Skew Strikes Back: New Developments in the Theory of Join Algorithms

TL;DR

This paper reviews recent advances in join algorithms that achieve worst-case optimality, offering a simplified and unified perspective valuable for researchers and system developers.

Contribution

It provides a comprehensive survey of new join algorithms with provable worst-case guarantees, unifying diverse approaches for better understanding.

Findings

Recent join algorithms have worst-case optimal runtime guarantees.

A unified description simplifies understanding of complex join algorithms.

The survey aids both theoretical and practical advancements in database systems.

Abstract

Evaluating the relational join is one of the central algorithmic and most well-studied problems in database systems. A staggering number of variants have been considered including Block-Nested loop join, Hash-Join, Grace, Sort-merge for discussions of more modern issues). Commercial database engines use finely tuned join heuristics that take into account a wide variety of factors including the selectivity of various predicates, memory, IO, etc. In spite of this study of join queries, the textbook description of join processing is suboptimal. This survey describes recent results on join algorithms that have provable worst-case optimality runtime guarantees. We survey recent work and provide a simpler and unified description of these algorithms that we hope is useful for theory-minded readers, algorithm designers, and systems implementors.