Efficient processing of window functions in analytical SQL queries
Leis V, Kundhikanjana K, Kemper A, Neumann T (2015)
Publication Type: Conference contribution
Publication year: 2015
Publisher: Association for Computing Machinery
Book Volume: 8
Pages Range: 1058-1069
Conference Proceedings Title: Proceedings of the VLDB Endowment
Abstract
Window functions, also known as analytic OLAP functions, have been part of the SQL standard for more than a decade and are now a widely-used feature. Window functions allow to elegantly express many useful query types including time series analysis, ranking, percentiles, moving averages, and cumulative sums. Formulating such queries in plain SQL-92 is usually both cumbersome and inefficient. Despite being supported by all major database systems, there have been few publications that describe how to implement an efficient relational window operator. This work aims at filling this gap by presenting an efficient and general algorithm for the window operator. Our algorithm is optimized for high-performance mainmemory database systems and has excellent performance on modern multi-core CPUs. We show how to fully parallelize all phases of the operator in order to effectively scale for arbitrary input distributions.
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Germany (DE)How to cite
APA:
Leis, V., Kundhikanjana, K., Kemper, A., & Neumann, T. (2015). Efficient processing of window functions in analytical SQL queries. In Proceedings of the VLDB Endowment (pp. 1058-1069). Association for Computing Machinery.
MLA:
Leis, Viktor, et al. "Efficient processing of window functions in analytical SQL queries." Proceedings of the 3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006 Association for Computing Machinery, 2015. 1058-1069.
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