Storage – native graph storage versus non-native graph storage

As with all database management systems, graph databases have the concept of storage and query engines, which deal with persistence and queries over connected data. The query engine of the database is responsible for running the queries and retrieving or modifying data. The query engine exposes the graph data model through Create, Read, Update, and Delete operations (commonly referred to as CRUD). Storage deals with how the data is stored physically and how it is represented logically when retrieved. Its knowledge can help in choosing a graph database.

Relationships are an important part of any domain model and need to be traversed frequently. In a graph database, the relationships are explicit rather than inferred. Making relationships explicit is achieved either via the query engine working on a non-native graph storage (such as RDBMS, column stores, document stores) or using a native graph storage.

In a graph database relying on non-native graph storage, relationships need to be inferred at runtime. For example, if we want to model a graph in an RDBMS, our processing engine will have to infer the relationships using foreign keys and reify the relationships at runtime. This problem is computationally expensive and is infeasible for traversing multiple relationships because of the recursive joins involved. There are other graph databases in which NoSQL stores such as HDFS, column stores such as Cassandra, or documents are used to store data and expose a Graph API. Though there are no joins in a graph database using NoSQL stores, the database still has to use index lookups. In cases where non-native storage is used, the query engines have to make more computational effort.

Neo4j uses a native graph storage. Each node has a handle to all the outgoing relationships it has and each relationship, in turn, knows its terminal nodes. At runtime, to find neighboring nodes, Neo4j doesn't have to do an index lookup. Instead, neighboring nodes can be identified by looking at the relationships of the current node. This feature is called index-free adjacency. Index-free adjacency is mechanically sympathetic and allows the Neo4j query engine to have a significant performance boost while traversing the graph.