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Data structures built with Ruby

A silly GIF of Jon Mulaney and Nick Kroll saying "ohhhh hello"

If you're like me and come from a non-Computer-Science background and have been curious about how data structures such as linked lists, hash maps, binary search trees and arrays work and what the trade-offs between them are, then this is the place for you.

We'll cover why you might want to use a given data structure and will give examples of places you've probably encountered them in your day-to-day as a programmer, perhaps without you even knowing it.

You'll also learn about the performance characteristics of each data structure, and how those characteristics can vary greatly from the average case to the worst case.

We'll see how you can use different data structures to implement things like stacks, queues, and deques.

But perhaps most importantly, these implementations are bare bones (for the love of God don't try to use them in production apps!) and are written in Ruby in the hopes of making them easy to understand and play around with. Make a PR for improving our hash function or allowing our arrays to efficiently add and remove items from the front!

Each data structure is described in detail below and comes with a set of tests that describe what's going on in plain English.

I hope you learn and enjoy! Any feedback positive or negative would be most welcome and can take the form of a GitHub issue, pull request, or email to

To test things outs, pull down the repo and run:

bundle exec rspec spec

Table of contents

Dynamic arrays

You can find the code for dynamically resizing arrays in resizing_array.rb and the corresponding spec file.

The Array class you're familiar with from Ruby's standard library is an implementation of a dynamic or automatically resizing array. If you think about how an array behaves in a lower-level language like C this terminology will begin to make sense.

In C you need to specify the size of the array you want. If that array fills up there is no built-in mechanism to increase the its size. You must manually allocate more memory to store the additional items and copy them over into a new array.

If you are removing elements from the array you'll probably want to de-allocate ("free") some of that memory so that your program isn't needlessly hogging your computer's resources.

The idea with our ResizingArray class is to show in Ruby how you might implement a dynamically resizing array, without leveraging the Array class from the standard library.

We use the methods instance_variable_get, instance_variable_set and remove_instance_variable to simulate allocating, de-allocating, and reading/writing to addresses in memory, as you would do in a language like C if you wanted to implement this data structure.

The array is initialized with some amount of capacity used to store its values, and that capacity is expanded as needed when values are added to the array. Capacity is reduced if there is a lot of unused space after an item is removed from the array.

The logic here is pretty unsophisticated: for example you could imagine adding an additional check to not decrease array capacity below a certain absolute level to avoid frequently resizing small arrays.

Sequential storage

Arrays store their values in a contiguous block of memory. This means that you can access arbitrary points in the array in constant time by simply supplying an index, which under the hood is used as an offset from the beginning of the array's address space. Concretely this means that if the VM or compiler knows both how much memory is used for each element in the array and what the address of the first value is, it can produce instructions to find any element via index by simply moving index * num_bytes_per_element from the start of the array.

A downside to this sequential storage approach is that adding or removing elements anywhere besides the tail (end) of the array means that all of the existing elements from the point of the operation on must be copied to new addresses in order to keep the data in sequence. Eg, if you have 10 elements in an array and delete the 5th one, elements 6-10 must be copied into positions 5-9. Similarly, if you have 10 elements and insert a new one at position 5 (zero-based index 4) you must move elements 5-10 to positions 6-11 (and you must resize the array if its current capacity is only 10).

Locality of reference

Finally, storing items at sequential addresses in memory provides good locality of reference, meaning that the computer can relatively easily store and re-use values from the array in its CPU caches. More concretely, when a computer reads from memory, it typically grabs as much data as it can process at once, rather than only grabbing the data at the specific memory address requested.

So if we have an array a and we read out the value stored at a[0] a 64 bit system might grab and 8 byte block of memory beginning at the address of a[0]. The computer can then store that entire block in a CPU cache, which will be much faster to access than going all the way back to memory.

This is good because, as mentioned in the locality of reference link above, computer systems tend to access the same values repeatedly in a short span of time, and they also tend to access things that are adjacent to one another in memory.

What this means is that programs that access a[0] will be likely to reference that value multiple times, and also will be likely to access data at a[1] and a[2]. So one nice thing about arrays is that they make it easy for computer hardware to optimize for those cases. Hooray!

