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" Adaptive query processing / "
Amol Deshpande, Zachary Ives, Vijayshankar Raman.
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BL
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| Record Number
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1009145
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b763515
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| Main Entry
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Deshpande, Amol.
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| Title & Author
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Adaptive query processing /\ Amol Deshpande, Zachary Ives, Vijayshankar Raman.
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| Publication Statement
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Boston :: Now,, ©2007.
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| Series Statement
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Foundations and trends in databases ;; v. 1, no. 1
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| Page. NO
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1 online resource (140 pages) :: illustrations (some color)
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| ISBN
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1601980353
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: 9781601980359
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9781601980342
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| Notes
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Offprint. Foundations and trends in databases (Online). Vol. 1, no. 1 (2007).
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Title from PDF title page (viewed Jan. 24, 2008).
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| Bibliographies/Indexes
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Includes bibliographical references.
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| Contents
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Abstract -- Introduction -- Query processing in relational database systems -- Motivations for AQP -- Road map -- Related work -- Background: conventional optimization techniques -- Query optimization -- Choosing an effective plan -- Summary -- Foundations of adaptive query processing -- New operators -- Adaptivity loop -- Post-mortem analysis of adaptive techniques -- Adaptivity loop and post-mortem in some example systems -- Scope of the remainder of the survey -- Adaptive selection ordering -- Adaptive greedy -- Adaptation using eddies -- Parallel and distributed scenarios -- Summary -- Adaptive join processing: overview -- Adaptive join processing: history-independent pipelined execution -- Pipelined plans with a single driver relation -- Pipelined plans with multiple drivers -- Adaptive caching (A-caching) -- Summary -- Adaptive join processing: history-dependent pipelined execution -- Corrective query processing -- Eddies with binary join operators -- Eddies with stairs -- Dynamic plan migration in CAPE -- Summary -- Adaptive join processing: non-pipelined execution -- Plan staging -- Mid-query reoptimization -- Query scrambling -- Summary and post-mortem analysis -- Summary and open questions -- Trade-offs and constraints -- Adaptive mechanisms -- Conclusions and challenge problems -- Acknowledgements -- References -- Updates.
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| Abstract
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As the data management field has diversified to consider settings in which queries are increasingly complex, statistics are less available, or data is stored remotely, there has been an acknowledgment that the traditional optimize-then-execute paradigm is insufficient. This has led to a plethora of new techniques, generally placed under the common banner of adaptive query processing, that focus on using runtime feedback to modify query processing in a way that provides better response time or more efficient CPU utilization. In this survey paper, we identify many of the common issues, themes, and approaches that pervade this work, and the settings in which each piece of work is most appropriate. Our goal with this paper is to be a value-add over the existing papers on the material, providing not only a brief overview of each technique, but also a basic framework for understanding the field of adaptive query processing in general. We focus primarily on intra-query adaptivity of long-running, but not full-fledged streaming, queries. We conclude with a discussion of open research problems that are of high importance.
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| Subject
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Querying (Computer science)
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Relational databases.
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COMPUTERS-- Database Management-- General.
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COMPUTERS-- Desktop Applications-- Databases.
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COMPUTERS-- System Administration-- Storage Retrieval.
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Querying (Computer science)
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Relational databases.
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| Dewey Classification
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005.74/1
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| LC Classification
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QA76.9.D3D47 2007eb
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| Added Entry
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Ives, Zachary.
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Raman, Vijayshankar.
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