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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)

Paper ID
3667
Venue
SIGMOD
Year
2005
Pagerank
9.5286436e-05
Overall Rank
2,111 | 85.32%
DOI
-

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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
14 Online Aggregation 1997 SIGMOD 0.0010801504
18 On Random Sampling over Joins 1999 SIGMOD 0.00092385438
99 On the Propagation of Errors in the Size of Join Results 1991 SIGMOD 0.00050022914
211 Join Synopses for Approximate Query Answering 1999 SIGMOD 0.00033981214
217 Ripple Joins for Online Aggregation 1999 SIGMOD 0.00033536712
220 Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans 1998 SIGMOD 0.00033194808
327 Balancing Histogram Optimality and Practicality for Query Result Size Estimation 1995 SIGMOD 0.00027308479
650 Robust Query Processing through Progressive Optimization 2004 SIGMOD 0.00018659177
1,228 Toward a Progress Indicator for Database Queries 2004 SIGMOD 0.00013164884
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