Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload
Summary: Prestroid uses tree-convolution to predict SQL query resource usage from traces, reducing encoding/padding waste in large-scale DL training. On 19k Presto queries over 20PB, it outperforms baselines and cuts memory 13.5x and epoch time 3.45x, with up to 13.2x Azure savings. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Johan Kok Zhi Kang (National University of Singapore)
- 2. Gaurav (GrabTaxi Holdings)
- 3. Sien Yi Tan (GrabTaxi Holdings)
- 4. Feng Cheng (GrabTaxi Holdings)
- 5. Shixuan Sun (National University of Singapore)
- 6. Bingsheng He (National University of Singapore)
BibTeX Citation
@inproceedings{kang_sigmod21,
title = {{Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload}},
author = {Kang, Johan Kok Zhi and Gaurav and Tan, Sien Yi and Cheng, Feng and Sun, Shixuan and He, Bingsheng},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3457546},
url = {https://dl.acm.org/doi/10.1145/3448016.3457546},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 16 of 16 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 23 | Spark SQL: Relational Data Processing in Spark | 2015 | SIGMOD | 0.00055384955 |
| 85 | Learned Cardinalities: Estimating Correlated Joins with Deep Learning | 2019 | CIDR | 0.00035876108 |
| 144 | Neo: A Learned Query Optimizer | 2019 | VLDB | 0.00029090793 |
| 462 | An End-to-End Learning-based Cost Estimator | 2020 | VLDB | 0.00017836105 |
| 1,155 | QuickSel: Quick Selectivity Learning with Mixture Models | 2020 | SIGMOD | 0.00011777046 |
| 2,211 | The Case for Predictive Database Systems: Opportunities and Challenges | 2011 | CIDR | 8.8308259e-05 |
| 3,057 | Towards a Hands-Free Query Optimizer through Deep Learning | 2019 | CIDR | 7.6960881e-05 |
| 4,913 | A Top-Down Approach to Achieving Performance Predictability in Database Systems | 2017 | SIGMOD | 6.3548977e-05 |
| 5,159 | Facilitating SQL Query Composition and Analysis | 2020 | SIGMOD | 6.2464819e-05 |
| 7,767 | AutoToken: Predicting Peak Parallelism for Big Data Analytics at Microsoft | 2020 | VLDB | 5.4550466e-05 |
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