research-article

Operator and Query Progress Estimation in Microsoft SQL Server Live Query Statistics

Authors:

Arnd Christian König,

Vivek Narasayya,

Surajit Chaudhuri,

Alexey Eksarevskiy,

Praneeta Prakash,

Jeff NaughtonAuthors Info & Claims

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

Pages 1753 - 1764

https://doi.org/10.1145/2882903.2903728

Published: 26 June 2016 Publication History

Abstract

We describe the design and implementation of the new Live Query Statistics (LQS) feature in Microsoft SQL Server 2016. The functionality includes the display of overall query progress as well as progress of individual operators in the query execution plan. We describe the overall functionality of LQS, give usage examples and detail all areas where we had to extend the current state-of-the-art to build the complete LQS feature. Finally, we evaluate the effect these extensions have on progress estimation accuracy with a series of experiments using a large set of synthetic and real workloads.

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Cited By

Lim WMa LZhang WButrovich MArch SPavlo A(2024)Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management SystemsProceedings of the VLDB Endowment10.14778/3681954.368203017:11(3680-3693)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3682030
Dong QLuo G(2024)Progress Estimation for End-to-End Training of Deep Learning Models With Online Data PreprocessingIEEE Access10.1109/ACCESS.2024.335999612(18658-18684)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3359996
Deshmukh HSundarmurthy BPatel J(2022)On inter-operator data transfers in query processing2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00066(820-832)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00066
Show More Cited By

Index Terms

Operator and Query Progress Estimation in Microsoft SQL Server Live Query Statistics
1. Information systems
  1. Data management systems
    1. Database administration
    2. Database management system engines
      1. Database query processing

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cover image ACM Conferences

SIGMOD '16: Proceedings of the 2016 International Conference on Management of Data

June 2016

2300 pages

ISBN:9781450335317

DOI:10.1145/2882903

General Chairs:
Fatma Özcan
IBM Research, USA
,
Georgia Koutrika
HP Labs, USA
,
Program Chair:
Sam Madden
Massachusetts Institute of Technology, USA

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 26 June 2016

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SIGMOD/PODS'16: International Conference on Management of Data

June 26 - July 1, 2016

California, San Francisco, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Lim WMa LZhang WButrovich MArch SPavlo A(2024)Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management SystemsProceedings of the VLDB Endowment10.14778/3681954.368203017:11(3680-3693)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3682030
Dong QLuo G(2024)Progress Estimation for End-to-End Training of Deep Learning Models With Online Data PreprocessingIEEE Access10.1109/ACCESS.2024.335999612(18658-18684)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3359996
Deshmukh HSundarmurthy BPatel J(2022)On inter-operator data transfers in query processing2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00066(820-832)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00066
Dong QZhang XLuo G(2022)Improving the Accuracy of Progress Indication for Constructing Deep Learning ModelsIEEE Access10.1109/ACCESS.2022.318149310(63754-63781)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3181493
Baresi LLeva AQuattrocchi G(2021)Fine-Grained Dynamic Resource Allocation for Big-Data ApplicationsIEEE Transactions on Software Engineering10.1109/TSE.2019.293153747:8(1668-1682)Online publication date: 1-Aug-2021
https://doi.org/10.1109/TSE.2019.2931537
Dong QLuo G(2020)Progress Indication for Deep Learning Model Training: A Feasibility DemonstrationIEEE Access10.1109/ACCESS.2020.29896848(79811-79843)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2989684
Marcus RPapaemmanouil O(2019)Plan-structured deep neural network models for query performance predictionProceedings of the VLDB Endowment10.14778/3342263.334264612:11(1733-1746)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.14778/3342263.3342646
Baresi LDenaro GQuattrocchi GDumas MPfahl DApel SRusso A(2019)Symbolic execution-driven extraction of the parallel execution plans of Spark applicationsProceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3338906.3338973(246-256)Online publication date: 12-Aug-2019
https://dl.acm.org/doi/10.1145/3338906.3338973
Kalmegh PBabu SBoncz PManegold SAilamaki ADeshpande AKraska T(2019)MIFOProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3319902(1678-1695)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3299869.3319902
Luo G(2018)Progress Indication for Machine Learning Model BuildingACM SIGKDD Explorations Newsletter10.1145/3299986.329998820:2(1-12)Online publication date: 11-Dec-2018
https://dl.acm.org/doi/10.1145/3299986.3299988
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