Hi there! This webpage covers the space and time BigO 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 Yahoo, eBay, LinkedIn, and Google, and each time that I prepared for an interview, I thought to myself "Why hasn't someone created a nice BigO cheat sheet?". So, to save all of you fine folks a ton of time, I went ahead and created one. Enjoy!  Eric
Excellent 
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Data Structure  Time Complexity  Space Complexity  

Average  Worst  Worst  
Access  Search  Insertion  Deletion  Access  Search  Insertion  Deletion  
Array  O(1) 
O(n) 
O(n) 
O(n) 
O(1) 
O(n) 
O(n) 
O(n) 
O(n) 
Stack  O(n) 
O(n) 
O(1) 
O(1) 
O(n) 
O(n) 
O(1) 
O(1) 
O(n) 
SinglyLinked List  O(n) 
O(n) 
O(1) 
O(1) 
O(n) 
O(n) 
O(1) 
O(1) 
O(n) 
DoublyLinked List  O(n) 
O(n) 
O(1) 
O(1) 
O(n) 
O(n) 
O(1) 
O(1) 
O(n) 
Skip List  O(log(n)) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(n) 
O(n) 
O(n) 
O(n) 
O(n log(n)) 
Hash Table   
O(1) 
O(1) 
O(1) 
 
O(n) 
O(n) 
O(n) 
O(n) 
Binary Search Tree  O(log(n)) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(n) 
O(n) 
O(n) 
O(n) 
O(n) 
Cartesian Tree   
O(log(n)) 
O(log(n)) 
O(log(n)) 
 
O(n) 
O(n) 
O(n) 
O(n) 
BTree  O(log(n)) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(n) 
RedBlack Tree  O(log(n)) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(n) 
Splay Tree   
O(log(n)) 
O(log(n)) 
O(log(n)) 
 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(n) 
AVL Tree  O(log(n)) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(n) 
Algorithm  Time Complexity  Space Complexity  

Best  Average  Worst  Worst  
Quicksort  O(n log(n)) 
O(n log(n)) 
O(n^2) 
O(log(n)) 
Mergesort  O(n log(n)) 
O(n log(n)) 
O(n log(n)) 
O(n) 
Timsort  O(n) 
O(n log(n)) 
O(n log(n)) 
O(n) 
Heapsort  O(n log(n)) 
O(n log(n)) 
O(n log(n)) 
O(1) 
Bubble Sort  O(n) 
O(n^2) 
O(n^2) 
O(1) 
Insertion Sort  O(n) 
O(n^2) 
O(n^2) 
O(1) 
Selection Sort  O(n^2) 
O(n^2) 
O(n^2) 
O(1) 
Shell Sort  O(n) 
O((nlog(n))^2) 
O((nlog(n))^2) 
O(1) 
Bucket Sort  O(n+k) 
O(n+k) 
O(n^2) 
O(n) 
Radix Sort  O(nk) 
O(nk) 
O(nk) 
O(n+k) 
Node / Edge Management  Storage  Add Vertex  Add Edge  Remove Vertex  Remove Edge  Query 

Adjacency list  O(V+E) 
O(1) 
O(1) 
O(V + E) 
O(E) 
O(V) 
Incidence list  O(V+E) 
O(1) 
O(1) 
O(E) 
O(E) 
O(E) 
Adjacency matrix  O(V^2) 
O(V^2) 
O(1) 
O(V^2) 
O(1) 
O(1) 
Incidence matrix  O(V ⋅ E) 
O(V ⋅ E) 
O(V ⋅ E) 
O(V ⋅ E) 
O(V ⋅ E) 
O(E) 
Type  Time Complexity  

Heapify  Find Max  Extract Max  Increase Key  Insert  Delete  Merge  
Linked List (sorted)   
O(1) 
O(1) 
O(n) 
O(n) 
O(1) 
O(m+n) 

Linked List (unsorted)   
O(n) 
O(n) 
O(1) 
O(1) 
O(1) 
O(1) 

Binary Heap  O(n) 
O(1) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(log(n)) 
O(m+n) 

Binomial Heap   
O(1) 
O(log(n)) 
O(log(n)) 
O(1) 
O(log(n)) 
O(log(n)) 

Fibonacci Heap   
O(1) 
O(log(n)) 
O(1) 
O(1) 
O(log(n)) 
O(1) 