Robust data partitioning for ad-hoc query processing

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.

Bibliographic Details
Main Author: Nguyen, Qui T
Other Authors: Samuel Madden.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2016
Subjects:
Online Access:http://hdl.handle.net/1721.1/106004
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author Nguyen, Qui T
author2 Samuel Madden.
author_facet Samuel Madden.
Nguyen, Qui T
author_sort Nguyen, Qui T
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description Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
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spelling mit-1721.1/1060042019-04-10T09:35:50Z Robust data partitioning for ad-hoc query processing Nguyen, Qui T Samuel Madden. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 59-62). Data partitioning can significantly improve query performance in distributed database systems. Most proposed data partitioning techniques choose the partitioning based on a particular expected query workload or use a simple upfront scheme, such as uniform range partitioning or hash partitioning on a key. However, these techniques do not adequately address the case where the query workload is ad-hoc and unpredictable, as in many analytic applications. The HYPER-PARTITIONING system aims to ll that gap, by using a novel space-partitioning tree on the space of possible attribute values to dene partitions incorporating all attributes of a dataset. The system creates a robust upfront partitioning tree, designed to benet all possible queries, and then adapts it over time in response to the actual workload. This thesis evaluates the robustness of the upfront hyper-partitioning algorithm, describes the implementation of the overall HYPER-PARTITIONING system, and shows how hyper-partitioning improves the performance of both selection and join queries. by Qui T. Nguyen. M. Eng. 2016-12-22T15:18:13Z 2016-12-22T15:18:13Z 2015 2015 Thesis http://hdl.handle.net/1721.1/106004 965799432 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 62 pages application/pdf Massachusetts Institute of Technology
spellingShingle Electrical Engineering and Computer Science.
Nguyen, Qui T
Robust data partitioning for ad-hoc query processing
title Robust data partitioning for ad-hoc query processing
title_full Robust data partitioning for ad-hoc query processing
title_fullStr Robust data partitioning for ad-hoc query processing
title_full_unstemmed Robust data partitioning for ad-hoc query processing
title_short Robust data partitioning for ad-hoc query processing
title_sort robust data partitioning for ad hoc query processing
topic Electrical Engineering and Computer Science.
url http://hdl.handle.net/1721.1/106004
work_keys_str_mv AT nguyenquit robustdatapartitioningforadhocqueryprocessing