You are previewing Graph Databases.

Graph Databases

Cover of Graph Databases by Ian Robinson... Published by O'Reilly Media, Inc.
  1. Special Upgrade Offer
  2. Foreword
    1. Graphs Are Everywhere, or the Birth of Graph Databases as We Know Them
  3. Preface
    1. About This Book
    2. Conventions Used in This Book
    3. Using Code Examples
    4. Safari® Books Online
    5. How to Contact Us
    6. Acknowledgments
  4. 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
  5. 2. Options for Storing Connected Data
    1. Relational Databases Lack Relationships
    2. NOSQL Databases Also Lack Relationships
    3. Graph Databases Embrace Relationships
    4. Summary
  6. 3. Data Modeling with Graphs
    1. Models and Goals
    2. The Property Graph Model
    3. Querying Graphs: An Introduction to Cypher
      1. Cypher Philosophy
      2. START
      3. MATCH
      4. RETURN
      5. 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. Avoiding Anti-Patterns
    8. Summary
  7. 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. Summary
  8. 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. Geo (Logistics)
    4. Summary
  9. 6. Graph Database Internals
    1. Native Graph Processing
    2. Native Graph Storage
    3. Programmatic APIs
      1. Kernel API
      2. Core (or “Beans”) API
      3. Traversal API
    4. Nonfunctional Characteristics
      1. Transactions
      2. Recoverability
      3. Availability
      4. Scale
    5. Summary
  10. 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
  11. 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
  12. Index
  13. About the Authors
  14. Colophon
  15. Special Upgrade Offer
  16. Copyright

Chapter 3. Data Modeling with Graphs

In previous chapters we’ve described the substantial benefits of the graph database when compared both with document, column family, and key-value NOSQL stores, and with traditional relational databases. But having chosen to adopt a graph database, the question arises: how do we model the world in graph terms?

This chapter focuses on graph modeling. Starting with a recap of the property graph model—the most widely adopted graph data model—we then provide an overview of the graph query language used for most of the code examples in this book: Cypher. Cypher is one of several languages for describing and querying property graphs. There is, as of today, no agreed-upon standard for graph query languages, as exists in the relational database management systems (RDBMS) world with SQL. Cypher was chosen in part because of the authors’ fluency with the language, but also because it is easy to learn and understand, and is widely used. With these fundamentals in place, we dive into a couple of examples of graph modeling. With our first example, that of a systems management domain, we compare relational and graph modeling techniques. With the second example, the production and consumption of Shakespearean literature, we use a graph to connect and query several disparate domains. We end the chapter by looking at some common pitfalls when modeling with graphs, and highlight some good practices.

Models and Goals

Before we dig deeper into modeling with graphs, ...

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