Quickstep: A Data Platform Based on the Scaling-In Approach
File(s)
Date
2017-06-19Author
Jignesh M. Patel
Harshad Deshmukh
Jianqiao Zhu
Hakan Memisoglu
Navneet Potti
Saket Saurabh
Marc Spehlmann
Zuyu Zhang
Metadata
Show full item recordAbstract
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.
Subject
Data Analytics
In-Memory Data Processing
Permanent Link
http://digital.library.wisc.edu/1793/76552Type
Technical Report
Citation
TR1847