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Graph Databases - New Opportunities for Connected Data(2nd) 无水印p....pdf

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    Cover
    Copyright
    Table of Contents
    Foreword
    Graphs Are Everywhere, or the Birth of Graph Databases as We Know Them
    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
    Represent Complex Value Types as Nodes
    Time
    Iterative and Incremental Development
    Application Architecture
    Embedded versus Server
    Clustering
    Load Balancing
    Testing
    Test-Driven Data Model Development
    Performance Testing
    Capacity Planning
    Optimization Criteria
    Performance
    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)
    Authorization and Access Control
    Geospatial and Logistics
    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
    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
    Appendix 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
    About the Authors
    2 n d E ditio n Graph Databases NEW OPPORTUNITIES FOR CONNECTED DATA Ian Robinson, Jim Webber & Emil Eifrem
    SECOND EDITION Graph Databases Ian Robinson, Jim Webber & Emil Eifrem
    Graph Databases by Ian Robinson, Jim Webber, and Emil Eifrem Copyright © 2015 Neo Technology, Inc. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://safaribooksonline.com). For more information, contact our corporate/ institutional sales department: 800-998-9938 or corporate@oreilly.com. Interior Designer: David Futato Cover Designer: Ellie Volckhausen Illustrator: Rebecca Demarest Editor: Marie Beaugureau Production Editor: Kristen Brown Proofreader: Christina Edwards Indexer: WordCo Indexing Services June 2013: June 2015: First Edition Second Edition Revision History for the Second Edition 2015-05-04: First Release See http://oreilly.com/catalog/errata.csp?isbn=9781491930892 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Graph Databases, the cover image of an European octopus, and related trade dress are trademarks of O’Reilly Media, Inc. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. 978-1-491-93200-1 [LSI]
    Table of Contents Foreword. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi 1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 What Is a Graph? 1 A High-Level View of the Graph Space 4 Graph Databases 5 Graph Compute Engines 7 The Power of Graph Databases 8 Performance 8 Flexibility 9 Agility 9 Summary 10 2. Options for Storing Connected Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Relational Databases Lack Relationships 11 NOSQL Databases Also Lack Relationships 15 Graph Databases Embrace Relationships 18 Summary 24 3. Data Modeling with Graphs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Models and Goals 25 The Labeled Property Graph Model 26 Querying Graphs: An Introduction to Cypher 27 Cypher Philosophy 28 MATCH 30 RETURN 30 iii
    Other Cypher Clauses 31 A Comparison of Relational and Graph Modeling 32 Relational Modeling in a Systems Management Domain 33 Graph Modeling in a Systems Management Domain 38 Testing the Model 39 Cross-Domain Models 41 Creating the Shakespeare Graph 45 Beginning a Query 46 Declaring Information Patterns to Find 48 Constraining Matches 49 Processing Results 50 Query Chaining 51 Common Modeling Pitfalls 52 Email Provenance Problem Domain 52 A Sensible First Iteration? 52 Second Time’s the Charm 55 Evolving the Domain 58 Identifying Nodes and Relationships 63 Avoiding Anti-Patterns 63 Summary 64 4. Building a Graph Database Application. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Data Modeling 65 Describe the Model in Terms of the Application’s Needs 66 Nodes for Things, Relationships for Structure 67 Fine-Grained versus Generic Relationships 67 Model Facts as Nodes 68 Represent Complex Value Types as Nodes 71 Time 72 Iterative and Incremental Development 74 Application Architecture 76 Embedded versus Server 76 Clustering 81 Load Balancing 82 Testing 85 Test-Driven Data Model Development 85 Performance Testing 91 Capacity Planning 95 Optimization Criteria 95 Performance 96 Redundancy 98 Load 98 iv | Table of Contents
    Importing and Bulk Loading Data 99 Initial Import 99 Batch Import 100 Summary 104 5. Graphs in the Real World. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Why Organizations Choose Graph Databases 105 Common Use Cases 106 Social 106 Recommendations 107 Geo 108 Master Data Management 109 Network and Data Center Management 109 Authorization and Access Control (Communications) 110 Real-World Examples 111 Social Recommendations (Professional Social Network) 111 Authorization and Access Control 123 Geospatial and Logistics 132 Summary 147 6. Graph Database Internals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Native Graph Processing 149 Native Graph Storage 152 Programmatic APIs 158 Kernel API 158 Core API 159 Traversal Framework 160 Nonfunctional Characteristics 162 Transactions 162 Recoverability 163 Availability 164 Scale 166 Summary 170 7. Predictive Analysis with Graph Theory. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Depth- and Breadth-First Search 171 Path-Finding with Dijkstra’s Algorithm 173 The A* Algorithm 181 Graph Theory and Predictive Modeling 182 Triadic Closures 182 Structural Balance 184 Local Bridges 188 Table of Contents | v
    Summary 190 A. NOSQL Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 vi | Table of Contents
    Foreword Graphs Are Everywhere, or the Birth of Graph Databases as We Know Them It was 1999 and everyone worked 23-hour days. At least it felt that way. It seemed like each day brought another story about a crazy idea that just got millions of dollars in funding. All our competitors had hundreds of engineers, and we were a 20-ish person development team. As if that was not enough, 10 of our engineers spent the majority of their time just fighting the relational database. It took us a while to figure out why. As we drilled deeper into the persistence layer of our enterprise content management application, we realized that our software was managing not just a lot of individual, isolated, and discrete data items, but also the connections between them. And while we could easily fit the discrete data in relational tables, the connected data was more challenging to store and tremendously slow to query. Out of pure desperation, my two Neo cofounders, Johan and Peter, and I started experimenting with other models for working with data, particularly those that were centered around graphs. We were blown away by the idea that it might be possible to replace the tabular SQL semantic with a graph-centric model that would be much easier for developers to work with when navigating connected data. We sensed that, armed with a graph data model, our development team might not waste half its time fighting the database. Surely, we said to ourselves, we can’t be unique here. Graph theory has been around for nearly 300 years and is well known for its wide applicability across a number of diverse mathematical problems. Surely, there must be databases out there that embrace graphs! vii
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