Data Chunk Compaction in Vectorized Execution
Summary: Addresses data-chunk fragmentation in vectorized analytics caused by hash-join processing. Proposes a runtime-learning compaction threshold and hash-join-specific logical compaction to minimize movement, delivering up to 63% speedups in DuckDB on TPC-H/DS. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yiming Qiao (Tsinghua University)
- 2. Huanchen Zhang (Shanghai Qi Zhi Institute; Tsinghua University)
BibTeX Citation
@inproceedings{qiao_sigmod25,
title = {{Data Chunk Compaction in Vectorized Execution}},
author = {Qiao, Yiming and Zhang, Huanchen},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709676},
url = {https://dl.acm.org/doi/10.1145/3709676},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,529 | Debunking the Myth of Join Ordering: Toward Robust SQL Analytics | 2025 | SIGMOD | 6.18591e-05 |
| 10,529 | Robust Predicate Transfer with Dynamic Execution | 2026 | VLDB | 5.093636e-05 |
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Showing 31 of 31 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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