Definition
A self-managing database system needs to gracefully handle variations in input workloads by adapting its internal structures and representation to changes in the environment. One approach to cope with evolving workloads is to periodically obtain the best possible configuration for a hypothetical “average” scenario. Unfortunately, this approach might be arbitrarily suboptimal for instances that lie outside the previously determined average case. An alternative approach is to require the database system to continuously tune its internal parameters in response to changes in the workload. This is the online tuning paradigm. Although solutions for different problems share the same underlying philosophy, the specific details are usually domain-specific. In the context of database systems, online tuning has been successfully applied to issues such as buffer pool management, statistics construction and maintenance, and physical design.
Historical Background
Database applications...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Recommended Reading
Aboulnaga A. and Chaudhuri S. Self-tuning histograms: building histograms without looking at data. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1999.
Brown K.P., Mehta M., Carey M.J., and Livny M. Towards automated performance tuning for complex workloads. In Proc. 20th Int. Conf. on Very Large Data Bases, 1994, pp. 72–84.
Bruno N. and Chaudhuri S. An online approach to physical design tuning. In Proc. 23rd Int. Conf. on Data Engineering, 2007.
Bruno N., Chaudhuri S., and Gravano L. STHoles: a multidimensional workload-aware histogram. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2001.
Chaudhuri S. and Narasayya V.R. Self-tuning database systems: a decade of progress. In Proc. 33rd Int. Conf. on Very Large Data Bases, 2007.
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.
Dageville B. and Zait M. SQL memory management in Oracle9i. In Proc. 28th Int. Conf. on Very Large Data Bases, 2002.
Diao Y., Hellerstein J.L., Parekh S.S., Griffith R., Kaiser G.E., and Phung D.B. Self-managing systems: a control theory foundation. In Proc. 12th IEEE Int. Conf. Engineering of Computer-Based Systems, 2005, pp. 441–448.
Markl V., Haas P.J., Kutsch M., Megiddo N., Srivastava U., and Tran T.M., Consistent selectivity estimation via maximum entropy. VLDB J., 16(1):55–76, 2007.
Srivastava U. et al. ISOMER: consistent histogram construction using query feedback. In Proc. 22nd Int. Conf. on Data Engineering, 2006.
Stillger M., Lohman G.M., Markl V., and Kandil M. LEO - DB2’s LEarning Optimizer. In Proc. 27th Int. Conf. on Very Large Data Bases, 2001, pp. 19–28.
Weikum G., König A.C., Kraiss A., and Sinnwell M. Towards self-tuning memory management for data servers. IEEE Data Eng. Bull., 22(2):3–11, 1999.
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
Bruno, N., Chaudhuri, S., Weikum, G. (2009). Database Tuning using Online Algorithms. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_335
Download citation
DOI: https://doi.org/10.1007/978-0-387-39940-9_335
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