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We study the index selection problem: Given a workload consisting of SQL statements on a database, and a user-specified storage constraint, recommend a set of indexes that have the maximum benefit for the given workload. We present a formal statement for this problem and show that it is computationally "hard” to solve or even approximate it. We develop a new algorithm for the problem which is based on treating the problem as a knapsack problem. The novelty of our approach lies in an LP (linear programming) based method that assigns benefits to individual indexes. For a slightly modified algorithm, that does more work, we prove that we can give instance specific guarantees about the quality of our solution. We conduct an extensive experimental evaluation of this new heuristic and compare it with previous solutions. Our results demonstrate that our solution is more scalable while achieving comparable quality.
Index selection, approximation, hardness result, NP-hardness, knapsack, linear programming, scalability.
Mayur Datar, Vivek Narasayya, Surajit Chaudhuri, "Index Selection for Databases: A Hardness Study and a Principled Heuristic Solution", IEEE Transactions on Knowledge & Data Engineering, vol. 16, no. , pp. 1313-1323, November 2004, doi:10.1109/TKDE.2004.75
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