Approximation Schemes for Many-Objective Query Optimization
Abstract
The goal of multi-objective query optimization (MOQO) is to find query plans that realize a good compromise between conflicting objectives such as minimizing execution time and minimizing monetary fees in a Cloud scenario. A previously proposed exhaustive MOQO algorithm needs hours to optimize even simple TPC-H queries. This is why we propose several approximation schemes for MOQO that generate guaranteed near-optimal plans in seconds where exhaustive optimization takes hours. We integrated all MOQO algorithms into the Postgres optimizer and present experimental results for TPC-H queries; we extended the Postgres cost model and optimize for up to nine conflicting objectives in our experiments. The proposed algorithms are based on a formal analysis of typical cost functions that occur in the context of MOQO. We identify properties that hold for a broad range of objectives and can be exploited for the design of future MOQO algorithms.
Cite
@article{arxiv.1404.0046,
title = {Approximation Schemes for Many-Objective Query Optimization},
author = {Immanuel Trummer and Christoph Koch},
journal= {arXiv preprint arXiv:1404.0046},
year = {2014}
}