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Native Graph Databases versus Non-Native Graph Databases

January 13, 2016

Ben Nussbaum

Relationships Matter: Non- and Native Graph Databases

Native Graph Databases and Index-Free Adjacency

Native Graph Databases Storage Techniques Used by ONgDB

native graph databases versus non-native graph databasesAs 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.

Relationships are integral in any domain and requires frequent transversal. In a graph database, relationships are strictly explicit instead of being inferred. Creating explicit relationships can be gained either through the query engine on a non-native or native graph storage.

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.

As a native graph database, ONgDB turns relationships into first class entities in data records at store levels. It doesn’t place a layer of graph on an existing database storage engine. At the store level, ONgDB writes and reads data from the disk using techniques that are optimized for graph transversal.

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.

ONgDB currently operates on a native graph storage. Each and every single node has a handle on all outbound relationships and in exchange, each relationship has knowledge of its terminal nodes. During runtime, ONgdB won’t have to rely on an index lookup. Rather, nearby nodes can be spotted by viewing relationships of the present node. This is the feature of an index-free adjacency. The adjacency is sympathetic and encourages a performance boost of the ONgDB query engine while traversing the graph.

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 ONgDB, which we use in our Connected Data Platform.