Neo4j - The Benefits of Graph Databases

Most applications today handle data that is deeply associative, i.e. structured as graphs (networks). The most obvious example of this is social networking sites, but even tagging systems, content management systems and wikis deal with inherently hierarchical or graph-shaped data.

This turns out to be a problem because it’s difficult to deal with recursive data structures in traditional relational databases. In essence, each traversal along a link in a graph is a join, and joins are known to be very expensive. Furthermore, with user-driven content, it is difficult to pre-conceive the exact schema of the data that will be handled. Unfortunately, the relational model requires upfront schemas and makes it difficult to fit this more dynamic and ad-hoc data.

A graph database uses nodes, relationships between nodes and key-value properties instead of tables to represent information. This model is typically substantially faster for associative data sets and uses a schema-less, bottoms-up model that is ideal for capturing ad-hoc and rapidly changing data.

This session will introduce an open source, high-performance, transactional and disk-based graph database called “Neo4j” (http://neo4j.org), which frequently outperforms relational backends with >1000x for many increasingly important use cases.