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Automatic Indexing for MongoDB

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New Trends in Database and Information Systems (ADBIS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1850))

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Abstract

We present a new method for automated index suggestion for MongoDB, based solely on the queries (called aggregation pipelines), without requiring data or usage information. The solution handles complex aggregations and is suitable for both cloud and standalone databases. We validated the algorithm on TPC-H and showed that all suggested indexes were used. We report on the performance and provide hints for further development of an automated method of index selection. Our algorithm is, to the best of our knowledge, the first query-based solution for automated indexing in MongoDB.

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Notes

  1. 1.

    https://github.com/pier4all/mongodb-auto-index.

  2. 2.

    https://www.npmjs.com/package/mongodb-auto-index.

  3. 3.

    https://github.com/antw0n/universal-database-optimizer-evaluation.

  4. 4.

    https://github.com/pier4all/auto-index.

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Acknowledgements

We acknowledge funding from www.innosuisse.ch, Grant No. 44824.1 IP-ICT and support from project 030TUKE-4/2023 from the Cultural and Education Grant Agency of the Slovak Ministry of Education. We thank Saverio Damiani and Anton Lorvi for their work and Prof. Christian Dolfus (HSLU) for providing the server. We have no conflicts of interest to disclose.

Author information

Authors and Affiliations

  1. School of Business, University of Applied Sciences and Arts Northwestern Switzerland FHNW, Riggenbachstrasse 16, 4600, Olten, Switzerland

    Lucia de Espona Pernas, Anton Vichalkovski & Ela Pustulka

  2. Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 1/9, 04200, Košice, Slovak Republic

    William Steingartner

Authors
  1. Lucia de Espona Pernas
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  2. Anton Vichalkovski
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  3. William Steingartner
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  4. Ela Pustulka
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Corresponding author

Correspondence to Ela Pustulka .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de Catalunya, Barcelona, Spain

    Alberto Abelló

  2. University of Ioannina, Ioannina, Greece

    Panos Vassiliadis

  3. Universitat Politècnica de Catalunya, Barcelona, Spain

    Oscar Romero

  4. Poznań University of Technology, Poznan, Poland

    Robert Wrembel

  5. University of Paris-Saclay, Gif-sur-Yvette, France

    Francesca Bugiotti

  6. Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Johann Gamper

  7. CNRS, Villeurbanne Cedex, France

    Genoveva Vargas Solar

  8. University of Calabria, Rende, Italy

    Ester Zumpano

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de Espona Pernas, L., Vichalkovski, A., Steingartner, W., Pustulka, E. (2023). Automatic Indexing for MongoDB. In: Abelló, A., et al. New Trends in Database and Information Systems. ADBIS 2023. Communications in Computer and Information Science, vol 1850. Springer, Cham. https://doi.org/10.1007/978-3-031-42941-5_46

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  • DOI: https://doi.org/10.1007/978-3-031-42941-5_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42940-8

  • Online ISBN: 978-3-031-42941-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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