Performance characteristics

  • Find/overwrite at index: O(1) - This runs in constant time, just move index * num_byte_per_element from the start of the array and read or write the value in question. In Ruby everything is an object and objects take up at most a set number of bytes.

  • Find by value: O(n) - This runs in linear time because in the worst case you'll need to iterate through the entire array, comparing the values of each element to the value you're searching for.

  • Insert/delete and at end ("tail") of the array: O(1) to O(n) - In the average case this runs in constant time because you're just writing or removing the value at the last address in memory (which is easy to find, just move (array_size - 1) * num_bytes_per_element from the start address of the array). However, as noted above, there are cases where this will be much slower. If the array is already full when we try to insert an item, or if the array has too much empty space when we delete an item a resizing will occur. This requires that all of the existing elements in the array be copied over to new space in memory.

  • Insert/delete not at the end of the array: O(n) - This is pretty slow! Operations not at the end of the array run in linear time because all existing values must be shifted over one place in memory. Why do we need to shift them? If we didn't our formula for quickly accessing elements by index (moving index * num_bytes_per_element from the start of the array) wouldn't work.

Stacks, queues, and deques

As written, operations on the head/front of the array (shift and unshift) operate in O(n) time because they are treated identically to operations at the middle of the array. However these operations can be made to run in constant time just like they are at the tail/end of the array. To do this, when the array is initialized a pointer is kept to both the first and last elements in the array (these point to the same address initially), and memory is allocated such that there is address space to either side of those pointers for values to be inserted into. As the array runs out of space on either end we must be able resize it by allocating memory on the appropriate side, expanding the array either towards the right or the left depending on where the value was inserted.

This variation of the array data structure is one way of implementing a Deque, or double-ended queue. Deques behave like both stacks (last in first out) and queues (first in, first out).

Making your array behave like a deque can cause there to be more frequent resizings (eg you could do one towards the right side, then on the very next insert have to do one towards the left side) and more unused space (because some empty space will tend to be kept both to the left and right ends).

So there are drawbacks, but if you need to be able to perform operations on either end of the array efficiently, such as with a queue where new values are pushed onto the right side and values are shifted off the left side, then it is a good thing to do.

Ruby's Array class does allow for efficient operations on either end:

require "benchmark"

a1 = { rand }
a2 = { rand }

queue_perf = Benchmark.realtime do
  1_000_000.times do
    rand > 0.5 ? a1.shift : a1.unshift(1)

stack_perf = Benchmark.realtime do
  1_000_000.times do
    rand > 0.5 ? a2.pop : a2.push(1)

puts queue_perf
# 0.10248183400017297

puts stack_perf
# 0.10594398699959129

Linked lists

You can find the code for linked lists in linked_list.rb and the corresponding spec file.

Unlike an array, which always stores its values sequentially in a contiguous block of memory addresses, linked lists do not need to keep their values adjacent to one another. Instead they rely on pointers from one node to the next/previous.

This means that they have worse locality of reference than arrays, and therefore the underlying hardware is less able to cache some contiguous block of memory with all the nodes in the list.

One trade-off there is that in a memory-constrained domain (the data you want to keep in memory is very large relative to the hardware) there must be a non-fragmented (contiguous) block of memory large enough to accommodate all the values of an array, whereas a linked list allows you to use all of the available memory regardless of how fragmented it is.

  • Find: O(n) - Runs in linear time because in the worst case we need to traverse he entire list from head to tail (or vice versa). There is no way to access items by index as you would in an array, or by key as you would in a hash map.

  • Insert / Delete: O(1) - Assuming you already have a reference to the node you want to insert or delete these operations run in constant time because the only work necessary is to adjust the references to/from the adjacent nodes.

Hash maps

You can find the code for hash maps in hash_map.rb and the corresponding spec file.

Hash maps (AKA hash tables or just "hashes" in Ruby) can read, write, and delete entries in O(1) (constant time) in the average case. However, in the worst case these operations occur in O(n) (linear time) because of the need for rehashing. What rehashing is and why it's necessary become apparent once you understand how hash maps work under the hood.

In this implementation we will create a hash map by using the linked list and dynamically resizing array data structures we've already built, along with a crucial third piece: a hash function.

