Graph Databases

✍ Scribed by Ian Robinson, Jim Webber, Emil Eifrem

• Publisher: O'Reilly Media
• Year: 2013
• Tongue: English
• Leaves: 223
• Edition: 1st
• Category: Library

✦ Synopsis

Discover how graph databases can help you manage and query highly connected data. With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems.
Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution.
Model data with the Cypher query language and property graph model
Learn best practices and common pitfalls when modeling with graphs
Plan and implement a graph database solution in test-driven fashion
Explore real-world examples to learn how and why organizations use a graph database
Understand common patterns and components of graph database architecture
Use analytical techniques and algorithms to mine graph database information

✦ Table of Contents

Copyright......Page 4
Table of Contents......Page 5
Graphs Are Everywhere, or the Birth of Graph Databases as We Know Them......Page 9
Preface......Page 11
Using Code Examples......Page 12
How to Contact Us......Page 13
Acknowledgments......Page 14
What Is a Graph?......Page 15
A High-Level View of the Graph Space......Page 18
Graph Databases......Page 19
Graph Compute Engines......Page 20
Flexibility......Page 22
Summary......Page 23
Relational Databases Lack Relationships......Page 25
NOSQL Databases Also Lack Relationships......Page 28
Graph Databases Embrace Relationships......Page 32
Summary......Page 37
Models and Goals......Page 39
The Property Graph Model......Page 40
Cypher Philosophy......Page 41
MATCH......Page 43
Other Cypher Clauses......Page 44
A Comparison of Relational and Graph Modeling......Page 45
Relational Modeling in a Systems Management Domain......Page 47
Graph Modeling in a Systems Management Domain......Page 50
Testing the Model......Page 52
Cross-Domain Models......Page 54
Creating the Shakespeare Graph......Page 58
Beginning a Query......Page 59
Declaring Information Patterns to Find......Page 60
Constraining Matches......Page 61
Processing Results......Page 62
Query Chaining......Page 63
A Sensible First Iteration?......Page 64
Second Time’s the Charm......Page 67
Evolving the Domain......Page 70
Summary......Page 75
Describe the Model in Terms of the Application’s Needs......Page 77
Nodes for Things, Relationships for Structure......Page 78
Fine-Grained versus Generic Relationships......Page 79
Model Facts as Nodes......Page 80
Represent Complex Value Types as Nodes......Page 83
Time......Page 84
Iterative and Incremental Development......Page 86
Application Architecture......Page 87
Embedded Versus Server......Page 88
Clustering......Page 92
Load Balancing......Page 93
Testing......Page 96
Test-Driven Data Model Development......Page 97
Performance Testing......Page 103
Optimization Criteria......Page 107
Performance......Page 108
Redundancy......Page 110
Load......Page 111
Summary......Page 112
Why Organizations Choose Graph Databases......Page 113
Social......Page 114
Recommendations......Page 115
Geo......Page 116
Network and Data Center Management......Page 117
Authorization and Access Control (Communications)......Page 118
Social Recommendations (Professional Social Network)......Page 119
Authorization and Access Control......Page 130
Geo (Logistics)......Page 138
Summary......Page 153
Native Graph Processing......Page 155
Native Graph Storage......Page 158
Programmatic APIs......Page 164
Core (or “Beans”) API......Page 165
Traversal API......Page 166
Nonfunctional Characteristics......Page 168
Transactions......Page 169
Recoverability......Page 170
Availability......Page 171
Scale......Page 173
Summary......Page 176
Depth- and Breadth-First Search......Page 177
Path-Finding with Dijkstra’s Algorithm......Page 178
The A* Algorithm......Page 187
Triadic Closures......Page 188
Structural Balance......Page 190
Local Bridges......Page 194
Summary......Page 196
The Rise of NOSQL......Page 197
ACID versus BASE......Page 198
Document Stores......Page 200
Key-Value Stores......Page 203
Column Family......Page 206
Graph Databases......Page 209
Property Graphs......Page 210
Hypergraphs......Page 211
Triples......Page 212
Index......Page 215
About the Authors......Page 223