英语学习打卡第21天

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 provide an overview of the graph query language used for most of the code examples in this book: Cypher. Though there are several graph query languages in existence, Cypher is the most widely deployed, making it the de facto standard. It is also easy to learn and understand, especially for those of us coming from a SQL background. With our first example, based on a systems management domain, we compare relational and graph modeling techniques. In the second example, the production and consumption of Shakespearean literature, we use a graph to connect and query several disparate domains. We end the chapter by looking at some common pitfalls when modeling with graphs, and highlight some good praactices.

Models and Goals

Before we dig deeper into modeling with graphs, a word on models in general. Modeling is an abstracting activity motivated by a particular need or goal. We model in order to bring specific facets of an unruly domain into a space where they can be structured and manipulated. There are no natural representations of the world the way it "really is", just many purposeful selections, abstractions, and simplifications, some of which are more useful than others for satisfying a particular goal.

Graph representations are no different in this respect. What perhaps differentiates them from many other data modeling techniques, however, is the close affinity between the logical and physical models. Relational data management techniques require us to deviate from our natural language representation of the domain: first by cajoling our representation into a logical model, and then by forcing it into a physical model. These transformations introduce semantic dissonance between our conceptualization of the world and the database's instantiation of that model. With graph databases, this gap shrinks considerably.