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ROOT’s RNTuple I/O Subsystem: The Path to Production

Conference · · EPJ Web Conf.

The RNTuple I/O subsystem is ROOT’s future event data file format and access API. It is driven by the expected data volume increase at upcoming HEP experiments, e.g. at the HL-LHC, and recent opportunities in the storage hardware and software landscape such as NVMe drives and distributed object stores. RNTuple is a redesign of the TTree binary format and API and has shown to deliver substantially faster data throughput and better data compression both compared to TTree and to industry standard formats. In order to let HENP computing workflows benefit from RNTuple’s superior performance, however, the I/O stack needs to connect efficiently to the rest of the ecosystem, from grid storage to (distributed) analysis frameworks to (multithreaded) experiment frameworks for reconstruction and ntuple derivation. With the RNTuple binary format soon arriving at its first production release, we present RNTuple’s feature set, integration efforts, and its performance impact on the time-to-solution. We show the latest performance figures of RDataFrame analysis code of realistic complexity, comparing RNTuple and TTree as data sources. We discuss RNTuple’s approach to functionality critical to the HENP I/O (such as multithreaded writes, fast data merging, schema evolution) and we provide an outlook on the road to its use in production.

Research Organization:
Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
DOE Contract Number:
AC02-07CH11359
OSTI ID:
2468767
Report Number(s):
FERMILAB-CONF-24-0690-CSAID; oai:inspirehep.net:2785522
Journal Information:
EPJ Web Conf., Journal Name: EPJ Web Conf. Vol. 295
Country of Publication:
United States
Language:
English

References (4)

RNTuple performance: Status and Outlook journal February 2023
Evolution of the ROOT Tree I/O journal January 2020
Exploring Object Stores for High-Energy Physics Data Storage journal January 2021
ROOT — An object oriented data analysis framework journal April 1997

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