  Compared to LHC Run 1 and Run 2, future HEP experiments, e.g., at the HL-LHC,
will increase the volume of generated data by an order of magnitude. In order
to sustain the expected analysis throughput, ROOT's RNTuple I/O subsystem has
been engineered to overcome the bottlenecks of the TTree I/O subsystem,
focusing also on a compact data format, asynchronous and parallel requests, and
a layered architecture that allows supporting distributed filesystem-less
storage systems, e.g. HPC-oriented object stores. In a previous publication, we
introduced and evaluated the RNTuple's native backend for Intel DAOS. Since its
first prototype, we carried out a number of improvements both on RNTuple and
its DAOS backend aiming to saturate the physical link, such as support for
vector writes and an improved RNTuple-to-DAOS mapping, only to name a few. In
parallel, the latest developments allow for better integration between RNTuple
and ROOT's storage-agnostic, declarative interface to write HEP analyses,
RDataFrame. In this work, we contribute with the following: (i) a redesign of
the RNTuple DAOS backend, including a mechanism for efficient population of the
object store based on existing data; and (ii) an experimental evaluation on a
single-node platform, showing a significant increase in the analysis throughput
for typical HEP workflows.
