Query optimizers use base-table statistics to derive statistics on the subplans that are enumerated during optimization. In practice, traditional optimizers rely on a number of simplifying assumptions, which can compromise the accuracy of cardinality estimates. To address this limitation, we had earlier introduced SITs, which are statistics built over query expressions, and we explained how a traditional optimizer can judiciously use SITs to sidestep the problem of inaccurate estimates. A significant challenge that was not addressed was how to build SITs efficiently in a database system. We present a family of techniques to create SITs. These techniques differ from each other in the trade-off they present between accuracy and efficiency of creation. We also present techniques to efficiently create multiple SITs by taking advantage of the commonalities among their generating query expressions.
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