Exploring Object Stores for High-Energy Physics Data Storage

TL;DR

This paper evaluates the integration of ROOT RNTuple with Intel DAOS object storage, demonstrating improved data access performance for high-energy physics datasets on modern hardware.

Contribution

It introduces a new RNTuple backend using Intel DAOS, enabling efficient data retrieval on object stores with minimal user code changes.

Findings

The DAOS backend outperforms existing compatibility solutions.

RNTuple with DAOS supports realistic HEP data analysis.

Minimal code modifications needed for users.

Abstract

Over the last two decades, ROOT TTree has been used for storing over one exabyte of High-Energy Physics (HEP) events. The TTree columnar on-disk layout has been proved to be ideal for analyses of HEP data that typically require access to many events, but only a subset of the information stored for each of them. Future colliders, and particularly HL-LHC, will bring an increase of at least one order of magnitude in the volume of generated data. Therefore, the use of modern storage hardware, such as low-latency high-bandwidth NVMe devices and distributed object stores, becomes more important. However, TTree was not designed to optimally exploit modern hardware and may become a bottleneck for data retrieval. The ROOT RNTuple I/O system aims at overcoming TTree's limitations and at providing improved efficiency for modern storage systems. In this paper, we extend RNTuple with a backend that uses Intel DAOS as the underlying storage, demonstrating that the RNTuple architecture can accommodate high-performance object stores. From the user perspective, data can be accessed with minimal changes to the code, that is by replacing a filesystem path by a DAOS URI. Our performance evaluation shows that the new backend can be used for realistic analyses, while outperforming the compatibility solution provided by the DAOS project.