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dc.contributor.authorAthanassoulis, Manoussosen_US
dc.contributor.authorBøgh, Kennethen_US
dc.contributor.authorIdreos, Stratosen_US
dc.date.accessioned2020-05-06T15:39:31Z
dc.date.available2020-05-06T15:39:31Z
dc.date.issued2019-09-01
dc.identifier.citationManoussos Athanassoulis, Kenneth Bøgh, Stratos Idreos. 2019. "Optimal Column Layout for Hybrid Workloads." Proceedings of the VLDB Endowment, Volume 12, Issue 13, pp. 2393 - 2407. https://doi.org/10.14778/3358701.3358707
dc.identifier.issn2150-8097
dc.identifier.urihttps://hdl.handle.net/2144/40617
dc.description.abstractData-intensive analytical applications need to support both efficient reads and writes. However, what is usually a good data layout for an update-heavy workload, is not well-suited for a read-mostly one and vice versa. Modern analytical data systems rely on columnar layouts and employ delta stores to inject new data and updates. We show that for hybrid workloads we can achieve close to one order of magnitude better performance by tailoring the column layout design to the data and query workload. Our approach navigates the possible design space of the physical layout: it organizes each column’s data by determining the number of partitions, their corresponding sizes and ranges, and the amount of buffer space and how it is allocated. We frame these design decisions as an optimization problem that, given workload knowledge and performance requirements, provides an optimal physical layout for the workload at hand. To evaluate this work, we build an in-memory storage engine, Casper, and we show that it outperforms state-of-the-art data layouts of analytical systems for hybrid workloads. Casper delivers up to 2.32x higher throughput for update-intensive workloads and up to 2.14x higher throughput for hybrid workloads. We further show how to make data layout decisions robust to workload variation by carefully selecting the input of the optimization.en_US
dc.description.urihttp://www.vldb.org/pvldb/vol12/p2393-athanassoulis.pdf
dc.format.extentp. 2393 - 2407en_US
dc.language.isoen_US
dc.publisherVLDB Endowmenten_US
dc.relation.ispartofProceedings of the VLDB Endowment
dc.rightsThis work is licensed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For any use beyond those covered by this license, obtain permission by emailing info@vldb.org. Copyright is held by the owner/author(s).en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectInformation systemsen_US
dc.subjectData management systemsen_US
dc.titleOptimal column layout for hybrid workloadsen_US
dc.typeArticleen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.14778/3358701.3358707
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Computer Scienceen_US
pubs.publication-statusPublisheden_US
dc.identifier.orcid0000-0003-1837-0010 (Athanassoulis, Manoussos)
dc.description.oaversionPublished version
dc.identifier.mycv534942


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This work is licensed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International License. To view a copy
of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For any use beyond those covered by this license, obtain permission by emailing info@vldb.org. Copyright is held by the owner/author(s).
Except where otherwise noted, this item's license is described as This work is licensed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For any use beyond those covered by this license, obtain permission by emailing info@vldb.org. Copyright is held by the owner/author(s).