[szarnyasg]

> Traditional relational OLTP databases such as Postgres are already faster than dedicated graph databases for certain graph related tasks

It is indeed quite common that relational databases outperform graph databases on certain graph processing problems such as subgraph queries (a.k.a. graph pattern matching). There are two key reasons for this: (1) most graph pattern matching operations can be formulated using relational operations such as natural joins, antijoins, and outer joins; and (2) relational databases have been around longer and have well-optimized operators.

A lot of the value that graph databases provide lies in their query languages which (for most systems) allow formulating path queries using a nice syntax (unlike SQL's WITH RECURSIVE which many people find difficult to read and write). Their property graph data model supports a schema-optional approach, which makes them better suited for storing semi-structured data. They also "provide efficient programmatic access to the graph, allowing one to write arbitrary algorithms against them if needed" [1].

With all these said, graph databases could be much faster on subgraph queries than relational databases and there are recent research results on the topic (worst-case optimal joins, A+ indexes, etc.). But these are not available in any production system yet.

[1] http://wp.sigmod.org/?p=1497