DMCA
Dynamic Query Evaluation Plans (1989)
| Venue: | Proc. ACM SIGMOD Con |
| Citations: | 158 - 5 self |
BibTeX
@INPROCEEDINGS{Cole89dynamicquery,
author = {Richard L. Cole},
title = {Dynamic Query Evaluation Plans},
booktitle = {Proc. ACM SIGMOD Con},
year = {1989}
}
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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 signifmantly 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. 1.
Keyphrases
dynamic plan dynamic query evaluation plan static plan start-up overhead run-time optimization accurate knowledge resource availability optimization effort host variable compile-time optimization plan optimization brute-force remedy previous work traditional optimizers technology described run-time parameter actual run-time invocation delayed decision choose-plan operator commercial query optimizers traditional query optimizers run-time primitive single static plan present paper introduces technique optimization decision prototype optimizer run-time system additional optimization embedded query novel optimization model query predicate complete optimization
