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How Good is Query Optimizer in Spark?

  • Zujie RenEmail author
  • Na Yun
  • Youhuizi Li
  • Jian Wan
  • Yuan Wang
  • Lihua Yu
  • Xinxin Fan
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 268)

Abstract

In the big data community, Spark plays an important role and is used to process interactive queries. Spark employs a query optimizer, called Catalyst, to interpret SQL queries to optimized query execution plans. Catalyst contains a number of optimization rules and supports cost-based optimization. Although query optimization techniques have been well studied in the field of relational database systems, the effectiveness of Catalyst in Spark is still unclear. In this paper, we investigated the effectiveness of rule-based and cost-based optimization in Catalyst, meanwhile, we obtained a set of comparative experiments by varying the data volume and the number of nodes. It is found that even when applied query optimizations, the execution time of most TPC-H queries were slightly reduced. Some interesting observations were made on Catalyst, which can enable the community to have a better understanding and improvement of the query optimizer in Spark.

Keywords

Spark SQL Catalyst Query optimization 

Notes

Acknowledgement

This work is supported by Key Research and Development Program of Zhejiang Province (No. 2018C01098), and the Natural Science Foundation of Zhejiang Province (NO. LY18F020014).

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Zujie Ren
    • 1
    Email author
  • Na Yun
    • 1
  • Youhuizi Li
    • 1
  • Jian Wan
    • 2
  • Yuan Wang
    • 3
  • Lihua Yu
    • 3
  • Xinxin Fan
    • 3
  1. 1.School of Computer ScienceHangzhou Dianzi UniversityHangzhouChina
  2. 2.Department of Software EngineeringZhejiang University of Science and TechnologyHangzhouChina
  3. 3.Key Enterprise Research Institute of NetEase Big Data of Zhejiang ProvinceNetease Hangzhou, Network Co. Ltd.HangzhouChina

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