Optimization of dynamic query evaluation plans Export

@inproceedings{citeulike:1889891, abstract = {Traditional query optimizers assume accurate knowledge of run-time parameters such as selectivities and resource availability during plan optimization, i.e., at compile time. In reality, however, this assumption is often not justified. Therefore, the ” static” plans produced by traditional optimizers may not be optimal for many of their actual run-time invocations. Instead, we propose a novel optimization model that assigns the bulk of the optimization effort to compile-time and delays carefully selected optimization decisions until run-time. Our previous work defined the run-time primitives, ” dynamic plans” using ” choose-plan” operators, for executing such delayed decisions, but did not solve the problem of constructing dynamic plans at compile-time. The present paper introduces techniques that solve this problem. Experience with a working prototype optimizer demonstrates (i) that the additional optimization and start-up overhead of dynamic plans compared to static plans is dominated by their advantage at run-time, (ii) that dynamic plans are as robust as the ” brute-force” remedy of run-time optimization, i.e., dynamic plans maintain their optimality even if parameters change between compile-time and run-time, and (iii) that the start-up overhead of dynamic plans is significantly less than the time required for complete optimization at run-time. In other words, our proposed techniques are superior to both techniques considered to-date, namely compile-time optimization into a single static plan as well as run-time optimization. Finally, we believe that the concepts and technology described can be transferred to commercial query optimizers in order to improve the performance of embedded queries with host variables in the query predicate and to adapt to run-time system loads unpredictable at compile time.}, address = {New York, NY, USA}, author = {Cole, Richard L. and Graefe, Goetz}, booktitle = {SIGMOD '94: Proceedings of the 1994 ACM SIGMOD international conference on Management of data}, citeulike-article-id = {1889891}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=191872}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/191839.191872}, doi = {10.1145/191839.191872}, isbn = {0897916395}, keywords = {adaptive, optimization, query-processing}, pages = {150--160}, posted-at = {2007-11-09 14:21:29}, priority = {2}, publisher = {ACM}, title = {Optimization of dynamic query evaluation plans}, url = {http://dx.doi.org/10.1145/191839.191872}, year = {1994} }

TY - CONF ID - citeulike:1889891 L3 - citeulike-article-id:1889891 N2 - Traditional query optimizers assume accurate knowledge of run-time parameters such as selectivities and resource availability during plan optimization, i.e., at compile time. In reality, however, this assumption is often not justified. Therefore, the “static” plans produced by traditional optimizers may not be optimal for many of their actual run-time invocations. Instead, we propose a novel optimization model that assigns the bulk of the optimization effort to compile-time and delays carefully selected optimization decisions until run-time. Our previous work defined the run-time primitives, “dynamic plans” using “choose-plan” operators, for executing such delayed decisions, but did not solve the problem of constructing dynamic plans at compile-time. The present paper introduces techniques that solve this problem. Experience with a working prototype optimizer demonstrates (i) that the additional optimization and start-up overhead of dynamic plans compared to static plans is dominated by their advantage at run-time, (ii) that dynamic plans are as robust as the “brute-force” remedy of run-time optimization, i.e., dynamic plans maintain their optimality even if parameters change between compile-time and run-time, and (iii) that the start-up overhead of dynamic plans is significantly less than the time required for complete optimization at run-time. In other words, our proposed techniques are superior to both techniques considered to-date, namely compile-time optimization into a single static plan as well as run-time optimization. Finally, we believe that the concepts and technology described can be transferred to commercial query optimizers in order to improve the performance of embedded queries with host variables in the query predicate and to adapt to run-time system loads unpredictable at compile time. SN - 0897916395 AD - New York, NY, USA EP - 160 TI - Optimization of dynamic query evaluation plans PB - ACM T2 - SIGMOD '94: Proceedings of the 1994 ACM SIGMOD international conference on Management of data SP - 150 KW - adaptive KW - optimization KW - query-processing AU - Cole, Richard AU - Graefe, Goetz PY - 1994/// UR - http://dx.doi.org/10.1145/191839.191872 ER -