Abstract
Remote data access from disparate sources across a wide-area network such as the Internet is problematic due to the unpredictable nature of the communications medium and the lack of knowledge about the load and potential delays at remote sites. Traditional, static, query processing approaches break down in this environment because they are unable to adapt in response to unexpected delays. Query scrambling has been proposed to address this problem. Scrambling modifies query execution plans on-the-fly when delays are encountered during runtime. In its original formulation, scrambling was based on simple heuristics, which although providing good performance in many cases, were also shown to be susceptible to problems resulting from bad scrambling decisions. In this paper we address these shortcomings by investigating ways to exploit query optimization technology to aid in making intelligent scrambling choices. We propose three different approaches to using query optimization for scrambling. These approaches vary, for example, in whether they optimize for total work or response-time, and whether they construct partial or complete alternative plans. Using a two-phase randomized query optimizer, a distributed query processing simulator, and a workload derived from queries of the TPCD benchmark, we evaluate these different approaches and compare their ability to cope with initial delays in accessing remote sources. The results show that cost-based scrambling can effectively hide initial delays, but that in the absence of good predictions of expected delay durations, there are fundamental tradeoffs between risk aversion and effectiveness.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.