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DunceCap: Query Plans Using Generalized Hypertree Decompositions

Published: 27 May 2015 Publication History

Abstract

Joins are central to data processing. However, traditional query plans for joins, which are based on choosing the order of pairwise joins, are provably suboptimal. They often perform poorly on cyclic graph queries, which have become increasingly important to modern data analytics. Other join algorithms exist: Yannakakis', for example, operates on acyclic queries in runtime proportional to the input size plus the output size \cite{yannakakis}. More recently, Ngo et al. published a join algorithm that is optimal on worst-case inputs \cite{worst}. My contribution is to explore query planning using these join algorithms. In our approach, every query plan can be viewed as a generalized hypertree decomposition (GHD). We score each GHD using the minimal fractional hypertree width, which Ngo et al. show allows us to bound its worst-case runtime. We benchmark our plans using datasets from the Stanford Large Network Dataset Collection \cite{dataset} and find that our performance compares favorably against that of LogicBlox, a commercial system that implements a worst-case optimal join algorithm.

References

[1]
C. R. Aberger, A. Nötzli, K. Olukotun, and C. Ré. EmptyHeaded: Boolean Algebra Based Graph Processing. ArXiv e-prints, Mar. 2015.
[2]
A. Atserias, M. Grohe, and D. Marx. Size bounds and query plans for relational joins, 2008.
[3]
G. Gottlob, Z. Miklós, and T. Schwentick. Generalized hypertree decompositions: Np-hardness and tractable variants. J. ACM, 56(6):30:1--30:32, Sept. 2009.
[4]
J. Leskovec and A. Krevl. SNAP Datasets: Stanford large network dataset collection, June 2014.
[5]
H. Q. Ngo, E. Porat, C. Ré, and A. Rudra. Worst-case optimal join algorithms. PODS '12, pages 37--48, New York, NY, USA, 2012. ACM.
[6]
H. Q. Ngo, C. Re, and A. Rudra. Skew strikes back: New developments in the theory of join algorithms. CoRR, abs/1310.3314, 2013.
[7]
M. Yannakakis. Algorithms for acyclic database schemes. VLDB '81, pages 82--94. VLDB Endowment, 1981.

Cited By

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  • (2024)Efficient Approximation of Fractional Hypertree Width2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS)10.1109/FOCS61266.2024.00053(754-779)Online publication date: 27-Oct-2024
  • (2023)Enhancing datalog reasoning with hypertree decompositionsProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/377(3383-3393)Online publication date: 19-Aug-2023
  • (2021)Complexity Analysis of Generalized and Fractional Hypertree DecompositionsJournal of the ACM10.1145/345737468:5(1-50)Online publication date: 13-Sep-2021
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cover image ACM Conferences
SIGMOD '15: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data
May 2015
2110 pages
ISBN:9781450327589
DOI:10.1145/2723372
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2015

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Author Tags

  1. graph processing
  2. joins
  3. query planning

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SIGMOD/PODS'15
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SIGMOD/PODS'15: International Conference on Management of Data
May 31 - June 4, 2015
Victoria, Melbourne, Australia

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SIGMOD '15 Paper Acceptance Rate 106 of 415 submissions, 26%;
Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

View all
  • (2024)Efficient Approximation of Fractional Hypertree Width2024 IEEE 65th Annual Symposium on Foundations of Computer Science (FOCS)10.1109/FOCS61266.2024.00053(754-779)Online publication date: 27-Oct-2024
  • (2023)Enhancing datalog reasoning with hypertree decompositionsProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/377(3383-3393)Online publication date: 19-Aug-2023
  • (2021)Complexity Analysis of Generalized and Fractional Hypertree DecompositionsJournal of the ACM10.1145/345737468:5(1-50)Online publication date: 13-Sep-2021
  • (2021)HyperBenchACM Journal of Experimental Algorithmics10.1145/344001526(1-40)Online publication date: 9-Jul-2021
  • (2020)Distributed subgraph countingProceedings of the VLDB Endowment10.14778/3407790.340784013:12(2493-2507)Online publication date: 14-Sep-2020
  • (2020)The Impact of Negation on the Complexity of the Shapley Value in Conjunctive QueriesProceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/3375395.3387664(285-297)Online publication date: 14-Jun-2020
  • (2020)SPRINTER: A Fast n-ary Join Query Processing Method for Complex OLAP QueriesProceedings of the 2020 ACM SIGMOD International Conference on Management of Data10.1145/3318464.3380565(2055-2070)Online publication date: 11-Jun-2020
  • (2019)HyperBenchProceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/3294052.3319683(464-480)Online publication date: 25-Jun-2019
  • (2019)Ranked Enumeration of Minimal TriangulationsProceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/3294052.3319678(74-88)Online publication date: 25-Jun-2019
  • (2018)General and Fractional Hypertree DecompositionsProceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/3196959.3196962(17-32)Online publication date: 27-May-2018
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