book

Graph Databases

by Jim Webber, Emil Eifrem, Ian Robinson

June 2013

Intermediate to advanced

221 pages

6h 16m

English

O'Reilly Media, Inc.

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Graphs Are Everywhere, or the Birth of Graph Databases as We Know Them
About This BookConventions Used in This BookUsing Code ExamplesSafari® Books OnlineHow to Contact UsAcknowledgments
What Is a Graph?A High-Level View of the Graph SpaceGraph DatabasesGraph Compute EnginesThe Power of Graph DatabasesPerformanceFlexibilityAgilitySummary
Relational Databases Lack RelationshipsNOSQL Databases Also Lack RelationshipsGraph Databases Embrace RelationshipsSummary
Models and GoalsThe Property Graph ModelQuerying Graphs: An Introduction to CypherCypher PhilosophySTARTMATCHRETURNOther Cypher ClausesA Comparison of Relational and Graph ModelingRelational Modeling in a Systems Management DomainGraph Modeling in a Systems Management DomainTesting the ModelCross-Domain ModelsCreating the Shakespeare GraphBeginning a QueryDeclaring Information Patterns to FindConstraining MatchesProcessing ResultsQuery ChainingCommon Modeling PitfallsEmail Provenance Problem DomainA Sensible First Iteration?Second Time’s the CharmEvolving the DomainAvoiding Anti-PatternsSummary
Data ModelingDescribe the Model in Terms of the Application’s NeedsNodes for Things, Relationships for StructureFine-Grained versus Generic RelationshipsModel Facts as NodesEmploymentPerformanceEmailingReviewingRepresent Complex Value Types as NodesTimeTimeline treesLinked listsVersioningIterative and Incremental DevelopmentApplication ArchitectureEmbedded Versus ServerEmbedded Neo4jServer modeServer extensionsClusteringReplicationBuffer writes using queuesGlobal clustersLoad BalancingSeparate read traffic from write trafficCache shardingRead your own writesTestingTest-Driven Data Model DevelopmentExample: A test-driven social network data modelTesting server extensionsPerformance TestingQuery performance testsApplication performance testsTesting with representative dataCapacity PlanningOptimization CriteriaPerformanceCalculating the cost of graph database performancePerformance optimization optionsRedundancyLoadSummary
Why Organizations Choose Graph DatabasesCommon Use CasesSocialRecommendationsGeoMaster Data ManagementNetwork and Data Center ManagementAuthorization and Access Control (Communications)Real-World ExamplesSocial Recommendations (Professional Social Network)Talent.net data modelInferring social relationsFinding colleagues with particular interestsAdding WORKED_WITH relationshipsAuthorization and Access ControlTeleGraph data modelFinding all accessible resources for an administratorDetermining whether an administrator has access to a resourceFinding administrators for an accountGeo (Logistics)Global Post data modelRoute calculationFinding the shortest delivery route using CypherImplementing route calculation with the traversal frameworkSummary
Native Graph ProcessingNative Graph StorageProgrammatic APIsKernel APICore (or “Beans”) APITraversal APINonfunctional CharacteristicsTransactionsRecoverabilityAvailabilityScaleCapacityLatencyThroughputSummary
Depth- and Breadth-First SearchPath-Finding with Dijkstra’s AlgorithmThe A* AlgorithmGraph Theory and Predictive ModelingTriadic ClosuresStructural BalanceLocal BridgesSummary
The Rise of NOSQLACID versus BASEThe NOSQL QuadrantsDocument StoresKey-Value StoresColumn FamilyQuery versus Processing in Aggregate StoresGraph DatabasesProperty GraphsHypergraphsTriples

Overview

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