AniPQO: almost non-intrusive parametric query optimization for nonlinear cost functions

The cost of a query plan depends on many parameters, such as predicate selectivities and available memory, whose values may not be known at optimization time. Parametric query optimization (PQO) optimizes a query into a number of candidate plans, each optimal for some region of the parameter space. We propose a heuristic solution for the PQO problem for the case when the cost functions may be nonlinear in the given parameters. This solution is minimally intrusive in the sense that an existing query optimizer can be used with minor modifications. We have implemented the heuristic and the results of the tests on the TPCD benchmark indicate that the heuristic is very effective. The minimal intrusiveness, generality in terms of cost functions and number of parameters and good performance (up to 4 parameters) indicate that our solution is of significant practical importance.