Introduction - Graph Databases - page 4

by following a relationship marked CURRENT ; PREVIOUS relationships then create a time
line of posts.
The Property Graph Model
In discussing Figure 1-2 we've also informally introduced the most popular variant of
graph model, the property graph (in Appendix A , we discuss alternative graph data
models in more detail). A property graph has the following characteristics:
• It contains nodes and relationships
• Nodes contain properties (key-value pairs)
• Relationships are named and directed, and always have a start and end node
• Relationships can also contain properties
Most people find the property graph model intuitive and easy to understand. Although
simple, it can be used to describe the overwhelming majority of graph use cases in ways
that yield useful insights into our data.
A High-Level View of the Graph Space
Numerous projects and products for managing, processing, and analyzing graphs have
exploded onto the scene in recent years. The sheer number of technologies makes it
difficult to keep track of these tools and how they differ, even for those of us who are
active in the space. This section provides a high-level framework for making sense of
the emerging graph landscape.
From 10,000 feet we can divide the graph space into two parts:
Technologies used primarily for transactional online graph persistence, typically ac‐
cessed directly in real time from an application
These technologies are called graph databases and are the main focus of this topic.
They are the equivalent of “normal” online transactional processing (OLTP) data‐
bases in the relational world.
Technologies used primarily for offline graph analytics, typically performed as a series
of batch steps
These technologies can be called graph compute engines . They can be thought of as
being in the same category as other technologies for analysis of data in bulk, such
as data mining and online analytical processing (OLAP).