Introduction
You’ve written a lot of code up to this point, and you’ve hopefully moved on from just trying to write code that works, to now considering code readability and maintainability. You might spend some time considering how you can create the necessary abstractions so that your code stays easy to work with even as the requirements for it grow.
Code readability and maintainability are super important. After all, you will likely spend as much, if not more, time reading code than writing it. You need to make sure new features are integrated with ease.
However, there is another consideration that can be just as important when writing code. Efficiency! You need to understand how the code you write will perform. You also need to understand how the choices you make impact that performance so that you can choose the right data structure and algorithm for your requirement.
In programming, there are two ways we can measure the efficiency of our code. We can measure the time complexity or the space complexity.
In this lesson, we’ll introduce the core concepts around measuring the time efficiency of the code you write.
Lesson overview
This section contains a general overview of topics that you will learn in this lesson.
 How the efficiency of an algorithm is measured.
 What is Big O.
 What are the Big O notations used to measure an algorithm’s efficiency.
 How else can we measure an algorithm’s efficiency.
 What to do when two algorithms have the same complexity.
Efficiency basics
The very first step in mastering efficient code is to understand how to measure it. Let’s take a look at a little program that prints out all odd numbers between 1 and 10.
def odd_numbers_less_than_ten
current_number = 1
while current_number < 10
if current_number % 2 != 0
puts current_number
end
current_number += 1
end
end
If you were to run this in your terminal, you should get the numbers 1
, 3
, 5
, 7
and 9
printed to the console. It probably took a fraction of a second to run. If you were to run it again, it might take the same time, or it might be faster or slower depending on what else your computer is doing. If you were to run it on a different computer, it would again run faster or slower. Therefore it’s important to understand that you never measure the efficiency of an algorithm by how long it takes to execute.
So how do we measure it?
The way to measure code efficiency is to evaluate how many ‘steps’ it takes to complete. If you know that one algorithm you write takes 5 steps and another one takes 20 steps to accomplish the same task, then you can say that the 5step algorithm will always run faster than the 20step algorithm on the same computer.
Let’s go back to our odd_numbers_less_than_ten method
. How many steps does our algorithm take?

We assign the number 1 to a variable. That’s one step.

We have a loop. For each iteration of the loop, we do the following:
 Compare
current_number
to see if it is less than 10. That is 1 step.  We then check if current_number is odd. That is 1 step.
 If it is then we output it to the terminal. That’s 1 step every 2 iterations.
 We increase
current_number
by 1. That is 1 step.
 Compare

