Institut für Wirtschaftsinformatik

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Automatic indexing for MongoDB
(Springer, 2023) Espona, Lucía; Vichalkovski, Anton; Steingartner, William; Pustulka, Elzbieta; Abelló, Alberto; Vassiliadis, Panos; Romero, Oscar; Wrembel, Robert; Bugiotti, Francesca; Gamper, Johann; Vargas Solar, Genoveva; Zumpano, Ester [in: New trends in database and information systems. ADBIS 2023 short Papers, doctoral consortium and workshops: AIDMA, DOING, K-Gals, MADEISD, PeRS, Barcelona, Spain, September 4–7, 2023, roceedings]
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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