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DunceCap: Compiling Worst-Case Optimal Query Plans

Published: 27 May 2015 Publication History

Abstract

Modern data analytics workloads frequently involve complex join queries where the pairwise-join-based algorithms used by most RDBMS engines are suboptimal. In this study, we explore two algorithms that are asymptotically faster than pairwise algorithms for a large class of queries. The first is Yannakakis' classical algorithm for acyclic queries. The second is a more recent algorithm which works for any query and which is optimal with respect to the worst-case size of the output. We introduce a query compiler, DunceCap, which uses these two algorithms and variations on them to produce optimal query plans, and find that these plans can outperform standard RDBMS algorithms as well as simple worst-case optimal algorithms by an order of magnitude on a variety of queries.

References

[1]
C. R. Aberger, A. Nötzli, K. Olukotun, and C. Ré. EmptyHeaded: Boolean Algebra Based Graph Processing, Mar. 2015.
[2]
J. Leskovec and A. Krevl. Snap datasets: Stanford large network dataset collection, June 2014.
[3]
H. Q. Ngo, E. Porat, C. Ré, and A. Rudra. Worst-case optimal join algorithms: {extended abstract}. In PODS '12, pages 37--48, New York, NY, USA, 2012. ACM.
[4]
T. L. Veldhuizen. Leapfrog triejoin: a worst-case optimal join algorithm. CoRR, abs/1210.0481, 2012.
[5]
M. Yannakakis. Algorithms for acyclic database schemes. VLDB '81, pages 82--94. VLDB Endowment, 1981.

Cited By

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  • (2021)HyperBenchACM Journal of Experimental Algorithmics10.1145/344001526(1-40)Online publication date: 9-Jul-2021
  • (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
  • Show More Cited By

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Published In

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. algorithms
  2. graph processing
  3. relational joins

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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
  • (2021)HyperBenchACM Journal of Experimental Algorithmics10.1145/344001526(1-40)Online publication date: 9-Jul-2021
  • (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
  • (2016)AJARProceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems10.1145/2902251.2902293(91-106)Online publication date: 15-Jun-2016

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