Progressive Parametric Query Optimization
Progressive Parametric Query Optimization
复制标题
渐进式参数查询优化
DOI：
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复制
发表时间：
2009
影响因子：
8.9
通讯作者：
D. DeWitt
中科院分区：
文献类型：
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作者：
P. Bizarro;Nicolas Bruno;D. DeWitt
商业应用程序通常依赖于预编译的参数化过程来与数据库交互。不幸的是，使用与编译时使用的参数不同的参数集执行过程可能是任意次优的。参数查询优化（PQO）试图通过在编译时穷举地确定参数空间的每个点处的最优计划来解决这个问题。但是，如果查询不经常执行，或者只使用参数空间子集内的值执行，则PQO可能不具有成本效益。在本文中，我们建议，而不是逐步探索参数空间，并建立一个参数化的计划，在几个相同的查询执行。我们介绍的算法，参数计划填充，能够经常绕过优化器，但仍然执行最佳或接近最佳的计划。
Commercial applications usually rely on pre-compiled parameterized procedures to interact with a database. Unfortunately, executing a procedure with a set of parameters different from those used at compilation time may be arbitrarily sub-optimal. Parametric query optimization (PQO) attempts to solve this problem by exhaustively determining the optimal plans at each point of the parameter space at compile time. However, PQO is likely not cost-effective if the query is executed infrequently or if it is executed with values only within a subset of the parameter space. In this paper we propose instead to progressively explore the parameter space and build a parametric plan during several executions of the same query. We introduce algorithms that, as parametric plans are populated, are able to frequently bypass the optimizer but still execute optimal or near-optimal plans.