educated decisions. This is the "engineering" part of software engineering. It's not the languages you know, the frameworks you use, or how retina-enabled your computer is. It's your ability to become completely engulfed in a problem, enough to sense its anatomy.

Take, for example, a recent project of mine: Pipes. Pipes evolved organically through deep discussion around the domain. Why does the probem exist, what are the currently known solutions, and how can we derive the best possible outcome? The question of "what design pattern should we use" never arrose. Design patterns should always be at the forefront of the discussion, however. Some of the architectural motivation was taken from the pipeline processing pattern. Studying the pipeline processing pattern evoked new ideas for which to draw transient conclusions. Ultimately, it was the exploration process combined with studying the pipeline processing pattern that lead to a solution I was happy to write home about.

Be a part of the exploration process. Discover how your solution fits into your domain, and your domain into your problem. It's more time consuming than jumping to a cookie-cutter solution, but it's lightyears more glorifying. The exploration process is what leads to interesting and eloquent implementations; ones that can be easily changed, apply to the domain, and have a dash of humanism. No matter how you program, being cognizant of design patterns is always desirable. Learning as much as possible and having varying perspectives is crucial.

Create your own design patterns. Solve problems how you would solve them, not how the Gang of Four solves them. Stay as well-informed as you can on known solutions and reflect on them regularly. Use design patterns as inspiration for better, more applicable solutions to your specific problem. Do not blindly apply them. Think first, then consider design patterns. This is software engineering.

Posted by Mike Pack on 10/02/2012 at 09:10AM

Tags: design patterns, ruby