实例介绍
【实例简介】图数据库第二版英文原版
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【实例截图】
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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
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