Introduction - Graph Databases - page 5

Another way to slice the graph space is to look at the graph models
employed by the various technologies. There are three dominant graph
data models: the property graph, Resource Description Framework
(RDF) triples, and hypergraphs. We describe these in detail in Appen‐
dix A . Most of the popular graph databases on the market use the prop‐
erty graph model, and in consequence, it's the model we'll use through‐
out the remainder of this topic.
Graph Databases
A graph database management system (henceforth, a graph database) is an online da‐
tabase management system with Create, Read, Update, and Delete (CRUD) methods
that expose a graph data model. Graph databases are generally built for use with trans‐
actional (OLTP) systems. Accordingly, they are normally optimized for transactional
performance, and engineered with transactional integrity and operational availability
in mind.
There are two properties of graph databases you should consider when investigating
graph database technologies:
The underlying storage
Some graph databases use native graph storage that is optimized and designed for
storing and managing graphs. Not all graph database technologies use native graph
storage, however. Some serialize the graph data into a relational database, an object-
oriented database, or some other general-purpose data store.
The processing engine
Some definitions require that a graph database use index-free adjacency , meaning
that connected nodes physically “point” to each other in the database. 2 Here we take
a slightly broader view: any database that from the user's perspective behaves like a
graph database (i.e., exposes a graph data model through CRUD operations) quali‐
fies as a graph database. We do acknowledge, however, the significant performance
advantages of index-free adjacency, and therefore use the term native graph pro‐
cessing to describe graph databases that leverage index-free adjacency.
2. See Rodriguez, M.A., Neubauer, P., “The Graph Traversal Pattern,” 2010 .