Skip to main content
Springer Nature Link
Log in

The LDBC Social Network Benchmark Interactive Workload v2: A Transactional Graph Query Benchmark with Deep Delete Operations

  • Conference paper
  • First Online:
Performance Evaluation and Benchmarking (TPCTC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14247))

Included in the following conference series:

  • 256 Accesses

Abstract

The LDBC Social Network Benchmark’s Interactive workload captures an OLTP scenario operating on a correlated social network graph. It consists of complex graph queries executed concurrently with a stream of updates operation. Since its initial release in 2015, the Interactive workload has become the de facto industry standard for benchmarking transactional graph data management systems. As graph systems have matured and the community’s understanding of graph processing features has evolved, we initiated the renewal of this benchmark. This paper describes the draft Interactive v2 workload with several new features: delete operations, a cheapest path-finding query, support for larger data sets, and a novel temporal parameter curation algorithm that ensures stable runtimes for path queries.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://ldbcouncil.org.

  2. 2.

    https://ldbcouncil.org/benchmarks/snb-interactive/.

  3. 3.

    https://ldbcouncil.org/tags/tuc-meeting/.

  4. 4.

    Most audited Interactive v1 implementations use 48 read and 32/64 write threads.

  5. 5.

    https://github.com/ldbc/ldbc_snb_datagen_spark.

  6. 6.

    For details on the optimization steps, see https://ldbcouncil.org/tags/datagen/.

  7. 7.

    The term shortest paths refers to the problem of finding unweighted shortest paths, which can be solved with the BFS algorithm. We use cheapest paths to refer to the weighted shortest paths problem which can be solved using e.g. Dijkstra’s algorithm.

  8. 8.

    Unlike in TPC-H  [25] and SNB BI  [24], which use geometric mean in their metrics.

  9. 9.

    github.com/ldbc/ldbc_snb_interactive_driver/releases/tag/v2.0.0-RC2.

  10. 10.

    https://github.com/ldbc/ldbc_snb_interactive_impls.

  11. 11.

    https://learn.microsoft.com/en-us/sql/relational-databases/graphs/sql-graph-overview?view=sql-server-ver16.

  12. 12.

    https://ldbcouncil.org/benchmarks/snb-interactive/.

References

  1. Angles, R., et al.: Foundations of modern query languages for graph databases. ACM Comput. Surv. 50(5), 68:1–68:40 (2017)

    Google Scholar 

  2. Angles, R., et al.: The LDBC Social Network Benchmark. CoRR, abs/2001.02299 (2020). http://arxiv.org/abs/2001.02299

  3. Angles, R., et al. PG-Keys: Keys for property graphs. In: SIGMOD (2021)

    Google Scholar 

  4. Boncz, P.A., et al.: TPC-H analyzed: hidden messages and lessons learned from an influential benchmark. In: TPCTC (2013)

    Google Scholar 

  5. Bonifati, A., et al.: Graph generators: state of the art and open challenges. ACM Comput. Surv. 53(2), 36:1–36:30 (2020)

    Google Scholar 

  6. Deutsch, A., et al.: Graph pattern matching in GQL and SQL/PGQ. In: SIGMOD (2022)

    Google Scholar 

  7. Erling, O., et al.: The LDBC social network benchmark: interactive workload. In: SIGMOD (2015)

    Google Scholar 

  8. Francis, N., et al.: Cypher: an evolving query language for property graphs. In: SIGMOD, pp. 1433–1445. ACM (2018)

    Google Scholar 

  9. Gray, J. (ed).: The Benchmark Handbook for Database and Transaction Systems. Morgan Kaufmann, 2nd edition (1993)

    Google Scholar 

  10. Gray, J., et al.: Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub totals. Data Min. Knowl. Discov. 1(1), 29–53 (1997)

    Article  Google Scholar 

  11. Green, A., et al.: Updating graph databases with Cypher. In: PVLDB (2019)

    Google Scholar 

  12. Gubichev, A., Boncz, P.A.: Parameter curation for benchmark queries. In: TPCTC (2014)

    Google Scholar 

  13. Lőrincz, L., Koltai, J., Győr, A.F., Takács, K.: Collapse of an online social network: burning social capital to create it? Soc. Networks 57, 43–53 (2019)

