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E-raamat: Graph Databases: New Opportunities for Connected Data

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  • Formaat: 238 pages
  • Ilmumisaeg: 10-Jun-2015
  • Kirjastus: O'Reilly Media
  • Keel: eng
  • ISBN-13: 9781491930847
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Graph Databases: New Opportunities for Connected DataSuurem pilt
  • Formaat: 238 pages
  • Ilmumisaeg: 10-Jun-2015
  • Kirjastus: O'Reilly Media
  • Keel: eng
  • ISBN-13: 9781491930847
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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.

This second edition includes new code samples and diagrams, using the latest Neo4j syntax, as well as information on new functionality. 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
Foreword vii
Preface xi
1 Introduction 1(10)
What Is a Graph?
1(3)
A High-Level View of the Graph Space
4(4)
Graph Databases
5(2)
Graph Compute Engines
7(1)
The Power of Graph Databases
8(2)
Performance
8(1)
Flexibility
9(1)
Agility
9(1)
Summary
10(1)
2 Options for Storing Connected Data 11(14)
Relational Databases Lack Relationships
11(4)
NOSQL Databases Also Lack Relationships
15(3)
Graph Databases Embrace Relationships
18(6)
Summary
24(1)
3 Data Modeling with Graphs 25(40)
Models and Goals
25(1)
The Labeled Property Graph Model
26(1)
Querying Graphs: An Introduction to Cypher
27(5)
Cypher Philosophy
28(2)
MATCH
30(1)
RETURN
30(1)
Other Cypher Clauses
31(1)
A Comparison of Relational and Graph Modeling
32(9)
Relational Modeling in a Systems Management Domain
33(5)
Graph Modeling in a Systems Management Domain
38(1)
Testing the Model
39(2)
Cross-Domain Models
41(11)
Creating the Shakespeare Graph
45(1)
Beginning a Query
46(2)
Declaring Information Patterns to Find
48(1)
Constraining Matches
49(1)
Processing Results
50(1)
Query Chaining
51(1)
Common Modeling Pitfalls
52(11)
Email Provenance Problem Domain
52(1)
A Sensible First Iteration?
52(3)
Second Time's the Charm
55(3)
Evolving the Domain
58(5)
Identifying Nodes and Relationships
63(1)
Avoiding Anti-Patterns
63(1)
Summary
64(1)
4 Building a Graph Database Application 65(40)
Data Modeling
65(11)
Describe the Model in Terms of the Application's Needs
66(1)
Nodes for Things, Relationships for Structure
67(1)
Fine-Grained versus Generic Relationships
67(1)
Model Facts as Nodes
68(3)
Represent Complex Value Types as Nodes
71(1)
Time
72(2)
Iterative and Incremental Development
74(2)
Application Architecture
76(9)
Embedded versus Server
76(5)
Clustering
81(1)
Load Balancing
82(3)
Testing
85(10)
Test-Driven Data Model Development
85(6)
Performance Testing
91(4)
Capacity Planning
95(4)
Optimization Criteria
95(1)
Performance
96(2)
Redundancy
98(1)
Load
98(1)
Importing and Bulk Loading Data
99(5)
Initial Import
99(1)
Batch Import
100(4)
Summary
104(1)
5 Graphs in the Real World 105(44)
Why Organizations Choose Graph Databases
105(1)
Common Use Cases
106(5)
Social
106(1)
Recommendations
107(1)
Geo
108(1)
Master Data Management
109(1)
Network and Data Center Management
109(1)
Authorization and Access Control (Communications)
110(1)
Real-World Examples
111(36)
Social Recommendations (Professional Social Network)
111(12)
Authorization and Access Control
123(9)
Geospatial and Logistics
132(15)
Summary
147(2)
6 Graph Database Internals 149(22)
Native Graph Processing
149(3)
Native Graph Storage
152(6)
Programmatic APIs
158(4)
Kernel API
158(1)
Core API
159(1)
Traversal Framework
160(2)
Nonfunctional Characteristics
162(8)
Transactions
162(1)
Recoverability
163(1)
Availability
164(2)
Scale
166(4)
Summary
170(1)
7 Predictive Analysis with Graph Theory 171(22)
Depth- and Breadth-First Search
171(2)
Path-Finding with Dijkstra's Algorithm
173(8)
The A* Algorithm
181(1)
Graph Theory and Predictive Modeling
182(6)
Triadic Closures
182(2)
Structural Balance
184(4)
Local Bridges
188(2)
Summary
190(3)
A NOSQL Overview 193(18)
Index 211
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