The VLDB Journal

, Volume 26, Issue 1, pp 107–124

Multi-objective parametric query optimization

Special Issue Paper

DOI: 10.1007/s00778-016-0439-0

Cite this article as:
Trummer, I. & Koch, C. The VLDB Journal (2017) 26: 107. doi:10.1007/s00778-016-0439-0


Classical query optimization compares query plans according to one cost metric and associates each plan with a constant cost value. In this paper, we introduce the multi-objective parametric query optimization (MPQO) problem where query plans are compared according to multiple cost metrics and the cost of a given plan according to a given metric is modeled as a function that depends on multiple parameters. The cost metrics may, for instance, include execution time or monetary fees; a parameter may represent the selectivity of a query predicate that is unspecified at optimization time. MPQO generalizes parametric query optimization (which allows multiple parameters but only one cost metric) and multi-objective query optimization (which allows multiple cost metrics but no parameters). We formally analyze the novel MPQO problem and show why existing algorithms are inapplicable. We present a generic algorithm for MPQO and a specialized version for MPQO with piecewise-linear plan cost functions. We prove that both algorithms find all relevant query plans and experimentally evaluate the performance of our second algorithm in multiple scenarios.


Query optimization Multi-objective optimization Parametric query optimization 

Funding information

Funder NameGrant NumberFunding Note
Google (US)
    European Research Council
    • 279804

    Copyright information

    © Springer-Verlag Berlin Heidelberg 2016

    Authors and Affiliations

    1. 1.Ecole Polytechnique Fèdèrale de LausanneLausanneSwitzerland
    2. 2.Cornell UniversityIthacaUSA

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