Quickstep: A Data Platform Based on the Scaling-In Approach

dc.contributor.authorJignesh M. Patel
dc.contributor.authorHarshad Deshmukh
dc.contributor.authorJianqiao Zhu
dc.contributor.authorHakan Memisoglu
dc.contributor.authorNavneet Potti
dc.contributor.authorSaket Saurabh
dc.contributor.authorMarc Spehlmann
dc.contributor.authorZuyu Zhang
dc.date.accessioned2017-06-19T20:36:31Z
dc.date.available2017-06-19T20:36:31Z
dc.date.issued2017-06-19T20:36:31Z
dc.description.abstractModern servers pack enough storage and computing power that just a decade ago was spread across a modest- sized cluster. This paper presents a prototype system, called Quickstep, to exploit the large amount of paral- lelism that is packed inside such modern servers. Quick- step builds on a vast body of previous work on meth- ods for organizing data, optimizing, scheduling and ex- ecuting queries, and brings them together in a single sys- tem. Quickstep also includes new query processing meth- ods that go beyond previous approaches. To keep the project focused, the project’s initial target is read-mostly in-memory data warehousing workloads in single-node settings. In this paper, we describe the design and imple- mentation of Quickstep for this target application space. In this paper, we also present experimental results com- paring the performance of Quickstep to a number of other systems. These experiments show that Quickstep is of- ten faster than many other contemporary systems, and in some cases faster by an order-of-magnitude. Quickstep is an Apache (incubating) project and lives at: https:// github.com/apache/incubator-quickstep.en
dc.identifier.citationTR1847eng
dc.identifier.urihttp://digital.library.wisc.edu/1793/76552
dc.language.isoen_USen
dc.relation.ispartofseriestech report;TR1847
dc.subjectData Analyticsen
dc.subjectIn-Memory Data Processingen
dc.titleQuickstep: A Data Platform Based on the Scaling-In Approachen
dc.typeTechnical Reporten

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