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.
Table of Contents (22 chapters)
Title Page
Dedication
Packt Upsell
Contributors
Preface
Index

The binary heap data structure


The binary heap is a special binary tree with the following two properties:

  • It is a complete binary tree, meaning all levels of the tree have both left and right children (with the exception of the last-level leaves), and the last level has all children as left as possible. This is called as shape prop­erty.
  • A binary heap is either a min heap or a max heap. The min heap allows you to quickly extract the minimum value of the tree, and the max heap allows you to quickly extract the maximum value of the tree. All nodes are either greater than or equal to (max heap), or less than or equal to (min heap), each of its child nodes. This is called heap prop­erty.

The following diagram contains some examples of invalid and valid heaps:

Although the binary heap is a binary tree, it is not necessarily a binary search tree (BST). In the binary heap, every child node needs to be greater than or equal to its parent node (min heap) or less than or equal to its parent node (max...