EPJ Web Conf.
Volume 214, 201923rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|Number of page(s)||8|
|Section||T4 - Data handling|
|Published online||17 September 2019|
The challenges of mining logging data in ATLAS
Department of Physics, University of Oxford, Denys Wilkinson Bldg, Keble Rd, Oxford OX1 3RH,
2 Department of Physics, University of Texas at Arlington, Arlington, Texas 76019, USA,
* Corresponding author: firstname.lastname@example.org
Published online: 17 September 2019
Processing ATLAS event data requires a wide variety of auxiliary information from geometry, trigger, and conditions database systems. This information is used to dictate the course of processing and refine the measurement of particle trajectories and energies to construct a complete and accurate picture of the remnants of particle collisions. Such processing occurs on a worldwide computing grid, necessitating wide-area access to this information.
Event processing tasks may deploy thousands of jobs. Each job calls for a unique set of information from the databases via SQL queries to dedicated squids in the ATLAS Frontier system, a system designed to pass queries to the database only if that result has not already been cached from another request. Many queries passing through Frontier are logged in an Elastic Search cluster along with pointers to the associated tasks and jobs, various metrics, and states at the time of execution. PanDA, which deploys the jobs, stores various configuration files as well as many log files after each job completes. Information is stored at each stage, but no system contains all information needed to draw a complete picture. This presentation describes the challenges of mining information from these sources to compile a view of database usage by jobs and tasks as well as assemble a global picture of the coherence and competition of tasks in resource usage to identify inefficiencies and bottlenecks within the overall system.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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