This project sets out an agenda to study in detail three key operators designed for adaptivity in query processing: Eddies serve to do continuous, adaptive reordering of operators in query plans in the face of changing data statistics and system behavior. SteMs expose the state management inherent in query processing as a first class operator in a physical algebra, allowing for adaptive competition and hybridization among query processing alternatives. FLuXen provide adaptive load balancing and fault tolerance across parallel machines. Using the Telegraph system infrastructure, this proposal's agenda is to investigate mechanisms and policies for efficient, robust eddies, SteMs and FluXen in the context of traditional and new query processing challenges. The end goal is to provide a parallel query processing architecture that can run nearly as well in perfect circumstances as a traditiona architecture, and -- more importantly -- that can automatically adapt to run efficiently and robustly when faced with the kinds of imperfections and variations commonly seen in large-scale environments.
