| Title |
Big Data Archiving From Oracle to Hadoop |
| Authors |
- I. Prieto Barreiro, M. Sobieszek
CERN, Meyrin, Switzerland
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| Abstract |
The CERN Accelerator Logging Service (CALS) is used to persist data of around 2 million predefined signals coming from heterogeneous sources such as the electricity infrastructure, industrial controls like cryogenics and vacuum, or beam related data. This old Oracle based logging system will be phased out at the end of the LHC’s Long Shut-down 2 (LS2) and will be replaced by the Next CERN Accelerator Logging Service (NXCALS) which is based on Hadoop. As a consequence, the different data sources must be adapted to persist the data in the new logging system. This paper describes the solution implemented to archive into NXCALS the data produced by QPS (Quench Protection System) and SCADAR (Supervisory Control And Data Acquisition Relational database) systems, which generate a total of around 175, 000 values per second. To cope with such a volume of data the new service has to be extremely robust, scalable and fail-safe with guaranteed data delivery and no data loss. The paper also explains how to recover from different failure scenarios like e.g. network disruption and how to manage and monitor this highly distributed service.
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| Paper |
download MOPHA117.PDF [0.544 MB / 5 pages] |
| Poster |
download MOPHA117_POSTER.PDF [1.227 MB] |
| Export |
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| Conference |
ICALEPCS2019 |
| Series |
International Conference on Accelerator and Large Experimental Physics Control Systems (17th) |
| Location |
New York, NY, USA |
| Date |
05-11 October 2019 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Karen S. White (ORNL, Oak Ridge, TN, USA); Kevin A. Brown (BNL, Upton, NY, USA); Philip S. Dyer (BNL, Upton, NY, USA); Volker RW Schaa (GSI, Darmstadt, Germany) |
| Online ISBN |
978-3-95450-209-7 |
| Online ISSN |
2226-0358 |
| Received |
29 September 2019 |
| Accepted |
10 October 2019 |
| Issue Date |
30 August 2020 |
| DOI |
doi:10.18429/JACoW-ICALEPCS2019-MOPHA117 |
| Pages |
497-501 |
| Copyright |
 Published by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |
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