Abstract
A query optimizer attempts to predict a performance metric based on the amount of time elapsed. Theoretically, this would necessitate the creation of a significant overhead on the core engine to provide the necessary query optimizing statistics. Machine learning is increasingly being used to improve query performance by incorporating regression models. To predict the response time for a query, most query performance approaches rely on DBMS optimizing statistics and the cost estimation of each operator in the query execution plan, which also focuses on resource utilization (CPU, I/O). Modeling query features is thus a critical step in developing a robust query performance prediction model. In this paper, we propose a new framework based on query feature modeling and ensemble learning to predict query performance and use this framework as a query performance predictor simulator to optimize the query features that influence query performance. In query feature modeling, we propose five dimensions used to model query features. The query features dimensions are syntax, hardware, software, data architecture, and historical performance logs. These features will be based on developing training datasets for the performance prediction model that employs the ensemble learning model. As a result, ensemble learning leverages the query performance prediction problem to deal with missing values. Handling overfitting via regularization. The section on experimental work will go over how to use the proposed framework in experimental work. The training dataset in this paper is made up of performance data logs from various real-world environments. The outcomes were compared to show the difference between the actual and expected performance of the proposed prediction model. Empirical work shows the effectiveness of the proposed approach compared to related work.
Highlights
A query optimizer attempts to make a comparable performance estimate
Select the type of model to run at each iteration. It has two options: gbtree: tree-based models gblinear: linear models The learning rate used to weight each model The maximum depth of each tree Gamma is a pseudo-regularization parameter Represents the fraction of observations to be sampled for each tree Number of features used in each tree reg: squarederror: for linear regression rmse –root mean square error
This paper proposed a framework for predicting query performance
Summary
This paper aims to propose a query performance prediction framework that can be used to estimate query performance. The objective of this study is to show how to extract features from hardware, software, SQL syntax, and data architecture
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