Big O Notation - Algorithm Benchmark

Compare algorithms and visualize their time complexity in real-time

Explore Algorithms

Click on any algorithm to learn more about its implementation

📚Array Random AccessO(1)🗺️Hash MapO(1)📚Stack PushO(1)🔍Binary SearchO(log n)🌳Heap InsertO(log n)🔄Array IterationO(n)🔗Linked List SearchO(n)👆Two PointersO(n)🪟Sliding WindowO(n)⚡Quick SortO(n log n)🔀Merge SortO(n log n)🌲Heap SortO(n log n)🫧Bubble SortO(n²)📝Insertion SortO(n²)🎯Selection SortO(n²)🔍BFSO(V+E)🌊DFS RecursiveO(V+E)📚DFS IterativeO(V+E)

Benchmark Configuration

Configure your benchmark settings and select algorithms to compare

Select Complexity Class

Select Algorithms to Compare (0 selected)

Array Random Access

Arrays/Strings

Accessing an element by index in an array

HashMap Get

Hash Maps/Sets

Getting a value from a hash map

Stack Push

Stacks/Queues

Pushing an element onto a stack

Data Sizes (n) to Test

1005001,0005,00010,000

Operations per Test

⚪ Real-time OFF

Sorting Visualizer

Watch sorting algorithms in action

How to use: Press "Play All", or choose the play button for the individual row/column to animate.

	Insertion	Selection	Bubble	Shell	Merge	Heap	Quick	Quick3
Random	Loading...	Loading...	Loading...	Loading...	Loading...	Loading...	Loading...	Loading...
Nearly Sorted	Loading...	Loading...	Loading...	Loading...	Loading...	Loading...	Loading...	Loading...
Reversed	Loading...	Loading...	Loading...	Loading...	Loading...	Loading...	Loading...	Loading...
Few Unique	Loading...	Loading...	Loading...	Loading...	Loading...	Loading...	Loading...	Loading...

About Big O Notation

Big O notation describes the performance or complexity of an algorithm. It shows how the time (or space) an algorithm takes scales with the input size (n).

Categories Covered:

Arrays/StringsHash Maps/SetsStacks/QueuesTrees/HeapsGraphsRecursion vs IterationSorting/SearchingTwo PointersSliding Window