Fast incremental maintenance of approximate histograms

Phillip B. Gibbons, Yossi Matias, Viswanath Poosala

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Many commercial database systems maintain histograms to summarize the contents of large relations and permit efficient estimation of query result sizes for use in query optimizers. Delaying the propagation of database updates to the histogram often introduces errors in the estimation. This paper presents new sampling-based approaches for incremental maintenance of approximate histograms. By scheduling updates to the histogram based on the updates to the database, our techniques are the first to maintain histograms effectively up-to-date at all times and avoid computing overheads when unnecessary. Our techniques provide highly-accurate approximate histograms belonging to the equi-depth and Compressed classes. Experimental results show that our new approaches provide orders of magnitude more accurate estimation than previous approaches. An important aspect employed by these new approaches is a backing sample, an up-to-date random sample of the tuples currently in a relation. We provide efficient solutions for maintaining a uniformly random sample of a relation in the presence of updates to the relation. The backing sample techniques can be used for any other application that relies on random samples of data.

Original languageEnglish
Title of host publicationProceedings of the 23rd International Conference on Very Large Databases, VLDB 1997
EditorsFred Lochovsky, Michael J. Carey, Matthias Jarke, Klaus R. Dittrich, Pericles Loucopoulos, Manfred A. Jeusfeld
PublisherMorgan Kaufmann
Number of pages10
ISBN (Electronic)1558604707, 9781558604704
StatePublished - 1997
Externally publishedYes
Event23rd International Conference on Very Large Databases, VLDB 1997 - Athens, Greece
Duration: 26 Aug 199729 Aug 1997

Publication series

NameProceedings of the 23rd International Conference on Very Large Databases, VLDB 1997


Conference23rd International Conference on Very Large Databases, VLDB 1997


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