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Efficient maintenance of materialized top-k views

机译:高效维护实例化top-k视图

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We tackle the problem of maintaining materialized top-k views. Top-k queries, including MIN and MAX as important special cases, occur frequently in common database workloads. A top-k view can be materialized to improve query performance, but in general it is not self-maintainable unless it contains all tuples in the base table. Deletions and updates on the base table may cause tuples to leave the top-k view, resulting in expensive queries over the base table to "refill" the view. We propose an algorithm that reduces the frequency of refills by maintaining a top-k' view instead of a top-k view, where k' changes at runtime between k and some k/sub max//spl ges/k. We show that in most practical cases, our algorithm can reduce the expected amortized cost of refill queries to O(1) while still keeping the view small. The optimal value of k/sub max/ depends on the update pattern and the costs of querying the base table and updating the view. Compared with the simple approach of maintaining either the top-k view itself or a copy of the base table, our algorithm can provide orders-of-magnitude improvements in performance with appropriate k/sub max/ values. We show how to choose k/sub max/ dynamically to adapt to the actual system workload and performance at runtime, without requiring accurate prior knowledge.
机译:我们解决了维持具体的前k个视图的问题。在常见的数据库工作负载中,经常发生top-k查询,包括MIN和MAX作为重要的特殊情况。可以实现top-k视图以提高查询性能,但是通常,除非它在基表中包含所有元组,否则它是不可自行维护的。在基表上的删除和更新可能会导致元组离开top-k视图,从而导致对基表进行“重新填充”视图的昂贵查询。我们提出了一种算法,该算法通过维护top-k'视图而不是top-k视图来减少重新填充的频率,其中k'在运行时在k和某些k / sub max // spl ges / k之间变化。我们表明,在大多数实际情况下,我们的算法可以将重新填充查询的预期摊销成本降低到O(1),同时仍保持视图较小。 k / sub max /的最佳值取决于更新模式以及查询基表和更新视图的成本。与仅维护top-k视图本身或基表副本的简单方法相比,我们的算法可以通过使用适当的k / sub max /值来提供性能上的数量级改进。我们展示了如何动态选择k / sub max /来适应运行时的实际系统工作负载和性能,而无需准确的先验知识。

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