Adaptive Query Processing
Foundations and Trends® in Databases
Volume 1 Issue 1
DOI: 10.1561/1900000001
Adaptive Query Processing
Amol Deshpande
University of Maryland, USA, amol@cs.umd.edu
Zachary Ives
University of Pennsylvania, USA, zives@cis.upenn.edu
Vijayshankar Raman
IBM Almaden, USA, ravijay@us.ibm.com
SUGGESTED CITATION:
Amol
Deshpande
Zachary
Ives
Vijayshankar
Raman
(2007)
"Adaptive Query Processing",
Foundations and Trends® in
Databases: Vol. 1: No 1, pp 1-140.
http:/dx.doi.org/10.1561/1900000001
Abstract
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.