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Automated statistics collection in action

Published: 14 June 2005 Publication History

Abstract

If presented with inaccurate statistics, even the most sophisticated query optimizers make mistakes. They may wrongly estimate the output cardinality of a certain operation and thus make sub-optimal plan choices based on that cardinality. Maintaining accurate statistics is hard, both because each table may need a specifically parameterized set of statistics and because statistics get outdated as the database changes. Automated Statistic Collection (ASC) is a new component in IBM DB2 UDB that, without any DBA intervention, observes and analyzes the effects of faulty statistics and, in response, it triggers actions that continuously repair the latter. In this demonstration, we will show how ASC works to alleviate the DBA from the task of maintaining fresh, accurate statistics in several challenging scenarios. ASC is able to reconfigure the statistics collection parameters (e.g, number of frequent values for a column, or correlations between certain column pairs) on a per-table basis. ASC can also detect and guard against outdated statistics caused by high updates/inserts/deletes rates in volatile, dynamic databases. We will also show how ASC works from the inside: from how cardinality mis-estimations are introduced in different kind of operators, to how this error is propagated to later operations in the plan, to how this influences plan choices inside the optimizer.

References

[1]
A. Aboulnaga, P. J. Haas, M. Kandil, S. Lightstone, G. M. Lohman, V. Markl, I. Popivanov, and V. Raman. Automated Statistics Collection in DB2 UDB. In Proc. of the Int'l Conf. on Very Large Databases (VLDB), pages 1146--1157, 2004.
[2]
M. Stillger, G. M. Lohman, V. Markl, and M. Kandil. LEO - DB2's Learning Optimizer. In Proc. of the Int'l Conf. on Very Large Databases (VLDB), pages 19--28, 2001.

Cited By

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  • (2019)SkinnerDBProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3300088(1153-1170)Online publication date: 25-Jun-2019
  • (2010)Autonomic View of Query Optimizers in Database Management SystemsProceedings of the 2010 Eighth ACIS International Conference on Software Engineering Research, Management and Applications10.1109/SERA.2010.11(3-8)Online publication date: 24-May-2010

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cover image ACM Conferences
SIGMOD '05: Proceedings of the 2005 ACM SIGMOD international conference on Management of data
June 2005
990 pages
ISBN:1595930604
DOI:10.1145/1066157
  • Conference Chair:
  • Fatma Ozcan
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 June 2005

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Cited By

View all
  • (2019)SkinnerDBProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3300088(1153-1170)Online publication date: 25-Jun-2019
  • (2010)Autonomic View of Query Optimizers in Database Management SystemsProceedings of the 2010 Eighth ACIS International Conference on Software Engineering Research, Management and Applications10.1109/SERA.2010.11(3-8)Online publication date: 24-May-2010

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