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
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Appendix A. NOSQL Overview

Recent years have seen a meteoric rise in popularity of a family of data storage technologies known as NOSQL (a cheeky acronym for Not Only SQL, or more confrontationally, No to SQL). But NOSQL as a term defines what those data stores are not—they’re not SQL-centric relational databases—rather than what they are, which is an interesting and useful set of storage technologies whose operational, functional, and architectural characteristics are many and varied.

Why were these new databases created? What problems do they address? Here we’ll discuss some of the new data challenges that have emerged in the past decade. We’ll then look at four families of NOSQL databases, including graph databases.

The Rise of NOSQL

Historically, most enterprise-level web apps ran on top of a relational database. But in the past decade, we’ve been faced with data that is bigger in volume, changes more rapidly, and is more structurally varied than can be dealt with by traditional RDBMS deployments. The NOSQL movement has arisen in response to these challenges.

It’s no surprise that as storage has increased dramatically, volume has become the principal driver behind the adoption of NOSQL stores by organizations. Volume may be defined simply as the size of the stored data.

As is well known, large datasets become unwieldy when stored in relational databases; in particular, query execution times increase as the size of tables and the number of joins grow (so-called join pain). This isn’t ...

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