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Predicting the execution time of queries is an important problem withapplications in scheduling, service level agreements and error detection.During query planning, a cost is associated with the chosen execution plan andused to rank competing plans. It would be convenient to use that cost topredict execution time, but it has been claimed in the literature that this isnot possible. In this paper, we thoroughly investigate this claim consideringboth linear and non-linear models. We find that the accuracy using more complexmodels with only the optimizer cost is comparable to the reported accuracy inthe literature. The most accurate method in the literature is nearest-neighbourregression which does not produce a model. The published results used a largefeature set to identify nearest neighbours. We show that it is possible toachieve the same level of accuracy using only the cost to identify nearestneighbours. Using a smaller feature set brings the advantages of reducedoverhead in terms of both storage space for the training data and the time toproduce a prediction.

Can the Optimizer Cost be Used to Predict Query Execution Times?