This is a continuation of my previous posts describing layers of code written in different programming languages. I have thought about the things involved for a while, and had several discussions with people about it. There were some parts that I didn’t describe as well as I thought in my posts, and I will try to do better in this one.
The core of these ideas are based on polyglot programming, the thinking that you should use several different languages in a project, based on which languages are better suited for different parts of it. Another term for this concept is Language-oriented programming. So how do you organize a polyglot system? The most natural way for me is to divide it into layers. In most cases you will find that different categories of languages will be better suited to different layers of the application.
In my original post I identified three layers that can be used to organize polyglot systems. These layers are the stable layer, the dynamic layer, and the domain layer. There are several reasons for organizing them this way, and I’ll take a harder look at each of the layers further down. But first let me note that these layers are usually depicted in the form of a pyramid, with the stable layer being that base. That is definitely not how I think about it. In fact, I see it as an inverted pyramid, where the stable layer is the tip of the pyramid, providing the base. The Dynamic layer is the middle part. The domain layer should be the largest part and will very often include more than one dynamic language. So in my mind I represent the different domain languages as smaller pyramids standing upside down, covering the base area. Now, the dynamic layer can also be divided into smaller parts like this, based on language or functionality. This is a bounded fractal representation, which is the reason for the title of this blog post.
This diagram shows how I think about it:
Of course, the smaller pyramids can be all the same language and system, or several different ones. It all depends on the application or system you are building. So you can for example use a combination of Ruby, Java and external or internal DSLs:
Or you could use Clojure, Scala and JavaScript:
Or any other combination you can imagine. As long as the combination is what’s best suited for the problem.
Let’s take a look at the definitions of the different layers. There have been some discussion about the names I’ve chosen for them, so let me describe a little more what the responsibility of each part is, and why it’s in that part of the system.
The Domain Layer
This layer is the simplest. This is where all the actual domain rules are defined. In general that means one or more domain specific languages. It doesn’t really matter if they are internal or external. This model see them as the same layer. This part of the system is what needs to be malleable enough that it should be possible to change rules in production, allow domain experts to do things with it, or just plain a very complicated configuration. The languages used in this layer are mostly external DSLs, but can also include extremely DSL-friendly languages like Ruby, Python or Groovy.
The Dynamic Layer
Neal Ford argues that this layer isn’t so uch about dynamic, as it is about essence. That was never my intention. The problem is that even if you take a language like Scala, which is usually classified as an essential language, Scala requires compilation. To me, compilation is ceremony, which means that it’s one extra thing you don’t want to care about when writing most of your application code. That’s why this layer needs to be dynamic. This is where languages like Ruby, Groovy, Python, JavaScript, Clojure and others live.
The Stable Layer
I view the stable layer as the core set of axioms, the hard kernel or the thin foundation that you can build the rest of your system in. There is definitely advantages to having this layer be written in an expressive language, but performance and static type checking is most interesting here. There is always a tradeof in giving up static typing, and the point of having this layer is to make that tradeof smaller. The dynamic layer runs on top of the stable layer, utilizing resources and services provided.
Another important feature of this layer is that this is where all interfaces are defined. By interfaces I mean external API’s. They need to be hard for other clients to be able to trust them. But the implementations for them lives in the dynamic layer, not in the stable. By doing it this way you can take advantage of static type information for your API’s while still retaining full flexbility in implementation of them. Languages in the stable layer can be Java, Scala or F#. It should be fairly small compared to the rest of the application, and just provide the base necessary services needed for everything to function.
The most common objection I hear from people about this strategy is the same as for the general polyglot programming idea: if we have a proliferation of languages in a system, it will be harder to find skilled programmers who can work with it.
This objection is true to a degree, but there are several ways around it. First, I have to say that I don’t believe this is such a big problem as many others think. Programmers nowadays depend on their tool chains quite heavily, all of them including many advanced features that takes lots of time to learn. But most programmers doesn’t even view their languages as tools. In my mind, the programming language is the most important tool. And once we start using better languages for systems, many of the things we need other tools for will disappear or become less of a problem.
I tend to believe that programming languages are quite easy to learn as soon as you understand the fundamental building blocks of programming languages. And if you don’t have a fair understanding of these building blocks, I would say that you probably aren’t using your current language as well as you should either. I see this as part of being responsible programmers.
I also believe quite strongly that if we used better languages for our code, many code bases would be smaller, easier to understand, easier to maintain and cost less – which means you could afford to find a more skilled programmer to do the work for you. This would mean that both parties win – the programmer gets more interesting work and better code, while the client gets more worth for his money in less time.
10 Comments | By Ola Bini | In: Uncategorized | tags: domain specific languages, fractal programming, polyglot, programming languages. | #
