QO-Insight: Inspecting Steered Query Optimizers

被引：0

作者：

Anneser, Christoph ^{[1
]}

Petruccelli, Mario ^{[1
]}

Tatbul, Nesime ^{[2
,3
]}

Cohen, David ^{[4
]}

Xu, Zhenggang ^{[5
]}

Pandian, Prithviraj ^{[5
]}

Laptev, Nikolay ^{[5
]}

Marcus, Ryan ^{[6
]}

Kemper, Alfons ^{[1
]}

机构：

[1] TUM, Munich, Germany

[2] Intel Labs, Hillsboro, OR USA

[3] MIT, Cambridge, MA 02139 USA

[4] Intel, Santa Clara, CA USA

[5] Meta, Menlo Pk, CA USA

[6] Univ Penn, Philadelphia, PA 19104 USA

来源：

PROCEEDINGS OF THE VLDB ENDOWMENT | 2023年 / 16卷 / 12期

关键词：

OPTIMIZATION;

D O I：

10.14778/3611540.3611586

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Steered query optimizers address the planning mistakes of traditional query optimizers by providing them with hints on a per-query basis, thereby guiding them in the right direction. This paper introduces QO-Insight, a visual tool designed for exploring query execution traces of such steered query optimizers. Although steered query optimizers are typically perceived as black boxes, QO-Insight empowers database administrators and experts to gain qualitative insights and enhance their performance through visual inspection and analysis.

引用

页码：3922 / 3925

页数：4

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