Shinobi : insert-aware partitioning and indexing techniques for skewed database workloads

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.

Department dc:contributor.department

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

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2010

Author dc:creator

• Wu, Eugene, Ph. D. Massachusetts Institute of Technology

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/1721.1/60824

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/60824