Graph Databases: New Opportunities for Connected Data

Graph Databases: New Opportunities for Connected Data

Language: English

Pages: 238

ISBN: 1491930896

Format: PDF / Kindle (mobi) / ePub

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

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Relationship’s name as we go. Doing so should create sensible sentences. For our data center example, we can read off sentences like “The App, which consists of App Instances 1, 2, and 3, uses the Database, which resides on Database Servers 1, 2 and 3,” and “Server 3 runs VM 31, which hosts App Instance 3.” If reading the graph in this way makes sense, we can be reasonably confident it is faithful to the domain. To further increase our confidence, we also need to consider the queries we’ll run.

"firstUser", firstUser ); params.put( "secondUser", secondUser ); return db.execute( query, params ); } // More queries } In the constructor for SocialNetworkQueries we store the supplied database instance in a member variable, which allows it to be reused over and again throughout the lifetime of the queries instance. The query itself we implement in the distance() method. Here we create a Cypher statement, initialize a map containing the query parameters, and execute the statement. If.

Networking preferences, and software as a service (SaaS) offerings, where they realize minutes to milliseconds increases in performance over their hand-rolled, relational predecessors. Real-World Examples In this section we describe three example use cases in detail: social and recommendations, authorization and access control, and logistics. Each use case is drawn from one or more production applications of a graph database (specifically in these cases, Neo4j). Company names, context,.

Introductions to graph theory, see Richard J. Trudeau, Introduction To Graph Theory (Dover, 1993) and Gary Chartrand, Introductory Graph Theory (Dover, 1985). For an excellent introduction to how graphs provide insight into complex events and behaviors, see David Easley and Jon Kleinberg, Networks, Crowds, and Markets: Reasoning about a Highly Connected World (Cambridge University Press, 2010). 2 See Rodriguez, Marko A., and Peter Neubauer. 2011. “The Graph Traversal Pattern.” In Graph Data.

Sparse tables with nullable columns require special checking in code, despite the presence of a schema. Several expensive joins are needed just to discover what a customer bought. Reciprocal queries are even more costly. “What products did a customer buy?” is relatively cheap compared to “which customers bought this product?”, which is the basis of recommendation systems. We could introduce an index, but even with an index, recursive questions such as “which customers buying this product also.

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