Graph Databases
Publisher: O'Reilly Media
Release Date: June 2013
Pages: 224
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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
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Chapter 1 Introduction
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What Is a Graph?
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A High-Level View of the Graph Space
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The Power of Graph Databases
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Summary
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Chapter 2 Options for Storing Connected Data
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Relational Databases Lack Relationships
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NOSQL Databases Also Lack Relationships
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Graph Databases Embrace Relationships
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Summary
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Chapter 3 Data Modeling with Graphs
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Models and Goals
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The Property Graph Model
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Querying Graphs: An Introduction to Cypher
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A Comparison of Relational and Graph Modeling
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Cross-Domain Models
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Common Modeling Pitfalls
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Avoiding Anti-Patterns
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Summary
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Chapter 4 Building a Graph Database Application
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Data Modeling
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Application Architecture
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Testing
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Capacity Planning
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Summary
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Chapter 5 Graphs in the Real World
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Why Organizations Choose Graph Databases
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Common Use Cases
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Real-World Examples
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Summary
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Chapter 6 Graph Database Internals
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Native Graph Processing
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Native Graph Storage
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Programmatic APIs
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Nonfunctional Characteristics
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Summary
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Chapter 7 Predictive Analysis with Graph Theory
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Depth- and Breadth-First Search
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Path-Finding with Dijkstra’s Algorithm
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The A* Algorithm
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Graph Theory and Predictive Modeling
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Local Bridges
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Summary
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Appendix NOSQL Overview
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The Rise of NOSQL
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ACID versus BASE
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The NOSQL Quadrants
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Document Stores
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Key-Value Stores
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Column Family
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Query versus Processing in Aggregate Stores
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Graph Databases
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Index
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Colophon