Adaptive Factorization Using Linear-Chained Hash Tables
Summary: Adds adaptive factorized aggregations and runtime-chosen worst-case-optimal joins to DuckDB via a collision-free linear-chained hash table that enables efficient factorized/WCOJ execution. Decisions use on-the-fly sketches, heuristics, and ML to obtain stats from subqueries/Parquet, yielding near-optimal performance with explainable low-cost alternatives. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Paul Groß (Centrum Wiskunde & Informatica)
- 2. Daniel ten Wolde (Centrum Wiskunde & Informatica)
- 3. Peter Boncz (Centrum Wiskunde & Informatica)
BibTeX Citation
@inproceedings{gro_cidr25,
address = {Amsterdam, Netherlands},
series = {{CIDR} '25},
title = {{Adaptive Factorization Using Linear-Chained Hash Tables}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Groß, Paul and Wolde, Daniel ten and Boncz, Peter},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,074 | Parachute: Single-Pass Bi-Directional Information Passing | 2025 | VLDB | 5.4929961e-05 |
| 11,012 | ScaleCache: Scalable and Production-grade Buffer Management for Disk-based Database Systems | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 17 of 17 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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