What is a data structure?

A data structure is **a specific way to organize & access data**.

Examples include:

- Arrays
- Binary trees
- Hashes

Different data structures excel at different tasks.

For example, hashes are great if you’re looking to store data that looks like a dictionary (word & definition), or a phone book (person name & number).

Knowing **what data structures are available**, and **the characteristics of each of them**, will make you a better Ruby developer.

That’s what you’ll learn in this article!

## Understanding Arrays

The array is the first data structure that you learn about when you start reading about programming.

Arrays use a contiguous chunk of memory where objects are stored one after another without gaps between them.

Unlike in lower-level programming languages, like C, Ruby does all the hard work of managing your memory, expanding the maximum array size & compacting it when you delete elements.

**Uses**:

- As a base for more advanced data structures
- To gather results from running a loop
- Collecting items

You’ll find arrays everywhere, like the `split`

& `chars`

methods, which break down a string into an array of characters.

**Example**:

out = []
10.times { |i| out << i }
out
# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

The following table shows you how the different array operations behave as the size of the array increases.

If you aren't familiar with time complexity notation read this article.

**Time complexity for arrays**:

Operation |
Complexity |

push |
O(1) |

pop |
O(1) |

access |
O(1) |

find |
O(n) |

delete |
O(n) |

**Why is this helpful?**

Because it tells you the performance characteristics of arrays.

If you're doing a lot of `find`

operations on a HUGE array that's going to be slow...

But if you know what indexes to use, then an array is going to be fast because of the `O(1)`

time complexity.

**Choose your data structure with this criteria**:

**Performance characteristics** => What are you doing with the data? How big is your dataset?
**Shape & form of your data** => What kind of data are you working with? Could you re-organize your data so it fits a better data structure?

## The Hash Data Structure

Do you have a mapping between country codes & country names?

Or maybe you just want to count stuff.

That's exactly what hashes are helpful for!

A hash is a data structure where **every value has a key & this key can be anything**, like a string, an integer, a symbol, etc.

Internally, a hash converts your key into a number (using the `hash`

method in Ruby) & then uses that number as the index.

**Uses**:

- Counting characters in a string
- Mapping words to definitions, names to phone numbers, etc.
- Find duplicates inside an array

**Example**:

"aaabcd"
.each_char
.with_object(Hash.new(0)) { |ch, hash| hash[ch] += 1 }
# {"a"=>3, "b"=>1, "c"=>1, "d"=>1}

**Time complexity**:

Operation |
Complexity |

store |
O(1) |

access |
O(1) |

delete |
O(1) |

find (value) |
O(n) |

Hashes are one of the most helpful data structures when it comes to performance because of the constant `O(1)`

store, delete & access time.

Find in the context of a hash means that you're looking for a specific value.

## Stacks

A stack is like a stack of plates, you put one plate on top of another & you can only remove the plate on top.

This is more useful than it sounds at first!

**Uses**:

- Replaces recursive methods with a regular loop
- Keep track of work left to do, leaving the most recent on top
- Reverse an array

**Example**:

stack = [1,2,3,4,5]
(1..stack.size).map { stack.pop }
# [5, 4, 3, 2, 1]

Yes, you can use `reverse`

instead.

This is only an example to show you this particular characteristic of stacks.

**Time complexity**:

Operation |
Complexity |

push |
O(1) |

pop |
O(1) |

find |
--- |

access |
--- |

Notice that stacks (and queues) only have two operations, `insert`

& `delete`

, or `push`

& `pop`

.

While it's possible to search inside a stack, it's very rare.

## How to Use Binary Trees

Most Ruby developers have probably heard about binary trees but never used one.

**Why is that?**

First, we don't have a built-in binary tree implementation.

Second, a binary tree is not that helpful for everyday programming challenges, unlike arrays & hashes which you use ALL the time.

But binary trees are a very **interesting data structure**.

In fact, there are many variations, like the Trie (covered in next section), multiway trees like the B-Tree (used in databases) & the Heap.

**Uses**:

**Example**:

# https://github.com/jamesconant/bstree
require 'bstree'
root = Bstree::Node.new(5)
root.insert(2)
root.insert(7)
root.search(3)
# nil

**Time complexity**:

Operation |
Complexity |

insert |
O(log n) |

delete |
O(log n) |

find |
O(log n) |

access |
--- |

A balanced binary tree is when all nodes have two children & all leaves have the same level

If a tree becomes unbalanced, the performance degrades to `O(n)`

.

In a **self-balanced binary tree** (like the Red-Black Tree, or AVL tree), every operation takes time proportional to the height (or level) of the tree.

Notice how there is no access time because to access a node you first have to find it...

In that case, you'll have `O(log n)`

for access.

But if keep a reference (as a variable) to a specific node, that would be `O(1)`

access time.

## The Trie Data Structure

A trie is a specialized tree-like data structure.

It's helpful for working with words, and then quickly searching for words that start with a prefix, or search for the full word.

**Uses**:

- Word games
- Spelling checker
- Autocomplete suggestions

**Example**:

# https://github.com/gonzedge/rambling-trie
require 'rambling-trie'
trie = Rambling::Trie.create('words.txt')
trie.include?('chocolate')
# true
trie.include?('salmon')
# true

**Time complexity**:

Operation |
Complexity |

add |
O(k) |

include? |
O(k) |

words |
O(k) |

In this table, I use `k`

to denote the size of the input string, while `n`

is used to denote the size of the data structure itself.

So for the word `apple`

, `k`

would be 5.

## Summary

You have learned about common data structures, their main uses & characteristics, and how to use them in Ruby.

When you apply this new knowledge you'll be able to solve problems faster!

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