Towards a Converged Relational-Graph Optimization Framework

TL;DR

This paper introduces RelGo, a unified optimization framework that enhances the efficiency of graph pattern matching queries in relational databases by combining relational and graph-specific optimization techniques, achieving significant speedups.

Contribution

We propose a converged relational-graph optimization framework called RelGo that effectively optimizes SPJM queries by integrating relational and graph query optimization strategies.

Findings

RelGo achieves an average speedup of 21.90x on benchmarks.

RelGo outperforms graph-agnostic optimizers in query execution plans.

The framework effectively leverages joint optimization for graph pattern matching.

Abstract

The recent ISO SQL:2023 standard adopts SQL/PGQ (Property Graph Queries), facilitating graph-like querying within relational databases. This advancement, however, underscores a significant gap in how to effectively optimize SQL/PGQ queries within relational database systems. To address this gap, we extend the foundational SPJ (Select-Project-Join) queries to SPJM queries, which include an additional matching operator for representing graph pattern matching in SQL/PGQ. Although SPJM queries can be converted to SPJ queries and optimized using existing relational query optimizers, our analysis shows that such a graph-agnostic method fails to benefit from graph-specific optimization techniques found in the literature. To address this issue, we develop a converged relational-graph optimization framework called RelGo for optimizing SPJM queries, leveraging joint efforts from both relational and graph query optimizations. Using DuckDB as the underlying relational execution engine, our experiments show that RelGo can generate efficient execution plans for SPJM queries. On well-established benchmarks, these plans exhibit an average speedup of 21.90x compared to those produced by the graph-agnostic optimizer.