基于声明式推理的高效协同查询处理技术  

Efficient Collaborative Query Processing Technique Based on Declarative Inference

作　　者：邱志林 寿黎但[1,2] 陈珂[1,2] 江大伟 骆歆远[1,2] 陈刚 QIU Zhi-Lin;SHOU Li-Dan;CHEN Ke;JIANG Da-Wei;LUO Xin-Yuan;CHEN Gang(College of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China;State Key Laboratory of Blockchain and Data Security(Zhejiang University),Hangzhou 310027,China)

机构地区：[1]浙江大学计算机科学与技术学院,浙江杭州310027 [2]区块链与数据安全全国重点实验室(浙江大学),浙江杭州310027

出　　处：《软件学报》2024年第12期5558-5581,共24页Journal of Software

基　　金：国家重点研发计划(2022YFB3304100);中央高校基本科研业务费专项资金(2021FZZX001-24)。

摘　　要：由于深度学习领域的不断进步,人们对用协同查询处理(CQP)技术扩展关系数据库以处理涉及结构化和非结构化数据的高级分析查询越来越感兴趣.最先进的CQP方法使用用户定义函数(UDFs)来实现深度神经网络(NN)模型来处理非结构化数据,并使用关系操作来处理结构化数据.基于UDF的方法简化了查询书写,允许用户使用单一的SQL提交分析查询,但要求在即席数据分析中能够根据所需性能指标手动选择合适且高效的模型,这对用户提出了很高的挑战.为了解决该问题,提出基于声明式推理函数(DIF)的协同查询处理技术,通过优化模型选择、执行方式、设备绑定等多个查询实现路径构建完整的协同查询处理框架.基于所提研究设计的成本模型和优化规则,查询处理器能够计算出不同查询计划的代价,并自动选择最优的物理查询计划.在4个数据集上的实验结果证实了提出的基于DIF的CQP方法的有效性和效率.Due to the continuous advancements in the field of deep learning,there is growing interest in extending relational databases with collaborative query processing(CQP)techniques to handle advanced analytical queries involving structured and unstructured data.State-of-the-art CQP methods employ user-defined functions(UDFs)to implement deep neural network(NN)models for processing unstructured data while utilizing relational operations for structured data.UDF-based approaches simplify query composition,allowing users to submit analytical queries with a single SQL statement.However,they require manual selection of appropriate and efficient models based on desired performance metrics during ad-hoc data analysis,posing significant challenges to users.To address this issue,this research proposes a CQP technique based on declarative inference functions(DIF),which constructs a complete CQP framework by optimizing model selection,execution strategies,and device bindings across multiple query execution paths.Leveraging the cost model and optimization rules designed in this study,the query processor is capable of estimating the cost of different query plans and automatically selecting the optimal physical query plan.Experimental results on four datasets validate the effectiveness and efficiency of the proposed DIF-based CQP approach.

关 键 词：数据库查询优化 声明式推理函数 协同查询处理 模型选择 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]