Graph Databases by Ian Robinson, Jim Webber -- Read -- Imperial Library of Trantor

Index

Foreword

Graphs Are Eating The World, And There’s No Going Back

Preface

About the Second Edition About This Book Conventions Used in This Book Using Code Examples Safari® Books Online How to Contact Us Acknowledgments

1. Introduction

What Is a Graph? A High-Level View of the Graph Space

Graph Databases Graph Compute Engines

The Power of Graph Databases

Performance Flexibility Agility

Summary

2. Options for Storing Connected Data

Relational Databases Lack Relationships NOSQL Databases Also Lack Relationships Graph Databases Embrace Relationships Summary

3. Data Modeling with Graphs

Models and Goals The Labeled Property Graph Model Querying Graphs: An Introduction to Cypher

Cypher Philosophy MATCH RETURN Other Cypher Clauses

A Comparison of Relational and Graph Modeling

Relational Modeling in a Systems Management Domain Graph Modeling in a Systems Management Domain Testing the Model

Cross-Domain Models

Creating the Shakespeare Graph Beginning a Query Declaring Information Patterns to Find Constraining Matches Processing Results Query Chaining

Common Modeling Pitfalls

Email Provenance Problem Domain A Sensible First Iteration? Second Time’s the Charm Evolving the Domain

Identifying Nodes and Relationships Avoiding Anti-Patterns Summary

4. Building a Graph Database Application

Data Modeling

Describe the Model in Terms of the Application’s Needs Nodes for Things, Relationships for Structure Fine-Grained versus Generic Relationships Model Facts as Nodes

Employment Performance Emailing Reviewing

Represent Complex Value Types as Nodes Time

Timeline trees Linked lists Versioning

Iterative and Incremental Development

Application Architecture

Embedded versus Server

Embedded Neo4j Server mode Server extensions

Clustering

Replication Buffer writes using queues Global clusters

Load Balancing

Separate read traffic from write traffic Cache sharding Read your own writes

Testing

Test-Driven Data Model Development

Example: A test-driven social network data model Testing server extensions

Performance Testing

Query performance tests Application performance tests Testing with representative data

Capacity Planning

Optimization Criteria Performance

Calculating the cost of graph database performance Performance optimization options

Redundancy Load

Importing and Bulk Loading Data

Initial Import Batch Import

Summary

5. Graphs in the Real World

Why Organizations Choose Graph Databases Common Use Cases

Social Recommendations Geo Master Data Management Network and Data Center Management Authorization and Access Control (Communications)

Real-World Examples

Social Recommendations (Professional Social Network)

Talent.net data model Inferring social relations Finding colleagues with particular interests Adding WORKED_WITH relationships

Authorization and Access Control

TeleGraph data model Finding all accessible resources for an administrator Determining whether an administrator has access to a resource Finding administrators for an account

Geospatial and Logistics

Global Post data model Route calculation Finding the shortest delivery route using Cypher Implementing route calculation with the Traversal Framework

Summary

6. Graph Database Internals

Native Graph Processing Native Graph Storage Programmatic APIs

Kernel API Core API Traversal Framework

Nonfunctional Characteristics

Transactions Recoverability Availability Scale

Capacity Latency Throughput

Summary

7. Predictive Analysis with Graph Theory

Depth- and Breadth-First Search Path-Finding with Dijkstra’s Algorithm The A* Algorithm Graph Theory and Predictive Modeling

Triadic Closures Structural Balance

Local Bridges Summary

A. NOSQL Overview

The Rise of NOSQL ACID versus BASE The NOSQL Quadrants Document Stores Key-Value Stores Column Family Query versus Processing in Aggregate Stores Graph Databases

Property Graphs Hypergraphs Triples

Index

← Prev
Back
Next →

← Prev
Back
Next →