An Hybrid Method to Robust Query Processing With Respect to Estimation Errors

Cost-based query optimizer chooses the most efficient execution plan for a given query using a cost model. The latter relies on the accuracy of estimated statistics. These estimates often differ significantly from those encountered during query execution, leading to poor plan choices. In this document, we present a method to query processing that is fully aware of estimation inaccuracies. This method produces execution plans that are likely to perform reasonably well over different run-time conditions, so called robust plans. Robust plans are then augmented with extra-operators. These operators collect statistics at run-time and check the robustness of the current plan. If the robustness is violated, extra-operators are able to make decisions for plan modifications to correct the robustness violation without a need to recall the optimizer. We present the results of performance studies of our method, which indicate that it provides significant improvements in the robustness of query processing. An Hybrid Method to Robust Query Processing With Respect to Estimation Errors Chiraz Moumen Franck Morvan Abdelkader Hameurlain