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
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
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
Proceedings of the 17th International Conference on Data Engineering
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
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
Adaptive Query Processing
The Picasso database query optimizer visualizer
Proceedings of the VLDB Endowment
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Predicate selectivity estimates are subject to considerable run-time variation relative to their compile-time estimates, often leading to poor plan choices that cause inflated response times. We present here a parametrized family of plan generation and selection algorithms that replace, whenever feasible, the optimizer's solely cost-conscious choice with an alternative plan that is (a) guaranteed to be near-optimal in the absence of selectivity estimation errors, and (b) likely to deliver comparatively stable performance in the presence of arbitrary errors. These algorithms have been implemented within the PostgreSQL optimizer, and their performance evaluated on a rich spectrum of TPC-H and TPC-DS-based query templates in a variety of database environments. Our experimental results indicate that it is indeed possible to identify robust plan choices that substantially curtail the adverse effects of erroneous selectivity estimates. In fact, the plan selection quality provided by our algorithms is often competitive with those obtained through apriori knowledge of the plan search and optimality spaces. The additional computational overheads incurred by the replacement approach are miniscule in comparison to the expected savings in query execution times. We also demonstrate that with appropriate parameter choices, it is feasible to directly produce anorexic plan diagrams, a potent objective in query optimizer design.