Introduction

If you’re learning Swift development you may be asking if you need to know AI and machine learning to be relevant, why learn algorithms when AI can generate code for me, or how do recommendation systems work—the ones that power Netflix and Spotify?

These are the right questions. The surprising answer is they all lead back to the same place—fundamental algorithms that have existed for decades.

These days technology is amazing. Generative AI understands your questions. Instagram knows what you’ll like next. Google autocomplete predicts what we’re typing after just three letters. Your iPhone unlocks instantly with Face ID. But here’s the secret: it’s not magic. It’s mathematics—specifically, algorithms that computer scientists developed between 1946 and the early 2000s, now applied in ways their creators never imagined.

The platform

When Swift debuted in 2014, it was Apple’s answer to modern iOS development. Ten years later, Swift has evolved into something far more powerful—a full-stack language that spans far beyond the iPhone. Today’s Swift gives you SwiftUI for declarative, reactive interfaces across iOS, macOS and watchOS. Swift Concurrency with async/await makes asynchronous code as readable as synchronous code—crucial for network requests, database operations, and AI model inference. Swift Package Manager lets you share your algorithm implementations as reusable libraries. Swift Testing provides modern, expressive test suites that verify your algorithms work correctly at scale. And solutions like Vapor take Swift to the server, allowing you to create backend APIs that power websites, online services, and cloud-based applications. You can build your iOS app and its backend API in the same language, sharing the same algorithm implementations.

The algorithm lineage

Understanding modern technology means understanding the classical algorithms that power it. Binary search, invented in 1946 for dictionary lookups, now powers vector database indexes in modern AI search systems. Tries, developed in 1968 for efficient string storage, became the foundation for Google’s autocomplete in 2004 and every modern suggestion system. PageRank, Larry Page’s 1998 breakthrough for ranking web pages, evolved into the attention mechanisms that power today’s transformer-based AI models. Hash tables from the 1960s, originally used for compiler symbol tables, now enable real-time session management in web applications and locality-sensitive hashing for vector search.

These algorithms didn’t just inspire modern systems—they are the building blocks. When we use semantic search in Chapter 22, we’re combining vector embeddings with k-nearest neighbors search, a technique from the 1960s. When we implement PageRank in Chapter 19, we’re learning the same iterative algorithm that influenced how neural networks process attention. When we build tries in Chapter 14, we’re using the exact data structure that made Google Suggest possible.

What we’ll learn

This book teaches fundamental algorithms and data structures through production Swift code. We’ll understand not just how algorithms work, but why they matter and where they’re used in modern development.

We’ll start with searching and sorting algorithms in Chapters 3-5, building intuition for algorithm design and performance analysis. Chapter 7 introduces Swift generics, showing how one algorithm can work with any data type—the foundation for every data structure in this book. Chapter 8 provides comprehensive performance analysis and Big O notation. Chapters 9-16 cover essential data structures: linked lists, stacks, queues, binary search trees, graphs, tries, hash tables, and heaps. Each chapter shows both the theory and the production implementation.

The advanced chapters demonstrate how these fundamentals power modern systems. Chapter 18 teaches dynamic programming, the optimization technique behind route planning and recommendation systems. Chapter 19 covers PageRank, Google’s original algorithm that revolutionized web search and influenced modern AI architectures. Chapters 20-21 introduce vectors and matrices with the Quiver framework, and Chapter 22 shows how to build semantic search systems using word embeddings and cosine similarity—the same techniques that power AI-driven search and recommendations.

By the end, we’ll understand how classical computer science enables modern AI, and we’ll have production-ready Swift implementations we can use in our own projects.

Audience

This book assumes we’re comfortable with Swift basics: variables, functions, classes, structs, optionals, and common design patterns like protocols and delegation. We should be able to read and write Swift code confidently, understand basic control flow, and be familiar with Swift collections like arrays and dictionaries.

We don’t need advanced computer science knowledge. Mathematical concepts like logarithms and vector operations are explained from first principles when needed. Prior experience with algorithms is helpful but not required—we build from fundamentals.