Robust data partitioning for ad-hoc query processing
Name
965799432-MIT.pdf
Description
Full printable version
Size
588.36 KB
Format
Adobe PDF
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Author(s)
Nguyen, Qui T
Advisor(s)
Samuel Madden.
Date Issued
2015
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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