Database partitioning strategies for social network data
Name
826515301-MIT.pdf
Description
Full printable version
Size
5.67 MB
Format
Adobe PDF
Checksum (MD5)
2ee9aa43f4b3ec37f72f623c1a0fd3e8
Author(s)
Moll Thomae, Oscar Ricardo
Advisor(s)
Stu Hood and Samuel R. Madden.
Date Issued
2012
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, I designed, prototyped and benchmarked two different data partitioning strategies for social network type workloads. The first strategy takes advantage of the heavy-tailed degree distributions of social networks to optimize the latency of vertex neighborhood queries. The second strategy takes advantage of the high temporal locality of workloads to improve latencies for vertex neighborhood intersection queries. Both techniques aim to shorten the tail of the latency distribution, while avoiding decreased write performance or reduced system throughput when compared to the default hash partitioning approach. The strategies presented were evaluated using synthetic workloads of my own design as well as real workloads provided by Twitter, and show promising improvements in latency at some cost in system complexity.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 64-66).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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reproduction or distribution in any format is prohibited without written
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