R* optimizer validation and performance evaluation for local queries
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
On the propagation of errors in the size of join results
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Accurate modeling of the hybrid hash join algorithm
SIGMETRICS '94 Proceedings of the 1994 ACM SIGMETRICS conference on Measurement and modeling of computer systems
A tight analysis of the greedy algorithm for set cover
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Efficient mid-query re-optimization of sub-optimal query execution plans
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
Least expected cost query optimization: an exercise in utility
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Self-tuning histograms: building histograms without looking at data
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Least expected cost query optimization: what can we expect?
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
LEO - DB2's LEarning Optimizer
Proceedings of the 27th International Conference on Very Large Data Bases
Robust query processing through progressive optimization
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Towards a robust query optimizer: a principled and practical approach
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Analyzing plan diagrams of database query optimizers
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Parametric query optimization for linear and piecewise linear cost functions
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
AniPQO: almost non-intrusive parametric query optimization for nonlinear cost functions
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
On the production of anorexic plan diagrams
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Foundations and Trends in Databases
A critical look at the TAB benchmark for physical design tools
ACM SIGMOD Record
Efficiently approximating query optimizer plan diagrams
Proceedings of the VLDB Endowment
Testing the accuracy of query optimizers
DBTest '12 Proceedings of the Fifth International Workshop on Testing Database Systems
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Estimates of predicate selectivities by database query optimizers often differ significantly from those actually encountered during query execution, leading to poor plan choices and inflated response times. In this paper, we investigate mitigating this problem by replacing selectivity error-sensitive plan choices with alternative plans that provide robust performance. Our approach is based on the recent observation that even the complex and dense "plan diagrams" associated with industrial-strength optimizers can be efficiently reduced to "anorexic" equivalents featuring only a few plans, without materially impacting query processing quality. Extensive experimentation with a rich set of TPC-H and TPC-DS-based query templates in a variety of database environments indicate that plan diagram reduction typically retains plans that are substantially resistant to selectivity errors on the base relations. However, it can sometimes also be severely counter-productive, with the replacements performing much worse. We address this problem through a generalized mathematical characterization of plan cost behavior over the parameter space, which lends itself to efficient criteria of when it is safe to reduce. Our strategies are fully non-invasive and have been implemented in the Picasso optimizer visualization tool.