Leigh Halliday

Tree Structures in your Rails models

published Feb 4, 2015
  • #ruby-on-rails

What are tree structures used for?

Tree structures are needed any time you want to insert a hierarchy into your data. It's when you want to store relationships between data of the same type, vertically. Think of categories, org charts, and family trees; all have a tree structure to them. Some examples of this are.


  • Electronics
    • Cameras
      • Bodies
      • Lenses

Or when working with organizational structures:

  • Product Team
    • IT Department
      • Team A
      • Team B
  • Finance Department
    • Auditing

3 Types of Trees

In a great article written about storing tree structures in the database, Alexey outlines 3 common patterns. They are parent-child, materialized path, and nested set.

Each of these patterns have different strengths and weaknesses, especially related to whether your application is read-heavy or write-heavy.


Pros: Simple, fast writes. Cons: Slow reads.

Parent-child tree structures are the easiest to understand, are very write-efficient, but can be extremely slow for reads, especially if your hierarchy spans more than 2 levels deep. The reason it is slow on reads is that it can't be done in a single query like the other patterns allow. If you're 5 levels deep and you want to get the top-level parent, you will need to perform 4 queries.

For this type of data, I don't use any sort of Gem to help me accomplish this because it is done very easily in plain Rails. Let's look into how we'd implement this for a Category class.

First, we need to add a parent_id column to the database. This is used to determine both the parent and the children of a specific category.

class AddParentIdToCategories < ActiveRecord::Migration
def change
add_column :category, :parent_id, :integer

Next, we'll add a parent relation to our model along with a children relation. Because we aren't following the normal Rails standards by naming the field like category_id, we have to tell it which class the relation refers to. I chose to use parent and children instead of category and categories, because I find it much clearer.

class Category < ActiveRecord::Base
belongs_to :parent, class_name: "Category"
has_many :children, class_name: "Category", foreign_key: "parent_id"

Now we can access our data like so:

category = Category.find 10 # Result: <Category id: 10, name: ....>
category.children.count # Result: 2
# SQL - SELECT COUNT(*) FROM "categories" WHERE "categories"."parent_id" = 10

If we wanted to add a method to category to get it's parent's name, or to check if it has a parent and/or children, we could do this:

def parent_name
# it may not have a parent

def has_parent?

def has_children?

Materialized Path

Pros: Fast reads. Cons: Slow writes, more complicated.

This is a pattern that you're most likely familiar with, even if you haven't heard of the name before. Think of it like how the Bible is organized: John, Chapter 3, Verse 16. Each record essentially stores the "path" through it's ancestry. So John, Chapter 3, Verse 16 has all of the information necessary to go straight from the verse to John, the book.

The ancestry gem uses the materialized path pattern to handle relationship data in your model. Inserts are fairly complicated, so thankfully the gem handles the complexity for us.

Nested Set

Pros: Fast reads. Cons: Slow writes, more complicated.

Nested sets are possibly the most complicated to explain, and have very complicated logic when modifying the hierarchy, but provide very fast read-access to the data. The way it works is by adding left and right bounds to your data. Awesome nested set is a great Gem implementing the nested set pattern..

Here's an example of Colombia with a couple of its provinces and cities:

  • 1 Colombia 10, L1
    • 2 Antioquia 7, L2
      • 3 Medellín 4, L3
      • 5 Rio Negro 6, L3
    • 8 Chocó 9, L2

Colombia has 4 children. 2 of them (Antioquia and Chocó) are direct descendants (1 level below), and the other 2 are second level descendants (Medellín and Rio Negro), children of children.

If you wanted to find direct descendants of Colombia, you could do so with a query like so:

SELECT * FROM locations WHERE left > 1 AND right < 10 AND level = 2;

Or if you wanted to find out how many cities are in Antioquia, you could do so with the following query:

SELECT count(*) FROM locations WHERE left > 2 AND right < 7 AND level = 3;


In summary, if you are working with simple data, not nested too deeply and which isn't too read-heavy, I recommend using the basic parent-child pattern. If your data is multiple levels deep and is read-heavy, I recommend using either the materialized path or the nested set patterns.