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Algorithms for processing distributed queries require a priori estimates of the size of intermediate relations. Most such algorithms take a “static” approach in which the algorithm is completely determined before processing begins. If size estimates are found to be inaccurate at some intermediate stage, there is no opportunity to re-schedule, and the result may be far from optimal. Adaptive query execution may be used to alleviate the problem. Care is necessary, though, to ensure that the delay associated with re-scheduling does not exceed the time saved through the use of a more efficient strategy. This paper presents a low overhead delay method to decide when to correct a strategy. Sampling is used to estimate the size of relations, and alternative heuristic strategies prepared in a background mode are used to decide when to correct. Correction is made only if lower overall delay is achieved, including correction time. Evaluation using a model of a distributed data base indicates that the heuristic strategies are near optimal. Moreover, it also suggests that it is usually correct to abort creation of an intermediate relation which is much larger than predicted.

Correcting execution of distributed queries