Data Structures and Algorithms in JavaScript

This repository covers the implementation of the classical algorithms and data structures in JavaScript.

Table of Contents

Book

You can check out the book that goes deeper into each topic and provide addtional illustrations and explanations.

Algorithmic toolbox to avoid getting stuck while coding.
Explains data structures similarities and differences.
Algorithm analysis fundamentals (Big O notation, Time/Space complexity) and examples.
Time/space complexity cheatsheet.

We are covering the following data structures.

Arrays: Built-in in most languages so not implemented here. Post.
Linked Lists: each data node has a link to the next (and previous). Code | Post.
Queue: data flows in a "first-in, first-out" (FIFO) manner. Code | Post
Stacks: data flows in a "last-in, first-out" (LIFO) manner. Code | Post.

Trees: data nodes has zero or more adjacent nodes a.k.a. children. Each node can only have one parent node otherwise is a graph not a tree. Code | Post
1. Binary Trees: same as tree but only can have two children at most. Code | Post
2. Binary Search Trees (BST): same as binary tree, but the nodes value keep this order left < parent < rigth. Code] | Post
3. AVL Trees: Self-balanced BST to maximize look up time. Code | Post
4. Red-Black Trees: Self-balanced BST more loose than AVL to maximize insertion speed. Code
Maps: key-value store.
1. Hash Maps: implements map using a hash function. Code | Post
2. Tree Maps: implement map using a self-balanced BST. Code
Graphs: data nodes that can have a connection or edge to zero or more adjacent nodes. Unlike trees, nodes can have multiple parents, loops. Code | Post

We cover the following algorithms and techniques.