Robust data partitioning for ad-hoc query processing

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2015

Author dc:creator

• Nguyen, Qui T

Advisor dc:contributor.advisor

• Samuel Madden.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/106004

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/106004