Laurelin: Java-native ROOT I/O for Apache Spark
Apache Spark[1] is one of the predominant frameworks in the big data space, providing a fully-functional query processing engine, vendor support for hardware accelerators, and performant integrations with scientific computing libraries. One difficulty in adopting conventional big data frameworks to...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2021-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2021/05/epjconf_chep2021_02072.pdf |
| _version_ | 1818715768403001344 |
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| author | Melo Andrew Shadura Oksana |
| author_facet | Melo Andrew Shadura Oksana |
| author_sort | Melo Andrew |
| collection | DOAJ |
| description | Apache Spark[1] is one of the predominant frameworks in the big data space, providing a fully-functional query processing engine, vendor support for hardware accelerators, and performant integrations with scientific computing libraries. One difficulty in adopting conventional big data frameworks to HEP workflows is the lack of support for the ROOT file format in these frameworks. Laurelin[6] implements ROOT I/O with a pure Java library, with no bindings to the C++ ROOT[2] implementation, and is readily installable via standard Java packaging tools. It provides a performant interface enabling Spark to read (and soon write) ROOT TTrees, enabling users to process these data without a pre-processing phase converting to an intermediate format. |
| first_indexed | 2024-12-17T19:08:37Z |
| format | Article |
| id | doaj.art-856bd54137cd40ce887f8792d0b33ff0 |
| institution | Directory Open Access Journal |
| issn | 2100-014X |
| language | English |
| last_indexed | 2024-12-17T19:08:37Z |
| publishDate | 2021-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj.art-856bd54137cd40ce887f8792d0b33ff02022-12-21T21:35:55ZengEDP SciencesEPJ Web of Conferences2100-014X2021-01-012510207210.1051/epjconf/202125102072epjconf_chep2021_02072Laurelin: Java-native ROOT I/O for Apache SparkMelo Andrew0Shadura Oksana1Vanderbilt UniversityUniversity of NebraskaApache Spark[1] is one of the predominant frameworks in the big data space, providing a fully-functional query processing engine, vendor support for hardware accelerators, and performant integrations with scientific computing libraries. One difficulty in adopting conventional big data frameworks to HEP workflows is the lack of support for the ROOT file format in these frameworks. Laurelin[6] implements ROOT I/O with a pure Java library, with no bindings to the C++ ROOT[2] implementation, and is readily installable via standard Java packaging tools. It provides a performant interface enabling Spark to read (and soon write) ROOT TTrees, enabling users to process these data without a pre-processing phase converting to an intermediate format.https://www.epj-conferences.org/articles/epjconf/pdf/2021/05/epjconf_chep2021_02072.pdf |
| spellingShingle | Melo Andrew Shadura Oksana Laurelin: Java-native ROOT I/O for Apache Spark EPJ Web of Conferences |
| title | Laurelin: Java-native ROOT I/O for Apache Spark |
| title_full | Laurelin: Java-native ROOT I/O for Apache Spark |
| title_fullStr | Laurelin: Java-native ROOT I/O for Apache Spark |
| title_full_unstemmed | Laurelin: Java-native ROOT I/O for Apache Spark |
| title_short | Laurelin: Java-native ROOT I/O for Apache Spark |
| title_sort | laurelin java native root i o for apache spark |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2021/05/epjconf_chep2021_02072.pdf |
| work_keys_str_mv | AT meloandrew laurelinjavanativerootioforapachespark AT shaduraoksana laurelinjavanativerootioforapachespark |