Database Research Group (CIS)

Document Type

Conference Paper

Date of this Version

June 1999

Comments

Postprint version. Copyright ACM, 1999. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in SIGMOD 1999, Volume 28, Issue 2, June 1999, pages 299-310.
Publisher URL: http://portal.acm.org/citation.cfm?id=304209&coll=portal&dl=ACM&CFID=21037932&CFTOKEN=45900175

NOTE: At the time of publication, the author Zachary G. Ives was affiliated with the University of Washington. Currently June 2007, he is a faculty member of the Department of Computer and Information Sciences at the University of Pennsylvania.

Abstract

Query processing in data integration occurs over network bound, autonomous data sources. This requires extensions to traditional optimization and execution techniques for three reasons: there is an absence of quality statistics about the data, data transfer rates are unpredictable and bursty, and slow or unavailable data sources can often be replaced by overlapping or mirrored sources. This paper presents the Tukwila data integration system, designed to support adaptivity at its core using a two-pronged approach. Interleaved planning and execution with partial optimization allows Tukwila to quickly recover from decisions based on inaccurate estimates. During execution, Tukwila uses adaptive query operators such as the double pipelined hash join, which produces answers quickly, and the dynamic collector, which robustly and efficiently computes unions across overlapping data sources. We demonstrate that the Tukwila architecture extends previous innovations in adaptive execution (such as query scrambling, mid-execution re-optimization, and choose nodes), and we present experimental evidence that our techniques result in behavior desirable for a data integration system.

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Date Posted: 11 June 2007

This document has been peer reviewed.