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There are some selfies being taken, but we really need to get started again, as we're on a tight schedule.
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Remember, otherwise I will take away your lunch break. And everybody's like, 'What's lunch break?'
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I need to take a picture with Mats. Okay, so we will have two talks, and they will be great.
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Then there will be a lunch break, which means you need to go outside. I've also been told that people should come on time.
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Schedule silence—I have no authority on this stage. Do I? No authority.
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I love that! Yes, yes.
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All right, we have to talk about the schedule. So, there will be a lunch break, as long as you cooperate with me.
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If you don't, you know the consequences. I have been told there will be coffee and such, whatever 'such' means, but I'm guessing it includes food and snacks in the next break.
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So, that's happening, and we need to do some technical stuff here. I'm just going to announce the next speaker right away to stay on schedule.
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Breaks are for talking, while conference room attendance is for not talking and for listening. Again, thank you to my kind volunteers in the audience for the clapping, it works really well.
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So, our next speaker is originally from Canada and now lives in Japan. He is the author of Mobility and an open-source contributor.
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He is also very active in the Japanese Ruby community, and he will be teaching us all about metaprogramming, which is the machinery that drives Ruby.
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We will be learning about writing modular, extensible code and tackling real hard problems. His goal is that we all walk away from here with a conceptual understanding to be more generalists.
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Because, according to Chris, that's what we need more of in this community. So, give it up for Chris!
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Look, okay, everybody, my name is Chris Salzberg. The title of my talk is 'Metaprogramming for Generalists.'
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I apologize—it's the first morning of the first day, and we're starting with metaprogramming. But hey, don't worry, it'll be interesting!
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Some quick points about me: my handle is 'ChillYama,' if you've seen that name around. I live in Tokyo, Japan.
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The background picture is from my neighborhood. Don't be fooled; I'm not Japanese—in case that wasn't obvious. I'm Canadian, originally from Montreal.
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I work at a company called Edgeago. We have a booth outside, so check us out.
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In the open-source world, I am the author of a few gems, with Mobility being the most well-known, which is for translating model data.
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I also write about things like the module builder pattern on my blog, Digimon.com. Okay, enough about me.
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This is a two-part talk. In the first part, I'm going to present a new way of thinking about metaprogramming that I think will be more relevant and meaningful for solving generic problems.
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In the second part, we're going to solve a generic problem and build what I'm going to refer to as generic software.
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So, talking about generalist metaprogramming, it's Ruby's 25th birthday, and I think more than any other area, metaprogramming is where Ruby truly stands out among programming languages.
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It's something that Ruby really embraces, and it underlies all the aspects of Ruby that Matz just mentioned—the human-like syntax, the DSLs, and the nice use of it.
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However, we still have a hard time defining this term. Programmers, in general, struggle with this concept.
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A basic definition states that if programming is writing code, then metaprogramming is writing code that writes code.
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A second definition, from Wikipedia, describes it as a technique in which computer programs can treat other programs as data, also known as reflection.
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It's a bit broader and also incorporates introspection, which is a way for a program to find out about itself or other programs.
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By the end of the day, though, people can't seem to agree, and we tend to throw our hands up in the air and refer to it as a 'bag of tricks.'
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This quote is from the book 'Metaprogramming Ruby,' which I highly recommend. What are those tricks? They include techniques such as monkey-patching, yield, self, and others.
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In practice, however, we often use a different working definition that identifies certain Ruby methods as metaprogramming methods.
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The classic one is 'evil', but also methods like 'define_method,' 'method_missing,' 'instance_eval,' and even 'send'—depending on where you draw your boundaries.
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This is what I will call the 'what' view of metaprogramming. It describes these methods and how to use them.
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However, I don’t want to focus on that today. If we zoom out on that view, we start to see a problem.
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This is my concern: how we see metaprogramming as a community. We often view it as a niche, fringe topic.
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It's often seen as scary, magical, or irrelevant to many of us. However, for another part of the community, which includes myself, this is not how we see it.
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Metaprogramming is central to Ruby and what we do with it. I believe this divide exists because, as I said, the materials out there do not answer the fundamental question: What is metaprogramming for?
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So that's what I want to tackle today. To do this, we're going to consider the role that metaprogramming plays in our ecosystem.
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We start with fire. We put Ruby in the fire, and Matz is feeling the heat.
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Down here, we have the Ruby committers building this language we love. Up here, we have the applications, where most of us spend our time.
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We are building things that pay the bills for us, using Ruby. We understand how to use it, but we don't always know its internals or care about them—unless something goes wrong.
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However, this picture is incomplete. I'm missing a layer here, and that layer is the libraries.
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I will use the terms 'libraries' and 'gems' interchangeably here. Libraries fill the gaps that multiple applications encounter, which the programming language itself does not provide.
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In Ruby, these libraries blend almost seamlessly into the programming language itself. ActiveSupport is a classic example of that.
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So what does this have to do with metaprogramming? The point here is that metaprogramming overwhelmingly lives in this middle layer.
