Graph Databases

Preface

Graph databases address one of the great macroscopic business trends of today: leveraging complex and dynamic relationships in highly connected data to generate insight and competitive advantage. Whether we want to understand relationships between customers, elements in a telephone or data center network, entertainment producers and consumers, or genes and proteins, the ability to understand and analyze vast graphs of highly connected data will be key in determining which companies outperform their competitors over the coming decade.

For data of any significant size or value, graph databases are the best way to represent and query connected data. Connected data is data whose interpretation and value requires us first to understand the ways in which its constituent elements are related. More often than not, to generate this understanding, we need to name and qualify the connections between things.

Although large corporates realized this some time ago and began creating their own proprietary graph processing technologies, we’re now in an era where that technology has rapidly become democratized. Today, general-purpose graph databases are a reality, enabling mainstream users to experience the benefits of connected data without having to invest in building their own graph infrastructure.

What’s remarkable about this renaissance of graph data and graph thinking is that graph theory itself is not new. Graph theory was pioneered by Euler in the 18th century, and has been actively researched and improved by mathematicians, sociologists, anthropologists, and others ever since. However, it is only in the past few years that graph theory and graph thinking have been applied to information management. In that time, graph databases have helped solve important problems in the areas of social networking, master data management, geospatial, recommendations, and more. This increased focus on graph databases is driven by twin forces: by the massive commercial success of companies such as Facebook, Google, and Twitter, all of whom have centered their business models around their own proprietary graph technologies; and by the introduction of general-purpose graph databases into the technology landscape.

About This Book

The purpose of this book is to introduce graphs and graph databases to technology practitioners, including developers, database professionals, and technology decision makers. Reading this book will give you a practical understanding of graph databases. We show how the graph model “shapes” data, and how we query, reason about, understand, and act upon data using a graph database. We discuss the kinds of problems that are well aligned with graph databases, with examples drawn from actual real-world use cases, and we show how to plan and implement a graph database solution.

Conventions Used in This Book

The following typographical conventions are used in this book:

Italic

Indicates new terms, URLs, email addresses, filenames, and file extensions.

Constant width

Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords.

Constant width bold

Shows commands or other text that should be typed literally by the user.

Constant width italic

Shows text that should be replaced with user-supplied values or by values determined by context.

Tip

This icon signifies a tip, suggestion, or general note.

Warning

This icon indicates a warning or caution.

Using Code Examples

This book is here to help you get your job done. In general, if this book includes code examples, you may use the code in this book in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission.

We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “Graph Databases by Ian Robinson, Jim Webber, and Emil Eifrem (O’Reilly). Copyright 2013 Neo Technology, Inc., 978-1-449-35626-2.”

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Acknowledgments

We would like to thank our technical reviewers: Michael Hunger, Colin Jack, Mark Needham, and Pramod Sadalage.

Our appreciation and thanks to our editor, Nathan Jepson.

Our colleagues at Neo Technology have contributed enormously of their time, experience, and effort throughout the writing of this book. Thanks in particular go to Anders Nawroth, for his invaluable assistance with our book’s toolchain; Andrés Taylor, for his enthusiastic help with all things Cypher; and Philip Rathle, for his advice and contributions to the text.

A big thank you to everyone in the Neo4j community for your many contributions to the graph database space over the years.

And special thanks to our families, for their love and support: Lottie, Tiger, Elliot, Kath, Billy, Madelene, and Noomi.