Graph Databases in Action

by Dave Bechberger, Josh Perryman

Overview

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

Product Details

ISBN-13:	9781617296376
Publisher:	Manning
Publication date:	11/24/2020
Series:	In Action
Pages:	366
Product dimensions:	7.38(w) x 9.25(h) x 0.60(d)

About the Author

Dave Bechberger has extensive experience using graph databases as a product architect and a consultant. He’s spent his career leveraging cutting- edge technologies to build software in complex data domains such as bioinformatics, oil and gas, and su

Josh Perryman is technologist with over two decades of diverse experience building and maintaining complex systems, including high performance computing (HPC) environments. Since 2014 he has focused on graph databases, especially in distributed or big data environments, and he regularly blogs and speaks at conferences about graph databases.

Table of Contents

Foreword ix

Preface xi

Acknowledgments xii

About this book xiii

About the authors xviii

About the cover illustration xix

Part 1 Getting started with graph databases 1

1 Introduction to graphs 3

1.1 What is a graph? 4

What is a graph database? 6

Comparison with other types of databases 7

Why can't I use SQL? 9

1.2 Is my problem a graph problem? 16

Explore the questions 16

I'm still confused. … Is this a graph problem? 19

2 Graph data modeling 24

2.1 The data modeling process 25

Data modeling terms 25

Four-step process for data modeling 27

2.2 Understand the problem 29

Domain and scope questions 30

Business entity questions 31

Functionality questions 32

2.3 Developing the whiteboard model 33

Identifying and grouping entities 33

Identifying relationships between entities 34

2.4 Constructing the logical data model 37

Translating entities to vertices 38

Translating relationships to edges 41

Finding and assigning properties 49

2.5 Checking our model 51

3 Running basic and recursive traversals 54

3.1 Setting up your environment 55

Starting the Gremlin Server 55

Starting the Gremlin Console, connecting to the Gremlin Server, and loading the data 56

3.2 Traversing a graph 57

Using a logical data model (schema) to plan traversals 58

Planning the steps through the graph data 59

Fundamental concepts of traversing a graph 61

Writing traversals in Gremlin 63

Retrieving properties with values steps 68

3.3 Recursive traversals 68

Using recursive logic 68

Writing recursive traversals in Gremlin 72

4 Pathfinding traversals and mutating graphs 80

4.1 Mutating a graph 81

Creating vertices and edges 82

Removing data from our graph 86

Updating a graph 88

Extending our graph 95

4.2 Paths 98

Cycles in graphs 100

Finding the simple path 101

4.3 Traversing and filtering edges 103

Introducing the E and V steps for traversing edges 103

Filtering with edge properties 108

Include edges in path results 109

Performant edge counts and denormalization 110

5 Formatting results 112

5.1 Review of values steps 113

5.2 Constructing our result payload 118

Applying aliases in Gremlin 120

Projecting results instead of aliasing 127

5.3 Organizing our results 131

Ordering results returned from a graph traversal 131

Grouping results returned from a graph traversal 133

Limiting results 135

5.4 Combining steps into complex traversals 137

6 Developing an application 142

6.1 Starting the project 143

Selecting our tools 144

Setting up the project 145

Obtaining a driver 145

Preparing the database server Instance 146

6.2 Connecting to our database 147

Building the cluster configuration 147

Setting up the GraphTraversalSource 148

6.3 Retrieving data 151

Retrieving a vertex 151

Using Gremlin language variants (GLVs) 153

Adding terminal steps 154

Creating the Java method in our application 155

6.4 Adding, modifying, and deleting data 156

Adding vertices 156

Adding edges 158

Updating properties 160

Deleting elements 160

6.5 Translating our list and path traversals 163

Getting a list of results 163

Implementing recursive traversals 164

Implementing paths 166

Part 2 Building on Graph Databases 169

7 Advanced data modeling techniques 171

7.1 Reviewing our current data models 172

7.2 Extending our logical data model 173

7.3 Translating entities to vertices 176

Using generic labels 177

Denormalizing graph data 183

Translating relationships to edges 188

Finding and assigning properties 190

Moving properties to edges 191

Checking our model 194

7.4 Extending our data model for personalization 194

7.5 Comparing the results 196

8 Building traversals using known walks 198

8.1 Preparing to develop our traversals 199

Identifying the required elements 199

Selecting a starting place 204

Setting up test data 205

8.2 Writing our first traversal 206

Designing our traversal 206

Developing the traversal code 210

8.3 Pagination and graph databases 214

8.4 Recommending the highest-rated restaurants 218

Designing our traversal 218

Developing the traversal code 220

8.5 Writing the last recommendation engine traversal 230

Designing our traversal 232

Adding this traversal to our application 234

9 Working with subgraphs 237

9.1 Working with subgraphs 238

Extracting a subgraph 238

Traversing a subgraph 244

9.2 Building a subgraph for personalization 246

9.3 Building the traversal 252

Reversing the traversing direction 253

Evaluating the individualized results of the subgraph 255

9.4 Implementing a subgraph with a remote connection 256

Connecting with TinkerPop's Client class 257

Adding this traversal to our application 258

Part 3 Moving Beyond the Basics 261

10 Performance, pitfalls, and anti-patterns 263

10.1 Slow performing traversals 264

Explaining our traversal 264

Profiling our traversal 266

Indexes 268

10.2 Dealing with supernodes 269

It's about instance data 270

It's about the database 270

What makes a supernode? 270

Monitoring for supernodes 271

What to do if you have a supernode 273

10.3 Application anti-patterns 277

Using graphs for non-graph use cases 277

Dirty data 278

Lack of adequate testing 280

10.4 Traversal anti-patterns 280

Not using parameterized, traversals 280

Using unlabeled filtering steps 283

11 What's next: Graph analytics, machine learning, and resources 286

11.1 Graph analytics 287

Pathfinding 287

Centrality 290

Community detection 292

Graphs and machine learning 296

Additional resources 297

11.2 Final thoughts 299

Appendix Apache TinkerPop installation and overview 301

Index 311