Graph Databases in Action

Graph Databases in Action takes the reader from a deceptively simple question—does this problem call for a graph?—to the practical work of modeling connected data and building an application around it. Dave Bechberger and Josh Perryman teach through Apache TinkerPop and Gremlin, grounding the concepts in a continuing social-network and restaurant-recommendation example.

The result is a hands-on guide for software developers who want to understand not just how to traverse a graph, but how to decide what belongs in one, shape a useful model, and put graph queries to work in an application.

Start with the shape of the problem

The opening chapters introduce graph concepts and compare graph databases with familiar relational approaches. Readers consider when relationships are central enough to make a graph a natural fit, then work through a systematic process for translating entities and their connections into vertices, edges, and properties.

Learn Gremlin by building useful traversals

With the modeling groundwork in place, the book develops Gremlin traversals for navigating, filtering, and retrieving graph data. It also covers paths, recursive traversals, result formatting, and combining steps into more complex queries. The progression helps connect the structure of a data model to the questions an application needs to answer.

Take the graph into an application

Examples move beyond the query console into application development. Readers work through connecting to a graph database, handling data changes, and implementing traversal patterns in Java. The recurring application scenario gives the code a practical purpose: using connected data to support social features, personalization, and restaurant recommendations.

Model for richer use cases

Later chapters explore more advanced modeling choices, including denormalization, generic labels, and placing properties on edges. The authors also show how subgraphs can support personalization and how recommendation-oriented traversals can be developed from known paths through the data.

Practical guidance for the rough edges

Graph applications bring their own performance questions and design pitfalls. The book discusses profiling slow traversals, indexes, supernodes, testing, dirty data, and anti-patterns such as applying graphs to problems that do not benefit from them. It closes by pointing toward graph analytics, machine learning, and further resources.

For developers building with connected data

Written for software developers, this book does not require previous experience with graph databases. Its comparisons with relational modeling make the material accessible to readers with database experience, while the step-by-step Gremlin and Java examples keep the focus on practical implementation.