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ABSTRACT
Many modern software systems provide progress indicators for long-running tasks. These progress indicators make systems more user-friendly by helping the user quickly estimate how much of the task has been completed and when the task will finish. However, none of the existing commercial RDBMSs provides a non-trival progress indicator for long-running queries. In this paper, we consider the problem of supporting such progress indicators. After discussing the goals and challenges inherent in this problem, we present a set of techniques sufficient for implementing a simple yet useful progress indicator for a large subset of RDBMS queries. We report an initial implementation of these techniques in PostgreSQL.
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CITED BY 18
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Stefan Krompass , Harumi Kuno , Janet L. Wiener , Kevin Wilkinson , Umeshwar Dayal , Alfons Kemper, Managing long-running queries, Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, March 24-26, 2009, Saint Petersburg, Russia
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Norman W. Paton , Jorge Buenabad-Chavez , Mengsong Chen , Vijayshankar Raman , Garret Swart , Inderpal Narang , Daniel M. Yellin , Alvaro A. Fernandes, Autonomic query parallelization using non-dedicated computers: an evaluation of adaptivity options, The VLDB Journal — The International Journal on Very Large Data Bases, v.18 n.1, p.119-140, January 2009
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