Graph Databases

【实例简介】
Graph Databases
Graph Databases by Ian Robinson, Jim Webber, and Emil Eifrem Copyright O 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, CA95472 OReilly books may be purchased for educational, business, or sales promotional use. Online editions are alsoavailableformosttitles(http://safaribooksonline.com).Formoreinformationcontactourcorporate institutionalsalesdepartment800-998-9938orcorporate@oreilly.com Editor: Marie Beaugureau Interior Designer: David Futato Production editor: Kristen brown Cover Designer: Ellie Volckhausen Proofreader: Christina edwards Illustrator: rebecca demarest Indexer: Word Co Indexing Services June 2013 First edition June 2015: Second edition Revision History for the Second Edition 2015-05-04: First Release Seehttp://oreilly.com/catalog/errata.csp?isbn=9781491930892forreleasedetails 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 ILSI Table of contents Foreword Preface 1. ntroduction 鲁鲁 What Is a graph? A High-Level View of the Graph Space Graph databases Graph Compute Engines The Power of Graph Databases Pe erformance Flexibility 14578899 gili ty Summar 2. Options for Storing Connected Data...................... 11 Relational Databases Lack relationships NOSQL Databases Also Lack Relationships 15 Graph databases Embrace relationships 18 S ummary 24 3. Data Modeling with Graphs. ................................................25 Models and goals 25 The Labeled Property Graph Model Querying Graphs: An Introduction to Cypher 27 Cypher Philosophy 28 MATCH RETURN 30 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 Testi ing the model 39 Cross-Domain models 41 Creating the Shakespeare Graph Beginning a Query 46 Declaring Information Patterns to Find 48 Constraining Matches Processing results 50 Query Chaining 51 Common Modeling pitfalls 52 Email provenance problem domain 52 A Sensible first iteration 52 Second Times the Charm 5 Evolving the domain 58 Identifying Nodes and Relationships Avoiding Anti-Patterns 63 Summary 4. Building a Graph Database Application Data modeling 65 Describe the Model in Terms of the applications Needs 66 Nodes for Things, Relationships for Structure Fine-Grained versus Generic Relationships 67 Model facts as nodes 68 Represent Complex Value Types as Nodes 71 Ti me Iterative and Incremental Development 74 Application Architecture Embedded versus server 76 Clustering 81 Load Balancing 82 Testing 85 Test-Driven Data Model development 85 Performance Testing 91 Capacit y P lanning 5 Optimization criteria 5 Performance Redundancy 98 98 iv Table of Contents Importing and bulk loading data 9 Initial Import 99 Batch Import 100 Summary 104 5. Graphs in the Real World ,105 Why Organizations Choose Graph Databases 105 Common use cases 106 106 Recommendations 107 108 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 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 Availabilit 164 166 ummary 170 7. Predictive Analysis with Graph Theory 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 Summary 190 A. NOSQL Overview. 93 Index ,,211 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! Well, we Alta Vistad' around the young Web and couldnt find any. After a few months of surveying, we (naively) set out to build, from scratch, a database that worked natively with graphs. Our vision was to keep all the proven features from the relational database(transactions, ACID, triggers, etc. )but use a data model for the 21st century. Project Neo was born, and with it graph databases as we know them The first decade of the new millennium has seen several world-changing new busi nesses spring to life, including Google, Facebook, and Twitter. And there is a com mon thread among them: they put connected data-graphs-at the center of their business. It's 15 years later and graphs are everywhere Facebook, for example, was founded on the idea that while there's value in discrete information about people-their names, what they do, etc -there's even more value in the relationships between them. Facebook founder Mark Zuckerberg built an empire on the insight to capture these relationships in the social graph Similarly, Google's Larry Page and Sergey Brin figured out how to store and process not just discrete web documents but how those web documents are connected. Goo gle captured the web graph, and it made them arguably the most impactful company of the previous decade Today, graphs have been successfully adopted outside the web giants. One of the big gest logistics companies in the world uses a graph database in real time to route phys- ical parcels; a major airline is leveraging graphs for its media content metadata; and a top-tier financial services firm has rewritten its entire entitlements infrastructure on Neo4j. Virtually unknown a few years ago, graph databases are now used in industries as diverse as healthcare, retail, oil and gas, media, gaming, and beyond, with every indication of accelerating their already explosive pace These ideas deserve a new breed of tools: general-purpose database management technologies that embrace connected data and enable graph thinking, which are the kind of tools I wish had been available off the shelf when we were fighting the rela tional database back in 1999 1 For the younger readers, it may come as a shock that there was a time in the history of mankind when google didn't exist. Back then, dinosaurs ruled the earth and search engines with names like Alta Vista, Lycos, and Excite were used, primarily to find ecommerce portals for pet food on the Internet I Foreword I hope this book will serve as a great introduction to this wonderful emerging world of graph technologies, and I hope it will inspire you to start using a graph database in your next project so that you too can unlock the extraordinary power of graphs Good luck! Emil eifrer Cofounder of Neo4j and CeO of Neo Technology Menlo park, california May 2013 【实例截图】
【核心代码】