在数据库负载管理、性能调优过程中，开销预测模型是提高其效率的关键技术.首先，由于数据库系统的复杂性和计算机资源的竞争，很难精确地估计不同操作的开销；其次，现有的研究大多没有真正预测查询的执行时间，而是预测了类似查询优化器中开销模型生成的开销；由于查询计划结构的复杂性，现有研究更多地使用了笼统的查询信息，而很少利用查询计划中操作层面的信息，并依据这些信息来获得开销模型.为了减少负载管理的复杂性，提出了基于循环神经网络的精细模型来预测查询开销，以查询计划中的操作行为及其实际运行时间作为特征提取的来源.特别地，考虑到查询计划结构的复杂性，采用一种特殊的循环神经网络——长短期记忆（long-short term memory，简称LSTM）.给一个特定的查询计划，在该计划实际执行之前，模型就能产生其预测的执行时间区间.这会比现有数据库的查询优化器产生的开销预估结果（任意单位）更具有参考性，也优于需要在执行开始之后才能预测的查询进度指示器.所提方法预测查询执行时间，可以解决数据库负载管理中的关键问题.通过实验验证，模型的正确率高于71%，在一定程度上证明了方法的可行性.

Query cost models are the key parts of database workload management and performance tuning. Firstly, it is difficult, even impossible, to precisely estimate the costs of different relational operators due to the complexity of database systems and competition of computer resources. Secondly, most existing research work uses general query information without taking advantage of actual operators because of the complexity of query plans. Thirdly, most previous research work does not address the problem of predicting actual execution time of a query but rather predicts the query performance by the cost the like query optimizers generate. To reduce the complexity of workload management, his paper proposes an elaborate cost prediction model based on recurrent neural network through learning from operator behavior and detailed runtime information. In particular, the model uses a special kind of recurrent neural network, called long-short term memory (LSTM). Given an ad-hoc query, the model is able to predict its running time before it starts to run. It is more meaningful than the state-of-the-art query optimizers of existing database systems which only estimate costs in arbitrary units. It is also better than query progress indicators which cannot predict cost before the query runs. This research provides a novel approach to solve the key problem in database workload management. Verified by the experiments, the accuracy of the model is over 71% which shows the method is feasible to some degree.

TP311

国家重点基础研究发展计划（973）（2015CB352400）；国家自然科学基金（61661146001，61472348，61672455）；浙江省自然科学基金（LY18F020005）