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Available viahttp://dbpubs.stanford.edu/pub/2005-1
Submitted on 20th of December 2004
Author Babu, Shivnath; Bizarro, Pedro
Title Adaptive Query Processing in the Looking Glass
Date of publication January 2005
Published in Proceedings of the Second Biennial Conference on Innovative Data Systems Research (CIDR), Jan. 2005
Citation Babu, Shivnath; Bizarro, Pedro. Adaptive Query Processing in the Looking Glass, Proceedings of the Second Biennial Conference on Innovative Data Systems Research (CIDR), Jan. 2005
Number of pages 12
Language English
Project STREAM
Type Other
Subject group Data Streams
Abstract A great deal of work on adaptive query processing has been done over the last few years: Adaptive query processing has been used to detect and correct optimizer errors due to incorrect statistics or simplified cost metrics; it has been applied to long-running continuous queries over data streams whose characteristics change over time; and routing-based adaptive query processing does away with the optimizer altogether. Despite this large body of interrelated work, no unifying comparison of adaptive query processing techniques or systems has been attempted; we tackle this problem. We identify three families of systems (plan-based, CQ-based, and routing-based), and compare them in detail with respect to the most important aspects of adaptive query processing: plan quality, statistics monitoring and re-optimization, plan migration, and scalability. We also suggest two new approaches to adaptive query processing that address some of the shortcomings revealed by our in-depth analysis: (1) "Proactive re-optimization," in which the optimizer chooses initial query plans with the expectation of re-optimization; and (2) "Plan logging," in which optimizer decisions under different conditions are logged over time, enabling plan re-use as well as analysis of relevant statistics and benefits of adaptivity.
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