To exit the loop, we need to compare
current_number
one last time to see that it is not less than ten any more. That is one last step.
So there are 3 steps for every loop iteration and it iterates 9 times which is 27 steps. Then we have one step which iterates for only half the loop iteration which is 5 steps. Assigning an initial value to current_number
and checking the exit condition of the loop is one step each. 27 + 5 + 1 + 1 = 34 steps.
Therefore, we can say our algorithm takes 34 steps to complete.
While this is useful to know, it isn’t actually helpful for comparing algorithms. To see why, let’s slightly modify our initial algorithm to take in a number instead of setting a hard default of 10.
def odd_numbers(max_number)
current_number = 1
while current_number < max_number
if current_number % 2 != 0
puts current_number
end
current_number += 1
end
end
How many steps does this algorithm take?
You’ve probably realised the answer is it depends. If you set max_number
to be 10, like we did before, the number of steps is 34, but if you enter another number then the number of steps changes. There is no concrete number we can use to measure the efficiency of our code because it changes based on an external input.
So what we really want to be able to measure is how the number of steps of our algorithm changes when the data changes. This helps us answer the question of whether the code we write will scale.
To do that, we need to delve into a new concept: Asymptotic Notations and, in particular, Big O.
Asymptotic notations
Asymptotic Notations are used to describe the running time of an algorithm. Because an algorithm’s running time can differ depending on the input, there are several notations that measure that running time in different ways. The 3 most common are as follows:
 Big O Notation  represents the upper bound of an algorithm. This means the worstcase scenario for how the algorithm will perform.
 Omega Notation  represents the lower bound of an algorithm. This is the bestcase scenario.
 Theta Notation  represents both the upper bound and lower bound and therefore analyses the average case complexity of an algorithm.
Big O is the one you’ll most commonly see referenced because you need to be sure the worstcase scenario for any code you write is scalable as the inputs grow in your application.
It’s also worth noting that the Notations given below for Big O also apply to Omega and Theta notations. The differences are in how they look to measure the efficiency of the algorithm and therefore which Notation should apply. This should become clearer as you read on.
What is big O?
Big O gives us a consistent way to measure the efficiency of an algorithm. It gives us a measurement for the time it takes for an algorithm to run as the input grows so that you can directly compare the performance of two algorithms and pick the best one.
Big O is not a piece of code you can put your algorithm into that tells you how efficient it is. You will need to measure how the number of steps changes as the data grows, and using this you can apply a Big O Notation to it and measure it against other algorithms. In many cases you’ll be using a data structure in which the ways you interact with it are well known, and in that case it’s easier to judge how it will scale as the input changes.
Firstly, we’ll summarise the Big O Notations and then provide a little more context for each one. The reading materials will dive into greater detail.
Big O notation
The Big O Notations in the order of speed from fastest to slowest are:
 O(1)  Constant Complexity
 O(log N)  Logarithmic Complexity
 O(N)  Linear Complexity
 O(N log N)  N x log N Complexity
 O(n²)  Quadratic Complexity
 O(n³)  Cubic Complexity
 O(2ⁿ)  Exponential Complexity
 O(N!)  Factorial Complexity
O(1)  constant complexity
To understand Constant Complexity, let’s use an array.
arr = [1, 2, 3, 4, 5]
If we want to look up what is at index 2, we can get to the element using arr[2]
which would give us back 3
. This takes just one step. If we double our array…
arr = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
We can still access any element in just one step. arr[7]
gives us 8
in a single step. Our array can keep growing and we can always access any element in a single step. It’s constant. Hence we have O(1)
.
Looking up something in one step is as good as it gets for time complexity.
While we’re looking at the basic form of Big O, let’s take a look at one of its little gotchas to keep in mind. You may have thought a moment ago, is it really just one step? The answer is technically no, in reality the computer must first look up where the array is in memory, then from the first element in the array it needs to jump to the index argument provided. That’s at least a couple of steps. So you wouldn’t be wrong for writing something like O(1 + 2(steps))
. However, the 2 steps are merely incidental. With an array of 10,000 elements, it still takes the same amount of steps as if the array was 2 elements. Because of this, Big O doesn’t concern itself with these incidental numbers. They don’t provide any context to how the complexity grows when the data size changes, because they are constant, and so in Big O they are dropped. Big O only wants to tell us an algorithm’s complexity relative to the size of the input.
Do the number of steps matter? Yes, they might. We’ll touch on when this may be the case a little later.
O(log N)  logarithmic complexity
Logarithmic Complexity tells us that the numbers of steps an algorithm takes increases by 1 as the data doubles. That’s still pretty efficient when you think about it. Going from 5,000 to 10,000 data elements and only taking one additional step can scale really well.
One such algorithm that does this is Binary Search. It only works on sorted arrays, but if you have an array of 10 items in sorted order
arr = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
and wanted to know if it had the number 7
, Binary Search would guess the middle item of the array and see what is there. Because the array is sorted, if the number at the middle index was 6
, then we know anything to the left of that index cannot be the number 7, as those items must be lower than 6 in a sorted array.
arr = [, , , , , 6, 7, 8, 9, 10]
Therefore in just one step, we’ve eliminated half of the array. We can do the same with the remaining half. We can guess the middle index and see if it’s 7. Half of that (half of an array) array eliminated again. In this case, the middle index would be 8, and we know that 7 is less than 8 so we can eliminate anything to the right of the number 8.
arr = [6, 7, 8, , ]
We can keep doing this until we have an array of just one item. If it matches the number we’re looking for, we’ve found it. If not, then it isn’t in the array.
The below table summarises the size of an array doubling and how many steps in Big O terms we would need to arrive at one element to see if it matches what we’re looking for:
Size  Steps 

