International Business Machines Corporation

Vasiliki Kantere

Vincent Corvinelli

Ning Wang

Brandon Lewis Frendo

Calisto Paul Zuzarte

Seyed Mohammad Amin Kamali

Calisto Zuzarte

Aspects of the disclosure include hybrid cost model-based techniques for evaluating query execution plans. A non-limiting example method includes inputting, to a plurality of base cost models including one or more learned cost models and a classic cost model, a query and a search space including a plurality of candidate query execution plans. Each base cost model outputs a predicted execution time or cost for each plan of the plurality of candidate query execution plans and a real execution time for each plan is determined. The method includes generating a training label including the query and a model of the base cost models having a highest correlation between the predicted and real execution times and training a query classifier on training data including the training label to predict which base cost model of the plurality of base cost models is a most suitable cost model for planning a given query.

Hybrid cost model for evaluating query execution plans

Aspects of the invention include techniques for robust query execution plan selection. A non-limiting example method includes training a model to estimate, for an input including a query and one or more plans, an execution time for each plan and a respective uncertainty of the execution time. A new query and a search space including a plurality of candidate plans are input to the model. Given an estimated distribution for the execution time of each candidate plan, a suboptimality risk is computed for each candidate plan. A plan of the plurality of candidate plans is selected according to a plan selection policy. The plan selection policy includes at least one of: selecting a plan by assuming that plans have higher costs proportional to an estimated standard deviation of their respective uncertainty; and selecting a plan with minimum risk using model uncertainty, data uncertainty, or a total uncertainty.

Robust query execution plan selection using machine learning with predictive uncertainties

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Ottawa, Canada

Vasiliki Kantere