Data Structures & Algorithms in Swift Updated for Swift 4.2

Happy Wednesday – it’s another iOS 12 Launch Party book release!

Today, we’re excited to announce that Data Structures & Algorithms in Swift has been updated for Swift 4.2 and Xcode 10!

We’ve also added a new chapter on time and space complexity that explains how to measure the performance and scalability of any algorithm.

This will be a free update for existing Data Structures and Algorithms in Swift digital edition customers — our way to say “thanks” to our readers for their support.

Don’t own Data Structures and Algorithms in Swift? Read on to see what’s inside, and how to take advantage of our launch discount!

What is Data Structures and Algorithms in Swift?

Understanding how data structures and algorithms work in code is crucial for creating efficient and scalable apps. Swift’s Standard Library has a small set of general purpose collection types, yet they don’t give you what you need for every case.

Moreover, you’ll find these concepts helpful for your professional and personal development as a developer.

When you interview for a software engineering position, chances are that you’ll be tested on data structures and algorithms. Having a strong foundation in data structures and algorithms is the “bar” for many companies with software engineering positions.

Knowing the strategies used by algorithms to solve tricky problems gives you ideas for improvements you can make to your own code. Knowing more data structures than just the standard array and dictionary also gives you a bigger collection of tools that you can use to build your own apps.

Here’s what’s contained in the updated release of the book:

Section I: Introduction

• Chapter 1: Why Learn Data Structures & Algorithms?: Data structures are a well-studied area, and the concepts are language agnostic; a data structure from C is functionally and conceptually identical to the same data structure in any other language, such as Swift. At the same time, the high-level expressiveness of Swift make it an ideal choice for learning these core concepts without sacrificing too much performance.
• Chapter 2: Swift Standard Library: Before you dive into the rest of this book, you’ll first look at a few data structures that are baked into the Swift language. The Swift standard library refers to the framework that defines the core components of the Swift language. Inside, you’ll find a variety of tools and types to help build your Swift apps.
• Chapter 3: Complexity (New!): When you’re working with a small amount of data, an expensive algorithm may still feel fast. However, as the amount of data increases, an expensive algorithm becomes crippling. So how bad can it get? Understanding how to quantify this is an important
  skill for you to have.

Section II: Elementary Data Structures

Easy-to-understand examples show key concepts, such as trees!

• Chapter 4: Linked List: A linked list is a collection of values arranged in a linear unidirectional sequence. A linked list has several theoretical advantages over contiguous storage options such as the Swift Array, including constant time insertion and removal from the front of the list, and other reliable performance characteristics.
• Chapter 6: Stacked Data Structure: The stack data structure is identical in concept to a physical stack of objects. When you add an item to a stack, you place it on top of the stack. When you remove an item from a stack, you always remove the topmost item. Stacks are useful, and also exceedingly simple. The main goal of building a stack is to enforce how you access your data.
• Chapter 8: Queues: Lines are everywhere, whether you are lining up to buy tickets to your favorite movie, or waiting for a printer machine to print out your documents. These real-life scenarios mimic the queue data structure. Queues use first-in-first-out ordering, meaning the first element that was enqueued will be the first to get dequeued. Queues are handy when you need to maintain the order of your elements to process later.


Easy-to-understand examples show key concepts, such as trees!

Easy-to-understand examples show key concepts, such as trees!

Section III: Trees

Helpful visuals demonstrate how to organize and sort data!

• Chapter 10: Trees: The tree is a data structure of profound importance. It is used to tackle many recurring challenges in software development, such as representing hierarchical relationships, managing sorted data, and facilitating fast lookup operations. There are many types of trees, and they come in various shapes and sizes.
• Chapter 12: Binary Trees: In the previous chapter, you looked at a basic tree where each node can have many children. A binary tree is a tree where each node has at most two children, often referred to as the left and right children. Binary trees serve as the basis for many tree structures and algorithms. In this chapter, you’ll build a binary tree and learn about the three most important tree traversal algorithms.
• Chapter 14: Binary Search Trees: A binary search tree facilitates fast lookup, addition, and removal operations. Each operation has an average time complexity of O(log n), which is considerably faster than linear data structures such as arrays and linked lists.
• Chapter 16: AVL Trees: In the previous chapter, you learned about the O(log n) performance characteristics of the binary search tree. However, you also learned that unbalanced trees can deteriorate the performance of the tree, all the way down to O(n). In 1962, Georgy Adelson-Velsky and Evgenii Landis came up with the first self-balancing binary search tree: the AVL Tree.


Helpful visuals demonstrate how to organize and sort data!

Helpful visuals demonstrate how to organize and sort data!

• Chapter 18: Tries: The trie (pronounced as “try”) is a tree that specializes in storing data that can be represented as a collection, such as English words. The benefits of a trie are best illustrated by looking at it in the context of prefix matching, which is what you’ll do in this chapter.
• Chapter 20: Binary Search: Binary search is one of the most efficient searching algorithms with a time complexity of O(log n). This is comparable with searching for an element inside a balanced binary search tree. To perform a binary search, the collection must be able to perform index manipulation in constant time, and must be sorted.
• Chapter 22: The Heap Data Structure: A heap is a complete binary tree, also known as a binary heap, that can be constructed using an array. Heaps come in two flavors: Max heaps and Min heaps. Have you seen the movie Toy Story, with the claw machine and the squeaky little green aliens? Imagine that the claw machine is operating on your heap structure, and will always pick the minimum or maximum value, depending on the flavor of heap.
• Chapter 24: Priority Queue: Queues are simply lists that maintain the order of elements using first-in-first-out (FIFO) ordering. A priority queue is another version of a queue that, instead of using FIFO ordering, dequeues elements in priority order. A priority queue is especially useful when you need to identify the maximum or minimum value given a list of elements.