Know Thy Complexities!

Hi there! This webpage covers the space and time Big-O complexities of common algorithms used in Computer Science. When preparing for technical interviews in the past, I found myself spending hours crawling the internet putting together the best, average, and worst case complexities for search and sorting algorithms so that I wouldn’t be stumped when asked about them. Over the last few years, I’ve interviewed at several Silicon Valley startups, and also some bigger companies, like Google, Facebook, Yahoo, LinkedIn, and eBay, and each time that I prepared for an interview, I thought to myself “Why hasn’t someone created a nice Big-O cheat sheet?”. So, to save all of you fine folks a ton of time, I went ahead and created one. Enjoy!

Common Data Structure Operations

Data Structure	Time Complexity								Space Complexity
	Average				Worst				Worst
	Access	Search	Insertion	Deletion	Access	Search	Insertion	Deletion
Array	`Θ(1)`	`Θ(n)`	`Θ(n)`	`Θ(n)`	`O(1)`	`O(n)`	`O(n)`	`O(n)`	`O(n)`
Stack	`Θ(n)`	`Θ(n)`	`Θ(1)`	`Θ(1)`	`O(n)`	`O(n)`	`O(1)`	`O(1)`	`O(n)`
Queue	`Θ(n)`	`Θ(n)`	`Θ(1)`	`Θ(1)`	`O(n)`	`O(n)`	`O(1)`	`O(1)`	`O(n)`
Singly-Linked List	`Θ(n)`	`Θ(n)`	`Θ(1)`	`Θ(1)`	`O(n)`	`O(n)`	`O(1)`	`O(1)`	`O(n)`
Doubly-Linked List	`Θ(n)`	`Θ(n)`	`Θ(1)`	`Θ(1)`	`O(n)`	`O(n)`	`O(1)`	`O(1)`	`O(n)`
Skip List	`Θ(log(n))`	`Θ(log(n))`	`Θ(log(n))`	`Θ(log(n))`	`O(n)`	`O(n)`	`O(n)`	`O(n)`	`O(n log(n))`
Hash Table	`N/A`	`Θ(1)`	`Θ(1)`	`Θ(1)`	`N/A`	`O(n)`	`O(n)`	`O(n)`	`O(n)`
Binary Search Tree	`Θ(log(n))`	`Θ(log(n))`	`Θ(log(n))`	`Θ(log(n))`	`O(n)`	`O(n)`	`O(n)`	`O(n)`	`O(n)`
Cartesian Tree	`N/A`	`Θ(log(n))`	`Θ(log(n))`	`Θ(log(n))`	`N/A`	`O(n)`	`O(n)`	`O(n)`	`O(n)`
B-Tree	`Θ(log(n))`	`Θ(log(n))`	`Θ(log(n))`	`Θ(log(n))`	`O(log(n))`	`O(log(n))`	`O(log(n))`	`O(log(n))`	`O(n)`
Red-Black Tree	`Θ(log(n))`	`Θ(log(n))`	`Θ(log(n))`	`Θ(log(n))`	`O(log(n))`	`O(log(n))`	`O(log(n))`	`O(log(n))`	`O(n)`
Splay Tree	`N/A`	`Θ(log(n))`	`Θ(log(n))`	`Θ(log(n))`	`N/A`	`O(log(n))`	`O(log(n))`	`O(log(n))`	`O(n)`
AVL Tree	`Θ(log(n))`	`Θ(log(n))`	`Θ(log(n))`	`Θ(log(n))`	`O(log(n))`	`O(log(n))`	`O(log(n))`	`O(log(n))`	`O(n)`
KD Tree	`Θ(log(n))`	`Θ(log(n))`	`Θ(log(n))`	`Θ(log(n))`	`O(n)`	`O(n)`	`O(n)`	`O(n)`	`O(n)`

Array Sorting Algorithms

Algorithm	Time Complexity			Space Complexity
	Best	Average	Worst	Worst
Quicksort	`Ω(n log(n))`	`Θ(n log(n))`	`O(n^2)`	`O(log(n))`
Mergesort	`Ω(n log(n))`	`Θ(n log(n))`	`O(n log(n))`	`O(n)`
Timsort	`Ω(n)`	`Θ(n log(n))`	`O(n log(n))`	`O(n)`
Heapsort	`Ω(n log(n))`	`Θ(n log(n))`	`O(n log(n))`	`O(1)`
Bubble Sort	`Ω(n)`	`Θ(n^2)`	`O(n^2)`	`O(1)`
Insertion Sort	`Ω(n)`	`Θ(n^2)`	`O(n^2)`	`O(1)`
Selection Sort	`Ω(n^2)`	`Θ(n^2)`	`O(n^2)`	`O(1)`
Tree Sort	`Ω(n log(n))`	`Θ(n log(n))`	`O(n^2)`	`O(n)`
Shell Sort	`Ω(n log(n))`	`Θ(n(log(n))^2)`	`O(n(log(n))^2)`	`O(1)`
Bucket Sort	`Ω(n+k)`	`Θ(n+k)`	`O(n^2)`	`O(n)`
Radix Sort	`Ω(nk)`	`Θ(nk)`	`O(nk)`	`O(n+k)`
Counting Sort	`Ω(n+k)`	`Θ(n+k)`	`O(n+k)`	`O(k)`
Cubesort	`Ω(n)`	`Θ(n log(n))`	`O(n log(n))`	`O(n)`

Category: Algorithm Warmup