Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (8): 49-55.doi: 10.11896/jsjkx.210700074

• Database & Big Data & Data Science • Previous Articles     Next Articles

Query Performance Prediction Based on Physical Operation-level Models

WANG Run-an, ZOU Zhao-nian   

  1. School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001,China
  • Received:2021-07-07 Revised:2021-12-09 Published:2022-08-02
  • About author:WANG Run-an,born in 1998,postgra-duate,is a student member of China Computer Federation.His main research interests include database systems and so on.
    ZOU Zhao-nian,born in 1979,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include database systems and big data analysis.
  • Supported by:
    National Natural Science Foundation of China(62072138) and Open Research Projects of Zhejiang Lab(2021KC0AB02).

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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.

Key words: Database system status, Neural network, Physical operation, Query performance prediction (QPP), Query plan

CLC Number: 

  • TP392
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