NDLI: COLT: continuous on-line tuning

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COLT: continuous on-line tuning

Content Provider	ACM Digital Library
Author	Abiteboul, Serge Polyzotis, Neoklis Schnaitter, Karl Milo, Tova
Abstract	The physical schema of a database plays a critical role in performance. Self-tuning is a cost-effective and elegant solution to optimize the physical configuration for the characteristics of the query load. Existing techniques operate in an off-line fashion, by choosing a fixed configuration that is tailored to a subset of the query load. The generated configurations therefore ignore any temporal patterns that may exist in the actual load submitted to the system.This demonstration introduces COLT (Continuous On-Line Tuning), a novel self-tuning framework that continuously monitors the incoming queries and adjusts the system configuration in order to maximize query performance. The key idea behind COLT is to gather performance statistics at different levels of detail and to carefully allocate profiling resources to the most promising candidate configurations. Moreover, COLT uses effective heuristics to regulate its own performance, lowering its overhead when the system is well-tuned, and being more aggressive when the workload shifts and it becomes necessary to re-tune the system. We present a specialization of COLT to the important problem of selecting an effective set of relational indices for the current query load. Our demonstration will use an implementation of our proposed framework in the PostgreSQL database system, showing the internal operation of COLT and the adaptive selection of indices as we vary the query load of the server.
Starting Page	793
Ending Page	795
Page Count	3
File Format	PDF
ISBN	1595934340
DOI	10.1145/1142473.1142592
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-06-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Self-tuning Colt Selection Index Demonstration On-line
Content Type	Text
Resource Type	Article

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