A graph database stores data in a graph, the most generic of data structures, capable of elegantly representing any kind of data in a highly accessible way. Let’s follow along some graphs, using them to express graph concepts. We’ll “read” a graph by following arrows around the diagram to form sentences.
“A Graph —records data in→ Nodes —which have→ Properties”
The simplest possible graph is a single Node, a record that has named values referred to as Properties. A Node could start with a single Property and grow to a few million, though that can get a little awkward. At some point it makes sense to distribute the data into multiple nodes, organized with explicit Relationships.
“Nodes —are organized by→ Relationships —which also have→ Properties”
Relationships organize Nodes into arbitrary structures, allowing a Graph to resemble a List, a Tree, a Map, or a compound Entity – any of which can be combined into yet more complex, richly inter-connected structures.
“A Traversal —navigates→ a Graph; it —identifies→ Paths —which order→ Nodes”
A Traversal is how you query a Graph, navigating from starting Nodes to related Nodes according to an algorithm, finding answers to questions like “what music do my friends like that I don’t yet own,” or “if this power supply goes down, what web services are affected?”
“An Index —maps from→ Properties —to either→ Nodes or Relationships”
Often, you want to find a specific Node or Relationship according to a Property it has. Rather than traversing the entire graph, use an Index to perform a look-up, for questions like “find the Account for username master-of-graphs.”
“A Graph Database —manages a→ Graph and —also manages related→ Indexes”
Neo4j is a commercially supported open-source graph database. It was designed and built from the ground-up to be a reliable database, optimized for graph structures instead of tables. Working with Neo4j, your application gets all the expressiveness of a graph, with all the dependability you expect out of a database.
