New Compressed Indices for Multijoins on Graph Databases

TL;DR

This paper introduces new compact indices for multijoins in graph databases that achieve efficient worst-case optimal performance while using significantly less space, leading to substantial improvements in query times.

Contribution

The paper presents novel compact indices and query strategies that enhance worst-case optimal multijoin processing in graph databases, reducing space and improving query efficiency.

Findings

Up to 13x faster query times with new indices

Reduced space usage by a factor of two

Potential for further improvements in query planning

Abstract

A recent surprising result in the implementation of worst-case-optimal (wco) multijoins in graph databases (specifically, basic graph patterns) is that they can be supported on graph representations that take even less space than a plain representation, and orders of magnitude less space than classical indices, while offering comparable performance. In this paper we uncover a wide set of new wco space-time tradeoffs: we (1) introduce new compact indices that handle multijoins in wco time, and (2) combine them with new query resolution strategies that offer better times in practice. As a result, we improve the average query times of current compact representations by a factor of up to 13 to produce the first 1000 results, and using twice their space, reduce their total average query time by a factor of 2. Our experiments suggest that there is more room for improvement in terms of generating better query plans for multijoins.