1  1 
2  2 
4  3 
8  4 
16  5 
32  6 
Pretty impressive eh!
O(N)  linear complexity
This one is pretty easy to wrap your head around. Linear Complexity just tells us that as the number of items grows, the number of steps grows at exactly the same rate. Every time you iterate over an array is an example of Linear Complexity. If you have an array of 5 items, then we can iterate every element in 5 steps. An array of 10 items can be iterated in 10 steps. If you come across any algorithm with a Big O efficiency of O(N)
, you know that the number of steps will increase in line with the number of elements in your data structure.
O(N log N)  N x log N complexity
You can’t say this one isn’t appropriately named. This notation means we have an algorithm which initially is O(log N)
such as our example earlier of Binary Search where it repeatedly breaks an array in half, but with O(N log N)
each of those array halves is processed by another algorithm with a complexity of O(N)
.
One such algorithm is the merge sort algorithm from our previous lesson. :)
O(n²)  quadratic complexity
You’ve probably written code with a Quadratic Complexity on your programming journey. It’s commonly seen when you loop over a data set and within each loop you loop over it again. For example, if our array has 3 items, the nested loops require 3² = 9 substeps. Adding just one more item to the array almost doubles this number to 4² = 16. Adding a 5th item takes us to 5² = 25 substeps. Then doubling the array size to 10 items increases the substeps from 25 to 100, so 4 times as much work needed! We hope you can see where we’re going with this…
O(n³)  cubic complexity
Think triple nested loops baby. If looping over an array with n items, 1 extra item adds an extra outer loop, an extra middle loop, and an extra innermost loop. When using such triply nested loops on an array of size n, we require a total of n³ substeps. For example, if our array has 3 items, the triplynested loops require a total of 3³ = 27 substeps. Adding one more item more than doubles this number to 4³ = 64 substeps. The task almost doubles again for 5 items, with 5³ = 125 substeps. Doubling our array size to 10 items means we require 10³ = 1000 substeps in total, 8 times as many as before! 100 items in the array require a total of 1,000,000 substeps. Ouch!
O(2ⁿ)  exponential complexity
Exponential Complexity means that with each item added to the data size, the number of steps doubles from the previous number of steps. Let’s provide a little table to see how quickly this can get out of hand.
Size  Steps 

