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@InProceedings{wozniak:icalepcs2019-wepha163,
author = {J.P. Wozniak and C. Roderick},
title = {{NXCALS - Architecture and Challenges of the Next CERN Accelerator Logging Service}},
booktitle = {Proc. ICALEPCS'19},
pages = {1465--1469},
paper = {WEPHA163},
language = {english},
keywords = {extraction, software, controls, operation, hardware},
venue = {New York, NY, USA},
series = {International Conference on Accelerator and Large Experimental Physics Control Systems},
number = {17},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2020},
issn = {2226-0358},
isbn = {978-3-95450-209-7},
doi = {10.18429/JACoW-ICALEPCS2019-WEPHA163},
url = {https://jacow.org/icalepcs2019/papers/wepha163.pdf},
note = {https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA163},
abstract = {CERN’s Accelerator Logging Service (CALS) is in production since 2003 and stores data from accelerator infrastructure and beam observation devices. Initially expecting 1 TB/year, the Oracle based system has scaled to cope with 2.5 TB/day coming from >2.3 million signals. It serves >1000 users making an average of 5 million extraction requests per day. Nevertheless, with a large data increase during LHC Run 2 the CALS system began to show its limits, particularly for supporting data analytics. In 2016 the NXCALS project was launched with the aim of replacing CALS from Run 3 onwards, with a scalable system using "Big Data" technologies. The NXCALS core is production-ready, based on open-source technologies such as Hadoop, HBase, Spark and Kafka. This paper will describe the NXCALS architecture and design choices, together with challenges faced while adopting these technologies. This includes: write/read performance when dealing with vast amounts of data from heterogenous data sources with strict latency requirements; how to extract, transform and load >1 PB of data from CALS to NXCALS. NXCALS is not CERN-specific and can be relevant to other institutes facing similar challenges.},
}