Overview of this book

Java 9 Data Structures and Algorithms covers classical, functional, and reactive data structures, giving you the ability to understand computational complexity, solve problems, and write efficient code. This book is based on the Zero Bug Bounce milestone of Java 9. We start off with the basics of algorithms and data structures, helping you understand the fundamentals and measure complexity. From here, we introduce you to concepts such as arrays, linked lists, as well as abstract data types such as stacks and queues. Next, we’ll take you through the basics of functional programming while making sure you get used to thinking recursively. We provide plenty of examples along the way to help you understand each concept. You will also get a clear picture of reactive programming, binary searches, sorting, search trees, undirected graphs, and a whole lot more!
Java 9 Data Structures and Algorithms
Credits
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Preface
Free Chapter
Why Bother? – Basic
Cogs and Pulleys – Building Blocks
Protocols – Abstract Data Types
Detour – Functional Programming
Efficient Searching – Binary Search and Sorting
Efficient Sorting – quicksort and mergesort
Concepts of Tree
More About Search – Search Trees and Hash Tables
Concepts of Graph
Reactive Programming
Index

Heap

A heap is a balanced binary tree that follows just two constraints:

• The value in any node is less than the value in either of the children. This property is also called the heap property.

• The tree is as balanced as possible—in the sense that any level is completely filled before a single node is inserted in the next level.

The following figure shows a sample heap:

Figure 1. A sample heap

It would not be really clear until we actually discuss how to insert elements and remove the least element. So let's jump into it.

Insertion

The first step of insertion is to insert the element in the next available position. The next available position is either another position in the same level or the first position in the next level; of course, this applies when there is no vacant position in the existing level.

The second step is to iteratively compare the element with its parent and keep switching until the element is bigger than the parent, thus restoring the constraints. The following figure shows the...