Variable-Length Path Query Evaluation Based on Worst-Case Optimal Joins

Variable-length path queries are essential for finding paths in a graph that adhere to a specified length constraint, utilizing only edges with labels from a restricted subset of the edge labels. These queries play a crucial role in graph analytics and are supported by practical graph query languages like Cypher in property graph systems and SPARQL 1.1 in RDF graph systems. In this paper, we present a novel solution for efficient evaluation of variable-length path queries, based on worst-case optimal joins. Our solution's core relies on a jumping-like worst-case optimal join technique, allowing us to select a query vertex order that differs completely from existing graph systems based on worst-case optimal joins. Furthermore, we introduce a cost-based dynamic programming optimizer that combines traditional and jumping-like worst-case optimal join techniques. We also propose an optimization technique to leverage intra-query parallelism during query evaluation. Through extensive experiments conducted on numerous synthetic and real RDF and property graphs, we demonstrate that the proposed technique achieves excellent performance.