Graph Databases, 2nd Edition

Table of contents

1. Foreword
   1. Graphs Are Eating The World, And There’s No Going Back
2. Preface
   1. About the Second Edition
   2. About This Book
   3. Conventions Used in This Book
   4. Using Code Examples
   5. Safari® Books Online
   6. How to Contact Us
   7. Acknowledgments
3. 1. Introduction
   1. What Is a Graph?
   2. A High-Level View of the Graph Space
      1. Graph Databases
      2. Graph Compute Engines
   3. The Power of Graph Databases
      1. Performance
      2. Flexibility
      3. Agility
   4. Summary
4. 2. Options for Storing Connected Data
   1. Relational Databases Lack Relationships
   2. NOSQL Databases Also Lack Relationships
   3. Graph Databases Embrace Relationships
   4. Summary
5. 3. Data Modeling with Graphs
   1. Models and Goals
   2. The Labeled Property Graph Model
   3. Querying Graphs: An Introduction to Cypher
      1. Cypher Philosophy
      2. MATCH
      3. RETURN
      4. Other Cypher Clauses
   4. A Comparison of Relational and Graph Modeling
      1. Relational Modeling in a Systems Management Domain
      2. Graph Modeling in a Systems Management Domain
      3. Testing the Model
   5. Cross-Domain Models
      1. Creating the Shakespeare Graph
      2. Beginning a Query
      3. Declaring Information Patterns to Find
      4. Constraining Matches
      5. Processing Results
      6. Query Chaining
   6. Common Modeling Pitfalls
      1. Email Provenance Problem Domain
      2. A Sensible First Iteration?
      3. Second Time’s the Charm
      4. Evolving the Domain
   7. Identifying Nodes and Relationships
   8. Avoiding Anti-Patterns
   9. Summary
6. 4. Building a Graph Database Application
   1. Data Modeling
      1. Describe the Model in Terms of the Application’s Needs
      2. Nodes for Things, Relationships for Structure
      3. Fine-Grained versus Generic Relationships
      4. Model Facts as Nodes
      5. Represent Complex Value Types as Nodes
      6. Time
      7. Iterative and Incremental Development
   2. Application Architecture
      1. Embedded versus Server
      2. Clustering
      3. Load Balancing
   3. Testing
      1. Test-Driven Data Model Development
      2. Performance Testing
   4. Capacity Planning
      1. Optimization Criteria
      2. Performance
      3. Redundancy
      4. Load
   5. Importing and Bulk Loading Data
      1. Initial Import
      2. Batch Import
   6. Summary
7. 5. Graphs in the Real World
   1. Why Organizations Choose Graph Databases
   2. Common Use Cases
      1. Social
      2. Recommendations
      3. Geo
      4. Master Data Management
      5. Network and Data Center Management
      6. Authorization and Access Control (Communications)
   3. Real-World Examples
      1. Social Recommendations (Professional Social Network)
      2. Authorization and Access Control
      3. Geospatial and Logistics
   4. Summary
8. 6. Graph Database Internals
   1. Native Graph Processing
   2. Native Graph Storage
   3. Programmatic APIs
      1. Kernel API
      2. Core API
      3. Traversal Framework
   4. Nonfunctional Characteristics
      1. Transactions
      2. Recoverability
      3. Availability
      4. Scale
   5. Summary
9. 7. Predictive Analysis with Graph Theory
   1. Depth- and Breadth-First Search
   2. Path-Finding with Dijkstra’s Algorithm
   3. The A* Algorithm
   4. Graph Theory and Predictive Modeling
      1. Triadic Closures
      2. Structural Balance
   5. Local Bridges
   6. Summary
10. A. NOSQL Overview
    1. The Rise of NOSQL
    2. ACID versus BASE
    3. The NOSQL Quadrants
    4. Document Stores
    5. Key-Value Stores
    6. Column Family
    7. Query versus Processing in Aggregate Stores
    8. Graph Databases
       1. Property Graphs
       2. Hypergraphs
       3. Triples
11. Index