fixed-precision estimation of join selectivity
We compare the performance of sampling-based procedures for estimation of the selectivity of an equijoin. While some of the procedures have been proposed in the database sampling literature, their relative performance has never been analyzed. A main result of this paper is a partial ordering that compares the variability of the estimators for the different procedures after an arbitrary fixed number of sampling steps. Prior to the current work, it was also unknown whether these fixed-step estimation procedures can be extended to asymptotically efficient fixed-precision estimation procedures. Our second main result is a general method for such an extension and a proof that the method is valid for all the estimation procedures under consideration. Finally, we show that, under reasonable assumptions on sampling costs, the partial ordering on the variability of the fixed-step estimation procedures implies a partial ordering on the cost of the corresponding fixed-precision estimation procedures. These results lead to a new algorithm for fixed-precision estimation of the selectivity of an equijoin. The algorithm appears to be the best available when there are no indices on the join key. Our results can be extended to general select-join queries.
estimates select performance sampling procedure database paper partial ordering variability efficiency general method extensibility cost algorithm queries precise relation