基于物理操作级模型的查询执行时间预测方法

Query Performance Prediction Based on Physical Operation-level Models

查询执行时间预测(Query Performance Prediction,QPP)是数据库系统中一个重要的研究问题。当数据库系统中存在并发执行的事务时,现有的QPP方法无法在不改变数据库查询性能的前提下建立准确的QPP模型。为此,提出了一种基于物理操作的查询执行时间预测新方法,该方法以查询的物理操作为单位建立单元预测模型,根据查询计划将单元预测模型组合为完整的QPP模型,把能够刻画数据库系统并发状态的统计信息纳入模型的输入特征。所提方法只须使用DBMS提供的基本手段即可获取构建模型所需的数据库统计信息,无须改变DBMS,也不会影响数据库系统上原有工作负载的执行。实验结果表明,所提方法无论在OLTP还是OLAP应用中,在不同的查询计划和并发度下的预测准确性均高于其他对比方法。

Query performance prediction(QPP)is an important issue in database systems.When there are concurrent transactions in a database system,the existing methods fail to establish an accurate model without changing query performance.In this paper,a new method is proposed to solve the QPP problem.The proposed method builds unit prediction models for various physical operations in the query and combines the unit models into a complete QPP model according to the query plan.It can describe the concurrency state of the database system by taking the statistical information as features.The proposed method only needs to use the basic means provided by the DBMS to obtain the database statistics required to build the model,without changing the DBMS or affecting the execution of the original workloads on the database system.We evaluate our techniques on various workloads including OLTP and OLAP.Experiments show that the proposed method outperforms the state-of-art QPP methods regardless of different query plans or different concurrency.