    Article  Google Scholar 

  14. Libkin, L., Vrgoc, D.: Regular path queries on graphs with data. In: ICDT (2012)

    Google Scholar 

  15. McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 9(1), 415–444 (2001)

    Google Scholar 

  16. Newman, M.E.J.: Power laws, Pareto distributions and Zipf’s law. Contemp. Phys. 46(5), 323–351 (2005)

    Article  Google Scholar 

  17. Püroja, D.: LDBC Social Network Benchmark Interactive v2. Master’s thesis, Universiteit van Amsterdam (2023). https://ldbcouncil.org/docs/papers/msc-thesis-david-puroja-snb-interactive-v2-2023.pdf

  18. Raasveldt, M., Mühleisen, H.: DuckDB: an embeddable analytical database. In: SIGMOD (2019)

    Google Scholar 

  19. Roditty, L., Zwick, U.: On dynamic shortest paths problems. In: ESA, (2004)

    Google Scholar 

  20. Sahu, S., Mhedhbi, A., Salihoglu, S., Lin, J., Özsu, M.T.: The ubiquity of large graphs and surprising challenges of graph processing: extended survey. The VLDB J. 29(2), 595–618 (2019). https://doi.org/10.1007/s00778-019-00548-x

    Article  Google Scholar 

  21. Sakr, S., et al.: The Future is Big Graphs: A Community View on Graph Processing Systems. ACM, Commun (2021)

    Book  Google Scholar 

  22. Shastri, S., et al.: Understanding and benchmarking the impact of GDPR on database systems. VLDB 13(7), 1064–1077 (2020)

    Google Scholar 

  23. Staudt, C.L., Sazonovs, A., Meyerhenke, H.: NetworKit: a tool suite for large-scale complex network analysis. Netw. Sci. 4(4), 508–530 (2016)

    Article  Google Scholar 

  24. Szárnyas, G., et al.: The LDBC Social Network Benchmark: Business Intelligence workload. In: PVLDB (2022)

    Google Scholar 

  25. TPC. TPC Benchmark H, revision 2.18.0. pages 1–138, (2017). http://www.tpc.org/tpc_documents_current_versions/pdf/tpc-h_v2.18.0.pdf

  26. Ugander, J., Karrer, B., Backstrom, L., Marlow, C.: The anatomy of the Facebook social graph. CoRR abs/1111.4503 (2011). http://arxiv.org/abs/1111.4503

  27. Watts, D.J., Strogatz, S.H.: Collective dynamics of “small-world” networks. Nature 393, 440–442 (1998)

    Google Scholar 

  28. Waudby, J., et al.: Supporting dynamic graphs and temporal entity deletions in the LDBC Social Network Benchmark’s data generator. In: GRADES-NDA (2020)

    Google Scholar 

  29. Waudby, J., et al.: Towards testing ACID compliance in the LDBC Social Network Benchmark. In: TPCTC (2020)

    Google Scholar 

Download references

Acknowledgements

We would like to thank our collaborators who contributed with feedback and implementations to the SNB Interactive v2 workload: Altan Birler, Arvind Shyamsundar, Benjamin A. Steer, and Dávid Szakállas. Jack Waudby was supported by the Engineering and Physical Sciences Research Council, Centre for Doctoral Training in Cloud Computing for Big Data [grant number EP/L015358/1].

Author information

Authors and Affiliations

  1. CWI, Amsterdam, The Netherlands

    David Püroja, Peter Boncz & Gábor Szárnyas

  2. Newcastle University, School of Computing, Newcastle upon Tyne, England

    Jack Waudby

Authors
  1. David Püroja
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jack Waudby
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter Boncz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gábor Szárnyas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Püroja .

Editor information

Editors and Affiliations

  1. Advanced Micro Devices Inc., Santa Clara, CA, USA

    Raghunath Nambiar

  2. Oracle Corporation, Redwood Shores, CA, USA

    Meikel Poess

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Püroja, D., Waudby, J., Boncz, P., Szárnyas, G. (2024). The LDBC Social Network Benchmark Interactive Workload v2: A Transactional Graph Query Benchmark with Deep Delete Operations. In: Nambiar, R., Poess, M. (eds) Performance Evaluation and Benchmarking. TPCTC 2023. Lecture Notes in Computer Science, vol 14247. Springer, Cham. https://doi.org/10.1007/978-3-031-68031-1_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-68031-1_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-68030-4

  • Online ISBN: 978-3-031-68031-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics