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Enhancements to SQL server column stores

Published:22 June 2013Publication History

ABSTRACT

SQL Server 2012 introduced two innovations targeted for data warehousing workloads: column store indexes and batch (vectorized) processing mode. Together they greatly improve performance of typical data warehouse queries, routinely by 10X and in some cases by a 100X or more. The main limitations of the initial version are addressed in the upcoming release. Column store indexes are updatable and can be used as the base storage for a table. The repertoire of batch mode operators has been expanded, existing operators have been improved, and query optimization has been enhanced. This paper gives an overview of SQL Server's column stores and batch processing, in particular the enhancements introduced in the upcoming release.

References

  1. Batory, D. S.: On searching transposed files. ACM Trans. Database Syst. 4, 4 (1979), 531--544. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. P. A. Boncz, M. Zukowski, and N.Nes, MonetDB/X100: Hyper-pipelining query execution. CIDR, 2005, 225--237.Google ScholarGoogle Scholar
  3. G. P. Copeland and S. Khoshafian, A decomposition storage model. SIGMOD, 1985, 268--279. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Harizopoulos, S., Liang, V., Abadi, D.J., and Madden, S.: Performance tradeoffs in read-optimized databases. VLDB, 2006, 487--498. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Sándor Héman, Marcin Zukowski, Niels J. Nes, Lefteris Sidirourgos, Peter A. Boncz: Positional update handling in column stores. SIGMOD, 2010: 543--554. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. J. A. Hoffer and D. G. Severance, The use of cluster analysis in physical data base design, VLDB, 1975, 69--86. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. M. Holsheimer and M. L. Kersten, Architectural support for data mining, KDD, 1994, 217--228.Google ScholarGoogle Scholar
  8. D. Inkster, M. Zukowski, and P. A. Boncz, Integration of VectorWise with Ingres, SIGMOD Record, 40(3):45--53, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. P.-Å. Larson, C. Clinciu, E. N. Hanson, A. Oks, S. L. Price, S. Rangarajan, A. Surna, and Q. Zhou, Sql Server column store indexes, SIGMOD, 2011, 1177--1184. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Microsoft, Column store Indexes in Books Online for SQL Server 2012, available at http://msdn.microsoft.com/en-us/library/gg492088.aspx.Google ScholarGoogle Scholar
  11. Microsoft, SQL Server Column store Index FAQ, http://social.technet.microsoft.com/wiki/contents/articles/3540.sql-server-column store-index-faq-en-us.aspx.Google ScholarGoogle Scholar
  12. M. Stonebraker et al. C-Store: A Column-oriented DBMS. VLDB, 2005, 553--564. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. TPC Benchmark DS (Decision Support), Draft Specification, Version 32, http://tpc.org/tpcds.Google ScholarGoogle Scholar
  14. ExaSolution, http://www.exasol.comGoogle ScholarGoogle Scholar
  15. Greenplum Database, http://www.greenplum.comGoogle ScholarGoogle Scholar
  16. InfoBright, http://www.infobright.comGoogle ScholarGoogle Scholar
  17. Actian VectorWise, http://www.actian.com/products/vectorwise.Google ScholarGoogle Scholar
  18. MonetDB, http://monetdb.cwi.nlGoogle ScholarGoogle Scholar
  19. ParAccel Analytic Database, http://paraccel.comGoogle ScholarGoogle Scholar
  20. SAND CDBMS, http://www.sand.comGoogle ScholarGoogle Scholar
  21. Sybase IQ Columnar database, http://www.sybase.com/products/datawarehousing/sybaseiqGoogle ScholarGoogle Scholar
  22. Teradata Columnar, http://www.teradata.com/products-and-services/database/teradata-14Google ScholarGoogle Scholar
  23. Vertica, http://www.vertica.comGoogle ScholarGoogle Scholar

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        cover image ACM Conferences
        SIGMOD '13: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
        June 2013
        1322 pages
        ISBN:9781450320375
        DOI:10.1145/2463676

        Copyright © 2013 ACM

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        Publication History

        • Published: 22 June 2013

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        SIGMOD '13 Paper Acceptance Rate76of372submissions,20%Overall Acceptance Rate785of4,003submissions,20%

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