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A classic example is ActiveRecord, where all your attribute methods and association methods are created using metaprogramming.
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However, it is not just Rails or ActiveRecord; many gems use metaprogramming significantly. When you arrive at the application layer, you don't see all of this very clearly.
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You might notice monkey-patching here and there, but you don’t often see a lot of metaprogramming in action.
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This leads to an important question: Why?
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Why is metaprogramming so prevalent in our libraries but virtually non-existent in our application code?
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I think that this is an important question that hasn't really been addressed, and it might seem obvious, but the answer is more subtle.
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To explore this, we need to consider another big word in my talk title: generalization.
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Generalization is what libraries do; they generalize problems faced by more than one application into generic components that we can use in all our applications.
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Here’s a definition from Wikipedia: Formulation of general concepts from specific instances by abstracting common properties.
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We will dive into the details of abstracting common properties in the second part of this talk, but for now, I want to focus on this: What role does metaprogramming play in developing general concepts?
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Because general concepts are what our libraries or gems implement for us.
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To illustrate this, we need a general concept. I'm going to pick an example that I believe is relevant for everyone here: the concept of attributes.
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To understand the role metaprogramming plays in the concept of attributes, let’s take a cue from genetics.
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If there are any biologists in the room, I apologize in advance. In genetics, to understand what role a gene plays in a mouse's DNA, one technique is to conduct a knockout experiment.
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You knock out the gene and then observe the differences between the mouse with the gene and the mouse without it.
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So, let’s do the same with the concept of attributes by removing metaprogramming and observing the results.
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This is our library example. The library gem has a single class called Model, which has an initializer that sets a blank hash of attributes.
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It has a single class method called 'define_attribute,' which takes an attribute name and creates both a setter and a getter. The getter fetches the value for that key in the hash while the setter sets the value.
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This is a very simple setup. Now, we’ll switch to the application perspective implementing this simple library.
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We create a class called Talk, which inherits from Model, and we call 'define_attribute' twice for two attributes: title and abstract.
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We create a talk, set the title, and retrieve it back. Similarly, we set the abstract and get it back. This is very simple stuff.
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The key point here is that the abstraction is transparent. It's invisible. In our example, we use the word 'attribute' when defining the attributes, but once they are defined, we don't see that abstraction at work.
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Now, let’s switch to the knockout case, which is without metaprogramming.
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In this scenario, we have the same class Model; it initializes the same way with a blank hash, but we can no longer define methods dynamically.
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So instead, the only thing we can do is define two instance methods: get_attribute and set_attribute.
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They perform the same functions as before but require the name of the attribute to be passed in to fetch or set the value from the hash.
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This forms our library example without metaprogramming. Now, if we check the application that implements this library, it again inherits from Model.
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We still create a talk, but since we can't define the attributes dynamically, we allow users to set or get anything they want.
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In this case, we create a talk and call 'set_attribute', passing the name of the attribute 'title' and its value, and we can get it back the same way.
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I want to highlight that this example is really simple, but there are significant subtleties. The abstraction is now visible; the abstraction is literally the term 'attribute.'
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When you want to set or get an attribute, you need to call 'set_attribute' or 'get_attribute'. The words 'set_attribute' and 'get_attribute' are right there.
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As for the attribute names themselves, they become second-class citizens, as we're passing them as arguments to these methods.
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Now, let's summarize the results of our little experiment between the application and library when comparing the cases with and without metaprogramming.
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In our control case—with metaprogramming—in the application, the abstraction was invisible. We simply call attributes directly without thinking about the abstraction involved.
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In this situation, we can say that the library speaks in the language of the domain, which is Talk, Title, and Abstract.
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However, in the knockout case—without metaprogramming—the abstractions become visible. You have to specifically call out those attributes when interacting with them.
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Here, the library speaks in the language of the abstraction, making it clear you have to work with the concept of attributes explicitly.
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Now, turning to the library perspective, the comparison was simple. Our library was really basic in its implementation.
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But in the knockout case without metaprogramming, the unknowns cannot refer to code; therefore, we must pass the name into the getter method, which then passes it to the hash.
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In general, this makes the knockout case without metaprogramming more manageable to understand, given there is no hidden abstraction.
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But if that's the case, why not eliminate metaprogramming entirely? Why would libraries and gems employ it when it arguably complicates things?
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The context for this question isn't straightforward. You could implement something like ActiveRecord without metaprogramming, but it would look like a mess.
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You would have to call 'get_attribute' and 'get_association' for every interaction, which is cumbersome and obscures the domain language.
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For the application developer, these variables become known. You can define them without metaprogramming. However, for a library, they are essentially unknown.
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This is where metaprogramming becomes invaluable for gem authors. It allows you to speak the language of the domain effectively.
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Metaprogramming levels the playing field by enabling libraries to translate unknowns into code.
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This perspective on metaprogramming is meaningful and actionable: it provides a working framework to understand it.
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For instance, the unknowns at the library level deal with column names and tables, while the unknowns at the application level tend to be user data.
