AutoAdmin 'What-if' Index Analysis Utility.

Surajit Chaudhuri, Vivek R. Narasayya: AutoAdmin 'What-if' Index Analysis Utility. SIGMOD Conference 1998: 367-378

@inproceedings{DBLP:conf/sigmod/ChaudhuriN98,
  author    = {Surajit Chaudhuri and
               Vivek R. Narasayya},
  editor    = {Laura M. Haas and
               Ashutosh Tiwary},
  title     = {AutoAdmin 'What-if' Index Analysis Utility},
  booktitle = {SIGMOD 1998, Proceedings ACM SIGMOD International Conference
               on Management of Data, June 2-4, 1998, Seattle, Washington, USA},
  publisher = {ACM Press},
  year      = {1998},
  isbn      = {0-89791-995-5},
  pages     = {367-378},
  ee        = {http://doi.acm.org/10.1145/276304.276337, db/conf/sigmod/ChaudhuriN98.html},
  crossref  = {DBLP:conf/sigmod/98},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

BibTeX

Abstract

As databases get widely deployed, it becomes increasingly important to reduce the overhead of database administration. An important aspect of data administration that critically influences performance is the ability to select indexes for a database. In order to decide the right indexes for a database, it is crucial for the database administrator (DBA) to be able to perform a quantitative analysis of the existing indexes. Furthermore, the DBA should have the ability to propose hypothetical ("what-if") indexes and quantitatively analyze their impact on performance of the system. Such impact analysis may consist of analyzing workloads over the database, estimating changes in the cost of a workload, and studying index usage while taking into account projected changes in the sizes of the database tables. In this paper we describe a novel index analysis utility that we have prototyped for Microsoft SQL Server 7.0. We describe the interfaces exposed by this utility that can be leveraged by a variety of front-end tools and sketch important aspects of the user interfaces enabled by the utility. We also discuss the implementation techniques for efficiently supporting "what-if" indexes. Our framework can be extended to incorporate analysis of other aspects of physical database design.

Copyright © 1998 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.

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BibTeX

Printed Edition

Laura M. Haas, Ashutosh Tiwary (Eds.): SIGMOD 1998, Proceedings ACM SIGMOD International Conference on Management of Data, June 2-4, 1998, Seattle, Washington, USA. ACM Press 1998, ISBN 0-89791-995-5 BibTeX , SIGMOD Record 27(2), June 1998
Contents

Online Edition: ACM SIGMOD

[Abstract]

References

[1]: ...
[2]: ...
[3]: Surajit Chaudhuri, Rajeev Motwani, Vivek R. Narasayya: Random Sampling for Histogram Construction: How much is enough? SIGMOD Conference 1998: 436-447 BibTeX
[4]: Surajit Chaudhuri, Vivek R. Narasayya: An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server. VLDB 1997: 146-155 BibTeX
[5]: Martin R. Frank, Edward Omiecinski, Shamkant B. Navathe: Adaptive and Automated Index Selection in RDBMS. EDBT 1992: 277-292 BibTeX
[6]: Sheldon J. Finkelstein, Mario Schkolnick, Paolo Tiberio: Physical Database Design for Relational Databases. ACM Trans. Database Syst. 13(1): 91-128(1988) BibTeX
[7]: Himanshu Gupta, Venky Harinarayan, Anand Rajaraman, Jeffrey D. Ullman: Index Selection for OLAP. ICDE 1997: 208-219 BibTeX
[8]: Venky Harinarayan, Anand Rajaraman, Jeffrey D. Ullman: Implementing Data Cubes Efficiently. SIGMOD Conference 1996: 205-216 BibTeX
[9]: Wilburt Labio, Dallan Quass, Brad Adelberg: Physical Database Design for Data Warehouses. ICDE 1997: 277-288 BibTeX
[10]: ...
[11]: Steve Rozen, Dennis Shasha: A Framework for Automating Physical Database Design. VLDB 1991: 401-411 BibTeX
[12]: Michael Stonebraker: Hypothetical Data Bases as Views. SIGMOD Conference 1981: 224-229 BibTeX

Referenced by

Stéphane Lopes, Jean-Marc Petit, Lotfi Lakhal: Efficient Discovery of Functional Dependencies and Armstrong Relations. EDBT 2000: 350-364
Surajit Chaudhuri, Eric Christensen, Goetz Graefe, Vivek R. Narasayya, Michael J. Zwilling: Self-Tuning Technology in Microsoft SQL Server. IEEE Data Eng. Bull. 22(2): 20-26(1999)
Surajit Chaudhuri, Vivek R. Narasayya: Index Merging. ICDE 1999: 296-303
Surajit Chaudhuri, Vivek R. Narasayya: Microsoft Index Tuning Wizard for SQL Server 7.0. SIGMOD Conference 1998: 553-554

BibTeX

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