Kwanchai Eurviriyanukul, Norman W. Paton, Alvaro A. A. Fernandes, Steven J. Lynden. Adaptive join processing in pipelined plans. In Ioana Manolescu, Stefano Spaccapietra, Jens Teubner, Masaru Kitsuregawa, Alain LÃ©ger, Felix Naumann, Anastasia Ailamaki, Fatma Ã–zcan, editors, EDBT 2010, 13th International Conference on Extending Database Technology, Lausanne, Switzerland, March 22-26, 2010, Proceedings. Volume 426 of ACM International Conference Proceeding Series, pages 183-194, ACM, 2010.
In adaptive query processing, the way in which a query is evaluated is changed in the light of feedback obtained from the environment during query evaluation. Such feedback may, for example, establish that misleading selectivity estimates were used when the query was compiled, leading to the optimizer choosing an inappropriate join order or unsuitable join algorithms. This paper describes how joins can be reordered, and the join algorithms used replaced, while they are being evaluated in pipelined plans. Where joins are reordered and/or replaced during their evaluation, the approach avoids duplicating work that has already been carried out, by resuming from where the previous plan left off. The approach has been evaluated empirically, and shown to be effective for improving query performance in the light of misleading selectivity estimates.