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https://doi.org/10.1109/icdew....
Article . 2012 . Peer-reviewed
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Application of Micro-specialization to Query Evaluation Operators

Authors: Rui Zhang 0035; Richard T. Snodgrass; Saumya Debray;

Application of Micro-specialization to Query Evaluation Operators

Abstract

Relational database management systems support a wide variety of data types and operations. Such generality involves much branch condition checking, which introduces inefficiency within the query evaluation loop. We previously introduced micro-specialization, which improves performance by eliminating unnecessary branching statements and the actual code branches by exploiting invariants present during the query evaluation loop. In this paper, we show how to more aggressively apply micro-specialization to each individual operator within a query plan. Rather than interpreting the query plan, the DBMS dynamically rewrites its object code to produce executable code tailored to the particular query. We explore opportunities for applying micro-specialization to DBMSes, focusing on query evaluation. We show through an examination of program execution profiles that even with a simple query in which just a few operators are micro-specialized, significant performance improvement can be achieved.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Top 10%
Average