Graph Databases in Action

Examples in Gremlin

Josh Perryman, Dave Bechberger

• Publisher: Manning
• Year: 2020
• Print ISBN: 9781617296376
• eBook ISBN: 9781638350101
• Topic: Ciencia de la computación
• Subtopic: Modelado y diseño de datos

Graph Databases in Action introduces you to graph database concepts by comparing them with relational database constructs. You'll learn just enough theory to get started, then progress to hands-on development. Discover use cases involving social networking, recommendation engines, and personalization.

Summary
Relationships in data often look far more like a web than an orderly set of rows and columns. Graph databases shine when it comes to revealing valuable insights within complex, interconnected data such as demographics, financial records, or computer networks. In Graph Databases in Action, experts Dave Bechberger and Josh Perryman illuminate the design and implementation of graph databases in real-world applications. You'll learn how to choose the right database solutions for your tasks, and how to use your new knowledge to build agile, flexible, and high-performing graph-powered applications!

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
Isolated data is a thing of the past! Now, data is connected, and graph databases—like Amazon Neptune, Microsoft Cosmos DB, and Neo4j—are the essential tools of this new reality. Graph databases represent relationships naturally, speeding the discovery of insights and driving business value.

About the book
Graph Databases in Action introduces you to graph database concepts by comparing them with relational database constructs. You'll learn just enough theory to get started, then progress to hands-on development. Discover use cases involving social networking, recommendation engines, and personalization.

What's inside
    Graph databases vs. relational databases
    Systematic graph data modeling
    Querying and navigating a graph
    Graph patterns
    Pitfalls and antipatterns

About the reader
For software developers. No experience with graph databases required.

About the author
Dave Bechberger and Josh Perryman have decades of experience building complex data-driven systems and have worked with graph databases since 2014.

Table of Contents

PART 1 - GETTING STARTED WITH GRAPH DATABASES

1 Introduction to graphs

2 Graph data modeling

3 Running basic and recursive traversals

4 Pathfinding traversals and mutating graphs

5 Formatting results

6 Developing an application

PART 2 - BUILDING ON GRAPH DATABASES

7 Advanced data modeling techniques

8 Building traversals using known walks

9 Working with subgraphs

PART 3 - MOVING BEYOND THE BASICS

10 Performance, pitfalls, and anti-patterns

11 What's next: Graph analytics, machine learning, and resources

Part 1. Getting started with graph databases

Journeys into new technologies take work, and in this book, our journey will extend your current knowledge of building relational database applications to demonstrate how you can solve complex data problems by building graph databases and graph-backed applications. In this first part, we ease into your journey by establishing concepts, terms, and processes, while highlighting the critical differences required when approaching a problem with a graph mindset.

Chapter 1 introduces the core concepts of graphs and discusses the types of problems that are well suited for these models. In chapter 2, we establish a data modeling methodology and build a simple data model for a social network that we’ll use in our example application, DiningByFriends. The next three chapters introduce the most common operations that you’ll use to find and manipulate data in graph databases. We approach these operations in three stages, starting with the basics of moving around a graph in chapter 3. Chapter 4 then covers how to perform basic CRUD (Create/Read/Update/Delete) operations before extending the work we did in chapter 3 to perform more complex recursive and pathfinding traversals. In chapter 5, we close our introduction by using simple graph operations to examine ways to organize your results. Chapter 6 completes this part by synthesizing the work from chapters 2 through 5 into our working Java application, DiningByFriends.

1 Introduction to graphs

This chapter covers

• An introduction to graphs and graph terminology
• How graph databases help solve highly connected data problems
• The advantages of graph databases over relational databases
• Identifying problems that make good candidates for using a graph database

Modern applications are built on data--data that is ever increasing in both size and complexity. Even as the complexity of our data grows, so do our expectations of what insight our applications can derive from that data. If you are old enough, you likely remember when applications took a long time to load data and had limited features. Today’s reality is different; applications provide powerful, flexible, and immediate insight into data. But for every 100 questions modern applications answer, the most common data tool these use (namely, a relational database) handles only about 88 of those questions well. That leaves 12 types of questions where relational databases struggle. These remaining questions deal with the links and connections within the data, those aspects of the data that can generate powerful and unique insights. This puts us at a crossroad: we can use the relational database “hammer” to pound away at those questions and make this work well enough, or we can take a step back and look at what other tools can answer these questions better, faster, and with less effort.

