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The challenges expected for the next era of the Large Hadron Collider (LHC),both in terms of storage and computing resources, provide LHC experiments witha strong motivation for evaluating ways of rethinking their computing models atmany levels. Great efforts have been put into optimizing the computing resourceutilization for the data analysis, which leads both to lower hardwarerequirements and faster turnaround for physics analyses. In this scenario, theCompact Muon Solenoid (CMS) collaboration is involved in several activitiesaimed at benchmarking different solutions for running High Energy Physics (HEP)analysis workflows. A promising solution is evolving software towards moreuser-friendly approaches featuring a declarative programming model andinteractive workflows. The computing infrastructure should keep up with thistrend by offering on the one side modern interfaces, and on the other sidehiding the complexity of the underlying environment, while efficientlyleveraging the already deployed grid infrastructure and scaling towardopportunistic resources like public cloud or HPC centers. This article presentsthe first example of using the ROOT RDataFrame technology to exploit suchnext-generation approaches for a production-grade CMS physics analysis. A newanalysis facility is created to offer users a modern interactive web interfacebased on JupyterLab that can leverage HTCondor-based grid resources ondifferent geographical sites. The physics analysis is converted from a legacyiterative approach to the modern declarative approach offered by RDataFrame anddistributed over multiple computing nodes. The new scenario offers not only anoverall improved programming experience, but also an order of magnitude speedupincrease with respect to the previous approach.

Prototyping a ROOT-based distributed analysis workflow for HL-LHC: the CMS use case