2026-09-19T20:42:52Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/626622022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Samuel Madden.
Muñiz Navarro, José Alberto
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2011-05-09T15:16:16Z
2011-05-09T15:16:16Z
2010
2010
http://hdl.handle.net/1721.1/62662
714212509
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 71-72).
This thesis presents a data structure which performs well for in-memory indexing of keys that are unevenly distributed into clusters with a high density of keys. This pattern is prevalent, for example, in systems that use tables with keys where one field is auto-incremented. These types of tables are widely used. The proposed data structure consists of a B+ Tree with intervals as keys, and arrays as values. Each array holds a cluster of values, while the clusters themselves are managed by the B+ Tree for space and cache efficiency. Using the H-Tree as an in-memory indexing structure for an implementation of the TPC-C benchmark sped up the transaction processing time by up to 50% compared to an implementation based on B+Trees, and showed even more dramatic performance gains in the presence of few and large clusters of data.
by José Alberto Muñiz Navarro.
M.Eng.
72 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
A hybrid data structure for dense keys in in-memory database systems
Thesis
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<Abstract>This thesis presents a data structure which performs well for in-memory indexing of keys that are unevenly distributed into clusters with a high density of keys. This pattern is prevalent, for example, in systems that use tables with keys where one field is auto-incremented. These types of tables are widely used. The proposed data structure consists of a B+ Tree with intervals as keys, and arrays as values. Each array holds a cluster of values, while the clusters themselves are managed by the B+ Tree for space and cache efficiency. Using the H-Tree as an in-memory indexing structure for an implementation of the TPC-C benchmark sped up the transaction processing time by up to 50% compared to an implementation based on B+Trees, and showed even more dramatic performance gains in the presence of few and large clusters of data.</Abstract>
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