Hash functions

Hash functions take in some arbitrary data and output an integer value known as a "hash", "hash digest", or "hash code" that has several useful properties:

  1. It is always of the same number of digits.
  2. Its input value cannot be deduced from its output value.
  3. It is uniformly random, meaning if the possible range of values for your hash function is 1-100, each of those numbers is equally likely to be produced. You won't get way more 50s than you will 2s.
  4. It is always the same for a given input, meaning the hash function is deterministic.
  5. There are few "collisions", which is when different inputs produce the same output.

Hash functions - if they are good - are also very fast. This is essential for the efficacy of the hash map. This speed, along with points 3-5 above make the hash map data structure possible.

We have written a very terrible hash function (HashCode.for(value)) for use in this hash map class. It takes the underlying bytes of the string representation of whatever you give it and squares them. Real world hash functions will probably do more than that, possibly shifting the sequence of bits or XORing the bits with some other, consistent string of bits.

You can even encrypt data using a cryptographic hash function that XORs the bits you want to encrypt using a secret, pseudo-random sequence of bits (this is the secret key in symmetric cryptography algorithms). But that's another topic for another day. Google it, jeeze.

Leveraging array access

OK, back to hash maps. Hash maps take advantage of the fact that reading, writing, or deleting the element at a given index in an array can happen in constant time.

The obvious limitation to this feature of arrays is that in order to take advantage of those constant time operations you must know the index of the element you'd like to access.

So, hash maps use hash functions to provide a way of deducing the desired array index given a "key" that identifies the value at that index. This is why hash maps are also known as associative arrays. They associate a key with an array index.

Hash maps take the key and run it through their hash function, producing a uniformly random integer hash digest for that key. They then use the modulus operator % to produce an integer that is within the size of the array. Eg, given a hash digest of 1001 and an array of size 10, 1001 % 10 gives you 1. We use this number as the array index for the key and its associated value.

Because a hash function always outputs the same digest given the same input we can look up this array index in the same way when it's time to read a value from the hash map by its key.

Resolving collisions

But what about the hash collisions mentioned above? It is possible to produce the same digest for multiple given inputs. To resolve such collisions we turn to the linked list. At each index in the array, rather than directly storing the value we want to write to the hash map, we store a linked list. Each node in the linked list contains both the key and the value. If there is a collision while writing to the hash map, we simply append another node to the list with the new key-value pair. When reading a value from the hash map we iterate through the linked list at the appropriate array index until we find the key we are interested in.


You may be asking yourself: "if we have to iterate through a list, how do hash maps have constant time lookups?". The answer is that we ensure there is never a linked list with a large number of nodes to be iterated through.

This is where rehashing comes in. As we insert key-value pairs into the hash map we increment a count. If the ratio of the number of key-value pairs (entries) relative to the capacity of the array rises above a certain level known as the load factor we will create a new, much larger, array and copy all of the existing values to it. Given a good hash function, this makes sure that we never have to do much iteration to find the key-value pair we are interested in.

Rehashing involves recalculating the hash and array index for every key, because the size of the array is now different, and so the denominator in our hash_digest % array_size function is also different.

Congratulations! Now you too can implement your own very, very terrible hash map!

Performance characteristics

Now that we understand how they work, we are in a position to understand some of the drawbacks to hash maps. Their guarantee of constant time operations in the average case is very groovy indeed. But we know that because of the need for rehashing every once and a while an insert operation will take linear time. So if you have a performance-critical application and need to store many thousands or millions of entries in memory and you cannot afford to have any operations take linear time, then a hash map may not be the best choice.

Further, hash maps don't keep an kind of order to their entries. So you'll need to do your own sorting if that's crucial to your application. This is more important than it might seem at first glance.

Consider that most database indexes are binary search trees (BSTs) under the hood, not hash maps. For example both PostgreSQL and MongoDB use BSTs as their default index data structure.

Why? If you want to query the database using any kind of comparison operation other than equality (AKA SELECT name FROM people WHERE name = "fred") a hash map will perform the search in linear time and therefore be useless as an index. The hash map knows how to look up the value associated with the key "fred", but it doesn't have an efficient way to compare the values of multiple keys with the value "fred". Read the section below on binary search trees to learn more about the many nice properties they provide.

