演进型OLAP（扩展版）的索引部署顺序优化
Optimizing Index Deployment Order for Evolving OLAP (Extended Version)
摘要：
OLAP应用程序中的查询工作负载和数据库模式正变得越来越复杂。此外，查询和模式必须不断地进行\textit{evolve}以满足业务需求。在这种重复的转换过程中，在设计物理模式（如索引和MV）时，必须考虑索引部署的\textit{order}。有效的索引部署顺序可以产生（1）迅速的查询运行时改进和（2）减少总部署时间。这两个都是快速发展数据库的设计工具的基本品质，但优化问题是具有挑战性的，因为复杂的索引交互和可能的解决方案的阶乘数。我们制定了一个数学模型的问题，并研究了解决索引排序问题的几种技术。我们证明了约束规划（CP）是一个更灵活，更有效的平台来解决这个问题比其他方法，如混合整数规划和A* 搜索。除了精确搜索技术，我们还研究了局部搜索算法，以快速找到近似最优解。我们对TPC-H数据集的实证分析表明，我们的修剪技术可以减少搜索空间的大小几十个数量级。使用TPC-DS数据集，我们验证了我们的局部搜索算法是一种高度可扩展和稳定的方法，可以快速找到接近最优的解决方案。
英文摘要：
Query workloads and database schemas in OLAP applications are becoming increasingly complex. Moreover, the queries and the schemas have to continually \textit{evolve} to address business requirements. During such repetitive transitions, the \textit{order} of index deployment has to be considered while designing the physical schemas such as indexes and MVs. An effective index deployment ordering can produce (1) a prompt query runtime improvement and (2) a reduced total deployment time. Both of these are essential qualities of design tools for quickly evolving databases, but optimizing the problem is challenging because of complex index interactions and a factorial number of possible solutions. We formulate the problem in a mathematical model and study several techniques for solving the index ordering problem. We demonstrate that Constraint Programming (CP) is a more flexible and efficient platform to solve the problem than other methods such as mixed integer programming and A* search. In addition to exact search techniques, we also studied local search algorithms to find near optimal solution very quickly. Our empirical analysis on the TPC-H dataset shows that our pruning techniques can reduce the size of the search space by tens of orders of magnitude. Using the TPC-DS dataset, we verify that our local search algorithm is a highly scalable and stable method for quickly finding a near-optimal solution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
