Empowering In-Memory Relational Database Engines with Native Graph Processing

TL;DR

This paper introduces GRFusion, a system that enables native graph processing within an in-memory relational database, significantly improving query performance for graph-relational data.

Contribution

It presents a novel approach to treat graphs as first-class citizens inside RDBMSs, enabling native graph operations and cross-model query execution.

Findings

Achieves up to four orders-of-magnitude speedup in query time

Successfully integrates graph processing into VoltDB

Demonstrates significant performance improvements over existing methods

Abstract

The plethora of graphs and relational data give rise to many interesting graph-relational queries in various domains, e.g., finding related proteins satisfying relational predicates 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 treat graphs as first-class citizens inside the relational engine so that operations on graphs are executed natively inside the RDBMS. We realize our approach inside VoltDB, an open-source in-memory relational database, and name this realization GRFusion. The SQL and the query engine of GRFusion are empowered to declaratively define graphs and execute cross-data-model query plans formed by graph and relational operators, resulting in up to four orders-of-magnitude in query-time speedup w.r.t. state-of-the-art approaches.