Abstract
In relational distributed databases a query cost consists of a local cost and a transmission cost. Query optimization is a combinatorial optimization problem. As the query size grows, the optimization methods based on exhaustive search become too expensive. We propose the following strategy for solving large distributed query optimization problems in relational database systems: (1) represent each query-processing schedule by a labeled directed graph; (2) reduce the number of different schedules by pruning away invalid or high-cost solutions; and (3) find a suboptimal schedule by combinatorial optimization. We investigate several combinatorial optimization techniques: random search, single start, multistart, simulated annealing, and a combination of random search and local simulated annealing. The utility of combinatorial optimization is demonstrated in the problem of finding the (sub)optimal semijoin schedule that fully reduces all relations of a tree query. The combination of random search and local simulated annealing was superior to other tested methods.
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