Learning JavaScript Data Structures and Algorithms - Third Edition

Book Image

Learning JavaScript Data Structures and Algorithms - Third Edition

Book Image

Learning JavaScript Data Structures and Algorithms - Third Edition

Overview of this book

A data structure is a particular way of organizing data in a computer to utilize resources efficiently. Data structures and algorithms are the base of every solution to any programming problem. With this book, you will learn to write complex and powerful code using the latest ES 2017 features. Learning JavaScript Data Structures and Algorithms begins by covering the basics of JavaScript and introduces you to ECMAScript 2017, before gradually moving on to the most important data structures such as arrays, queues, stacks, and linked lists. You will gain in-depth knowledge of how hash tables and set data structures function as well as how trees and hash maps can be used to search files in an HD or represent a database. This book serves as a route to take you deeper into JavaScript. You’ll also get a greater understanding of why and how graphs, one of the most complex data structures, are largely used in GPS navigation systems in social networks. Toward the end of the book, you’ll discover how all the theories presented in this book can be applied to solve real-world problems while working on your own computer networks and Facebook searches.

Title Page

Dedication

Packt Upsell

Contributors

Preface

Free Chapter

JavaScript – A Quick Overview

JavaScript – A Quick Overview

JavaScript data structure and algorithms

Setting up the environment

JavaScript basics

Debugging and tools

ECMAScript and TypeScript Overview

ECMAScript and TypeScript Overview

ECMAScript or JavaScript?

ECMAScript 2015+ functionalities

Introducing TypeScript

Arrays

Why should we use arrays?

Creating and initializing arrays

Adding elements

Removing elements

Adding and removing elements from a specific position

Two-dimensional and multi-dimensional arrays

References for JavaScript array methods

The TypedArray class

Arrays in TypeScript

Stacks

Creating a JavaScript data structure and algorithm library

The stack data structure

Creating a JavaScript object-based Stack class

Protecting the internal elements of the data structure

Solving problems using stacks

Queues and Deques

Queues and Deques

The queue data structure

The deque data structure

Solving problems using queues and deques

Linked Lists

The linked list data structure

Doubly linked lists

Circular linked lists

Sorted linked lists

Creating the StackLinkedList class

Sets

Structuring a dataset

Creating a Set class

ECMAScript 2015 – the Set class

Multisets or bags

Dictionaries and Hashes

Dictionaries and Hashes

The dictionary data structure

The ES2015 Map class

The ES2015 WeakMap and WeakSet classes

Recursion

Understanding recursion

Calculating the factorial of a number

The Fibonacci sequence

Why use recursion? Is it faster?

Trees

The tree data structure

Tree terminology

The binary and binary search trees

Searching for values in a tree

Self-balancing trees

Binary Heap and Heap Sort

Binary Heap and Heap Sort

The binary heap data structure

The heap sort algorithm

Graphs

Graph terminology

Representing a graph

Creating the Graph class

Graph traversals

Shortest path algorithms

Minimum spanning tree (MST)

Sorting and Searching Algorithms

Sorting and Searching Algorithms

Sorting algorithms

Searching algorithms

Shuffle algorithms

Algorithm Designs and Techniques

Algorithm Designs and Techniques

Divide and conquer

Dynamic programming

Greedy algorithms

Backtracking algorithms

Introduction to functional programming

Algorithm Complexity

Algorithm Complexity

Having fun with algorithms

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Self-balancing trees

Now that you have learned how to work with BST, you can dive into the study of trees if you want to.

BST has a problem: depending on how many nodes you add, one of the edges of the tree can be very deep, meaning a branch of the tree can have a high level and another branch can have a low level, as shown in the following diagram:

This can cause performance issues when adding, removing, and searching for a node on a particular edge of the tree. For this reason, there is a tree called the Adelson-Velskii and Landi's tree (AVL tree). The AVL tree is a self-balancing BST, which means the height of both the left and right subtrees of any node differ by 1 at most. You will learn more about the AVL tree in the following topic.

Adelson-Velskii and Landi’s tree (AVL tree)

The AVL tree is a self-balancing tree, meaning the tree tries to self-balance whenever a node is added to it or removed from it. The height of the left or right subtree of any node (and any level) differs by 1 at...