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Let's try summarize in a table what we discussed until this point. For example, in Makengo I can define tasks that depend on more than a single task passing multiple arguments to DependsOn: BTW, Go has good support for varargs that combined with types reflection, types assertion and interfaces, are good substitutes for literal lists. Like maps, literal lists in Go have not optional delimiters so it's not convenient to use them as function arguments inside DSL. Moreover, Ruby allows you omit the delimiters for a literal map. In Ruby, a literal map could be passed as argument to a function allowing for more expressive function calls inside the DSL: This comes in handy, for example, in variadic functions (see below). Regarding metaprogramming, Go has an interesting reflect package that allows the manipulation of objects with arbitrary types. Dynamic Reception, combined with the metaprogramming magic of ruby, allows for very readable DSL (taken from Machinist): This language feature is known as Dynamic Reception. In ruby, when an unknown method is sent to an object the object responds executing method_missing. Go lacks of a ruby-like method_missing method. Actually, the Task function returns a task object and task object can receive the DependsOn method allowing for method chaining: In Makengo, this fact is exploited to express dependencies among tasks. Also, considering that since Go is a strongly typed language, anonymous function arguments need their own type declaration. This is due to the necessity of a func keyword and to the use of many parentheses. However, compared with Ruby, Go's anonymous functions syntax appears to be a lot more verbose. For example:Ĭompared with C, this a great step towards expressivity. In makengo, I use anonymous an function to define what a task does.Īlso, anonymous functions can take arguments in order to push values in the block. The first thing that comes to mind are anonymous functions that act as closures. Go has many features that help building DSL. The retrospection on Makengo raises the main question of this post: is Go suitable for building internal DSLs? Let's try answering, exploring Go's DSL capability in more detail. I could shortening the DependsOn() function name and gain some chars but what about expressivity? Rake DSL use Ruby's hash syntax to express dependency but I can't find nothing so effective in Go. In order to express the dependency we need the long named DependsOn function rather than the concise key => value hash syntax.The block syntax is burdened by the func keyword.String names are longer than symbol names (:hello vs 'hello', :world vs 'world').How does it look compared to the ruby version? First of all, the Go version takes 117 chars against the 75 chars of the Ruby version. Using makengo DSL, the previous tasks definitions become: So, I started writing a small experimental project named makengo. It's a joy to use.'īelonging from years of experience with dynamic languages such as ruby and enjoying its capability to build DSL, I was curious to see how much the compiled and strongly typed nature of Go can be bended in this sense. 'It feels like a dynamic language but has the speed and safety of a static language. When I approached to Go the first time I was intrigued by what is stated in its homepage:

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Since rake DSL uses valid ruby code to express itself we name it an internal DSL. task is a ruby method that accepts an hash argument.The neat fact about the code above is that it is perfectly legal ruby code. This will result in the string 'Hello World!' printed on the console. In other words, when the :world task is executed, the :hello task will be executed first. They state that the :world task is dependent from the :hello task. In particular, they define two dependent tasks. Tasks definitions above are written using Rake DSL. Rake, like make or ant, is a DSL for defining tasks. In this blog post I'll explore Go's capability in building DSL. But of course, the mean of expressivity is relative to the problem domain. A DSL tries to describe the problem with expressivity. There are modern and general-purpose languages like Ruby and Python that are suitable for building a sort of internal language that is specific for the problem domain: a Domain Specific Language (DSL). Computer problems can be approached easier once they are expressed in an effective way.

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