Survivorship Bias in Industrial Database Workloads
Summary: Industrial workload traces reflect a negotiation: users adapt queries to exploit platform strengths and avoid weak features, so traces are biased toward "surviving" patterns. This survivorship bias undermines assuming traces fully represent user needs and calls for contextualized workload collection, analysis, and benchmark design. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Ryan Marcus (University of Pennsylvania)
- 2. Jeffrey Tao (University of Pennsylvania)
- 3. Peizhi Wu (University of Pennsylvania)
- 4. Zijie Zhao (University of Pennsylvania)
BibTeX Citation
@inproceedings{marcus_cidr26,
address = {Amsterdam, Netherlands},
series = {{CIDR} '26},
title = {{Survivorship Bias in Industrial Database Workloads}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Marcus, Ryan and Tao, Jeffrey and Wu, Peizhi and Zhao, Zijie},
year = {2026}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,941 | Ultron: History-Based Query Optimization at Databricks | 2026 | VLDB | 4.9793485e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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