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dc.contributor.authorAthanassoulis, Manosen_US
dc.contributor.authorBogh, Kenneth S.en_US
dc.contributor.authorIdreos, Stratosen_US
dc.coverage.spatialoriginally Tokyo Japan (online due to COVID)en_US
dc.date.accessioned2021-05-03T19:01:45Z
dc.date.available2021-05-03T19:01:45Z
dc.date.issued2020-09-01
dc.identifierhttps://vldb2020.org/program_flat.html?p=14F-1
dc.identifier.citationManos Athanassoulis. 2020. "Optimal Column Layout for Hybrid Workloads (VLDB 2020 talk)." https://doi.org/10.14778/3358701.3358707
dc.identifier.urihttps://hdl.handle.net/2144/42459
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 2:32 higher throughput for update-intensive workloads and up to 2:14 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.language.isoen_US
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.titleOptimal column layout for hybrid workloads (VLDB 2020 talk)en_US
dc.typePresentationen_US
dc.identifier.doi10.14778/3358701.3358707
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: No embargoen_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
dc.identifier.orcid0000-0003-1837-0010 (Athanassoulis, Manoussos)
dc.description.oaversionPublished version
dc.identifier.mycv597637


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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).