Plan Bouquets: A Fragrant Approach to Robust Query Processing

摘要

Identifying efficient execution plans for declarative OLAP queries typically entails estimation of several predicate selectivities. In practice, these estimates often differ significantly from the values actually encountered during query execution, leading to poor plan choices and grossly inflated response times. We propose here a conceptually new approach to address this classical problem, wherein the compile-time estimation process is completely eschewed for error-prone selectivities. Instead, from the set of optimal plans in the query's selectivity error space, a limited subset, called the "plan bouquet," is selected such that at least one of the bouquet plans is 2-optimal at each location in the space. Then, at run time, a sequence of cost-budgeted executions from the plan bouquet is carried out, eventually finding a plan that executes to completion within its assigned budget. The duration and switching of these executions is controlled by a graded progression of isosurfaces projected onto the optimal performance profile. We prove that this construction results, for the first time, in guarantees on worst-case performance sub-optimality. Moreover, it ensures repeatable execution strategies across different invocations of a query.