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Shared Index Scans for Data Warehouses

  • Yannis Kotidis1⋆
  • Yannis Sismanis
  • Nick Roussopoulos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2114)

Abstract

In this paper we propose a new “transcurrent execution model” (TEM) for concurrent user queries against tree indexes. Our model exploits intra-parallelism of the index scan and dynamically decomposes each query into a set of disjoint “query patches”. TEM integrates the ideas of prefetching and shared scans in a new framework, suitable for dynamic multi-user environments. It supports time constraints in the scheduling of these patches and introduces the notion of data flow for achieving a steady progress of all queries. Our experiments demonstrate that the transcurrent query execution results in high locality of I/O which in turn translates to performance benefits in terms of query execution time, buffer hit ratio and disk throughput. These benefits increase as the workload in the warehouse increases and offer a scalable solution to the I/O problem of data warehouses.

Keywords

Query Execution Page Request Query Execution Time Bitmap Index Disk Schedule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    P. Cao, E.W. Felten, A.R. Karlin, and K. Li. Implementation and Performance of Integrated Application-Controlled File Caching, Prefetching, and Disk Scheduling. ACM Transactions on Computer Systems, 14(4):311–343, 1996.CrossRefGoogle Scholar
  2. 2.
    C.Y. Chan and Y. Ioannidis. Bitmap Index Design and Evaluation. In Proceedings of ACM SIGMOD, pages 355–366, Seattle, Washington, USA, June 1998.Google Scholar
  3. 3.
    S. Chaudhuri and U. Dayal. An Overview of Data Warehousing and OLAP Technology. SIGMOD Record, 26(1), September 1997.Google Scholar
  4. 4.
    C.M. Chen and N. Roussopoulos. Adaptive Database Buffer Allocation Using Query Feedback. In Procs. of VLDB Conf., Dublin, Ireland, August 1993.Google Scholar
  5. 5.
    J. Cheng, D. Haderle, R. Hedges, B. Iyer, T. Messinger, C. Mohan, and Y. Wang. An Efficient Hybrid Join Algorithm: A DB2 Prototype. In Proceedings of ICDE, pages 171–180, Kobe, Japan, April 1991.Google Scholar
  6. 6.
    H. Chou and D. DeWitt. An Evaluation of Buffer Management Strategies for Relational Database Systems. In Procs. of VLDB, Sweden, August 1985.Google Scholar
  7. 7.
    W. Effelsberg and T. Haerder. Principles of Database Buffer Management. ACM TODS, 9(4):560–595, 1984.CrossRefGoogle Scholar
  8. 8.
    R. Geist and S. Daniel. A Continuum of Disk Scheduling Algorithms. ACM Transactions on Computer Systems, 5(1):77–92, 1987.CrossRefGoogle Scholar
  9. 9.
    J. Gray. The Benchmark Handbook for Database and Transaction Processing Systems-2nd edition. Morgan Kaufmann, San Franscisco, 1993.Google Scholar
  10. 10.
    J. Gray, P. Sundaresan, S. Englert, K. Baclawski, and P. Weiberger. Quickly Generating Billion-Record Synthetic Databases. In Proc. of the ACM SIGMOD, pages 243–252, Minneapolis, May 1994.Google Scholar
  11. 11.
    A. Guttman. R-Trees: A Dynamic Index Structure for Spatial Searching. In Proceedings of the ACM SIGMOD, Boston, MA, June 1984.Google Scholar
  12. 12.
    Y. Kotidis and N. Roussopoulos. An Alternative Storage Organization for ROLAP Aggregate Views Based on Cubetrees. In Proceedings of ACM SIGMOD, pages 249–258, Seattle, Washington, June 1998.Google Scholar
  13. 13.
    R.T. Ng, C. Faloutsos, and T. Sellis. Flexible Buffer Allocation Based on Marginal Gains. In Procs. of ACM SIGMOD, pages 387–396, Denver, Colorado, May 1991.Google Scholar
  14. 14.
    C. Nyberg. Disk Scheduling and Cache Replacement for a Database Machine. Master’s thesis, UC Berkeley, July 1984.Google Scholar
  15. 15.
    E.J. O’Neil, P.E. O’Neil, and G. Weikum. The LRU-K Page Replacement Algorithm for Database Disk Buffering. In Proceedings of ACM SIGMOD Intl. Conf. on Management of Data, pages 297–306, Washington D.C., May 26-28 1993.Google Scholar
  16. 16.
    P. O’Neil and D. Quass. Improved Query Performance with Variant Indexes. In Proceedings of ACM SIGMOD, Tucson, Arizona, May 1997.Google Scholar
  17. 17.
    A. Reiter. A Study of Buffer Management Policies for Data Management Systems. Technical Report TR-1619, University of Wisconsin-Madison, 1976.Google Scholar
  18. 18.
    N. Roussopoulos and H. Kang. Principles and Techniques in the Design of ADMS±. IEEE Computer, 19(12):19–25, December 1986.Google Scholar
  19. 19.
    N. Roussopoulos, Y. Kotidis, and M. Roussopoulos. Cubetree: Organization of and Bulk Incremental Updates on the Data Cube. In Proceedings of ACM SIGMOD, pages 89–99, Tucson, Arizona, May 1997.Google Scholar
  20. 20.
    N. Roussopoulos and D. Leifker. Direct Spatial Search on Pictorial Databases Using Packed R-trees. In Procs. of ACM SIGMOD, pages 17–31, Austin, 1985.Google Scholar
  21. 21.
    G.M. Sacco. Index Access with a Finite Buffer. In Proceedings of 13th International Conference on VLDB, pages 301–309, Brighton, England, September 1987.Google Scholar
  22. 22.
    A. Shoshani, L.M. Bernardo, H. Nordberg, D. Rotem, and A. Sim. Multidimensional Indexing and Query Coordination for Tertiary Storage Management. In Proceedings of SSDBM, pages 214–225, Cleveland, Ohio, July 1999.Google Scholar
  23. 23.
    B.L. Worthington, G.R. Ganger, and Y.N. Patt. Scheduling Algorithms for Modern Disk Drives. In SIGMETRICS, Santa Clara, CA, May 1994Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Yannis Kotidis1⋆
    • 1
  • Yannis Sismanis
    • 2
  • Nick Roussopoulos
    • 2
  1. 1.AT&T Labs ResearchFlorham ParkUSA
  2. 2.University of Maryland

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