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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.

Table of Content
1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . 1

  • What Is a Graph? 1
  • A High-Level View of the Graph Space 4
  • Graph Databases 5
  • Graph Compute Engines 6
  • The Power of Graph Databases 8
  • Performance 8
  • Flexibility 8
  • Agility 9
  • Summary 9

2. Options for Storing Connected Data. . . . . . . . . . . . . . . . . 11

  • Relational Databases Lack Relationships 11
  • NOSQL Databases Also Lack Relationships 14
  • Graph Databases Embrace Relationships 18
  • Summary 23

3. Data Modeling with Graphs. .. . . . . . . . . . . . . . . 25

  • Models and Goals 25
  • The Property Graph Model 26
  • Querying Graphs: An Introduction to Cypher 27
  • Cypher Philosophy 27
  • START 29
  • MATCH 29
  • RETURN 30
  • Other Cypher Clauses 30
  • A Comparison of Relational and Graph Modeling 31
  • Relational Modeling in a Systems Management Domain 33
  • Graph Modeling in a Systems Management Domain 36
  • Testing the Model 38
  • Cross-Domain Models 40
  • Creating the Shakespeare Graph 44
  • Beginning a Query 45
  • Declaring Information Patterns to Find 46
  • Constraining Matches 47
  • Processing Results 48
  • Query Chaining 49
  • Common Modeling Pitfalls 50
  • Email Provenance Problem Domain 50
  • A Sensible First Iteration? 50
  • Second Time’s the Charm 53
  • Evolving the Domain 56
  • Avoiding Anti-Patterns 61
  • Summary 61

4. Building a Graph Database Application. . . . . . . . . . . . . . . . . . 63

  • Data Modeling 63
  • Describe the Model in Terms of the Application’s Needs 63
  • Nodes for Things, Relationships for Structure 64
  • Fine-Grained versus Generic Relationships 65
  • Model Facts as Nodes 66
  • Represent Complex Value Types as Nodes 69
  • Time 70
  • Iterative and Incremental Development 72
  • Application Architecture 73
  • Embedded Versus Server 74
  • Clustering 78
  • Load Balancing 79
  • Testing 82
  • Test-Driven Data Model Development 83
  • Performance Testing 89
  • Capacity Planning 93
  • Optimization Criteria 93
  • Performance 94
  • Redundancy 96
  • Load 97
  • Summary 98

5. Graphs in the Real World. .. . . . . . . . . . . . . . . . . 99

  • Why Organizations Choose Graph Databases 99
  • Common Use Cases 100
  • Social 100
  • Recommendations 101
  • Geo 102
  • Master Data Management 103
  • Network and Data Center Management 103
  • Authorization and Access Control (Communications) 104
  • Real-World Examples 105
  • Social Recommendations (Professional Social Network) 105
  • Authorization and Access Control 116
  • Geo (Logistics) 124
  • Summary 139

6. Graph Database Internals. .  . . . . . . . . . . . . . . . . 141

  • Native Graph Processing 141
  • Native Graph Storage 144
  • Programmatic APIs 150
  • Kernel API 151
  • Core (or “Beans”) API 151
  • Traversal API 152
  • Nonfunctional Characteristics 154
  • Transactions 155
  • Recoverability 156
  • Availability 157
  • Scale 159
  • Summary 162

7. Predictive Analysis with Graph Theory. . .. . . . . . . . . . . . . . . . 163

  • Depth- and Breadth-First Search 163
  • Path-Finding with Dijkstra’s Algorithm 164
  • The A* Algorithm 173
  • Graph Theory and Predictive Modeling 174
  • Triadic Closures 174
  • Structural Balance 176
  • Local Bridges 180
  • Summary 182

A. NOSQL Overview. . . .. . . . .. . . . . . . . . . . 183
Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201

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