PathFinder: A unified approach for handling paths in graph query languages

TL;DR

PathFinder offers a unified, efficient approach for handling complex path queries across various graph query languages, significantly improving performance and stability on large datasets.

Contribution

It introduces a novel, unified algorithmic framework that efficiently processes diverse path queries in multiple graph query languages, supporting all common path modes.

Findings

PathFinder outperforms many modern graph engines by an order of magnitude.

It maintains stable performance even on large datasets and complex queries.

The approach is validated through a comprehensive implementation and testing on real-world data.

Abstract

Path queries are a core feature of modern graph query languages such as Cypher, SQL/PGQ, and GQL. These languages provide a rich set of features for matching paths, such as restricting to certain path modes (shortest, simple, trail) and constraining the edge labels along the path by a regular expression. In this paper we present PathFinder, a unifying approach for dealing with path queries in all these query languages. PathFinder leverages a compact representation of the (potentially exponential number of) paths that can match a given query, extends it with pipelined execution, and supports all commonly used path modes. In the paper we describe the algorithmic backbone of PathFinder, provide a reference implementation, and test it over a large set of real-world queries and datasets. Our results show that PathFinder exhibits very stable behavior, even on large data and complex queries, and its performance is an order of magnitude better than that of many modern graph engines.