Efficient generation of query plans containing group-by, join, and groupjoin


Eich, Marius ; Fender, Pit ; Moerkotte, Guido


DOI: https://doi.org/10.1007/s00778-017-0476-3
URL: https://link.springer.com/article/10.1007/s00778-0...
Additional URL: https://www.semanticscholar.org/paper/Efficient-ge...
Document Type: Article
Year of publication: 2018
The title of a journal, publication series: The VLDB Journal : The International Journal on Very Large Data Bases
Volume: 27
Issue number: 5
Page range: 617-641
Place of publication: Berlin ; Heidelberg
Publishing house: Springer
ISSN: 1066-8888 , 0949-877X
Publication language: English
Institution: School of Business Informatics and Mathematics > Praktische Informatik III (Moerkotte)
Subject: 004 Computer science, internet
Abstract: It has been a recognized fact for many years that query execution can benefit from pushing grouping operators down in the operator tree and applying them before a join. This so-called eager aggregation reduces the size(s) of the join argument(s), making join evaluation faster. Lately, the idea enjoyed a revival when it was applied to outer joins for the first time and incorporated in a state-of-the-art plan generator. However, the recent approach is highly dependent on the use of heuristics because of the exponential growth of the search space that goes along with eager aggregation. Finding an optimal solution for larger queries calls for effective optimality-preserving pruning mechanisms to reduce the search space size as far as possible. By a more thorough investigation of functional dependencies and keys, we provide a set of new pruning criteria and extend the idea of eager aggregation further by combining it with the introduction of groupjoins. We evaluate the resulting plan generator with respect to runtime and memory consumption.

Dieser Eintrag ist Teil der Universitätsbibliographie.




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Eich, Marius ; Fender, Pit ; Moerkotte, Guido (2018) Efficient generation of query plans containing group-by, join, and groupjoin. The VLDB Journal : The International Journal on Very Large Data Bases Berlin ; Heidelberg 27 5 617-641 [Article]


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