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KOBE: Cloud-Native Open Benchmarking Engine for Federated Query Processors

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12731)

Abstract

In the SPARQL query processing community, as well as in the wider databases community, benchmark reproducibility is based on releasing datasets and query workloads. However, this paradigm breaks down for federated query processors, as these systems do not manage the data they serve to their clients but provide a data-integration abstraction over the actual query processors that are in direct contact with the data. As a consequence, benchmark results can be greatly affected by the performance and characteristics of the underlying data services. This is further aggravated when one considers benchmarking in more realistic conditions, where internet latency and throughput between the federator and the federated data sources is also a key factor. In this paper we present KOBE, a benchmarking system that leverages modern containerization and Cloud computing technologies in order to reproduce collections of data sources. In KOBE, data sources are formally described in more detail than what is conventionally provided, covering not only the data served but also the specific software that serves it and its configuration as well as the characteristics of the network that connects them. KOBE provides a specification formalism and a command-line interface that completely hides from the user the mechanics of provisioning and orchestrating the benchmarking process on Kubernetes-based infrastructures; and of simulating network latency. Finally, KOBE automates the process of collecting and comprehending logs, and extracting and visualizing evaluation metrics from these logs.

Keywords

  • Benchmarking
  • Federated query processing
  • Cloud-native

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Notes

  1. 1.

    Previously demonstrated in ISWC 2020, with extended abstract proceedings [6].

  2. 2.

    See https://github.com/semagrow/kobe.

  3. 3.

    cf. https://kubernetes.io.

  4. 4.

    cf. https://istio.io.

  5. 5.

    cf. https://www.elastic.co/elasticsearch.

  6. 6.

    cf. https://www.fluentd.org.

  7. 7.

    cf. https://www.elastic.co/kibana.

  8. 8.

    https://semagrow.github.io/kobe/references/api.

  9. 9.

    cf. https://virtuoso.openlinksw.com.

  10. 10.

    cf. http://strabon.di.uoa.gr.

  11. 11.

    https://semagrow.github.io/kobe/use/create_benchmark.

  12. 12.

    https://semagrow.github.io/kobe/use/tune_network.

  13. 13.

    https://semagrow.github.io/kobe/use/create_experiment.

  14. 14.

    https://semagrow.github.io/kobe/extend/add_evaluator.

  15. 15.

    https://semagrow.github.io/kobe/extend/add_metrics.

  16. 16.

    https://semagrow.github.io/kobe/extend/add_dataset_server.

  17. 17.

    https://semagrow.github.io/kobe/extend/add_federator.

  18. 18.

    Specifically, see the first step of the walk-through for adding a new federator. See also details about collecting logs to compute evaluation metrics https://semagrow.github.io/kobe/extend/support_metrics.

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Acknowledgments

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825258. Please see http://earthanalytics.eu for more details.

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Correspondence to Stasinos Konstantopoulos .

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Kostopoulos, C., Mouchakis, G., Troumpoukis, A., Prokopaki-Kostopoulou, N., Charalambidis, A., Konstantopoulos, S. (2021). KOBE: Cloud-Native Open Benchmarking Engine for Federated Query Processors. In: , et al. The Semantic Web. ESWC 2021. Lecture Notes in Computer Science(), vol 12731. Springer, Cham. https://doi.org/10.1007/978-3-030-77385-4_40

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  • DOI: https://doi.org/10.1007/978-3-030-77385-4_40

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