NOTE: This issue of Practicing Ruby was one of several content experiments that was run in Volume 6. It uses a cookbook format (e.g. problem -> solution -> discussion) instead of the traditional long-form article format we use in most Practicing Ruby articles.

Problem: Code for data munging projects can easily become brittle.

Whenever you work on a project that involves a significant amount of data munging, you can expect to get some mud on your boots. Even if the individual aggregation and transformation steps are simple, complexity arises from messy process of assembling a useful data processing pipeline. With each new change in requirements, this problem can easily be compounded in brittle systems that have not been designed with malleability in mind.

As an example, imagine that you are implementing a tool that delivers auto-generated email newsletters by aggregating and filtering links from Reddit. The following workflow provides a rough outline of what that sort of program would need to do in order to complete its task:

  1. Map the raw JSON data from Reddit’s API to an intermediate format that can be used throughout the rest of the program.

  2. Apply filters to ignore links that have already been included in a previous newsletter, or fall below a minimum score threshold.

  3. Convert the curated list of links into a human readable format.

  4. Send out the formatted list via email using GMail’s SMTP servers.

Some will look at this set of steps and see a standalone script as the right tool for the job: the individual steps are simple, and the time investment is small enough that you could throw the entire script away and start again if you end up facing significant changes in requirements.

Others will see this as a perfect opportunity to put together an elegant domain model that supports a classic object-oriented design style. By encapsulating all of these ideas in generalized abstractions, endless changes would be possible in the future, thus justifying the upfront design cost.

Both of these perspectives have merit, but it would be unwise to set up a false dichotomy between formal design and skipping the design process entirely. Interesting solutions to this problem also exist in the space between these two extremes, and so we’ll take a look at one of them now.

Solution: Reduce the cost of rework by organizing your codebase into isolated single-purpose components.

Unlike the typical web application which has a wide range of end-points serving orthogonal concerns, the workflow for data munging projects often more closely resembles a flow-chart, with clearly defined beginning and end points. The step-by-step nature of data munging projects makes them most naturally fit the procedural programming paradigm. This is a source of tension in Ruby, because of its heavy object-oriented bias at the language level.

A reasonable compromise is to embrace “procedural programming with objects”. Rather than discussing this technique in the abstract, we will instead explore what it looks like in practice by seeing how it can be used to build the Reddit curation tool we discussed earlier.

Let’s start with the script that implements the core workflow of the program:

require_relative "../lib/spyglass/actions/load_history_file"
require_relative "../lib/spyglass/actions/fetch_links"
require_relative "../lib/spyglass/actions/format_message"
require_relative "../lib/spyglass/actions/deliver_message"

basedir = File.dirname(__FILE__)

history   = Spyglass.load_history_file("#{basedir}/")     #1
min_score = 20

selected_links = Spyglass.fetch_links("ruby").select do |link|         #2
  link.score >= min_score &&                        #3

history.update(selected_links)                                         #4

message = Spyglass.format_message(links: selected_links, 
                                  template: "#{basedir}/message.erb")  #5

Spyglass.deliver_message(subject: "Links for you!!!!!!",               #6
                         message: message)

This code looks a bit different than the typical Ruby snippet, because rather than instantiating objects directly and then calling methods on them, it is simply calling methods on the Spyglass module. It is obvious from the require_relative calls that these features have been individually enabled, which is also a non-standard way of doing thing.

If you set aside the quirks of this code for a moment, it should still be fairly easy to read. Here’s a rough English translation of what’s going on:

  1. A history log is being loaded from a file.
  2. Links in the “ruby” sub-reddit are being fetched and filtered
  3. Links are filtered out if they’re below a score threshold or have been selected in a previous run of the program.
  4. The history log is updated with the newly selected links
  5. The selected links are formatted into a human readable message
  6. The message is delivered with the subject “Links for you!!!!!!”

Because the six steps above pretty much directly line up with the high-level requirements of the project, it is safe to say that this code is sufficiently expressive. But to properly evaluate the overall design, we’ll need to dig into the code that implements these features. Let’s proceed by walking through the features in the order that they are used.

First up is the Spyglass.load_history_file procedure:

require_relative "../data/history"

module Spyglass
  def self.load_history_file(filename)

This method is a trivial stub that creates an instance of the Spyglass::Data::History class shown below:

require "pstore"

module Spyglass
  module Data
    class History
      def initialize(filename)
        @store =

      def new?(link)
        @store.transaction { @store[link.url].nil? }

      def update(links)
        @store.transaction do
          links.each { |link| @store[link.url] = true }

From this definition, we can infer that the job of the History object is to keep track of which URLs have been selected in previous runs of the program. It uses a PStore object as its persistence method, but that is mostly an implementation detail.

