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...
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Aboulnaga A, Chaudhuri S. Self-tuning histograms: building histograms without looking at data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1999.
Brown KP, Mehta M, Carey MJ, Livny M. Towards automated performance tuning for complex workloads. In: Proceedings of the 20th International Conference on Very Large Data Bases; 1994. p. 72–84.
Bruno N, Chaudhuri S. An online approach to physical design tuning. In: Proceedings of the 23rd International Conference on Data Engineering; 2007.
Bruno N, Chaudhuri S, Gravano L. STHoles: a multidimensional workload-aware histogram. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2001.
Chaudhuri S, Narasayya VR. Self-tuning database systems: a decade of progress. In: Proceedings of the 33rd International Conference on Very Large Data Bases; 2007.
Chen C-M, Roussopoulos N. Adaptive selectivity estimation using query feedback. In: Proceedinds of the ACM SIGMOD International Conference on Management of Data; 1994. p. 161–72.
Dageville B, Zait M. SQL memory management in Oracle9i. In: Proceedings of the 28th International Conference on Very Large Data Bases; 2002.
Diao Y, Hellerstein JL, Parekh SS, Griffith R, Kaiser GE, Phung DB. Self-managing systems: a control theory foundation. In: Proceedings of the 12th IEEE International Conference on Engineering of Computer-Based Systems; 2005. p. 441–8.
Markl V, Haas PJ, Kutsch M, Megiddo N, Srivastava U, Tran TM. Consistent selectivity estimation via maximum entropy. VLDB J. 2007;16(1):55–76.
Srivastava U, et al. ISOMER: consistent histogram construction using query feedback. In: Proceedings of the 22nd International Conference on Data Engineering; 2006.
Stillger M, Lohman GM, Markl V, Kandil M. LEO – DB2’s LEarning optimizer. In: Proceedings of the 27th International Conference on Very Large Data Bases; 2001. p. 19–28.
Weikum G, König AC, Kraiss A, Sinnwell M. Towards self-tuning memory management for data servers. IEEE Data Eng Bull. 1999;22(2):3–11.
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Bruno, N., Chaudhuri, S., Weikum, G. (2018). Database Tuning Using Online Algorithms. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_335
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DOI: https://doi.org/10.1007/978-1-4614-8265-9_335
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