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A Reliable Large Distributed Object Store Based Platform for Collecting Event Metadata


The Large Hadron Collider (LHC) is about to enter its third run at unprecedented energies. The experiments at the LHC face computational challenges with enormous data volumes that need to be analysed by thousands of physics users. The ATLAS EventIndex project, currently running in production, builds a complete catalogue of particle collisions, or events, for the ATLAS experiment at the LHC. The distributed nature of the experiment data model is exploited by running jobs at over one hundred Grid data centers worldwide. Millions of files with petabytes of data are indexed, extracting a small quantity of metadata per event, that is conveyed with a data collection system in real time to a central Hadoop instance at CERN. After a successful first implementation based on a messaging system, some issues suggested performance bottlenecks for the challenging higher rates in next runs of the experiment. In this work we characterize the weaknesses of the previous messaging system, regarding complexity, scalability, performance and resource consumption. A new approach based on an object-based storage method was designed and implemented, taking into account the lessons learned and leveraging the ATLAS experience with this kind of systems. We present the experiment that we run during three months in the real production scenario worldwide, in order to evaluate the messaging and object store approaches. The results of the experiment show that the new object-based storage method can efficiently support large-scale data collection for big data environments like the next runs of the ATLAS experiment at the LHC.

Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.


  1. 1.

    ATLAS Collaboration: The ATLAS experiment at the CERN Large Hadron Collider. J. Instrum. 3(08), S08003 (2008)

    Google Scholar 

  2. 2.

    Barberis, D., Cárdenas Zárate, SE, Cranshaw, J., Favareto, A., Fernández Casaní, A, Gallas, E.J., Glasman, C., González De La Hoz, S, Hřivnáč, J, Malon, D., Prokoshin, F., Salt Cairols, J., Sánchez, J, Többicke, R, Yuan, R.: The ATLAS EventIndex: Architecture, design choices, deployment and first operation experience. J. Phys.: Conf. Ser. 664(4), 042003 (2015)

    Google Scholar 

  3. 3.

    Barberis, D., Cranshaw, J., Favareto, A., Fernández Casaní, A, Gallas, E., González de la Hoz, S, Hřivnáč, J, Malon, D., Nowak, M., Prokoshin, F., Salt, J., Sánchez Martínez, J, Többicke, R, Yuan, R.: The ATLAS EventIndex: Full chain deployment and first operation. Nuclear and Particle Physics Proceedings 273-275, 913–918 (2016)

    Article  Google Scholar 

  4. 4.

    White, T.: Hadoop: The definitive guide. O’Reilly Media, Inc. (2012)

  5. 5.

    Sánchez, J, Casaní, AF, de la Hoz, S.G.: Distributed data collection for the ATLAS EventIndex. J. Phys.: Conf. Ser. 664(4), 042046 (2015)

    Google Scholar 

  6. 6.

    Salomoni, D., Campos, I., Gaido, L., de Lucas, J.M., Solagna, P., Gomes, J., Matyska, L., Fuhrman, P., Hardt, M., Donvito, G., et al: Indigo-datacloud: A platform to facilitate seamless access to e-infrastructures. J Grid Computing 16(3), 381–408 (2018).

    Article  Google Scholar 

  7. 7.

    Krašovec, B, Filipčič, A: Enhancing the grid with cloud computing. J Grid Computing 17(1), 119–135 (2019)

    Article  Google Scholar 

  8. 8.

    Hagras, T., Atef, A., Mahdy, Y.B.: Greening duplication-based dependent-tasks scheduling on heterogeneous large-scale computing platforms. J. Grid Comput. 19(1), 13 (2021)

    Article  Google Scholar 

  9. 9.


  10. 10.

    Fernandez Casani, A., Sanchez, J., Gonzalez de la Hoz, S., Orduña, JM: Designing Alternative Transport Methods for the Distributed Data Collection of ATLAS EventIndex Project. (2016)

  11. 11.

    Karol, M., Hluchyj, M., Morgan, S.: Input versus output queueing on a space-division packet switch. IEEE Transactions on Communications 35(12), 1347–1356 (1987).

    Article  Google Scholar 

  12. 12.

    Mesnier, M., Ganger, G.R., Riedel, E.: Object-based storage. IEEE Commun. Mag. 41(8), 84–90 (2003).

    Article  Google Scholar 

  13. 13.


  14. 14.

    Apache flume,

  15. 15.

    Logstash data processing pipeline,

  16. 16.

    Kreps, J., Narkhede, N., Rao, J., et al.: Kafka: A distributed messaging system for log processing. In: Proceedings of NetDB, pp 1–7 (2011)

  17. 17.

    Dobbelaere, P., Esmaili, K.S.: Kafka versus rabbitmq: A comparative study of two industry reference publish/subscribe implementations: Industry paper. In: Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems, DEBS ’17, ACM, New York, NY, USA, pp 227–238. (2017)

  18. 18.

    Bird, I.G.: Lhc computing (wlcg): Past, present, and future. Grid and Cloud Computing: Concepts and Practical Applications 192, 1 (2016)

    Google Scholar 

  19. 19.

    Garonne, V., Vigne, R., Stewart, G., Barisits, M., Lassnig, M., Serfon, C., Goossens, L., Nairz, A., Collaboration, A., et al: Rucio–the next generation of large scale distributed system for atlas data management. In: Journal of Physics: Conference Series, IOP Publishing, 513, pp 042021 (2014)

  20. 20.

    Calafiura, P., De, K., Guan, W., Maeno, T., Nilsson, P., Oleynik, D., Panitkin, S., Tsulaia, V., Gemmeren, P.V., Wenaus, T.: The atlas event service: A new approach to event processing. J. Phys.: Conf. Ser. 664(6), 062065 (2015)

    Google Scholar 

  21. 21.

    Amazon simple storage service developer guide,

  22. 22.

    Weil, S.A., Brandt, S.A., Miller, E.L., Long, D.D.E., Maltzahn, C.: Ceph: A scalable, high-performance distributed file system. In: Proceedings of the 7th Symposium on Operating Systems Design and Implementation, OSDI ’06, USENIX Association, Berkeley, CA, USA, pp 307–320 (2006)

  23. 23.


  24. 24.

    Amazon s3, cloud computing storage for files, images, videos.

  25. 25.

    Google protocol buffers: Google’s data interchange format.

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This work has been partially supported by MICINN in Spain under grants FPA2016-75141-C2-1-R, PID2109-104301RB-C21 and TIN2015-66972-C5-5-R, which include FEDER funds from the European Union. This work was done as part of the databases applications research and development programme of the ATLAS Collaboration, and we thank the collaboration for its support and cooperation.


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Correspondence to Álvaro Fernández Casaní.

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Fernández Casaní, Á., Orduña, J.M., Sánchez, J. et al. A Reliable Large Distributed Object Store Based Platform for Collecting Event Metadata. J Grid Computing 19, 39 (2021).

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  • Grid computing
  • Hadoop file system
  • Object-Based storage