As with any graph database management system, native graph databases revolve around the idea of storage and use of query engines, which deals specifically with connected data persistence and traversal queries. The database query engine is in charge of operating queries, modifying, and extracting data. Native graph databases showcase the traversal of the graph data model paired with strategic index usage for locating the starting nodes for such operations. Storage involves how data can be physically housed and how it can be represented during extraction. Understanding graph database storage nuances is key to selecting the right graph database for your use case.
A graph database that depends on a non-native graph storage has relationships that will need to be inferred at runtime. For instance, if we intend to model an RDBMS graph, the processing engine will need to infer relationships through foreign keys while making the relationship concrete at runtime. This would be an expensive approach and won’t be feasible to traversing relationships due to the involvement of recursive joins.
Native graph databases utilizes a method known as the “index-free” adjacency.” It means that every data element is aimed directly to its incoming and outgoing relationships. This, in turn, point towards related nodes and etc… This method allows millions of records to be traversed in a single second.
Native graph processing is deemed to be a highly efficient way of graph data processing since it intertwined nodes can “direct” each other within the database. Few of the popular graph database today utilize native graph storage and native graph processing because it’s more complex to develop and takes a longer time to perfect. Native graph databases perform and scale much better than their non-native siblings and currently the fastest most scalable native graph database is Neo4j, which we recommend.