Publications associated with ATLAS Oxford Group


The atlas eventindex and its evolution based on Apache Kudu storage

CEUR Workshop Proceedings 2267 (2019) 18-25

D Barberis, F Prokoshin, E Alexandrov, I Alexandrov, Z Baranowski, L Canali, G Dimitrov, AF Casani, E Gallas, CG Montoro, SG De La Hoz, J Hrivnac, A Iakovlev, A Kazymov, M Mineev, G Rybkin, J Sanchez, JS Cairols, P Vasileva, MV Perez

© 2018Dario Barberis, Fedor Prokoshin, Evgeny Alexandrov, Igor Alexandrov, Zbigniew Baranowski, Luca Canali, Gancho Dimitrov, Alvaro Fernandez Casani, Elizabeth Gallas, Carlos Garcia Montoro, Santiago Gonzalez de la Hoz, Julius Hrivnac, Aleksandr Iakovlev, Andrei Kazymov, Mikhail Mineev, Grigori Rybkin, Javier Sanchez, José Salt Cairols, Petya Vasileva, Miguel Villaplana Perez. The ATLAS experiment produced hundreds of petabytes of data and expects to have one order of magnitude more in the future. This data are spread among hundreds of computing Grid sites around the world. The EventIndex catalogues the basic elements of these data: real and simulated events. It provides the means to select and access event data in the ATLAS distributed storage system, and provides support for completeness and consistency checks and data overlap studies. The EventIndex employs various data handling technologies like Hadoop and Oracle databases, and is integrated with other elements of the ATLAS distributed computing infrastructure, including systems for data, metadata, and production management (AMI, Rucio and PanDA). The project is in operation since the start of LHC Run 2 in 2015, and is in permanent development in order to fit the analysis and production demands and follow technology evolutions. The main data store in Hadoop, based on MapFiles and HBase, can work for the rest of Run 2 but new solutions are explored for the future. Kudu offers an interesting environment, with a mixture of BigData and relational database features, which looked promising at the design level and is now used to build a prototype to measure the scaling capabilities as a function of data input rates, total data volumes and data query and retrieval rates. An extension of the EventIndex functionalities to support the concept of Virtual Datasets produced additional requirements that are tested on the same Kudu prototype, in order to estimate the system performance and response times for different internal data organisations. This paper reports on the current system performance and on the first measurements of the new prototype based on Kudu.


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