Proceedings Article10.1109/ICDE.2009.68
Power Hints for Query Optimization
Nicolas Bruno,Surajit Chaudhuri,Ravi Ramamurthy +2 more
- 29 Mar 2009
- pp 469-480
TL;DR: A hinting framework that enables the specification of rich constraints to influence the optimizer to pick better plans and can be implemented efficiently with moderate changes to current optimizers is introduced.
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Abstract: Commercial database systems expose query hints to address situations in which the optimizer chooses a poor plan for a given query. However, current query hints are not flexible enough to deal with a variety of non-trivial scenarios. In this paper, we introduce a hinting framework that enables the specification of rich constraints to influence the optimizer to pick better plans. We show that while our framework unifies previous approaches, it goes considerably beyond existing hinting mechanisms, and can be implemented efficiently with moderate changes to current optimizers.
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Citations
Detecting optimization bugs in database engines via non-optimizing reference engine construction
Manuel Rigger,Zhendong Su +1 more
- 08 Nov 2020
TL;DR: In this article, the authors propose Non-Optimizing Reference Engine Construction (NoREC), a fully-automatic approach to detect optimization bugs in DBMS, which aims to evaluate a query by an optimizing and a nonoptimizing version of a DBMS and then detect differences in their returned result set, which would indicate a bug in the DBMS.
Query optimizers: time to rethink the contract?
Surajit Chaudhuri
- 29 Jun 2009
TL;DR: It is argued that it is worth rethinking this prevalent model of the optimizer to benefit from leveraging rich usage data and from application input to further advance query optimization technology.
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Exploring Query Optimization Techniques in Relational Databases
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TL;DR: This paper reviews different query optimization techniques and approaches discussed in the contemporary literature for both centralized and distributed databases and highlights merits of these techniques by critically analyzing them.
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Processing and optimizing main memory spatial-keyword queries
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TL;DR: This work employs a cost-based optimizer to process spatial-keyword queries using a spatial index and a keyword index, and introduces five optimization techniques that efficiently reduce the search space and produce a query plan with low cost.
Xplus: a SQL-tuning-aware query optimizer
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TL;DR: Xplus is designed, implements, and evaluates Xplus, which, to this knowledge, is the first query optimizer to provide this feature, and shows the effectiveness of Xplus on real-life tuning scenarios created using TPC-H queries on a PostgreSQL database.
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