Don't want to get left behind? Learn to build with A.I. now 🤖

Big O Cheat Sheet ⭕

We created this Big O Cheat Sheet initially for students of Master the Coding Interview: Data Structures + Algorithms (my Coding Interview Bootcamp course) but we're now sharing it with any and all Developers that want to learn and remember the basics of Big O.

Want to download a PDF version of this Big O Cheat Sheet?

Enter your email below and we'll send it to you 👇

Unsubscribe anytime.

If you’ve stumbled across this page and are just starting to learn Big O, nice work! Big O is one of the most important topics for any software developer or engineer.

Even if (... when!) you're coding 10 years from now, this is a topic that will be around for a long time and will make you a better developer. This was true for me and will be for you as well.

Many of the biggest tech companies (Google, Amazon, Facebook... aka Meta, etc.) ask questions about Big O as part of their coding interview process for good reason.

That said, if you're currently stuck in an endless cycle of YouTube tutorials and not having success getting hired as a developer at the company of your dreams, I can help.

Come join the Zero To Mastery Academy and take my Ultimate Coding Interview Bootcamp.

You'll not only learn data structures and algorithms (and Big O) but also the exact steps to take to get more interviews, more job offers, and a higher salary.

I've also made the entire Big O introduction section of my Coding Interview Bootcamp completely free (no signup or credit card necessary) so that you can learn more about Big O for free.

You can also watch it on Youtube right here 👇

Please enjoy this cheatsheet and if you'd like to submit any corrections or suggestions, feel free to email us at

Big O's

O(1) Constant - no loops

O(log N) Logarithmic - usually searching algorithms have log n if they are sorted (Binary Search)

O(n) Linear - for loops, while loops through n items

O(n log(n)) Log Linear - usually sorting operations

O(n^2) Quadratic - every element in a collection needs to be compared to ever other element. Two nested loops

O(2^n) Exponential - recursive algorithms that solves a problem of size N

O(n!) Factorial - you are adding a loop for every element

Iterating through half a collection is still O(n)

Two separate collections: O(a * b)

Big O Name Description
1 Constant statement, one line of code
log(n) Logarithmic Divide and conquer (binary search)
n Linear Loop
n*log(n) Linearithmic Effective sorting algorithms
n^2 Quadratic Double loop
n^3 Cubic Triple loop
2^n Exponential Complex full search

What Can Cause Time in a Function?

  • Operations (+,-, \*, /)
  • Comparisons (<, >, ===)
  • Looping (for, while)
  • Outside Function call (function())

Sorting Algorithms

Sorting Algorithms Space complexity Time complexity Time complexity
Worst case Best case Worst case
Insertion Sort O(1) O(n) O(n^2)
Selection Sort O(1) O(n^2) O(n^2)
Bubble Sort O(1) O(n) O(n^2)
Mergesort O(n) O(n log n) O(n log n)
Quicksort O(log n) O(n log n) O(n^2)
Heapsort O(1) O(n log n) O(n log n)

Common Data Structure Operations

Worst Case→ Access Search Insertion Deletion Space Complexity
Array O(1) O(n) O(n) O(n) O(n)
Stack O(n) O(n) O(1) O(1) O(n)
Queue O(n) O(n) O(1) O(1) O(n)
Singly-Linked List O(n) O(n) O(1) O(1) O(n)
Doubly-Linked List O(n) O(n) O(1) O(1) O(n)
Hash Table N/A O(n) O(n) O(n) O(n)

Rule Book

Rule 1: Always worst Case

Rule 2: Remove Constants

Rule 3:

  • Different inputs should have different variables: O(a + b).
  • A and B arrays nested would be: O(a * b)

+ for steps in order

* for nested steps

Rule 4: Drop Non-dominant terms

What Causes Space Complexity?

  • Variables
  • Data Structures
  • Function Call
  • Allocations