Functions in disguise

David Wagner

Mainstream programming languages support structured programming. Functions, subroutines and methods make code more expressive and reduce repetition.

Pure functions, constructs that produce values by using only their input arguments and nothing else, are of particular importance.

Pure functions express intent explicitly because, by definition, they don’t rely on side effects. Pure functions are easier to test and reason about than those which have observable side-effects. This post is only about pure functions, so I’m dropping the “pure” qualifier.

In functional programming programs are composed of expressions and declarations of functions. In this niche domain the utility of functions is well studied and understood. Ideas from purely functional programming languages percolate into mainstream programming languages, but functional programming is far from mainstream.

There is, however, a form of functional programming practiced by every developer every day. They write configuration files. This is the hardest form of functional programming where functions are not used at all.

Functional configuration

Every non-trivial software requires some configuration. When a program reads its configuration it executes a small, often trivial functional program. Let me show you what I mean with an example.

Consider the following, INI-style, configuration file:



Let’s say that this is a configuration file of a simple service which is deployed into a specific environment. First, we deploy it to staging and, when all the tests pass, we promote the application to production. Naturally, the service’s address is different in these two environments.

This is pretty standard, we see similar configuration blocks everywhere. Where’s the functional programming here?

With the configuration blocks we implicitly created a simple function. Its imaginary type signature is:

configuration :: Environment -> Url

This reads: the configuration is a function from an environment to a URL. If we saw such a function signature in an application, we could implement it like this:

def configuration(environment):
   return {
     url: '' if environment == 'production' else ''

For clarity I used Python’s familiar syntax, but this is not important.

Conceptually we can think that after the configuration file is parsed, this function is evaluated. In practice, we usually don’t see this function written out, but it’s hidden somewhere between the configuration format parser library and our application’s initialization.

What we see is that a function, a universally useful concept in programming, is not being used explicitly to define the program’s configuration.

But the configuration is simple

Let’s continue on the previous example and add more configuration parameters:



Here I made the database backend of our service configurable. It’s a contrived example, but not completely unrealistic: in staging we use our own Postgres database engine as opposed to production where we wish to store our data in Amazon’s database-as-service offering called Relational Database Service (RDS).

Note that I reused the value of db_backend in the definition of db_address because I wanted to avoid duplication. You can parse this configuration file with Python’s configparser module.

Configuration formats with sections and custom interpolation rules are very common. Still, there are some interesting questions to ask:

  • What is a valid section name?
  • What happens if I repeat the same entry in a section?
  • What happens if I omit an entry in a section?
  • What are the interpolation rules of the percent expressions?

In this specific case, we find the answers in the not so short documentation, but the point is that a concept like ‘configuration block’ looks deceptively simple. It takes quite some effort to precisely explain what a file like this means.

Now let’s see how this configuration file looks if we represent it explicitly as a function:

def configuration(environment):
  if environment == 'production':
    url = ''
    db_backend = 'postgres'
    db_suffix = ''
    url = ''
    db_backend = 'rds'
    db_suffix = ''

  return {
    url: url,
    db_backend: db_backend
    db_address: db_backend + db_suffix

Is this better than the INI syntax? Well, not necessarily, but it triggers different kinds of thoughts:

  • Should we avoid repeating the domain?
  • Should we introduce a richer data type to represent the database configuration?
  • Should we split the database configuration in a separate function?

The answers to these questions depend on the specific context. Note, however, that these questions are programming questions. We raise similar questions when we write the core of our applications. Why doesn’t configuration deserve the same level of scrutiny?

Again, I gave the example in Python’s syntax for simplicity, but the configuration language doesn’t have to be Python, but it could support function definitions.

Functions hide everywhere

Let’s leave our toy example and look for functions elsewhere.


Packer is a tool for building machine images. Its documentation explains how to write configuration templates and use variables to further specialize them from the command line.

The appearance of the word ‘variable’ suggests that some function-related business may be going on here. Indeed, Packer templates are functions from user variables to build instructions. Interestingly, Packer templates themselves can call functions too.

Now Packer is a wonderful tool and I’m not claiming that there’s anything wrong with it. I just want you to realize that in a Packer configuration there lies an ad hoc, mini functional program.

Rolling your own implicitly functional configuration language is a lot of work and it develops some warts. For example, there are restrictions where you can or cannot use variables in a Packer template. Again, this is not a problem, you can use Packer just fine. It’s just a pity because variable scoping is pretty well understood since the development of ALGOL in the 1950s.


Terraform allows you to define your cloud infrastructure as code. I use Terraform every day and I cannot imagine my work without it.

Terraform’s configuration language already supports various programming constructs, but remains implicitly functional. You cannot define a function explicitly, but a “module”. Just observe the language used to describe them. These are quotes from the documentation, the emphasis is mine:

  • Input variables to accept values from the calling module.
  • Output values to return results to the calling module.
  • To call a module means to include the contents of that module into the configuration with specific values for its input variables.

It’s no surprise that modules actually are functions, or they should be.

You could imagine that when you import a module you compute a value which contains a bunch of other functions, those that are defined in the module. In fact, this is exactly how JavaScript modules worked before the language standard introduced the import keyword.

Ansible and Salt

Configuration management systems such as Ansible and Salt allow you to manage a large number of machines. You specify your servers’ configuration in a YAML file and these systems make sure that the desired files, software, etc., are deployed on them.

A core feature of these tools is the ability to specify configuration file templates which are rendered using context specific parameters (for example: the server’s assigned IP address). These templates are functions which take user parameters as arguments and return the rendered configuration file as a string.

Salt and Ansible also support structuring your configuration into separate files. Sadly they are not called modules, but roles and formulas.

If you take a look at the documentation of Ansible roles you won’t be surprised to find word “variable” again. The second paragraph of Ansible’s documentation claims: Ansible’s main goals are simplicity and ease of use. Take a look how “simple” it is to use variables.

Warm and fuzzy names

What’s wrong with the names “template”, “role” and “formula”? Just replace all their occurrences with the word “function” and we’re done. Well, no. It’s not at all about linguistics.

The problem is that your preferred warm and fuzzy name is inevitably more ambiguous than the concept of a function. When you refuse to admit that you’re manipulating functions in your software configuration you’re throwing away 60+ years worth of computer science. Additionally, you’re creating extra work for yourself to come up with some idiosyncratic rules for your users to write modular and comprehensible configuration files for your system.

Why not use functions as the core abstraction to build up configuration files? Functions can be composed using rigorous, well-defined mathematical rules. A functional configuration language could provide few, but powerful concepts to author configuration files for complex software systems.

Of course this not to say just use Maths and you’re done. There’s a lot more to a good configuration language than that. Nevertheless, I firmly believe that it’s possible to provide developer ergonomics, readability on top of solid concepts borrowed from Mathematics and Computer Science.


Today, configuration files are written in ad hoc, implicitly functional programming languages where functions don’t appear explicitly but disguised. This leads to the proliferation of ill-defined concepts and idiosyncratic rules.

This makes extremely valuable tools, like those mentioned in this article and many others, unnecessarily hard to understand, configure and use.

It is possible to define a disciplined, functional programming language for configuration files. I encourage you to take a look at Dhall. Get over its unusual syntax and devote some time understanding the fundamental role of functions in software configuration and in programming in general.


I’m grateful to Kyle M. Douglass for reviewing an early draft of this article and for providing valuable feedback.