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You, as an application developer, don't know what kind of user input will come through. You may not want to translate user input into code.
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This clarifies the divide between applications and libraries: for libraries, metaprogramming serves as an enabler, while for applications, it can be viewed as an obstacle.
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Moreover, we can see issues with our working definition of metaprogramming as a collection of methods.
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Our assumption was that using any of these methods qualifies as metaprogramming, but I want to illustrate why this assumption can lead to misunderstandings.
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Let's examine the use of 'eval': if we write 'eval foo,' our understanding of metaprogramming implies it is metaprogramming.
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But what does this evaluate to? It simply calls the method for the variable 'foo' without translating unknowns into code.
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'eval' may seem like metaprogramming, but it falls into a category of its own known as automatic programming.
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Now consider the case when we append a string to 'foo': if we see 'foo = #{foo}', that still doesn’t change the situation.
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The only way to make something unknown is to wrap it in a method; we name this method 'foo_gal'. It takes a string and appends it to foo.
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This method still earns the title of metaprogramming as it is translating unknowns into code.
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Using 'defined_writer' is similar to the second half of 'define_attribute.' Here, we create a method with 'defined_method'.
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While its use of 'define_method' qualifies it as metaprogramming according to the original working definition, it does not actually translate unknowns into code.
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Thus, we need a definition that differentiates the context of whether the attribute name is known or unknown.
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For example, if we use 'auto_writer,' by passing a string or a symbol, those are generally known values.
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In contrast, if we pass in an unknown value, it would accurately qualify as metaprogramming. So we can visualize these results in terms of our definitions.
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The original 'what' definition encompasses numerous metaprogramming methods, and on the other side, the knockout does not.
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As we introduce additional data points, we see conflicts, such as the eval example where it shouldn’t belong here.
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Thus, we will establish a new definition grounded in the idea of 'how' instead of merely 'what'.
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Therefore, we define metaprogramming based on its capacity to convert unknowns into code.
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This revised definition will considerably enrich our understanding of metaprogramming.
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Now we can explore how to apply this new understanding and how libraries can use metaprogramming to solve generic problems.
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The second half of my talk is about generic software. This concept isn't my own; it comes from Jeremy Evans, the author of several gems including the ORM 'Sequel.'
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In a talk from 2012 about the development of Sequel, he mentioned that one of the best ways to write flexible software is by writing generic software.
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Instead of designing a single API that completely handles a specific case, you can create multiple APIs that handle smaller, more generic parts of that use case.
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Then handling the entire case becomes simply a matter of gluing those pieces together.
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Now, let's build an example to illustrate this concept—one about equality. In Ruby, there are several kinds of equality.
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We will use double equals equality, which checks if two objects with similar content are equal.
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For instance, two strings that have the same characters will be considered equal.
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To provide an example, I didn't go to Sequel this time; instead, we are going to Rails.
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After searching through Rails code, we found a test file with two classes intertwined with our topic.
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The first class is called Address, where an 'add_reader' method takes street, city, and country.
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In addition, there's an equals method that compares the street, city, and country attributes of two Address objects.
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The other class, GPSLocation, captures latitude and longitude, with its own equality check.
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Now, let’s align these two classes to extract similarities that will help us build our generic solution.
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The differences are trivial, so we will focus on code that checks equality by comparing the attributes.
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We can create an array of keys representing attributes in both classes and then utilize the all method to verify equality.
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Next, we will create a method called 'equalize,' distinguishing keys as our unknowns across the classes.
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The use of defined_method allows us to dynamic create methods based on our array of keys.
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This, however, falls under the previous working definition of metaprogramming, as it converts unknowns into functionalities.
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No change occurs aside from our extraction of commonality using the module called Equalizer.
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We have successfully derived a gem that allows us to call equality checks for both classes through a unified, generic interface.
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This method now provides a more efficient approach and makes our code reusable across different objects with minimal adjustments.
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We can build on this abstraction further, creating methods for inspecting attributes, returning and checking hashes associated with attributes.
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This modular approach allows for easy further development and enhancements to our applications, striking a balance of performance and readability.
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Lastly, what I want to stress is that all of the concepts we've talked about fall under the broader umbrella of generic software.
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Dry Equalizer is a gem built upon these principles, implicating that we have already implemented much of this work before.
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In addition, taking a glance at the DryRB ecosystem demonstrates how these components harmoniously interact.
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As we reach the end of this talk, let's return to the title, 'Metaprogramming for Generalists.'
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The term generalist often refers to someone with broad knowledge across fields, but I defined it differently.
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In my interpretation, a generalist is someone willing to dig deeply into core concepts that address various problems—like attributes and equality.
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To summarize, we need more community members to become these generalists who can build and share generic software.
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By solving larger, generic problems, we make specific instances easier to handle.
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This is the magic of metaprogramming—the metaphor that forms the backbone of the Ruby ecosystem.
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That's what makes Ruby the powerful language that it is.
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Thank you!
