Advertisement

Aging Locality Awareness in Cost Estimation for Database Query Optimization

  • Chihiro Kato
  • Yuto Hayamizu
  • Kazuo Goda
  • Masaru Kitsuregawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9828)

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.

Keywords

Database systems Query optimizer Database aging 

References

  1. 1.
    Heyman, D.P.: Mathematical models of database degradation. ACM Trans. Database Syst. (TODS) 7(4), 615–631 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Sockut, G.H., Goldberg, R.P.: Database reorganization-principles and practice. ACM Comput. Surv. (CSUR) 11(4), 371–395 (1979)CrossRefGoogle Scholar
  3. 3.
    Shneiderman, B.: Optimum data base reorganization points. Commun. ACM 16(6), 362–365 (1973)CrossRefzbMATHGoogle Scholar
  4. 4.
    Bing Yao, S., Sundar Das, K., Teorey, T.J.: A dynamic database reorganization algorithm. ACM Trans. Database Syst. (TODS) 1(2), 159–174 (1976)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    Jarke, M., Koch, J.: Query optimization in database systems. ACM Comput. Surv. (CsUR) 16(2), 111–152 (1984)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Graefe, G.: The cascades framework for query optimization. Data Eng. Bull. 18(3), 19–29 (1995)Google Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    Chen, C.M., Roussopoulos, N.: The implementation and performance evaluation of the ADMS query optimizer: Integrating query result caching and matching. Springer, Heidelberg (1994)Google Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    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)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Chihiro Kato
    • 1
  • Yuto Hayamizu
    • 1
  • Kazuo Goda
    • 1
  • Masaru Kitsuregawa
    • 1
    • 2
  1. 1.The University of TokyoTokyoJapan
  2. 2.National Institute of InformaticsTokyoJapan

Personalised recommendations