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An Adaptive Probe-Based Technique to Optimize Join Queries in Distributed Internet Databases

An Adaptive Probe-Based Technique to Optimize Join Queries in Distributed Internet Databases

Latifur Khan, Dennis McLeod, Cyrus Shahabi
Copyright: © 2001 |Volume: 12 |Issue: 4 |Pages: 12
ISSN: 1063-8016|EISSN: 1533-8010|ISSN: 1063-8016|EISBN13: 9781615200689|EISSN: 1533-8010|DOI: 10.4018/jdm.2001100101
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MLA

Khan, Latifur, et al. "An Adaptive Probe-Based Technique to Optimize Join Queries in Distributed Internet Databases." JDM vol.12, no.4 2001: pp.3-14. http://doi.org/10.4018/jdm.2001100101

APA

Khan, L., McLeod, D., & Shahabi, C. (2001). An Adaptive Probe-Based Technique to Optimize Join Queries in Distributed Internet Databases. Journal of Database Management (JDM), 12(4), 3-14. http://doi.org/10.4018/jdm.2001100101

Chicago

Khan, Latifur, Dennis McLeod, and Cyrus Shahabi. "An Adaptive Probe-Based Technique to Optimize Join Queries in Distributed Internet Databases," Journal of Database Management (JDM) 12, no.4: 3-14. http://doi.org/10.4018/jdm.2001100101

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Abstract

An adaptive probe-based optimization technique is developed and demonstrated in the context of an Internet-based distributed database environment. More and more common are database systems, which are distributed across servers communicating via the Internet where a query at a given site might require data from remote sites. Optimizing the response time of such queries is a challenging task due to the unpredictability of server performance and network traffic at the time of data shipment; this may result in the selection of an expensive query plan using a static query optimizer. We constructed an experimental setup consisting of two servers running the same DBMS connected via the Internet. Concentrating on join queries, we demonstrate how a static query optimizer might choose an expensive plan by mistake. This is due to the lack of a priori knowledge of the run-time environment, inaccurate statistical assumptions in size estimation, and neglecting the cost of remote method invocation. These shortcomings are addressed collectively by proposing a probing mechanism. Furthermore, we extend our mechanism with an adaptive technique that detects sub-optimality of a plan during query execution and attempts to switch to the cheapest plan while avoiding redundant work and imposing little overhead. An implementation of our run-time optimization technique for join queries was constructed in the Java language and incorporated into an experimental setup. The results demonstrate the superiority of our probe-based optimization over a static optimization.

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