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A distributed data processing scheme based on Hadoop for synchrotron radiation experiments
Ist Teil von
Journal of synchrotron radiation, 2024-05, Vol.31 (Pt 3), p.635-645
Ort / Verlag
United States: John Wiley & Sons, Inc
Erscheinungsjahr
2024
Link zum Volltext
Quelle
Wiley Online Library - AutoHoldings Journals
Beschreibungen/Notizen
With the development of synchrotron radiation sources and high-frame-rate detectors, the amount of experimental data collected at synchrotron radiation beamlines has increased exponentially. As a result, data processing for synchrotron radiation experiments has entered the era of big data. It is becoming increasingly important for beamlines to have the capability to process large-scale data in parallel to keep up with the rapid growth of data. Currently, there is no set of data processing solutions based on the big data technology framework for beamlines. Apache Hadoop is a widely used distributed system architecture for solving the problem of massive data storage and computation. This paper presents a set of distributed data processing schemes for beamlines with experimental data using Hadoop. The Hadoop Distributed File System is utilized as the distributed file storage system, and Hadoop YARN serves as the resource scheduler for the distributed computing cluster. A distributed data processing pipeline that can carry out massively parallel computation is designed and developed using Hadoop Spark. The entire data processing platform adopts a distributed microservice architecture, which makes the system easy to expand, reduces module coupling and improves reliability.