1  2 
2  4 
3  8 
4  16 
5  32 
6  64 
7  128 
8  256 
9  512 
10  1024 
You want to avoid this if at all possible, otherwise you won’t be processing much data quickly.
O(N!)  factorial complexity
A factorial is the product of the sequence of n integers. For example, the factorial of 4 (written as 4!) is 4 * 3 * 2 * 1.
You will come across Factorial Complexity if you ever need to calculate permutations or combinations. If you have an array and have to work out all the combinations you can make from the array, that is a Factorial complexity. It’s manageable for a small number of items, but the leap with each new item in a dataset can be huge.
The factorial of 3 is 6 (3 * 2 * 1). The factorial of 4 is 24. The factorial of 10? 3,628,800. So you can see how quickly things can get out of hand.
Alternatives to big O
If Big O gives us the worstcase scenario of how our algorithm will scale, what alternatives are there?
Big Ω (omega notation)
Omega Notations gives us the bestcase scenario for an algorithm. To understand where this might be, let’s look at a method and discuss how we can measure its complexity.
def find_value(arr)
arr.each do item
return item if item == 1
end
end
In the worst case (Big O), which would happen if the item is not in the array, we would say it had linear complexity O(N)
. This is because the item
we are looking for is not in the array, so our code must iterate on every value. If the array input doubles in size then the worst case also means our method must double the number of iterations looking for the item
.
However, in the bestcase scenario the value we are looking for will be the first item in the array. In this case our algorithm takes just one step. This has a complexity of Ω(1)
. This is its Omega Complexity.
Omega Notation isn’t considered as useful because it is unlikely our item will often be the first item in our data structure search, so it doesn’t give us any idea how well the algorithm will scale.
BigΘ (bigtheta notation)
While Omega Notation measures the bestcase scenario for an algorithm’s efficiency, and Big O measures the worst case, Theta looks to give the exact value or a useful range between narrow upper and lower bounds.
If we had some code that looped every item in an array, then it doesn’t matter the size of the array. Our algorithm will always run in O(N)
time in its bestcase and worstcase scenarios. In that case we know its exact performance in all scenarios is Θ(N)
, and that is the Theta performance of our algorithm. For other algorithms, Theta may represent both the lower and upper bound of an algorithm that has different complexities. We won’t get into this more here because Big O is the primary notation used for general algorithm time complexity.
This is just a simplistic explanation to try to make the topic approachable. If you do happen to be mathematically minded, then you’ll find more detailed explanations with a quick search online.
Why big O
Now that we’ve touched on the different ways of quantifying an algorithm’s efficiency, hopefully it’s clear why we choose to use the worstcase scenario when measuring the efficiency of that algorithm.
Using a worstcase scenario we can make sure our algorithm will scale in all outcomes. If we write an algorithm that could potentially run in constant time, but could also run in linear time in the worst case, it can only scale as the input grows if it still works when the worst case does happen. You need to be confident your code won’t lock up and leave users frustrated if you suddenly get an input of a million items instead of 10.
Algorithms with the same complexity
If we write two algorithms with the same complexity, does that mean they’re equally good to use? We’ll answer this question with two code examples which we’ll then discuss a bit further to try and answer the question.
The first example is some code we’ve seen already, our odd_numbers
method.
def odd_numbers(max_number)
current_number = 1
while current_number < max_number
if current_number % 2 != 0
puts current_number
end
current_number += 1
end
end
The time complexity of this algorithm is O(N)
. As the data size increases, the number of steps of our algorithm increases at the same rate.
Let’s look at another version:
def odd_numbers(max_number)
current_number = 1
while current_number < max_number
if current_number % 2 != 0
puts current_number
end
current_number += 2
end
end
Not much of a change, but this time we increase current_number
by 2. How does this affect our algorithm runtime? Well, for an input of n
, the number of steps is approximately half as we iterate by 2 each time. This is an algorithm of O(N/2)
but as mentioned earlier, Big O doesn’t concern itself with constants because they aren’t relative to how an algorithm scales as the input changes and it wouldn’t be fun or easy to have to compare an algorithm of O(N/2 + 5 N)
against O(N + 5 / 2N)
. Therefore, the Big O efficiency of both algorithms is O(N)
. They scale at the same rate as the input grows.
Therefore, you also need to ensure the code you write is as efficient as it can be within its time complexity.
Assignment
 Read through A Rubyists Guide to BigO Notation. The last section talks about how this applies to database queries in a web app, which you won’t have covered yet, but it’s something to keep in mind as you move forward into the Rails materials.
 The BigO cheat sheet is an amazing resource. It gives a complexity chart where you can see how the different algorithms perform as the data size increases and also gives the time complexity for common data structure operations along with those for common sorting algorithms.
 This Guide to Time Complexity for Ruby Developers gives some more insight into applying a BigO notation to your own code.
Knowledge check
This section contains questions for you to check your understanding of this lesson on your own. If you’re having trouble answering a question, click it and review the material it links to.
Additional resources
This section contains helpful links to related content. It isn’t required, so consider it supplemental.

It’s not a free resource but A common sense guide to data structures and algorithms does a great job making these topics approachable to people not familiar with some of the mathematical terminology used.

In this video, Introduction to Big O Notation and Time Complexity, the author provides a stepbystep process for how to analyze and understand time complexity for various algorithms.