Graph databases are a type of NoSQL database, created to address the limitations of relational databases. While the graph model explicitly lays out the dependencies between nodes of data, the relational model and other NoSQL database models link the data by implicit connections. Graph databases, by design, allow simple and fast retrieval of complex hierarchical structures that are difficult to model in relational systems.
They are based on graph theory, and employ nodes, edges, and properties. Nodes represent entities or instances such as people, businesses, accounts, or any other item to be tracked. Edges represent the relationships between the nodes. Properties are details or attributes that relate to the nodes. The ability to have multiple relationships is one of the factors that differentiates graph databases from relational databases.
Definition and Explanation
A graph database is a database that uses graph structures with nodes, edges, and properties to represent and store data. A key concept of the system is the graph (or edge or relationship). The graph relates the data items in the store to a collection of nodes and edges, the edges representing the relationships between the nodes. The relationships allow data in the store to be linked together directly and, in many cases, retrieved with one operation.
Graph databases are more flexible than relational databases because they contain no rigid schema that confines the types of relationships a database can have. This means that as new business requirements arise, graph databases can be easily adapted to meet these new demands. This flexibility also means that graph databases are a good choice for storing data that has complex relationships and dynamic schema.
Nodes
Nodes are the entities in the graph. They can hold any number of attributes (key-value pairs). Nodes can be tagged with labels representing their different roles in your domain. In a graph database, looking up nodes by their ID, their properties, or their labels is very efficient.
Nodes are often used to represent entities, but depending on the graph design they can also represent other elements of the domain model. For example, in a time tree model, individual time instants or intervals may be represented as nodes.
Edges
Edges or relationships are used to connect nodes in the graph and represent the relationship between them. Relationships can also hold attributes. They are always directed and always have a start and end node.
Relationships are equally important to the data model as nodes. By connecting nodes, relationships give the data structure and context. They can be dynamically added to the graph structure, making them ideal for representing data that changes over time.
History of Graph Databases
The concept of graph databases has been around since the mid-1960s, but it wasn't until the early 21st century that they began to be used in a major way. The term "graph" was coined by the mathematician Leonhard Euler in 1736 to describe a system where dots, called vertices or nodes, were connected by lines, called edges or arcs.
Graph databases emerged as a significant technology in the late 2000s, under the NoSQL banner. They were developed to address the limitations of relational databases, particularly when dealing with large data sets. The rise of social networks, which required the management of complex relationships between entities, also contributed to the popularity of graph databases.
Early Development
One of the first graph databases was the Network Database Management System (NDBMS), which was developed in the 1960s. This system was based on the hierarchical and network database models, and it was used to manage large amounts of data for complex manufacturing projects.
However, the NDBMS had limitations, particularly when it came to the flexibility of the data model and the efficiency of data retrieval. These limitations led to the development of the relational database model, which became the dominant database technology for several decades.
Modern Graph Databases
The modern concept of a graph database was first introduced by Marko A. Rodriguez in his 2005 paper "A Graph-Based Movie Recommender". This paper described a system that used a graph database to provide recommendations based on user behavior. This concept was later developed into the Titan graph database, which was one of the first open-source graph databases.
Since then, many other graph databases have been developed, including Neo4j, the most popular graph database as of 2021, and Amazon Neptune, a fully managed graph database service that is part of Amazon Web Services (AWS).
Use Cases of Graph Databases
Graph databases are used in a wide variety of applications, from social networks to logistics, due to their ability to efficiently manage and query highly connected data. They are particularly useful in any scenario where relationships are as important as the individual entities.
Some common use cases for graph databases include social networking, recommendation engines, fraud detection, network and IT operations, identity and access management, and master data management. In all these cases, the ability to quickly traverse and analyze complex relationships gives graph databases a significant advantage over other database models.
Social Networking
Social networks are a natural fit for graph databases, as they can efficiently model and query the complex and dynamic relationships between entities. For example, a graph database can easily model the relationships between users, their friends, and the various posts, comments, and likes that connect them.
Graph databases can also be used to perform complex queries on this data, such as finding the shortest path between two users, identifying clusters of users with similar interests, or recommending new friends based on shared connections.
Recommendation Engines
Recommendation engines are another common use case for graph databases. These systems need to analyze complex relationships between entities, such as the relationships between customers, products, and purchase history, to provide personalized recommendations.
Graph databases can easily model these relationships and perform complex queries on them. For example, a graph database can be used to find products that are often purchased together, identify customers with similar purchase histories, or recommend products based on a customer's past purchases and the purchases of similar customers.
Specific Examples of Graph Databases
There are several examples of graph databases in use today, each with its own unique features and advantages. Some of the most popular include Neo4j, Amazon Neptune, and Microsoft Azure Cosmos DB.
These databases are used by a wide range of organizations, from small startups to large corporations, to manage and analyze their data. They are particularly popular in industries that deal with large amounts of complex, interconnected data, such as social media, e-commerce, and logistics.
Neo4j
Neo4j is the most popular graph database as of 2021. It is an open-source database that is written in Java and Scala. Neo4j is ACID-compliant and has a powerful query language called Cypher, which is specifically designed for querying graph data.
Neo4j is used by a wide range of organizations, from small startups to large corporations. Some of its notable users include eBay, Walmart, and NASA. These organizations use Neo4j to manage and analyze their data, from social networks to logistics to scientific research.
Amazon Neptune
Amazon Neptune is a fully managed graph database service that is part of Amazon Web Services (AWS). It is designed to be highly available and durable, with built-in support for data replication and backup. Neptune supports both the Property Graph model and the RDF model, and it has a flexible query language that supports SPARQL and Gremlin.
Neptune is used by a wide range of organizations, from small startups to large corporations. Some of its notable users include Siemens, Thomson Reuters, and the Financial Industry Regulatory Authority (FINRA). These organizations use Neptune to manage and analyze their data, from social networks to logistics to financial transactions.
Conclusion
Graph databases are a powerful tool for managing and analyzing complex, interconnected data. They offer significant advantages over other database models, particularly when dealing with large data sets and complex relationships.
With the rise of social networks, recommendation engines, and other applications that require the analysis of complex relationships, the popularity of graph databases is likely to continue to grow. Whether you're a small startup or a large corporation, if you're dealing with complex, interconnected data, a graph database could be the right solution for you.