A novel query execution time prediction approach based on operator iterate-aware of the execution plan on the graph database

IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-07-01 DOI:10.1016/j.jksuci.2024.102125
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Abstract

Query execution time prediction is essential for database query optimization tasks, such as query scheduling, progress monitoring, and resource allocation. In the query execution time prediction tasks, the query plan is often used as the modeling object of a prediction model. Although the learning-based prediction models have been proposed to capture plan features, there are two limitations need to be considered more. First, the parent–child dependencies between plan operators can be captured, but the operator’s branch independence cannot be distinguished. Second, each operator’s output row is its following operator input, but the data iterate transfer operations between operators are ignored. In this study, we propose a graph query execution time prediction model containing a plan module, a query module, a plan-query module, and a prediction module to improve prediction effectiveness. Specifically, the plan module is used to capture the data iterate transfer operations and distinguish independent of branch operators; the query module is used to learn features of query terms that have an influence on the composition of operators; the plan-query interaction module is used to learn the logical correlations of plan and query. The experiment on datasets proves the effectiveness of the operator iterate-aware and query-plan interaction method in our proposed graph query execution prediction model.

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基于操作员迭代感知图数据库执行计划的新型查询执行时间预测方法
查询执行时间预测对于查询调度、进度监控和资源分配等数据库查询优化任务至关重要。在查询执行时间预测任务中,查询计划通常被用作预测模型的建模对象。虽然基于学习的预测模型已被提出来捕捉计划特征,但有两个局限性需要更多考虑。首先,计划运算符之间的父子依赖关系可以捕获,但运算符分支的独立性无法区分。其次,每个操作符的输出行都是其后续操作符的输入,但忽略了操作符之间的数据迭代传输操作。在本研究中,我们提出了一种包含计划模块、查询模块、计划-查询模块和预测模块的图查询执行时间预测模型,以提高预测效果。具体来说,计划模块用于捕捉数据迭代转移操作并区分独立于分支算子的算子;查询模块用于学习对算子组成有影响的查询词特征;计划-查询交互模块用于学习计划和查询的逻辑关联。数据集实验证明了在我们提出的图查询执行预测模型中运算符迭代感知和查询计划交互方法的有效性。
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来源期刊
CiteScore
10.50
自引率
8.70%
发文量
656
审稿时长
29 days
期刊介绍: In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.
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