On efficiently storing huge property graphs in relational database management systems

Graph structured data can be found in an increasing amount of use-cases. While there exists a considerable number of solutions to store graphs in NoSQL databases, the combined storage of relationally stored data with huge graph structured data within the same relational database system is not well researched. We present a relational approach for storing and querying huge property graphs by combining NoSQL features, provided by nearly any state-of-the-art database system, and an adjacency table approach. Our approach is optimized for read-only queries but also performs well on update queries. Through an empirical evaluation we show that we achieve a 10 times higher throughput than previous works on a graph with up to 650 million edges. This way, we can use all the advantages of full-fledged relational database systems and seamlessly integrate classical relational data with graph-structured data in an efficient way.