HomeRun: A Cardinality Estimation Advisor for Graph Databases
Article No.: 6, Pages 1 - 9
Abstract
Database systems depend on cardinality estimates for generation of optimal query execution plans. Selecting an appropriate cardinality estimation technique involves navigating trade-offs, including the accuracy of estimates, time required for estimation, and necessary statistics. These trade-offs can lead to different choices based on the dataset and query workload. Unfortunately there is limited support for advising graph database users in exploring these trade-offs and making the right choices for their scenarios. To address this critical gap, we introduce an advisor tool, HomeRun, which analyzes the performance of various cardinality estimation techniques in given usage scenarios. We explain HomeRun's capabilities using the industry-standard LSQB benchmark and synthetic scenarios. HomeRun reveals how minor changes in the dataset can significantly impact the conclusions about the performance of cardinality estimation techniques.
References
[1]
The LDBC Social Network Benchmark (version 2.2.2)
[2]
Aboulnaga, A., Alameldeen, A.R., Naughton, J.F.: Estimating the selectivity of xml path expressions for internet scale applications. In: VLDB. vol. 1, pp. 591--600. Citeseer (2001)
[3]
Cai, W., Balazinska, M., Suciu, D.: Pessimistic cardinality estimation: Tighter upper bounds for intermediate join cardinalities. In: Proceedings of the 2019 International Conference on Management of Data. pp. 18--35 (2019)
[4]
Han, Y., Wu, Z., Wu, P., Zhu, R., Yang, J., Tan, L.W., Zeng, K., Cong, G., Qin, Y., Pfadler, A., et al.: Cardinality estimation in dbms: A comprehensive benchmark evaluation. arXiv preprint arXiv:2109.05877 (2021)
[5]
Hilprecht, B., Schmidt, A., Kulessa, M., Molina, A., Kersting, K., Binnig, C.: Deepdb: Learn from data, not from queries! arXiv preprint arXiv:1909.00607 (2019)
[6]
Kipf, A., Kipf, T., Radke, B., Leis, V., Boncz, P., Kemper, A.: Learned cardinalities: Estimating correlated joins with deep learning. arXiv preprint arXiv:1809.00677 (2018)
[7]
van Leeuwen, W., Fletcher, G., Yakovets, N.: A general cardinality estimation framework for subgraph matching in property graphs. IEEE Transactions on Knowledge and Data Engineering 35(6), 5485--5505 (2023). https://doi.org/10.1109/TKDE.2022.3161328
[8]
van Leeuwen, W., Mulder, T., van de Wall, B., Fletcher, G., Yakovets, N.: Avantgraph query processing engine. Proceedings of the VLDB Endowment 15(12), 3698--3701 (2022)
[9]
Leis, V., Gubichev, A., Mirchev, A., Boncz, P., Kemper, A., Neumann, T.: How good are query optimizers, really? Proceedings of the VLDB Endowment 9(3), 204--215 (2015)
[10]
Leis, V., Radke, B., Gubichev, A., Kemper, A., Neumann, T.: Cardinality estimation done right: Index-based join sampling. In: Cidr (2017)
[11]
Leis, V., Radke, B., Gubichev, A., Mirchev, A., Boncz, P., Kemper, A., Neumann, T.: Query optimization through the looking glass, and what we found running the join order benchmark. The VLDB Journal 27, 643--668 (2018)
[12]
Li, F., Wu, B., Yi, K., Zhao, Z.: Wander join: Online aggregation via random walks. In: Proceedings of the 2016 International Conference on Management of Data. pp. 615--629 (2016)
[13]
Mhedhbi, A., Lissandrini, M., Kuiper, L., Waudby, J., Szárnyas, G.: Lsqb: a large-scale subgraph query benchmark. In: Proceedings of the 4th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA). pp. 1--11 (2021)
[14]
Neumann, T., Moerkotte, G.: Characteristic sets: Accurate cardinality estimation for rdf queries with multiple joins. In: 2011 IEEE 27th International Conference on Data Engineering. pp. 984--994. IEEE (2011)
[15]
Poosala, V., Haas, P.J., Ioannidis, Y.E., Shekita, E.J.: Improved histograms for selectivity estimation of range predicates. ACM Sigmod Record 25(2), 294--305 (1996)
[16]
Selinger, P.G., Astrahan, M.M., Chamberlin, D.D., Lorie, R.A., Price, T.G.: Access path selection in a relational database management system. In: Proceedings of the 1979 ACM SIGMOD international conference on Management of data. pp. 23--34 (1979)
[17]
Stefanoni, G., Motik, B., Kostylev, E.V.: Estimating the cardinality of conjunctive queries over rdf data using graph summarisation. In: Proceedings of the 2018 World Wide Web Conference. pp. 1043--1052 (2018)
[18]
Wu, W.: Sampling-based cardinality estimation algorithms: A survey and an empirical evaluation
[19]
Wu, Z., Shaikhha, A., Zhu, R., Zeng, K., Han, Y., Zhou, J.: Bayescard: Revitilizing bayesian frameworks for cardinality estimation. arXiv preprint arXiv:2012.14743 (2020)
[20]
Yang, Z., Kamsetty, A., Luan, S., Liang, E., Duan, Y., Chen, X., Stoica, I.: Neurocard: one cardinality estimator for all tables. arXiv preprint arXiv:2006.08109 (2020)
Recommendations
FactorJoin: A New Cardinality Estimation Framework for Join Queries
PACMMODCardinality estimation is one of the most fundamental and challenging problems in query optimization. Neither classical nor learning-based methods yield satisfactory performance when estimating the cardinality of the join queries. They either rely on ...
Accurate summary-based cardinality estimation through the lens of cardinality estimation graphs
This paper is an experimental and analytical study of two classes of summary-based cardinality estimators that use statistics about input relations and small-size joins in the context of graph database management systems: (i) optimistic estimators that ...
Comments
Information & Contributors
Information
Published In
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 09 June 2024
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
Conference
SIGMOD/PODS '24
Sponsor:
Acceptance Rates
Overall Acceptance Rate 29 of 61 submissions, 48%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 78Total Downloads
- Downloads (Last 12 months)78
- Downloads (Last 6 weeks)17
Reflects downloads up to 04 Oct 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in