By reading this book, you decided to take a step back from your relational database hammer and investigate a road less traveled: graph databases. This book is written for developers, engineers, and architects who are interested in other ways to solve problems specific to working with highly connected data. We assume you are already familiar with relational databases but are interested in learning when, where, and how graph databases are a better tool.

Our goal with this book is to equip you with the techniques needed to add graph databases as another tool in your toolbelt. We like to think of this book as the guide that we wish we had when we started building graph-backed applications. Throughout this book, we’ll demonstrate common graph patterns that highlight how graph databases enable navigation and exploration of data in ways not easily accomplished with a traditional relational database.

Our primary approach is through an example of building a fictitious restaurant review and recommendation application we call “DiningByFriends.” As we move through the software development life cycle from planning, to analysis, to design, and on to implementation, this application demonstrates how to think about and work with graph data. Each chapter builds on the previous chapter, and by the end of this book, we’ll have created a functioning application on a graph database. We believe that putting the concepts immediately to work by solving a realistic set of problems, even if they are somewhat simplistic, is the best way to get comfortable using a new technology. Let’s begin our journey with an introduction to what graphs and graph databases are and how they compare with traditional tools such as relational databases.

1.1 What is a graph?

When you look at a road map, examine an organizational chart, or use social networks such as Facebook, LinkedIn, or Twitter, you use a graph. Graphs are a nearly ubiquitous way to think about real-world scenarios as these abstract out the items and the relationships being represented, and this abstraction allows for quick and efficient processing of the connections within the data.

Let’s demonstrate with a common task: going to the supermarket. Take out a piece of paper and draw out a plan for getting from your house to your supermarket. Chances are it looks something like figure 1.1.

Figure 1.1 A graph representing directions to the supermarket

Figure 1.1 shows a graph where the key items and relationships are represented by abstractions. First, we abstracted key locations, like intersections, and represented these as circles. We then designated the connections between these key intersections as lines, showing how the key intersections are related. This is just one example of how we naturally represent real-world problems as graphs.

It is human nature to abstract real-world entities and their relationships, and the mathematical name for this abstract construct is a graph. When thinking about a set of data that contains a vast array of highly interconnected items, we might also describe this data set as a web of interconnected things, which is just another way of saying a graph.

On maps, cities are frequently represented by circles, and the roads that connect these are represented by lines. On an organizational chart (org chart), a circle usually represents a person, normally with an associated title, and lines that connect these people together show the employer-employee relationship. In a social network, people connect to one another via friending or following. This process of generalizing entities and the connections between them is the fundamental basis for graphs and graph theory. Because graphs have been defined and studied by mathematicians for centuries, we can offer these definitions used in graph theory as our starting terms:

• Graph --A set of vertices (singular, vertex) and edges
• Vertex --A point in a graph where zero or more edges meet, also referred to as a node or an entity
• Edge --A relationship between two vertices with...

Table of contents

• Graph Databases in Action
• copyright
• contents
• foreword
• preface
• acknowledgments
• about this book
• about the authors
• about the cover illustration
• Part 1 Getting started with graph databases
• 1 Introduction to graphs
• 2 Graph data modeling
• 3 Running basic and recursive traversals
• 4 Pathfinding traversals and mutating graphs
• 5 Formatting results
• 6 Developing an application
• Part 2 Building on Graph Databases
• 7 Advanced data modeling techniques
• 8 Building traversals using known walks
• 9 Working with subgraphs
• Part 3 Moving Beyond the Basics
• 10 Performance, pitfalls, and anti-patterns
• 11 What’s next: Graph analytics, machine learning, and resources
• appendix A Apache TinkerPop installation and overview