Abstract
A number of insertions, updates and deletions eventually deteriorate the structural efficiency of database storage, and then cause performance degradation. This phenomenon is called “aging.” In real-world database systems, aging often exhibits strong locality because of the inherent skewness of data access; specifically speaking, the cost of I/O operations is not uniform throughout the storage space. Potentially query execution cost is influenced by the aging. However, conventional query optimizers do not consider the aging locality; thus they cannot accurately estimate the cost of query execution plans at times. In this paper, we propose a novel method of cost estimation that has the key capability of accurately determining aging phenomena, even though such phenomena are non-uniformly incurred. Our experiment on PostgreSQL and TPC-H data sets showed that the proposed method can accurately estimate the query execution cost even if it is influenced by the aging.
C. Kato — Currently, Fujitsu Laboratories.
The original version of this chapter was revised: The authors’ affiliations were incorrect. An erratum to this chapter can be found at 10.1007/978-3-319-44406-2_40
An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-44406-2_40
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Heyman, D.P.: Mathematical models of database degradation. ACM Trans. Database Syst. (TODS) 7(4), 615–631 (1982)
Sockut, G.H., Goldberg, R.P.: Database reorganization-principles and practice. ACM Comput. Surv. (CSUR) 11(4), 371–395 (1979)
Shneiderman, B.: Optimum data base reorganization points. Commun. ACM 16(6), 362–365 (1973)
Bing Yao, S., Sundar Das, K., Teorey, T.J.: A dynamic database reorganization algorithm. ACM Trans. Database Syst. (TODS) 1(2), 159–174 (1976)
Sockut, G.H., Beavin, T.A., Chang, C.-C.: A method for on-line reorganization of a database. IBM Syst. J. 36(3), 411–436 (1997)
Omiecinski, E., Scheuermann, P.: A global approach to record clustering and file reorganization. In: Proceedings of the Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 201–219. British Computer Society (1984)
Kitsuregawa, M., Goda, K., Hoshino, T.: Storage fusion. In: Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication (ICUIMC2008), pp. 270–277. ACM (2008)
Ghandeharizadeh, S., Gao, S., Gahagan, C., Krauss, R.: An on-line reorganization framework for SAN file systems. In: Manolopoulos, Y., Pokorný, J., Sellis, T.K. (eds.) ADBIS 2006. LNCS, vol. 4152, pp. 399–414. Springer, Heidelberg (2006)
Chaudhuri, S.: An overview of query optimization in relational systems. In: Proceedings of the 17th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp. 34–43. ACM (1998)
Jarke, M., Koch, J.: Query optimization in database systems. ACM Comput. Surv. (CsUR) 16(2), 111–152 (1984)
Graefe, G.: The cascades framework for query optimization. Data Eng. Bull. 18(3), 19–29 (1995)
Waas, F.M., Hellerstein, J.M.: Parallelizing extensible query optimizers. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data, pp. 871–878. ACM (2009)
Chen, C.M., Roussopoulos, N.: The implementation and performance evaluation of the ADMS query optimizer: Integrating query result caching and matching. Springer, Heidelberg (1994)
Perez, L.L., Jermaine, C.M.: History-aware query optimization with materialized intermediate views. In: 2014 IEEE 30th International Conference on Data Engineering (ICDE), pp. 520–531. IEEE (2014)
Haas, L.M., Carey, M.J., Livny, M., Shukla, A.: Seeking the truth about ad hoc join costs. The VLDB J. 6(3), 241–256 (1997)
Ghodsnia, P., Bowman, I.T., Nica, A.: Parallel I/O aware query optimization. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 349–360. ACM (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Kato, C., Hayamizu, Y., Goda, K., Kitsuregawa, M. (2016). Aging Locality Awareness in Cost Estimation for Database Query Optimization. In: Hartmann, S., Ma, H. (eds) Database and Expert Systems Applications. DEXA 2016. Lecture Notes in Computer Science(), vol 9828. Springer, Cham. https://doi.org/10.1007/978-3-319-44406-2_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-44406-2_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44405-5
Online ISBN: 978-3-319-44406-2
eBook Packages: Computer ScienceComputer Science (R0)