Definition
Execution costs, or simply costs, is a generic term to collectively refer to the various goals or objectives of database query optimization. Optimization aims at finding the “cheapest” (“best” or at least a “reasonably good”) query execution plan (QEP) among semantically equivalent alternative plans for the given query. Cost is used as a metric to compare plans. Depending on the application different types of costs are considered. Traditional optimization goals include minimizing response time (for the first answer or the complete result), minimizing resource consumption (like CPU time, I/O, network bandwidth, or amount of memory required), or maximizing throughput, i.e., the number of queries that the system can answer per time. Other, less obvious objectives – e.g., in a mobile environment – may be to minimize the power consumption needed to answer the query or the on-line time being connected to a remote database server.
Obviously, evaluating a QEP to measure its...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Recommended Reading
Blohsfeld B., Korus D., and Seeger B. A comparison of selectivity estimators for range queries on metric attributes. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1999, pp. 239–250.
Chakrabarti K., Garofalakis M.N., Rastogi R., and Shim K. Approximate query processing using wavelets. In Proc. 26th Int. Conf. on Very Large Data Bases, 2000, pp. 111–122.
Chaudhuri S., Motwani R., and Narasayya V.R. On random sampling over joins. In Proc. ACM SIGMOD Int. Conf. on Management of Data, Philadephia, PA, USA, June 1999, pp. 263–274.
Chen C.M. and Roussopoulos N. Adaptive selectivity estimation using query feedback. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1994, pp. 161–172.
Garcia-Molina H., Ullman J.D., and Widom J. Database Systems: The Complete Book. Prentice Hall, Englewood Cliffs, NJ, USA, 2002.
Gibbons P.B. and Matias Y. Synopsis data structures for massive data sets. In Proc. 10th Annual ACM-SIAM Symp. on Discrete Algorithms, 1999, pp. 909–910.
Gibbons P.B., Matias P.B., and Poosala V. Fast incremental maintenance of approximate histograms. In Proc. 23th Int. Conf. on Very Large Data Bases, 1997, pp. 466–475.
Haas P.J., Naughton J.F., Seshadri S., and Swami A.N. Selectivity and cost estimation for joins based on random sampling. J. Comput. Syst. Sci., 52(3):550–569, 1996.
Ioannidis Y.E. and Christodoulakis S. On the propagation of errors in the size of join results. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1991, pp. 268–277.
Ioannidis Y.E. and Poosala V. Histogram-based approximation of set-valued query-answers. In Proc. 25th Int. Conf. on Very Large Data Bases, 1999, pp. 174–185.
König A.C. and Weikum G. Combining histograms and parametric curve fitting for feedback-driven query result-size estimation. In Proc. 25th Int. Conf. on Very Large Data Bases, 1999, pp. 423–434.
König A.C. and Weikum G. Auto-tuned spline synopses for database statistics management. In Proc. Int. Conf. on Management of Data, 2000.
Korth H. and Silberschatz A. Database Systems Concepts. McGraw-Hill, Inc., New York, San Francisco, Washington, DC, USA, 1991.
Lu H., Tan K.L., and Shan M.C. Hash-based join algorithms for multiprocessor computers. In Proc. 16th Int. Conf. on Very Large Data Bases, 1990, pp. 198–209.
Manegold S. Understanding, Modeling, and Improving Main-Memory Database Performance. PhD Thesis, Universiteit van Amsterdam, Amsterdam, The Netherlands, December 2002.
Manegold S., Boncz P.A., and Kersten M.L. Generic database cost models for hierarchical memory systems. In Proc. 28th Int. Conf. on Very Large Data Bases, 2002, pp. 191–202.
Matias Y., Vitter J.S., and Wang M. Wavelet-based histograms for selectivity estimation. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1998, pp. 448–459.
Selinger P.G., Astrahan M.M., Chamberlin D.D., Lorie R.A., and Price T.G. Access path selection in a relational database management system. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1979, pp. 23–34.
Spiliopoulou M. and Freytag J.-C. Modelling resource utilization in pipelined query execution. In Proc. European Conference on Parallel Processing, 1996, pp. 872–880.
Sun W., Ling Y., Rishe N., and Deng Y. An instant and accurate size estimation method for joins and selection in a retrieval-intensive environment. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1993, pp. 79–88.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this entry
Cite this entry
Manegold, S. (2009). Cost Estimation. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_857
Download citation
DOI: https://doi.org/10.1007/978-0-387-39940-9_857
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-35544-3
Online ISBN: 978-0-387-39940-9
eBook Packages: Computer ScienceReference Module Computer Science and Engineering