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Raven: Benchmarking Monetary Expense and Query Efficiency of OLAP Engines on the Cloud

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Database Systems for Advanced Applications (DASFAA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13946))

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Abstract

Nowadays, it is prevalent to build OLAP services on cloud platforms. Cloud OLAP adopters are eager to understand and characterize the performance of OLAP engines on the cloud. However, traditional OLAP benchmarks are usually designed for on-premise environments. When evaluating cloud OLAP engines, they have limitations on cloud environment adaption and cloud scenario benchmark execution. To address these issues, this paper proposes Raven, a cloud-oriented OLAP benchmark with flexible system architecture and diversified workloads. Raven supports cloud service deployment and various cloud OLAP engine integration. In addition, to simulate complex cloud query scenarios, we design a group of timeline-based and service-oriented workloads. We implement Raven on the Amazon AWS cloud platform and use it to evaluate typical types of widely-used OLAP engines, including Presto, SparkSQL, Kylin, and Athena. Experimental results show that Raven can effectively benchmark diversified OLAP engines. Besides, Raven can benchmark various configuration settings of an identical OLAP engine. We also explore an OLAP case study on the cloud using Raven.

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Acknowledgments

This work is funded in part by the China National Science Foundation (No. 62072230, U1811461), the Fundamental Research Funds for the Central Universities (No. 020214380089, 020214380098), Jiangsu Province Science and Technology Key Program (No. BE2021729), and the Collaborative Innovation Center of Novel Software Technology and Industrialization.

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Tongyu Wu, Rong Gu, Yi Chen, Ying Zhu & Yihua Huang

  2. Kyligence, Inc., Shanghai, China

    Yang Li, Hongbin Ma, Xiaoxiang Yu & Tengting Xu

Authors
  1. Tongyu Wu
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  2. Rong Gu
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  3. Yang Li
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  4. Hongbin Ma
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  5. Yi Chen
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  6. Ying Zhu
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  7. Xiaoxiang Yu
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  8. Tengting Xu
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  9. Yihua Huang
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Corresponding authors

Correspondence to Rong Gu or Yihua Huang .

Editor information

Editors and Affiliations

  1. Tianjin University, Tianjin, China

    Xin Wang

  2. University of Torino, Turin, Italy

    Maria Luisa Sapino

  3. POSTECH, Pohang, Korea (Republic of)

    Wook-Shin Han

  4. University of California Santa Barbara, Santa Barbara, CA, USA

    Amr El Abbadi

  5. University of Auckland, Auckland, New Zealand

    Gill Dobbie

  6. Tianjin University, Tianjin, China

    Zhiyong Feng

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Yingxiao Shao

  8. The University of Queensland, Brisbane, QLD, Australia

    Hongzhi Yin

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Wu, T. et al. (2023). Raven: Benchmarking Monetary Expense and Query Efficiency of OLAP Engines on the Cloud. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13946. Springer, Cham. https://doi.org/10.1007/978-3-031-30678-5_45

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  • DOI: https://doi.org/10.1007/978-3-031-30678-5_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30677-8

  • Online ISBN: 978-3-031-30678-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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