With an understanding of how Spyglass.load_history_file works and what type of object it returns, we can now move on to investigating the Spyglass.fetch_links procedure:

require "json"
require "open-uri"

require_relative "../data/link"

module Spyglass
  def self.fetch_links(category)
    document = open("{category}?limit=100").read

    JSON.parse(document)["data"]["children"].map do |e|
      e = e["data"] e["url"], score: e["score"], title: e["title"])

This method is responsible for making an HTTP request to the Reddit API to capture a JSON document representing the raw data about links in a particular subreddit. It then parses that document and transforms it into Data::Link objects. A quick look at the class definition for Data::Link reveals that it is a straightforward value object with no interesting business logic:

module Spyglass
  module Data
    class Link
      def initialize(url: raise, score: raise, title: raise)
        @url   = url
        @score = score
        @title = title

      attr_reader :url, :score, :title

As simple as it is, the Data::Link object is a very important part of this program, because every other feature that refers to links assumes that they conform to this interface. In other words, we’ve set in stone here that the data our program is interested in when it comes to links are its score, url, and title. Any changes to this interface would require widespread changes throughout our program.

Based on what you’ve seen so far, you should be able to understand exactly how this program works up to step #4 in its main script. Only two steps remain: formatting the list of curated links into a human readable message, and delivering it to someone.

The formatting procedure (i.e. Spyglass.format_message) is extremely basic, as it is nothing more than a minimal wrapper around the ERB standard library:

require "erb"

module Spyglass
  def self.format_message(links: raise, template: raise), nil, "-").result(binding)

This code is somewhat generalized, allowing an arbitrary template to present the list of links. In the case of this particular script, we use a simple text-based template that looks like this:

Here are some links you might enjoy!
<% links.each do |link| -%>

  <%= link.title %>:
  <%= link.url   %>
<% end %>
Have fun!

When evaluated, this template spits out plain-text output that looks similar to what you see below:

Here are some links you might enjoy!

  _why updated his site:

  Teabag: A Javascript test runner built on top of Rails:

  Ruby 2.0 Works Hard So You Can Be Lazy:

Have fun!

From here, all that remains is to fire this message out via email, which is handled by the Spyglass.deliver_message procedure:

require "mail"

Mail.defaults do
  delivery_method :smtp, {
    :address              => '',
    :port                 => '587',
    :user_name            => ENV["GMAIL_USER"],
    :password             => ENV["GMAIL_PASSWORD"],
    :authentication       => :plain,
    :enable_starttls_auto => true

module Spyglass  
  def self.deliver_message(message: raise, subject: raise)
    mail =


    mail.subject = subject
    mail.body    = message 


This is not as easy to read as many of the previous procedures, because it involves some configuration code. However, on closer investigation we can easily see that this is a thin wrapper around the Mail gem, and that it uses three environment variables for its settings: GMAIL_USER, GMAIL_PASSWORD, and SPYGLASS_RECIPIENT. This means that the main script for this program needs to have these values set before it can be run, as in the example below:

$ GMAIL_USER="[email protected]" GMAIL_PASSWORD="password" \
  SPYGLASS_RECIPIENT="[email protected]" ruby examples/reddit.rb

If you have a GMail account, you can actually give this a try by cloning the practicing-ruby-examples repository and running something similar to the line shown above in the v6/009 folder. But as long as you understand the general idea behind this program, don’t worry if you can’t test it for yourself right now.

Assuming that you have been able to understand this walk-through, you may already have some sense of why this solution is a reasonable middle ground between ad hoc scripting and formal object-oriented design. However, we should discuss the benefits and costs in more detail before we wrap things up here.


The primary difference between object-oriented programming and “procedural programming with objects” is that the former binds certain behaviors to encapsulated data, and the latter decouples its data from its behavior.

Object-oriented design is best suited for problems where most of the interesting details exist in the messages that are passed between objects. In other words, when you have a complex set of interactions between a network of communicating objects, it makes good sense to tightly bind together state and behavior. However, this comes at the cost of indirection, and so it becomes hard to keep a mental model in your mind of what the call graph looks like for even a single request.

By contrast, data munging projects are procedural in nature, and so you have a good sense of what needs to happen at each step in the process. The final program represents a chain of transformations and filters on relatively simple data structures, with some side effects thrown in along the way. Because each step tends to be a very concrete action, the abstraction benefit that objects can offer is negated by the fact that so much is subject to change in the whole system.

If you go back and read through the codebase we discussed in this article, you will find that the data objects are trivially understandable, and the actions are context-independent. Although they are not pure functions, each of the actions can be fully understood in terms of its inputs, outputs, and external dependencies. This makes it possible to make changes to the internals without thinking about their impact on the overall program, as long as the return values do not change.

Another interesting benefit of “procedural programming with objects” is that a lack of internal behavioral dependencies makes it so that you can easily change the signature of a single action without requiring a cascade of changes throughout the system. The main script might need to be updated, but such revisions would be trivial.

However, it is important to remember that all of these benefits come from the fact that data munging projects occupy a special domain where certain benefits of object-oriented programming are not especially important. You may want to consider adopting a traditional object-oriented design if any of the following conditions apply:

  • You have actions that need to store data in instance variables, rather than simply returning value objects or using repository objects like the History object in this example.

  • You have actions that need to call other actions in order to get their job done, rather than relying on simple data objects that rarely change.

  • You have actions that need to operate as a multi-step state machine, rather than a single-purpose procedure that you can fire and forget.

All of the above are symptoms that the benefits of object-oriented design will outweigh its costs, and Ruby is a deeply object-oriented language, so you won’t lose out by heading in that direction. However, if you are stuck in the place between a throwaway script and a full object-oriented program, the example shown in this article might help you find a nice compromise.