CGO: a sound genetic optimizer for cyclic query graphs

The increasing number of applications requiring the use of large join queries reinforces the search for good methods to determine the best execution plan. This is especially true, when the large number of joins occurring in a query prevent traditional optimizers from using dynamic programming. In this paper we present the Carquinyoli Genetic Optimizer (CGO). CGO is a sound optimizer based on genetic programming that uses a subset of the cost-model of IBM®DB2®Universal DatabaseTM(DB2 UDB) for selection in order to produce new generations of query plans. Our study shows that CGO is very competitive either as a standalone optimizer or as a fast post-optimizer. In addition, CGO takes into account the inherent characteristics of query plans like their cyclic nature.