Finally, hashes also have poor locality of reference because, like linked lists they don't store values sequentially in memory.

Binary search trees

You can find the code for binary search trees in avl_tree.rb and the corresponding spec file. AVL trees are a type of binary search tree that is self-balancing.

Self-balancing binary search trees perform reads, writes, and deletes in O(log n) in the worst case. So if avoiding slow operations is imperative to your application then you may want a tree rather than a hash map, despite the tree's slower average operation time.

Further, trees order nodes as they are inserted, so you easily can traverse the entire tree in sorted order in log(n) time.

But wait, what's a binary search tree, and why does it need to be self-balancing? Whatever that means.

Binary search

You may already be familiar with the binary search algorithm. It's simple but powerful. If you have some set of data in sorted order you can use binary search for a given value in log(n) time. To put that in perspective log base 2 of 1,000,000 is roughly 20. So that's at most 20 operations to find a particular value in a data set of one million entries. Noice!

Binary search starts at the middle value (the halfway point of the sorted data) and compares the value it finds there to the one it's looking for. If it doesn't find a matching value there it repeats the process, but this time it searches only half of the data: if the middle value was greater than the value being searched for then the search is repeated to the left of the middle value, otherwise it is repeated on the data to the right (assuming the data was sorted in ascending order).

You repeat this process as many times as necessary, effectively ignoring half of the remaining data on each iteration.

So what's the problem with just using a sorted array and doing binary search when you want to find something? The problem is that, as discussed above, when you want to insert or delete an element from the array it can be very slow depending on how full the array is and at what position in the array you're operating.


Binary search trees solve this by organizing data in nodes that link to up to two child nodes. Every node has a value. If a node is a left-hand child of a given parent node, its value is less than its parent's. If a node is right-hand child of a parent node then its value is greater than than that of its parent's.

With trees a picture is worth a thousand words:

t =

#            5
#          /   \
#         1     7
#        /     / \
#      -3     7   33
#                /
#               9

Binary search trees combine the good properties of a linked list and doing a binary search on a sorted array. You can find a node in log(n) time via binary search by starting at the root node (the top of the tree) and navigating left if the value you want is less than the node's value, and going to the right if the value you're looking for is greater.

However, once you've found a given node in the tree deleting it, or inserting one above or below it occurs in constant time just like with a linked list! All you need to do is update the references from one node to another.

Thus, all operations occur in roughly log(n) time.

Rebalancing via tree rotations

Tree rotations are a mechanism for our tree to self-balance, which prevents our tree from becoming degenerate. That is, as nodes are inserted and deleted some sections ("subtrees") of the tree may become much deeper than others.

To see why this is a problem imagine the extreme case: a "tree" where all child nodes are the right-hand child of their parent. This is just a linked list, which takes O(n) to find any given node. Eg, a degenerate binary search tree that is essentially just a linked list:


Rotations move nodes around such that no subtree is more than one layer deeper/ taller than its sibling subtree. The term "rotation" makes visual sense as you typically adjust the nodes so that a child from one side comes up to occupy the position the parent is currently in, and the parent is moved down and over to the side opposite the one that first node came from. Eg, our degenerate tree above can be rotated so that 2 comes up to where 1 is, and 1 moves down and over to the left side:

 / \
1   3

But how do we know when to perform a rotation? First, we specify an invariant constraint for the system. This is just a rule that we want to never be violated. For AVL trees the invariant is that no node should have one subtree be more than 1 layer deeper/taller than its sibling subtree.

Next, we keep track of a balance attribute on each node, and adjust the balances of relevant nodes whenever a node is inserted or deleted from the tree. When that happens we traverse up the tree from the node that was inserted/deleted adjusting the balances of each ancestor node along the way. If at any point we encounter a node whose updated balance would violate our constraint we perform the appropriate rotation for that node's subtrees.

Depending on the shape of the subtree the rotations look a little different. These specs demonstrate how rotations work for different shapes of subtrees.

Confused? Here's a great visualization of tree rotations from the University of San Francisco.


Linked lists, resizing arrays, hashmaps, binary search trees, oh my!







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