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Abstract

Very recently, Cao et al.\ presented the MAPLE approach, which accelerates queries with multiple instances of the same relation by sharing their scan operator. The principal idea is to derive, in a first phase, a non-shared tree-shaped plan via a traditional plan generator. In a second phase, common instances of a scan are detected and shared by turning the operator tree into an operator DAG (directed acyclic graph). The limits of their approach are obvious. (1) Sharing more than scans is often possible and can lead to considerable performance benefits. (2) As sharing influences plan costs, a separation of the optimization into two phases comprises the danger of missing the optimal plan, since the first optimization phase does not know about sharing. We remedy both points by introducing a general framework for reasoning about sharing: plans can be shared whenever they are {\em share equivalent} and not only if they are scans of the same relation. Second, we sketch how this framework can be integrated into a plan generator, which then constructs optimal DAG-structured plans.