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Domain Query Optimization: Adapting the General-Purpose Database System Hyper for Tableau Workloads

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2019

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Gesellschaft für Informatik, Bonn

Zusammenfassung

The Hyper database system was started as an academic project at Technical University Munich. In 2016, the commercial spin-off of the academic Hyper database system was acquired by Tableau, a leader in the analytics and business intelligence (BI) platforms market. As a human-in-the-loop BI platform, Tableau products machine-generate query workloads with characteristics that differ from human-written queries and queries represented in industry-standard database system benchmarks. In this work, we contribute optimizations we developed for one important class of queries typically generated by Tableau products: retrieving (aggregates of) the domain of a column. We devise methods for leveraging the compression of the database column in order to efficiently retrieve the duplicate-free value set, i.e., the domain. Our extensive performance evaluation of a synthetic benchmark and over 60 thousand real-world workbooks from Tableau Public shows that our optimization enables query latencies for domain queries that allow self-service ad-hoc data exploration.

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Vogelsgesang, Adrian; Muehlbauer, Tobias; Leis, Viktor; Neumann, Thomas; Kemper, Alfons (2019): Domain Query Optimization: Adapting the General-Purpose Database System Hyper for Tableau Workloads. BTW 2019. DOI: 10.18420/btw2019-19. Gesellschaft für Informatik, Bonn. PISSN: 1617-5468. ISBN: 978-3-88579-683-1. pp. 313-333. Industriebeiträge. Rostock. 4.-8. März 2019

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