| Issue |
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
|
|
|---|---|---|
| Article Number | 03009 | |
| Number of page(s) | 7 | |
| Section | 3 - Middleware and Distributed Computing | |
| DOI | https://doi.org/10.1051/epjconf/202024503009 | |
| Published online | 16 November 2020 | |
https://doi.org/10.1051/epjconf/202024503009
Distributed data analysis with ROOT RDataFrame
1
CERN
2
Università degli Studi di Milano-Bicocca (IT)
3
University of Oldenburg (DE)
* e-mail: vincenzo.eduardo.padulano@cern.ch
** e-mail: javier.cervantes.villanueva@cern.ch
*** e-mail: enrico.guiraud@cern.ch
**** e-mail: enric.tejedor.saavedra@cern.ch
Published online: 16 November 2020
Widespread distributed processing of big datasets has been around for more than a decade now thanks to Hadoop, but only recently higher-level abstractions have been proposed for programmers to easily operate on those datasets, e.g. Spark. ROOT has joined that trend with its RDataFrame tool for declarative analysis, which currently supports local multi-threaded parallelisation. However, RDataFrame’s programming model is general enough to accommodate multiple implementations or backends: users could write their code once and execute it as-is locally or distributedly, just by selecting the corresponding backend.
This abstract introduces PyRDF, a new python library developed on top of RDataFrame to seamlessly switch from local to distributed environments with no changes in the application code. In addition, PyRDF has been integrated with a service for web-based analysis, SWAN, where users can dynamically plug in new resources, as well as write, execute, monitor and debug distributed applications via an intuitive interface.
© The Authors, published by EDP Sciences, 2020
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