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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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.
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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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