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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Mathematical Chemistry 47(4): 1209-1223. Chapter 3. Data Modeling with Graphs In previous chapters we’ve described the substantial benefits of the graph database when compared both with other NOSQL stores and with traditional relational databases. But having chosen to adopt a graph database, the question arises: how do we model in graphs? This chapter focuses on graph modeling. Starting with a recap of the labeled property graph model — the most widely adopted graph data model — we then

In our graph we can find the faulty equipment with the following query: MATCH (user:User)-[*1..5]-(asset:Asset) WHERE user.name = 'User 3' AND asset.status = 'down' RETURN DISTINCT asset The MATCH clause here describes a variable length path between one and five relationships long. The relationships are unnamed and undirected (there’s no colon or relationship name between the square brackets, and no arrow-tip to indicate direction). This allows us to match paths such as:

By this time, our data model looks something like the one shown in Figure 4-8. DELIVERY_ADDRESS specializes the data on behalf of the application’s fulfillment needs; BILLING_ADDRESS specializes the data on behalf of the application’s billing needs; and ADDRESS specializes the data on behalf of the application’s customer management needs. Figure 4-8. Different relationships for different application needs Just because we can add new relationships to meet new application goals, doesn’t

as production data is available to verify our assumptions. Although ideally we would always test with a production-sized dataset, it is often not possible or desirable to reproduce extremely large volumes of data in a test environment. In such cases, we should at least ensure that we build a representative dataset whose size exceeds our capacity to hold the entire graph in main memory. That way, we’ll be able to observe the effect of cache evictions, and query for portions of the graph not

must do so according to the routes scheduled for a particular period. Parcel routes change throughout the year, with more trucks, delivery people, and collections over the Christmas period than during the summer, for example. The engine must, therefore, apply its calculations using only those routes that are available for a particular period. On top of accommodating different routes and levels of parcel traffic, the new parcel network must also allow for significant change and evolution. The

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