Fast Parallel Hypertree Decompositions in Logarithmic Recursion Depth

TL;DR

This paper introduces a novel parallel algorithm for computing hypertree decompositions with logarithmic recursion depth, significantly improving efficiency for complex query processing in databases.

Contribution

The paper presents the first parallel algorithm for hypertree decomposition with logarithmic recursion depth, enabling more efficient query processing.

Findings

Effective for complex queries

Logarithmic recursion depth achieved

High parallelisation efficiency

Abstract

Modern trends in data collection are bringing current mainstream techniques for database query processing to their limits. Consequently, various novel approaches for efficient query processing are being actively studied. One such approach is based on hypertree decompositions (HDs), which have been shown to carry great potential to process complex queries more efficiently and with stronger theoretical guarantees. However, using HDs for query execution relies on the difficult task of computing decompositions of the query structure, which guides the efficient execution of the query. From theoretical results we know that the performance of purely sequential methods is inherently limited, yet the problem is susceptible to parallelisation. In this paper we propose the first algorithm for computing hypertree decompositions that is well-suited for parallelisation. The proposed algorithm log-k-decomp requires only a logarithmic number of recursion levels and additionally allows for highly parallelised pruning of the search space by restriction to balanced separators. We provide detailed experimental evaluation over the HyperBench benchmark and demonstrate that our approach is highly effective especially for complex queries.