Query Performance Prediction Based on Physical Operation-level Models

Abstract

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.

Keywords

• query performance prediction (qpp)|physical operation|query plan|database system status|neural network