Database Reference
In-Depth Information
This is exactly how the Neo4j engine works in the example—it visits nodes connected to
the starting node, at a predictable speed. Even when the number of nodes in the whole
graph increases (given similar node density), the performance can remain predictably fast.
If you apply the same football analogy to the relational database queries, you'd count all
the people in the stadium and then remove those not around you, which is not the most ef-
ficient strategy given the interconnectivity of the data.
These experiments demonstrate that the Neo4j graph database is significantly faster in
querying graph data than using a relational database. In addition, a single Neo4j instance
can handle data sets ofthree orders ofmagnitude without performance penalties. The inde-
pendence of traversal performance on graph size is one of the key aspects that make Neo4j
an ideal candidate for solving graph problems, even when data sets are very large.
In the next section we'll try to answer the question of what graph data actually is, and how
Neo4j can help you model your data models as natural graph structures.
1.5. Graphs around you
Graphs are considered the most ubiquitous natural structures. The first scientific paper on
graph theory is considered to be a solution of the historical seven bridges of Königsberg
problem,writtenbySwissmathematicianLeohnardEulerin1774.Thesevenbridgesprob-
lem started what is now known as graph theory, and its model and algorithms are now ap-
plied across wide scientific and engineering disciplines.
There are a lot of examples of graph usage in modern science. For example, graphs are
used to describe the relative positions of the atoms and molecules in chemical compounds.
The laws of atomic connections in physics are also described using graphs. In biology, the
evolution tree is actually a special form of graph. In linguistics, graphs are used to model
semantic rules and syntax trees. Network analysis is based on graph theory as well, with
applications in traffic modeling, telecommunications, and topology.
In computer science, a lot of problems are modeled as graphs: social networks, access con-
trol lists, network topologies, taxonomy hierarchies, recommendation engines, and so on.
