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Extending In-Memory Relational Database Engines with Native Graph Support
Abstract and Figures
The plethora of graphs and relational data give rise to many interesting graph-relational queries in various domains, e.g., finding related proteins retrieved by a relational subquery in a biological network. The maturity of RDBMSs motivated academia and industry to invest efforts in leveraging RDBMSs for graph processing , where efficiency is proven for vital graph queries. However, none of these efforts process graphs natively inside the RDBMS, which is particularly challenging due to the impedance mismatch between the relational and the graph models. In this paper, we propose to manage graphs as first-class citizens inside the rela-tional engine. We realize our approach inside VoltDB , an open-source in-memory relational database, and name this realization GRFusion. The SQL and query engine of GRFusion are empowered to declaratively define graphs and execute cross-data-model query plans acting on graphs and relations, resulting in up to four orders-of-magnitude in query-time speedup w.r.t. state-of-the-art approaches.
Figures - uploaded by Mohammad Sadoghi
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