The implementation and performance evaluation of the ADMS query optimizer: Integrating query result caching and matching

  • ChungMin Melvin Chen
  • Nicholas Roussopoulos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 779)

Abstract

In this paper, we describe the design and evaluation of the ADMS optimizer. Capitalizing on a structure called Logical Access Path Schema to model the derivation relationship among cached query results, the optimizer is able to perform query matching coincidently with the optimization and generate more efficient query plans using cached results. The optimizer also features data caching and pointer caching, alternative cache replacement strategies, and different cache update strategies. An extensive set of experiments were conducted and the results showed that pointer caching and dynamic cache update strategies substantially speedup query computations and, thus, increase query throughput under situations with fair query correlation and update load. The requirement of the cache space is relatively small and the extra computation overhead introduced by the caching and matching mechanism is more than offset by the time saved in query processing.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [AL80]
    M.E. Adiba and B. G. Lindsay. Database snapshots. In Procs. of 6th VLDB, 1980.Google Scholar
  2. [BDT83]
    D. Bitton, D.J. DeWitt, and C. Turbyfill. Benchmarking database systems, a systematic approach. In Procs. of 9th VLDB, 1983.Google Scholar
  3. [BLT86]
    J.A. Blakeley, P. Larson, and F.W. Tompa. Efficiently updating materialized views. In Procs. of ACM-SIGMOD, 1986.Google Scholar
  4. [CL73]
    C. L. Chang and C. T. Lee. Symbolic Logic and Mechanical Theorem Proving. Academic Press, 1973.Google Scholar
  5. [CR93]
    C.M. Chen and N. Roussopoulos. Implementation and performance evaluation of the ADMS query optimizer. Technical Report CS-TR-3159, Dept. of Comp. Sci., University of Maryland, College Park, 1993.Google Scholar
  6. [DR92]
    A. Delis and N. Roussopoulos. Evaluation of an enhanced workstation-server DBMS architecture. In Procs. of 18th VLDB, 1992.Google Scholar
  7. [Fin82]
    S. Finkelstein. Common expression analysis in database applications. In Procs. of ACM-SIGMOD, pages 235–245, 1982.Google Scholar
  8. [Han87]
    E.N. Hanson. A performance analysis of view materialization strategies. In Procs. of ACM-SIGMOD, pages 440–453, 1987.Google Scholar
  9. [HS93]
    J.M. Hellerstein and M. Stonebraker. Predicate migration: Optimizing queries with expensive predicates. In Procs. of ACM-SIGMOD, 1993.Google Scholar
  10. [J+93]
    C. S. Jensen et al. Using differential techniques to efficiently support transaction time. VLDB Journal, 2(1):75–111, 1993.Google Scholar
  11. [Jhi88]
    A. Jhingran. A performance study of query optimization algorithms on a database system supporting procedures. In Procs. of 14th VLDB, 1988.Google Scholar
  12. [LHM86]
    B. Lindsay, L. Haas, and C. Mohan. A snapshot differential refresh algorithm. In Procs. of ACM-SIGMOD, pages 53–60, 1986.Google Scholar
  13. [LW86]
    S. Lafortune and E. Wong. A state transition model for distributed query processing. ACM TODS, 11(3):294–322, 1986.Google Scholar
  14. [LY85]
    P.-Å. Larson and H. Z. Yang. Computing queries from derived relations. In Procs. of 11th VLDB, pages 259–269, 1985.Google Scholar
  15. [Nil80]
    N.J. Nilsson. Principles of Artificial Intelligence. Tioga Pub. Co., 1980.Google Scholar
  16. [RES93]
    N. Roussopoulos, N. Economou, and A. Stamenas. ADMS: A testbed for incremental access methods. To appear in IEEE Trans, on Knowledge and Data Engineering, 1993.Google Scholar
  17. [RK86]
    N. Roussopoulos and H. Kang. Principles and techniques in the design of ADMS±. Computer, 19(12):19–25, 1986.Google Scholar
  18. [Rou82a]
    N. Roussopoulos. The logical access path schema of a database. IEEE Trans, on Software Engineering, SE-8(6):563–573, 1982.Google Scholar
  19. [Rou82b]
    N. Roussopoulos. View indexing in relational databases. ACM TODS, 7(2):258–290, 1982.Google Scholar
  20. [Rou91]
    N. Roussopoulos. An incremental access method for ViewCache: Concept, algorithms, and cost analysis. ACM TODS, 16(3):535–563, 1991.Google Scholar
  21. [S+79]
    P. G. Selinger et al. Access path selection in a relational database management system. In Procs. of ACM-SIGMOD, pages 23–34, 1979.Google Scholar
  22. [Sel87]
    T. Sellis. Efficiently supporting procedures in relational database systems. In Procs. of ACM-SIGMOD, 1987.Google Scholar
  23. [Sel88]
    T. K. Sellis. Intelligent caching and indexing techniques for relational database systems. Inform. Systems, 13(2), 1988.Google Scholar
  24. [SWK76]
    M. Stonebraker, E. Wong, and P. Kreps. The design and implementation of INGRES. ACM TODS, 1(3):189–222, 1976.Google Scholar
  25. [Val87]
    P. Valduriez. Join indices. ACM TODS, 12(2):218–246, 1987.Google Scholar

Copyright information

© Springer-Verlag 1994

Authors and Affiliations

  • ChungMin Melvin Chen
    • 1
  • Nicholas Roussopoulos
    • 1
  1. 1.Department of Computer ScienceUniversity of MarylandCollege ParkUSA

Personalised recommendations