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 kmax≥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 kmax 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 kmax values. We show how to choose kmax dynamically to adapt to the actual system workload and performance at runtime, without requiring accurate prior knowledge.
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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。基表上的删除和更新可能导致元组离开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 max ≥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至O（1），同时仍保持视图较小。 k 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适当的k max 值提供性能的数量级改进。我们展示了如何动态地选择k max ，以在运行时适应实际的系统工作负载和性能，而无需准确的先验知识。
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