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

arXiv: https://arxiv.org/abs/1310.3314 · HTML: https://mlsanity.com/paper/1310.3314/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2013.

## Built on by (most cited first, 3 of 3)

- [A Neuro-Symbolic Framework for Answering Graph Pattern Queries in Knowledge Graphs](https://mlsanity.com/paper/2310.04598/) (2023)
- [Graphical Join: A New Physical Join Algorithm for RDBMSs](https://mlsanity.com/paper/2206.10435/) (2022)
- [Enhancing Datalog Reasoning with Hypertree Decompositions](https://mlsanity.com/paper/2305.06854/) (2023)

## Abstract (arXiv)

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.