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The plethora of graphs and relational data give rise to many interestinggraph-relational queries in various domains, e.g., finding related proteinssatisfying relational predicates in a biological network. The maturity ofRDBMSs motivated academia and industry to invest efforts in leveraging RDBMSsfor graph processing, where efficiency is proven for vital graph queries.However, none of these efforts process graphs natively inside the RDBMS, whichis particularly challenging due to the impedance mismatch between therelational and the graph models. In this paper, we propose to treat graphs asfirst-class citizens inside the relational engine so that operations on graphsare executed natively inside the RDBMS. We realize our approach inside VoltDB,an open-source in-memory relational database, and name this realizationGRFusion. The SQL and the query engine of GRFusion are empowered todeclaratively define graphs and execute cross-data-model query plans formed bygraph and relational operators, resulting in up to four orders-of-magnitude inquery-time speedup w.r.t. state-of-the-art approaches.

Empowering In-Memory Relational Database Engines with Native Graph Processing