Towards Cross-Model Efficiency in SQL/PGQ

TL;DR

This paper discusses the integration of graph querying into relational systems via SQL/PGQ, highlighting performance gaps between models and advocating for a unified optimization approach to improve efficiency.

Contribution

It identifies performance disparities between SQL and graph query models and proposes a future direction for unified optimization strategies.

Findings

Performance gaps exist between SQL and graph query models.

Current approaches optimize each formalism separately.

Unified optimization could mitigate performance disparities.

Abstract

SQL/PGQ is a new standard that integrates graph querying into relational systems, allowing users to freely switch between graph patterns and SQL. Our experiments show performance gaps between these models, as queries written in both formalisms can exhibit varying performance depending on the formalism used, suggesting that current approaches handle each query type separately, applying distinct optimizations to each formalism. We argue that a holistic optimization is necessary, where the system internally decides on the best algorithms regardless of whether queries are written in SQL or as graph patterns. We propose possible future research direction to unify these optimizations and mitigate performance gaps.