Like the name says, a linked list is a way to store data in a list format (thanks, Captain Obvious!).
The “linked” part comes from the fact that data is stored in a node & these nodes are linked to each other in a sequential manner.
How is this different from an array?
Linked List vs Array
A linked list has different performance characteristics than an array. That’s one reason why you may want to pick one over the other.
This means that a linked list can be more efficient for certain tasks than an array.
In a linked list there is no indexing, no random access, meaning that you can’t reach out for an item in the middle of the list.
You have to start at the “head” of the list & follow the links until you find the node you want or until the end of the list.
Removing (or adding) an item from the middle of a linked list is a lot faster.
All you have to do is change the “next” pointer for one node.
But if you want to delete an element from the middle of an array you will leave a gap & to close that gap you have to move all the elements on the right of the deleted element.
Not very efficient if you have to do this often!
If we want to insert in the middle of an array we have to move all the array elements to create an empty space.
Let’s see a code example:
a = [1,2,3,4,5,6,7,8]
def insert(arr, item, pos)
tmp = arr[pos]
arr[pos] = item
arr.replace(arr[0..pos] + [tmp] + arr[pos+1..-1])
end
insert(a, 99, 3)
p a
Note: There is also a built-in Array#insert method, but I wanted to show you how this method works.
What is the performance impact of inserting an item in the middle of a list?
Big difference here, but it also depends a lot on the size of the LinkedList since we have to search for the node.
Another way to compare two data structures is to look at a time complexity table (this assumes you don’t have to search a node for insertion & deletion):
Data Structure
Access
Search
Insertion
Deletion
Array
O(1)
O(n)
O(n)
O(n)
Linked List
O(n)
O(n)
O(1)
O(1)
Looking for real-world applications?
Here’s a pull request by Aaron Patterson where he speeds up Ruby Gems by using a linked list:
Ruby doesn’t include a LinkedList class so we need to create our own.
We want the following operations to be available:
append (to the end of the list)
append_after
delete
find
Here’s one possible implementation:
class LinkedList
def initialize
@head = nil
end
def append(value)
if @head
find_tail.next = Node.new(value)
else
@head = Node.new(value)
end
end
def find_tail
node = @head
return node if !node.next
return node if !node.next while (node = node.next)
end
def append_after(target, value)
node = find(target)
return unless node
old_next = node.next
node.next = Node.new(value)
node.next.next = old_next
end
def find(value)
node = @head
return false if !node.next
return node if node.value == value
while (node = node.next)
return node if node.value == value
end
end
def delete(value)
if @head.value == value
@head = @head.next
return
end
node = find_before(value)
node.next = node.next.next
end
def find_before(value)
node = @head
return false if !node.next
return node if node.next.value == value
while (node = node.next)
return node if node.next && node.next.value == value
end
end
def print
node = @head
puts node
while (node = node.next)
puts node
end
end
end
This particular implementation doesn’t keep track of the tail, it finds the tail when appending a new item. This makes the append operation linear time (O(n)). In the video below I show you another implementation where I append the elements at the front.
And here is our node class:
class Node
attr_accessor :next
attr_reader :value
def initialize(value)
@value = value
@next = nil
end
def to_s
"Node with value: #{@value}"
end
end
This is a basic “Singly-Linked List” implementation.
You also have other types of linked list:
Doubly-Linked List
Circular Linked List
In the doubly-linked list every node has two pointers: one to the next node & another to the previous node.
This allows for more flexibility when searching the list, but also requires extra work when making changes to the list.
A circular linked list is the same than a doubly-linked list, but the last node is connected to the head node.
Video
Summary
You have learned about linked lists, a data structure which you could consider if you are doing a lot of adding & removing of elements in the middle of an array.
It could also come up in a coding interview, so it’s worth learning even if it’s just for that.
Don’t forget to share this post using the buttons below so more people can benefit from this knowledge 🙂
I too have been going through some typical computer science exercises and wrote a doubly-linked list implementation in ruby. Check it out: https://github.com/jgnagy/linked_list
