Shinobi : insert-aware partitioning and indexing techniques for skewed database workloads
Name
697282348-MIT.pdf
Description
Full printable version
Size
7.36 MB
Format
Adobe PDF
Checksum (MD5)
5401fa06554207a8bdf6af17c9638a62
Author(s)
Wu, Eugene, Ph. D. Massachusetts Institute of Technology
Advisor(s)
Samuel Madden.
Alternative Title
Insert-aware partitioning and indexing techniques for skewed database workloads
Date Issued
2010
Publisher
Massachusetts Institute of Technology
Abstract
Many data-intensive websites are characterized by a dataset that grows much faster than the rate that users access the data and possibly high insertion rates. In such systems, the growing size of the dataset leads to a larger overhead for maintaining and accessing indexes even while the query workload becomes increasingly skewed. Additionally, the database index update costs can be a non-trivial proportion of the overall system cost. Shinobi introduces a cost model that takes index update costs account, and proposes database design algorithms that optimally partition tables and drop indexes from partitions that are not queried often, and that maintain these partitions as workloads change. We show a 60x performance improvement over traditionally indexed tables using a real-world query workload derived from a traffic monitoring application and over 8x improvement for a Wikipedia workload.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 65-68).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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