论文标题
自动存储结构选择混合工作负载
Automatic Storage Structure Selection for hybrid Workload
论文作者
论文摘要
在使用数据库系统时，存储引擎和数据模型的设计直接影响执行查询时数据库的性能。因此，数据库的用户需要根据遇到的工作负载选择存储引擎和设计数据模型。但是，在混合工作负载中，数据库的查询集正在动态变化，其最佳存储结构的设计也在改变。在此激励的情况下，我们提出了一个基于学习成本的自动存储结构选择系统，该系统用于动态选择混合工作负载下数据库的最佳存储结构。在系统中，我们介绍了一种机器学习方法，以建立存储引擎的成本模型，并以列为导向的数据布局生成算法。实验结果表明，建议的系统可以根据当前的工作量选择存储引擎和数据模型的最佳组合，从而大大提高了默认存储结构的性能。该系统旨在与不同的存储引擎兼容，以便于实用应用。
In the use of database systems, the design of the storage engine and data model directly affects the performance of the database when performing queries. Therefore, the users of the database need to select the storage engine and design data model according to the workload encountered. However, in a hybrid workload, the query set of the database is dynamically changing, and the design of its optimal storage structure is also changing. Motivated by this, we propose an automatic storage structure selection system based on learning cost, which is used to dynamically select the optimal storage structure of the database under hybrid workloads. In the system, we introduce a machine learning method to build a cost model for the storage engine, and a column-oriented data layout generation algorithm. Experimental results show that the proposed system can choose the optimal combination of storage engine and data model according to the current workload, which greatly improves the performance of the default storage structure. And the system is designed to be compatible with different storage engines for easy use in practical applications.
