GSLPI: a Cost-based Query Progress Indicator

被引:15
|
作者
Li, Jiexing [1 ]
Nehme, Rimma V. [2 ]
Naughton, Jeffrey [1 ]
机构
[1] Univ Wisconsin, Madison, WI 53706 USA
[2] Microsoft Jim Gray Syst Lab, Madison, WI USA
关键词
D O I
10.1109/ICDE.2012.74
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Progress indicators for SQL queries were first published in 2004 with the simultaneous and independent proposals from Chaudhuri et al. and Luo et al. In this paper, we implement both progress indicators in the same commercial RDBMS to investigate their performance. We summarize common cases in which they are both accurate and cases in which they fail to provide reliable estimates. Although there are differences in their performance, much more striking is the similarity in the errors they make due to a common simplifying uniform future speed assumption. While the developers of these progress indicators were aware that this assumption could cause errors, they neither explored how large the errors might be nor did they investigate the feasibility of removing the assumption. To rectify this we propose a new query progress indicator, similar to these early progress indicators but without the uniform speed assumption. Experiments show that on the TPC-H benchmark, on queries for which the original progress indicators have errors up to 30X the query running time, the new progress indicator is accurate to within 10 percent. We also discuss the sources of the errors that still remain and shed some light on what would need to be done to eliminate them.
引用
收藏
页码:678 / 689
页数:12
相关论文
共 50 条
  • [1] Cost-Based Query-Rewriting for DynamoDB (Work in Progress)
    Chawathe, Sudarshan S.
    [J]. 2019 IEEE 18TH INTERNATIONAL SYMPOSIUM ON NETWORK COMPUTING AND APPLICATIONS (NCA), 2019, : 101 - 103
  • [2] Cost-based Query Optimization for XPath
    Li, Dong
    Chen, Wenhao
    Liang, Xiaochong
    Guan, Jida
    Xu, Yang
    Lu, Xiuyu
    [J]. APPLIED MATHEMATICS & INFORMATION SCIENCES, 2014, 8 (04): : 1935 - 1948
  • [3] Cost-based Optimization of Multistore Query Plans
    Forresi, Chiara
    Francia, Matteo
    Gallinucci, Enrico
    Golfarelli, Matteo
    [J]. INFORMATION SYSTEMS FRONTIERS, 2023, 25 (05) : 1925 - 1951
  • [4] Cost-based Optimization of Multistore Query Plans
    Chiara Forresi
    Matteo Francia
    Enrico Gallinucci
    Matteo Golfarelli
    [J]. Information Systems Frontiers, 2023, 25 : 1925 - 1951
  • [5] A review of different cost-based distributed query optimizers
    Manik Sharma
    Gurvinder Singh
    Rajinder Singh
    [J]. Progress in Artificial Intelligence, 2019, 8 : 45 - 62
  • [6] A review of different cost-based distributed query optimizers
    Sharma, Manik
    Singh, Gurvinder
    Singh, Rajinder
    [J]. PROGRESS IN ARTIFICIAL INTELLIGENCE, 2019, 8 (01) : 45 - 62
  • [7] Geno: Cost-based Heterogeneous Fusion Query Optimizer
    Tu, Yao-Feng
    Chen, Xiao-Qiang
    Zhou, Shi-Jun
    Bian, Fu-Sheng
    Wu, Fei
    Chen, Bing
    [J]. Ruan Jian Xue Bao/Journal of Software, 2022, 33 (03): : 774 - 796
  • [8] Cost-Based Query Optimization via AI Planning
    Robinson, Nathan
    McIlraith, Sheila A.
    Toman, David
    [J]. PROCEEDINGS OF THE TWENTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2014, : 2344 - 2351
  • [9] Cost-based query optimization for multi reachability joins
    Cheng, Jiefeng
    Yu, Jeffrey Xu
    Ding, Bolin
    [J]. ADVANCES IN DATABASES: CONCEPTS, SYSTEMS AND APPLICATIONS, 2007, 4443 : 18 - +
  • [10] Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection
    Yu, Xiang
    Chai, Chengliang
    Li, Guoliang
    Liu, Jiabin
    [J]. PROCEEDINGS OF THE VLDB ENDOWMENT, 2022, 15 (13): : 3924 - 3936