Shinobi : insert-aware partitioning and indexing techniques for skewed database workloads
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2011
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| Online Access: | http://hdl.handle.net/1721.1/60824 |
| _version_ | 1826214554300841984 |
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| author | Wu, Eugene, Ph. D. Massachusetts Institute of Technology |
| author2 | Samuel Madden. |
| author_facet | Samuel Madden. Wu, Eugene, Ph. D. Massachusetts Institute of Technology |
| author_sort | Wu, Eugene, Ph. D. Massachusetts Institute of Technology |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010. |
| first_indexed | 2024-09-23T16:07:37Z |
| format | Thesis |
| id | mit-1721.1/60824 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T16:07:37Z |
| publishDate | 2011 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/608242019-04-12T20:13:55Z Shinobi : insert-aware partitioning and indexing techniques for skewed database workloads Insert-aware partitioning and indexing techniques for skewed database workloads Wu, Eugene, Ph. D. Massachusetts Institute of Technology Samuel Madden. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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). 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. by Eugene Wu. S.M. 2011-01-26T14:30:36Z 2011-01-26T14:30:36Z 2010 2010 Thesis http://hdl.handle.net/1721.1/60824 697282348 eng 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. http://dspace.mit.edu/handle/1721.1/7582 68 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Wu, Eugene, Ph. D. Massachusetts Institute of Technology Shinobi : insert-aware partitioning and indexing techniques for skewed database workloads |
| title | Shinobi : insert-aware partitioning and indexing techniques for skewed database workloads |
| title_full | Shinobi : insert-aware partitioning and indexing techniques for skewed database workloads |
| title_fullStr | Shinobi : insert-aware partitioning and indexing techniques for skewed database workloads |
| title_full_unstemmed | Shinobi : insert-aware partitioning and indexing techniques for skewed database workloads |
| title_short | Shinobi : insert-aware partitioning and indexing techniques for skewed database workloads |
| title_sort | shinobi insert aware partitioning and indexing techniques for skewed database workloads |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/60824 |
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