Graph Database White Papers

Neo4j

- 3 minutes read - 534 words

Graph databases let you model data the way you actually think about it: as connected entities with relationships, not rows in a table. If your project involves networks, hierarchies, or any question where “how are these things connected?” matters more than “what are the individual fields?”, a graph database is worth exploring.

What Is a Graph Database and How Might It Be Used?

A graph database stores data as nodes, relationships, and properties instead of in tables or documents. This means you can organize your data in a similar way to sketching ideas on a whiteboard.

And since graph databases are not restricted to a pre-defined model, you can take more flexible approaches and strategies when working with them.

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Figure 1. A visual way of matching patterns and relationships using Cypher

How It Works

Graph databases are structured through nodes and relationships.

Nodes are entities in the graph which can:

  • Be tagged with labels representing their different roles in a domain (e.g., ==Person==).
  • Hold any number of key-value pairs as properties (e.g., ==name==).
  • Be indexed and bound by constraints.

Relationships provide named connections between two nodes (e.g., Person - ==LOVES== - Person) and they:

  • Must always have a start node, an end node, and exactly one type.
  • Must have a direction.
  • Can have properties, like nodes.
  • Nodes can have multiple relationships of various types without sacrificing performance.

In summary, nodes and relationships are an efficient and flexible way to store data since they allow you to:

  • Create traversals in big graphs for both depth and breadth.
  • Scale up your database to billions of nodes.
  • Design flexible property graph data models that can adapt over time.

Why Use a Graph Database

Projects often deal with large amounts of complex data, and graph databases can be a powerful tool for that.

There are other ways to store data as objects and connections, such as relational databases. However, relational databases rely on computationally expensive ==JOIN== operations or cross-lookups, which are often tied to a rigid data model.

Graph databases do not use JOINs. Rather, relationships are stored natively alongside the data elements (nodes) in a more flexible format, which allows the optimization of data traversal and millions of connections to be accessed per second.

Moreover, many daily challenges and tasks can be viewed from a graph perspective, which allows you to:

  • Navigate deep hierarchies.
  • Find hidden connections between distant items.
  • Discover inter-relationships between items.

Where Graphs Apply

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Whether it is a social network or a road network, all networks can be structured as an interconnected graph of relationships. Many times, a project’s questions and challenges revolve around the relationship between elements, not the elements themselves (e.g., how to get from A to B, instead of what A is and what B is). For this reason, graphs can be applied to many areas of society and to a wide variety of projects.

Query Language

Graph databases use a new open standard known as Cypher. Cypher is similar to and based on SQL language models, but it follows a language design that is more like spoken language while also using ASCII art.

Open Cypher Standard

https://opencypher.org/

Neo4j Cypher Cheat Sheet

https://neo4j.com/docs/cypher-cheat-sheet/current/

References