Book Image

Learning JavaScript Data Structures and Algorithms - Second Edition

By : Loiane Groner
Book Image

Learning JavaScript Data Structures and Algorithms - Second Edition

By: Loiane Groner

Overview of this book

This book begins by covering basics of the JavaScript language and introducing ECMAScript 7, before gradually moving on to the current implementations of ECMAScript 6. You will gain an in-depth knowledge of how hash tables and set data structure functions, as well as how trees and hash maps can be used to search files in a HD or represent a database. This book is an accessible route deeper into JavaScript. Graphs being one of the most complex data structures you’ll encounter, we’ll also give you a better understanding of why and how graphs are largely used in GPS navigation systems in social networks. Toward the end of the book, you’ll discover how all the theories presented by this book can be applied in real-world solutions while working on your own computer networks and Facebook searches.

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Learning JavaScript Data Structures and Algorithms - Second Edition
Loiane Groner
Jun, 2016 | 314 pages
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Table of Contents (18 chapters)
Learning JavaScript Data Structures and Algorithms - Second Edition
Learning JavaScript Data Structures and Algorithms - Second Edition
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
JavaScript—A Quick Overview
JavaScript—A Quick Overview
JavaScript data structure and algorithms
Setting up the environment
JavaScript basics
Control structures
Functions
Object-oriented programming in Javascript
Debugging and tools
Introducing ECMAScript
ECMAScript 6 functionalities
ECMAScript 7 functionalities
Summary
2
Arrays
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 multidimensional arrays
References for JavaScript array methods
The TypedArray class
Summary
3
Stacks
Stacks
The stack data structure
EcmaScript 6 and the Stack class
Solving problems using stacks
Summary
4
Queues
Queues
The queue data structure
Creating a queue
The Queue class using ECMAScript 6 syntax
The priority queue
The circular queue - Hot Potato
JavaScript task queues
Summary
5
Linked Lists
Linked Lists
The linked list data structure
Creating a linked list
Doubly linked lists
Circular linked lists
Summary
6
Sets
Sets
Structuring a dataset
Creating a set
Set operations
ES6 – the Set class
Summary
7
Dictionaries and Hashes
Dictionaries and Hashes
Dictionaries
The hash table
The ES6 Map class
The ES6 WeakMap and WeakSet classes
Summary
8
Trees
Trees
The tree data structure
Tree terminology
The binary and binary search trees
Tree traversal
Searching for values in a tree
Self-balancing trees
Summary
9
Graphs
Graphs
Graph terminology
Representing a graph
Creating the Graph class
Graph traversals
Shortest path algorithms
Minimum spanning tree (MST)
Summary
10
Sorting and Searching Algorithms
Sorting and Searching Algorithms
The sorting algorithms
Searching algorithms
Summary
11
Patterns of Algorithm
Patterns of Algorithm
Recursion
Dynamic programming
Greedy algorithms
Introduction to functional programming
Summary
12
Algorithm Complexity
Algorithm Complexity
Big-O notation
Having fun with algorithms
Summary

Big-O notation

In Chapter 10 , Sorting and Searching Algorithms, we introduced the concept of big-O notation. What does this mean exactly? It is used to describe the performance or complexity of an algorithm.

When analyzing algorithms, the following classes of functions are the most commonly encountered:

Notation

Name

O(1)

Constant

O(log(n))

Logarithmic

O((log(n))c)

Poly-logarithmic

O(n)

Linear

O(n2)

Quadratic

O(nc)

Polynomial

O(cn)

Exponential

Understanding big-O notation

How do we measure the efficiency of an algorithm? We usually use resources such as CPU (time) usage, memory usage, disk usage, and network usage. When talking about big-O notation, we usually consider CPU (time) usage.

Let's try to understand how big-O notation works using some examples.

O(1)

Consider the following function:

function increment(num){ 
  return ++num; 
}

If we try to execute the increment(1) function, we will have an execution time equal to x. If...

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