Quickstep: A Data Platform Based on the Scaling-In Approach
| dc.contributor.author | Jignesh M. Patel | |
| dc.contributor.author | Harshad Deshmukh | |
| dc.contributor.author | Jianqiao Zhu | |
| dc.contributor.author | Hakan Memisoglu | |
| dc.contributor.author | Navneet Potti | |
| dc.contributor.author | Saket Saurabh | |
| dc.contributor.author | Marc Spehlmann | |
| dc.contributor.author | Zuyu Zhang | |
| dc.date.accessioned | 2017-06-19T20:36:31Z | |
| dc.date.available | 2017-06-19T20:36:31Z | |
| dc.date.issued | 2017-06-19T20:36:31Z | |
| dc.description.abstract | Modern 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.citation | TR1847 | eng |
| dc.identifier.uri | http://digital.library.wisc.edu/1793/76552 | |
| dc.language.iso | en_US | en |
| dc.relation.ispartofseries | tech report;TR1847 | |
| dc.subject | Data Analytics | en |
| dc.subject | In-Memory Data Processing | en |
| dc.title | Quickstep: A Data Platform Based on the Scaling-In Approach | en |
| dc.type | Technical Report | en |