摘要:数据库索引是关系数据库系统实现快速查询的有效方式之一.智能索引调优技术可以有效地对数据库实例进行索引调节，从而保持数据库高效的查询性能.现有的方法大多利用了数据库实例的查询日志，它们先从查询日志中得到候选索引，再利用人工设计的模型选择索引，从而调节索引.然而，从查询日志中产生出的候选索引可能并未实际存在于数据库实例中，因此导致这些方法不能有效地估计这类索引对于查询的优化效果.首先，设计并实现了一种面向关系数据库的智能索引调优系统；其次，提出了一种利用机器学习方法来构造索引的量化模型，根据该模型，可以准确地对索引的查询优化效果进行估计；接着设计了一种高效的最优索引选择算法，实现快速地从候选索引空间中选择满足给定大小约束的最优的索引组合；最后，通过实验测试不同场景下智能索引调优系统的调优性能.实验结果表明，所提出的技术可以在不同的场景下有效地对索引进行优化，从而实现数据库系统查询性能的提升.



Abstract:Indexing is one of the most effective techniques for relational databases to achieve fast queryprocessing. The intelligent index tuning technique can effectively adjust the index of the database instance to obtain efficient query performance. Most of the existing methods utilize the query log to generate candidate indices, and then use the artificially designed models to select indices, thereby the indices are adjusted. However, the candidate indices generated from the query log may not exist in the database instance, so they cannot precisely estimate the effects of such indices on the query processing. This study first designs and implements an intelligent index tuning system for the relational database. Secondly, it proposes a learning-based method to model the effects of indices for query processing, accordingly, the query optimization effect of an index can be accurately estimated when selecting optimized indices. Then, an efficient optimal index selection algorithm is designed to select a set of indices with the maximal utility from candidate indices, which satisfy the space threshold. Finally, experiments are conducted to test the performance of the proposed system in different settings. The experimental results show that the proposed technique can effectively adjust the index and achieve a significant improvement in query performance for a relational database.



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