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LSShare: an efficient multiple query optimization system in the cloud

机译:LSShare:高效的云中多查询优化系统

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摘要

Multiple query optimization (MQO) in the cloud has become a promising research direction due to the popularity of cloud computing, which runs massive data analysis queries (jobs) routinely. These CPU/IO intensive analysis queries are complex and time-consuming but share common components. It is challenging to detect, share and reuse the common components among thousands of SQL-like queries. Previous solutions to MQO, heuristic or genetic based, are not appropriate for the large growing query set situation. In this paper, we develop a sharing system called LSShare using our proposed Lineage-Signature approach. By LSShare, we can efficiently solve the MQO problem in a recurring query set situation in the cloud. Our system has been prototyped in a distributed system built for massive data analysis based on Alibaba's cloud computing platform. Experimental results on real data sets demonstrate the efficiency and effectiveness of the proposed approach.
机译:由于云计算的普及,云中的多查询优化(MQO)已成为一个有前途的研究方向,该计算通常会运行大量的数据分析查询(作业)。这些CPU / IO密集型分析查询既复杂又耗时,但共享相同的组件。在成千上万个类似SQL的查询中检测,共享和重用公共组件具有挑战性。基于MQO的先前解决方案(基于启发式或基于遗传的)不适用于庞大的增长查询集情况。在本文中,我们使用我们提出的沿袭签名方法开发了一个名为LSShare的共享系统。通过LSShare,我们可以有效地解决云中重复出现的查询集情况下的MQO问题。我们的系统已在基于阿里巴巴云计算平台的用于海量数据分析的分布式系统中进行了原型设计。在真实数据集上的实验结果证明了该方法的有效性和有效性。

著录项

  • 来源
    《Distributed and Parallel Databases》 |2014年第4期|583-605|共23页
  • 作者单位

    Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;

    Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;

    Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;

    Alibaba Cloud Computing Company, Hangzhou, China;

    Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;

    Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;

    Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Multiple query optimization; Query processing; SQL-rewriting; Subexpression identification;

    机译:多重查询优化;查询处理;SQL重写;亚表达识别;

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