Journal of Computer Science and Technology ›› 2020, Vol. 35 ›› Issue (1): 194-208.doi: 10.1007/s11390-020-9536-z

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CirroData: Yet Another SQL-on-Hadoop Data Analytics Engine with High Performance

Zheng-Hao Jin1, Haiyang Shi2, Ying-Xin Hu1, Li Zha3,4, Member, CCF, Xiaoyi Lu2, Member, ACM, IEEE   

  1. 1 Business-Intelligence of Oriental Nations Corporation Ltd., Beijing 100102, China;
    2 Department of Computer Science and Engineering, The Ohio State University, Ohio 43210, U.S.A;
    3 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
    4 University of Chinese Academy of Sciences, Beijing 101408, China
  • Received:2019-07-15 Revised:2019-10-14 Online:2020-01-05 Published:2020-01-14
  • About author:Zheng-Hao Jin is the chief architect of Business-Intelligence of Oriental Nations Corporation, Beijing. He received his Master's degree in computer software and theory from Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, in 2001. His current research interests include distributed DBMS, Big Data, cloud computing, and data science.
  • Supported by:
    This research is supported in part by the Strategic Priority Research Program of the Chinese Academy of Sciences of China under Grant No. XDA19020400.

This paper presents CirroData, a high-performance SQL-on-Hadoop system designed for Big Data analytics workloads. As a home-grown enterprise-level online analytical processing (OLAP) system with more than seven-year research and development (R&D) experiences, we share our design details to the community about how to achieve high performance in CirroData. Multiple optimization techniques have been discussed in the paper. The effectiveness and the efficiency of all these techniques have been proved by our customers' daily usage. Benchmark-level studies, as well as several real application case studies of CirroData, have been presented in this paper. Our evaluations show that CirroData can outperform various types of counterpart database systems in the community, such as "Spark+Hive", "Spark+HBase", Impala, DB-X/Y, Greenplum, HAWQ, and others. CirroData can achieve up to 4.99x speedup compared with Greenplum, HAWQ, and Spark in the standard TPC-H queries. Application-level evaluations demonstrate that CirroData outperforms "Spark+Hive" and "Spark+HBase" by up to 8.4x and 38.8x, respectively. In the meantime, CirroData achieves the performance speedups for some application workloads by up to 20x, 100x, 182.5x, 92.6x, and 55.5x as compared with Greenplum, DB-X, Impala, DB-Y, and HAWQ, respectively.

Key words: CirroData, high performance, SQL-on-Hadoop, online analytical processing (OLAP), Big Data

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