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Distributed Query Engine for Multiple-Query Optimization over Data Stream

  • Junye Yang
  • Yong ZhangEmail author
  • Jin Wang
  • Chunxiao Xing
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11448)

Abstract

Query processing over data stream has attracted much attention in real-time applications. While many efforts have been paid for query processing of data streams in distributed environment, no previous study focused on multiple-query optimization. To address this problem, we propose EsperDist, a distributed query engine for multiple-query optimization over data stream. EsperDist can significant reduce the overhead of network transmission and memory usage by reusing operators in the query plan. Moreover, EsperDist also makes best effort to minimize the query cost so as to avoid resource bottle neck in a single machine. In this demo, we will present the architecture and work-flow of EsperDist using datasets collected from real world applications. We also propose a user-friendly to monitor query results and interact with the system in real time.

Notes

Acknowledgement

This work was supported by NSFC (91646202), National Key R&D Program of China (SQ2018YFB140235), and the 1000-Talent program.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Junye Yang
    • 1
  • Yong Zhang
    • 1
    Email author
  • Jin Wang
    • 2
  • Chunxiao Xing
    • 1
  1. 1.RIIT, TNList, Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.Computer Science DepartmentUniversity of CaliforniaLos AngelesUSA

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