JavaScript Algorithms and Data Structures

This repository contains JavaScript based examples of many popular algorithms and data structures.

Each algorithm and data structure has its own separate README with related explanations and links for further reading (including ones to YouTube videos).

Read this in other languages: 简体中文繁體中文한국어

We’re writing a book that will clearly explain, in detail, the main algorithms. If you’d like to be notified when the “JavaScript Algorithms” book launches, click here.

Data Structures

A data structure is a particular way of organizing and storing data in a computer so that it can be accessed and modified efficiently. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data.

B – Beginner, A – Advanced

Algorithms

An algorithm is an unambiguous specification of how to solve a class of problems. It is a set of rules that precisely define a sequence of operations.

B – Beginner, A – Advanced

Algorithms by Topic

Algorithms by Paradigm

An algorithmic paradigm is a generic method or approach which underlies the design of a class of algorithms. It is an abstraction higher than the notion of an algorithm, just as an algorithm is an abstraction higher than a computer program.

How to use this repository

Install all dependencies

npm install

Run ESLint

You may want to run it to check code quality.

npm run lint

Run all tests

npm test

Run tests by name

npm test -- 'LinkedList'

Playground

You may play with data-structures and algorithms in ./src/playground/playground.js file and write tests for it in ./src/playground/__test__/playground.test.js.

Then just simply run the following command to test if your playground code works as expected:

npm test -- 'playground'

Useful Information

References

▶ Data Structures and Algorithms on YouTube

Big O Notation

Order of growth of algorithms specified in Big O notation.

Big O graphs

Source: Big O Cheat Sheet.

Below is the list of some of the most used Big O notations and their performance comparisons against different sizes of the input data.

Big O NotationComputations for 10 elementsComputations for 100 elementsComputations for 1000 elements
O(1)111
O(log N)369
O(N)101001000
O(N log N)306009000
O(N^2)100100001000000
O(2^N)10241.26e+291.07e+301
O(N!)36288009.3e+1574.02e+2567

Data Structure Operations Complexity

Data StructureAccessSearchInsertionDeletionComments
Array1nnn
Stacknn11
Queuenn11
Linked Listnn11
Hash TablennnIn case of perfect hash function costs would be O(1)
Binary Search TreennnnIn case of balanced tree costs would be O(log(n))
B-Treelog(n)log(n)log(n)log(n)
Red-Black Treelog(n)log(n)log(n)log(n)
AVL Treelog(n)log(n)log(n)log(n)
Bloom Filter11False positives are possible while searching

Array Sorting Algorithms Complexity

NameBestAverageWorstMemoryStableComments
Bubble sortnn2n21Yes
Insertion sortnn2n21Yes
Selection sortn2n2n21No
Heap sortn log(n)n log(n)n log(n)1No
Merge sortn log(n)n log(n)n log(n)nYes
Quick sortn log(n)n log(n)n2log(n)NoQuicksort is usually done in-place with O(log(n)) stack space
Shell sortn log(n)depends on gap sequencen (log(n))21No
Counting sortn + rn + rn + rn + rYesr – biggest number in array
Radix sortn * kn * kn * kn + kYesk – length of longest key
Jonny Richards

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