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A Divergent Index Advisor Using Deep Reinforcement Learning

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Database and Expert Systems Applications (DEXA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13426))

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Abstract

A divergent design index is a tuning method that employs replication to specialize the index configuration of each replica in a replicated database for a subset of a workload to minimize the total processing cost of the workload. Studies show that this tuning method improves the workload performance in comparison with the case that all replicas have the same index configuration. Current divergent design algorithms do not have any mechanism to learn about the effectiveness of the recommended index sets. Moreover, they solely rely on the query optimizer’s cost estimation, which can be inaccurate.

To tackle these problems, we introduce a new divergent index advisor, DINA, that learns the goodness of the workload partitioning among replicas and the efficiency of their index configurations by employing a Deep Reinforcement Learning (DRL) algorithm. The DRL agent explores various possible workload partitions and learns the benefit of their index configurations via performance observation. We conduct experiments using the TPC-H and TPC-DS database benchmarks to evaluate the performance of DINA. The experiments show that DINA yields better query execution time than the existing algorithms.

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Authors and Affiliations

  1. University of Oklahoma, Norman, OK, USA

    Zahra Sadri & Le Gruenwald

Authors
  1. Zahra Sadri
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  2. Le Gruenwald
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Correspondence to Zahra Sadri .

Editor information

Editors and Affiliations

  1. University of Vienna, Vienna, Austria

    Christine Strauss

  2. University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

  3. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Sadri, Z., Gruenwald, L. (2022). A Divergent Index Advisor Using Deep Reinforcement Learning. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13426. Springer, Cham. https://doi.org/10.1007/978-3-031-12423-5_11

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  • DOI: https://doi.org/10.1007/978-3-031-12423-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12422-8

  • Online ISBN: 978-3-031-12423-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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