Abstract
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.
Research supported by the IBM Toronto Lab Center for Advanced Studies and UPC Barcelona. The authors from UPC want to thank Generalitat de Catalunya for its support through grant GRE-00352.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Bennett, K., Ferris, M.C., Ioannidis, Y.E.: A genetic algorithm for database query optimization. In: Belew, R., Booker, L. (eds.) Proceedings of the Fourth International Conference on Genetic Algorithms, San Mateo, CA, pp. 400–407. Morgan Kaufmann, San Francisco (1991)
Holland, J.: Adaption in natural and artificial systems. The University of Michigan Press, Ann Arbor (1975)
Ioannidis, Y.E., Wong, E.: Query optimization by simulated annealing. In: SIGMOD 1987: Proceedings of the 1987 ACM SIGMOD international conference on Management of data, pp. 9–22. ACM Press, New York (1987)
Muntes, V., Aguilar, J., Zuzarte, C., Markl, V., Larriba, J.L.: Genetic evolution in query optimization: a complete analysis of a genetic optimizer. Technical Report UPC-DAC-RR-2005-21, Dept. d’Arqu. de Computadors. Universitat Politecnica de Catalunya (2005), http://www.dama.upc.edu
PostgreSQL, http://www.postgresql.org/
Griffiths Selinger, P., Astrahan, M.M., Chamberlin, D.D., Lorie, R.A., Price, T.G.: Access path selection in a relational database management system. In: Proceedings of the 1979 ACM SIGMOD international conference on Management of data, pp. 23–34. ACM Press, New York (1979)
Steinbrunn, M., Moerkotte, G., Kemper, A.: Heuristic and randomized optimization for the join ordering problem. VLDB Journal: Very Large Data Bases 6(3), 191–208 (1997)
Stillger, M., Spiliopoulou, M.: Genetic programming in database query optimization. In: Koza, J.R., Goldberg, D.E., Fogel, D.B., Riolo, R.L. (eds.) Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, pp. 28–31. MIT Press, Cambridge (1996)
Swami, A.: Optimization of large join queries: combining heuristics and combinatorial techniques. In: SIGMOD 1989: Proceedings of the 1989 ACM SIGMOD international conference on Management of data, pp. 367–376. ACM Press, New York (1989)
Swami, A., Gupta, A.: Optimization of large join queries. In: SIGMOD 1988: Proceedings of the 1988 ACM SIGMOD international conference on Management of data, pp. 8–17. ACM Press, New York (1988)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Muntés-Mulero, V., Aguilar-Saborit, J., Zuzarte, C., Larriba-Pey, JL. (2006). CGO: A Sound Genetic Optimizer for Cyclic Query Graphs. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3991. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758501_25
Download citation
DOI: https://doi.org/10.1007/11758501_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34379-0
Online ISBN: 978-3-540-34380-6
eBook Packages: Computer ScienceComputer Science (R0)