Article

Power Hints for Query Optimization

Authors:

Nicolas Bruno,

Surajit Chaudhuri,

Ravi RamamurthyAuthors Info & Claims

ICDE '09: Proceedings of the 2009 IEEE International Conference on Data Engineering

Pages 469 - 480

https://doi.org/10.1109/ICDE.2009.68

Published: 29 March 2009 Publication History

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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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Pasupuleti KDas DValluri SZait MAl Hasan MXiong L(2022)Observability of SQL Hints in OracleProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557124(3441-3450)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557124
Li TShi HLu Xde Supinski BHall MGamblin T(2021)HatRPCProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3458817.3476191(1-14)Online publication date: 14-Nov-2021
https://dl.acm.org/doi/10.1145/3458817.3476191
Song WYang YEo JSeo JKim JLee SLee GUm TCho HChun B(2020)Apache Nemo: A Framework for Optimizing Distributed Data ProcessingACM Transactions on Computer Systems10.1145/346814438:3-4(1-31)Online publication date: 30-Nov-2020
https://dl.acm.org/doi/10.1145/3468144
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Power Hints for Query Optimization
1. Information systems
  1. Data management systems
    1. Database management system engines
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory

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ICDE '09: Proceedings of the 2009 IEEE International Conference on Data Engineering

March 2009

1772 pages

ISBN:9780769535456

Publisher

IEEE Computer Society

United States

Publication History

Published: 29 March 2009

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Pasupuleti KDas DValluri SZait MAl Hasan MXiong L(2022)Observability of SQL Hints in OracleProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557124(3441-3450)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557124
Li TShi HLu Xde Supinski BHall MGamblin T(2021)HatRPCProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3458817.3476191(1-14)Online publication date: 14-Nov-2021
https://dl.acm.org/doi/10.1145/3458817.3476191
Song WYang YEo JSeo JKim JLee SLee GUm TCho HChun B(2020)Apache Nemo: A Framework for Optimizing Distributed Data ProcessingACM Transactions on Computer Systems10.1145/346814438:3-4(1-31)Online publication date: 30-Nov-2020
https://dl.acm.org/doi/10.1145/3468144
Rigger MSu ZDevanbu PCohen MZimmermann T(2020)Detecting optimization bugs in database engines via non-optimizing reference engine constructionProceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3368089.3409710(1140-1152)Online publication date: 8-Nov-2020
https://dl.acm.org/doi/10.1145/3368089.3409710
Yang YEo JKim GKim JLee SSeo JSong WChun BDan TDahlia M(2019)Apache nemoProceedings of the 2019 USENIX Conference on Usenix Annual Technical Conference10.5555/3358807.3358824(177-190)Online publication date: 10-Jul-2019
https://dl.acm.org/doi/10.5555/3358807.3358824
Damasio GCorvinelli VGodfrey PMierzejewski PMihaylov ASzlichta JZuzarte C(2019)Guided automated learning for query workload re-optimizationProceedings of the VLDB Endowment10.14778/3352063.335212012:12(2010-2021)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.14778/3352063.3352120
Ding BDas SWu WChaudhuri SNarasayya V(2018)Plan stitchProceedings of the VLDB Endowment10.14778/3231751.323176111:10(1123-1136)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.14778/3231751.3231761
Chandramouli BFernandez RGoldstein JEldawy AQuamar A(2016)QuillProceedings of the VLDB Endowment10.14778/3007328.30073299:14(1623-1634)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.14778/3007328.3007329
Lee TPark JLee SHwang SElnikety SHe Y(2015)Processing and optimizing main memory spatial-keyword queriesProceedings of the VLDB Endowment10.14778/2850583.28505889:3(132-143)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.14778/2850583.2850588
Herodotou HBabu S(2010)XplusProceedings of the VLDB Endowment10.14778/1920841.19209843:1-2(1149-1160)Online publication date: 1-Sep-2010
https://dl.acm.org/doi/10.14778/1920841.1920984
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The use of Hints in SQL-Nested query optimization

Interactive plan hints for query optimization

Query Optimization for Ontology-Mediated Query Answering

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