Perhaps you sometimes wonder how your e-commerce application always recommends the right things for you. Or, you are amazed at the lightning speeds of your purchase transactions. Both these scenarios utilize graph databases to detect relationship patterns to prevent online fraud or make product recommendations.

If you are wondering, “what is a graph database?” read on to find out.

How a Graph Database Works

Essentially, a graph database can be defined as a storage solution that connects and links elements together. The graph-oriented structures store data without using an index in vertices and edges. These are referred to as entities and relationships.

Graph databases are optimized for queries and start from a root node to leverage the proximity of data. Here, there is no need to declare data types. In contrast, this is what you do in relational databases that are table-oriented and more constrained.

Graph vs. Relational Databases

A graph database allows you to explore data structured like graphs. Ideally, you dereference a physical pointer to traverse the graph and access the neighbors of an entity.

Relationships are stored in a graph database itself, and hence, querying will be faster. Also, retrieval is complete in a single operation for many cases.

But to issue global queries, you need an indexation mechanism that works by crafting supernodes into the graph. On the other hand, relational database use cases need indices that are logical pointers. Graph databases address these limitations.

Types of Graph Database Models

Primarily, there are three main types of graph database:

  1. True graph
  2. Triple
  3. Conventional

Of these, conventional databases have some graphical capabilities.

While a true graph supports index-free adjacency, you need an index to traverse a triple graph database. Triple stores, also called Resource Description Framework databases, store data as subject-predicate-object.

Besides, a true graph supports attributes associated with entities. A Triple or RDF graph integrates heterogeneous data and makes links between datasets.

Concerning storage models, graph databases are further as follows:

Native Graph Storage: Data is stored on a disk as a vertex and edge.

Relational Storage: This is a relational model where the stored data is in vertex tables and edge tables.

Key-Value Storage: This storage uses NoSQL databases.

If you want to know what is a graph database using the data model, here are some examples:


Hypergraph data model relationships can connect several nodes. Since there can be multiple nodes at each end, it is useful for data with many-to-many relationships, like HyperGraph DB.

Triple Store

RDF graph notations represent the subject and the object with nodes and the predicate with an arc. Triple stores are not optimized to support property graphs as they are not index-free. Also, they scale horizontally but add latency to every query, and hence, they are more suitable for offline analytics, like AllegroGraph.

Property Graph

Entities, represented by nodes, hold any number of key-value pairs or attributes called properties. Labels tagged with nodes have different roles and attach metadata in the domain. Relationships, which can also have properties, provide semantically relevant connections between entities, like AWS Neptune.

The world of today is struggling to handle immense amounts of data from various sources. Graph databases strive to resolve this issue with network analysis, fraud detection, metadata management, and more.