Graph Database

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Graph Database:

A graph database is a database designed to treat the relationships between data as a first-class citizen in the data model.

Only a database that natively embraces relationships is able to store, process, and query connections efficiently. While other databases compute relationships at query time through expensive JOIN operations, a graph database stores connections as first class citizens.

Accessing nodes and relationships in a native graph database is an efficient, constant-time operation and allows you to quickly traverse millions of connections per second per core.

The Property Graph Model

Nodes are the entities in the graph. They can hold any number of attributes (key-value-pairs) called properties. Nodes can be tagged with labels representing their different roles in your domain. In addition to contextualizing node and relationship properties, labels may also serve to attach metadata—​index or constraint information—​to certain nodes.

Relationships provide directed, named, semantically relevant connections between two node-entities (eg Employee WORKS_FOR Company). A relationship always has a direction, a type, a start node, and an end node. Like nodes, relationships can also have properties. In most cases, relationships have quantitative properties, such as weights, costs, distances, ratings, time intervals, or strengths. As relationships are stored efficiently, two nodes can share any number or type of relationships without sacrificing performance. Note that although they are directed, relationships can always be navigated efficiently in either direction.

There is one core consistent rule in a graph database: “No broken links”. Since a relationship always has a start and end node, you can’t delete a node without also deleting its associated relationships. You can also always assume that an existing relationship will never point to a non-existing endpoint.

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Neo4j:

Neo4j is referred to as a native graph database because it implements the Property Graph Model efficiently down to the storage level. As opposed to graph processing or in-memory libraries, Neo4j provides full database characteristics including ACID transaction compliance, cluster support, and runtime failover, making it suitable to use graph data in production scenarios.

Features:

Cypher, a declarative query language similar to SQL, but optimized for graphs. Now used by other databases like SAP HANA Graph and Redis graph via the openCypher project.

Constant time traversals in big graphs both in depth and in breadth due to efficient representation of nodes and relationships. Enables scale-up to billions of nodes on moderate hardware.

Flexible property graph schema that can adapt over time, making it possible to materialize and use new relationships later on to “shortcut” and speed up the domain data when the business needs change.

Drivers for popular programming languages, including Java, JavaScript, .NET and Python.