When Can We Trust Progress Estimators for SQL Queries?
Summary: Worst-case: for SQL progress estimation, no estimator beats the trivial 0–100% bound; they propose an optimal error-bound estimator for that regime. In typical SQL workloads, progress estimates are accurate with small errors; empirical results show such good scenarios are common, and combining estimators improves robustness. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Surajit Chaudhuri (Microsoft)
- 2. Raghav Kaushik (Microsoft)
- 3. Ravishankar Ramamurthy (Microsoft)
BibTeX Citation
@inproceedings{chaudhuri_sigmod05,
title = {{When Can We Trust Progress Estimators for SQL Queries?}},
author = {Chaudhuri, Surajit and Kaushik, Raghav and Ramamurthy, Ravishankar},
series = {{SIGMOD} '05},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1066157.1066223},
url = {https://dl.acm.org/doi/10.1145/1066157.1066223},
year = {2005}
}
Incoming Citations (Sorted by Pagerank)
Showing 13 of 13 citing papers.
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9 | Online Aggregation | 1997 | SIGMOD | 0.00076265429 |
| 57 | On Random Sampling over Joins | 1999 | SIGMOD | 0.00040095727 |
| 91 | On the Propagation of Errors in the Size of Join Results | 1991 | SIGMOD | 0.00034748721 |
| 135 | Ripple Joins for Online Aggregation | 1999 | SIGMOD | 0.00029858107 |
| 138 | Join Synopses for Approximate Query Answering | 1999 | SIGMOD | 0.00029618887 |
| 149 | Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans | 1998 | SIGMOD | 0.00028977821 |
| 284 | Balancing Histogram Optimality and Practicality for Query Result Size Estimation | 1995 | SIGMOD | 0.00022205848 |
| 471 | Robust Query Processing through Progressive Optimization | 2004 | SIGMOD | 0.00017744392 |
| 1,158 | Toward a Progress Indicator for Database Queries | 2004 | SIGMOD | 0.00011767292 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 5,658 | Uncertainty Aware Query Execution Time Prediction | 2014 | VLDB |
| 2 | 7,492 | Non-Invasive Progressive Optimization for In-Memory Databases | 2016 | VLDB |
| 3 | 6,664 | Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation | 2023 | SIGMOD |
| 4 | 1,428 | Knowing When You’re Wrong: Building Fast and Reliable Approximate Query Processing Systems | 2014 | SIGMOD |
| 5 | 6,243 | Operator and Query Progress Estimation in Microsoft SQL Server Live Query Statistics | 2016 | SIGMOD |
| 6 | 1,158 | Toward a Progress Indicator for Database Queries | 2004 | SIGMOD |
| 7 | 13,056 | Online Estimation For Subset-Based SQL Queries | 2005 | VLDB |
| 8 | 682 | Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques | 2012 | VLDB |
| 9 | 1,588 | Estimating Progress of Execution for SQL Queries | 2004 | SIGMOD |
| 10 | 6,458 | A Statistical Approach Towards Robust Progress Estimation | 2012 | VLDB |