research-article

ROX: run-time optimization of XQueries

Authors:

Riham Abdel Kader,

Stefan Manegold,

Maurice van KeulenAuthors Info & Claims

SIGMOD '09: Proceedings of the 2009 ACM SIGMOD International Conference on Management of data

Pages 615 - 626

https://doi.org/10.1145/1559845.1559910

Published: 29 June 2009 Publication History

Abstract

Optimization of complex XQueries combining many XPath steps and joins is currently hindered by the absence of good cardinality estimation and cost models for XQuery. Additionally, the state-of-the-art of even relational query optimization still struggles to cope with cost model estimation errors that increase with plan size, as well as with the effect of correlated joins and selections.

In this research, we propose to radically depart from the traditional path of separating the query compilation and query execution phases, by having the optimizer execute, materialize partial results, and use sampling based estimation techniques to observe the characteristics of intermediates. The proposed technique takes as input a Join Graph where the edges are either equi-joins or XPath steps, and the execution environment provides value- and structural-join algorithms, as well as structural and value-based indices.

While run-time optimization with sampling removes many of the vulnerabilities of classical optimizers, it brings its own challenges with respect to keeping resource usage under control, both with respect to the materialization of intermediates, as well as the cost of plan exploration using sampling. Our approach deals with these issues by limiting the run-time search space to so-called "zero-investment algorithms for which sampling can be guaranteed to be strictly linear in sample size. All operators and XML value indices used by ROX for sampling have the zero-investment property.

We perform extensive experimental evaluation on large XML datasets that shows that our run-time query optimizer finds good query plans in a robust fashion and has limited run-time overhead.

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Cited By

Zhu RWeng LWei WWu DPeng JWang YDing BLian DZheng BZhou J(2024)PilotScope: Steering Databases with Machine Learning DriversProceedings of the VLDB Endowment10.14778/3641204.364120917:5(980-993)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.14778/3641204.3641209
Pavlopoulou CCarey MTsotras V(2023)Revisiting Runtime Dynamic Optimization for Join Queries in Big Data Management SystemsACM SIGMOD Record10.1145/3604437.360446052:1(104-113)Online publication date: 8-Jun-2023
https://dl.acm.org/doi/10.1145/3604437.3604460
Wang FYan XYiu MLI SMao ZTang B(2023)Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality EstimationProceedings of the ACM on Management of Data10.1145/35887081:1(1-25)Online publication date: 30-May-2023
https://doi.org/10.1145/3588708
Show More Cited By

Index Terms

ROX: run-time optimization of XQueries
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Database query processing
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query processing and optimization (theory)

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Published In

cover image ACM Conferences

SIGMOD '09: Proceedings of the 2009 ACM SIGMOD International Conference on Management of data

June 2009

1168 pages

ISBN:9781605585512

DOI:10.1145/1559845

Editors:
Carsten Binnig,
Benoit Dageville,
General Chairs:
Uğur Çetintemel
Brown University, USA
,
Stan Zdonik
Brown University, USA
,
Program Chair:
Donald Kossmann
ETH Zurich, Switzerland

Copyright © 2009 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 ACM 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: 29 June 2009

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SIGMOD/PODS '09: International Conference on Management of Data

June 29 - July 2, 2009

Rhode Island, Providence, USA

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Cited By

Zhu RWeng LWei WWu DPeng JWang YDing BLian DZheng BZhou J(2024)PilotScope: Steering Databases with Machine Learning DriversProceedings of the VLDB Endowment10.14778/3641204.364120917:5(980-993)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.14778/3641204.3641209
Pavlopoulou CCarey MTsotras V(2023)Revisiting Runtime Dynamic Optimization for Join Queries in Big Data Management SystemsACM SIGMOD Record10.1145/3604437.360446052:1(104-113)Online publication date: 8-Jun-2023
https://dl.acm.org/doi/10.1145/3604437.3604460
Wang FYan XYiu MLI SMao ZTang B(2023)Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality EstimationProceedings of the ACM on Management of Data10.1145/35887081:1(1-25)Online publication date: 30-May-2023
https://doi.org/10.1145/3588708
Borovica-Gajic RIdreos SAilamaki AZukowski MFraser C(2018)Smooth ScanThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-018-0507-827:4(521-545)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s00778-018-0507-8
Leis VRadke BGubichev AMirchev ABoncz PKemper ANeumann T(2018)Query optimization through the looking glass, and what we found running the Join Order BenchmarkThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-017-0480-727:5(643-668)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s00778-017-0480-7
Gai LWang XWang T(2018)ROSIE: Runtime Optimization of SPARQL Queries over RDF Using Incremental EvaluationKnowledge Science, Engineering and Management10.1007/978-3-319-99247-1_11(117-131)Online publication date: 11-Aug-2018
https://doi.org/10.1007/978-3-319-99247-1_11
(2016)On the Use of Abstract Models for RDF/S ProvenanceLinked Data Management10.1201/b16859-26(419-440)Online publication date: 19-Apr-2016
https://doi.org/10.1201/b16859-26
Leis VGubichev AMirchev ABoncz PKemper ANeumann T(2015)How good are query optimizers, really?Proceedings of the VLDB Endowment10.14778/2850583.28505949:3(204-215)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.14778/2850583.2850594
Choi RWong R(2015)VXQInformation Systems Frontiers10.1007/s10796-013-9480-317:4(961-981)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1007/s10796-013-9480-3
Kersten MManegold SMullender S(2012)The database architectures research group at CWIACM SIGMOD Record10.1145/2094114.209412440:4(39-44)Online publication date: 11-Jan-2012
https://dl.acm.org/doi/10.1145/2094114.2094124
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