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The 3D Hash Join: Building On Non-Unique Join Attributes

Summary: Introduces 3D Hash Join: cluster collision chains by distinct build-key values to avoid long chains from duplicates/skew, improving probe locality and cutting memory accesses. Adds deferred unnesting for multi-join evaluation; shows up to 3.5×–5.7× speedups. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
444
Venue
CIDR
Year
2022
Pagerank
5.9429869e-05
Overall Rank
6,207 | 57.42%
DOI
-

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{flachs_cidr22,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '22},
        title = {{The 3D Hash Join: Building On Non-Unique Join Attributes}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Flachs, Daniel and Müller, Magnus and Moerkotte, Guido},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
3,622 Robust Join Processing with Diamond Hardened Joins 2024 VLDB 7.2465862e-05
6,999 DuckPGQ: Efficient Property Graph Queries in an analytical RDBMS 2023 CIDR 5.7291934e-05
8,232 Adaptive Factorization Using Linear-Chained Hash Tables 2025 CIDR 5.4612012e-05
10,888 Saving Private Hash Join 2025 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 15 of 15 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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