CERN Accelerating science

Article
Title	Let’s get our hands dirty: a comprehensive evaluation of DAQDB, key-value store for petascale hot storage
Author(s)	Abed Abud, Adam (CERN) ; Cicalese, Danilo (CERN) ; Jereczek, Grzegorz (IMP, Gdansk) ; Goff, Fabrice Le (CERN) ; Lehmann Miotto, Giovanna (CERN) ; Love, Jeremy (Argonne (main)) ; Maciejewski, Maciej (IMP, Gdansk) ; Mommsen, Remigius K (Fermilab) ; Radtke, Jakub (IMP, Gdansk) ; Schmiegel, Jakub (IMP, Gdansk) ; Szychowska, Malgorzata (IMP, Gdansk)
Publication	2020
Number of pages	7
In:	EPJ Web Conf. 245 (2020) 10004
In:	24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.10004
DOI	10.1051/epjconf/202024510004
Subject category	Computing and Computers
Abstract	Data acquisition systems are a key component for successful data taking in any experiment. The DAQ is a complex distributed computing system and coordinates all operations, from the data selection stage of interesting events to storage elements. For the High Luminosity upgrade of the Large Hadron Collider, the experiments at CERN need to meet challenging requirements to record data with a much higher occupancy in the detectors. The DAQ system will receive and deliver data with a significantly increased trigger rate, one million events per second, and capacity, terabytes of data per second. An effective way to meet these requirements is to decouple real-time data acquisition from event selection. Data fragments can be temporarily stored in a large distributed key-value store. Fragments belonging to the same event can be then queried on demand, by the data selection processes. Implementing such a model relies on a proper combination of emerging technologies, such as persistent memory, NVMe SSDs, scalable networking, and data structures, as well as high performance, scalable software. In this paper, we present DAQDB (Data Acquisition Database) — an open source implementation of this design that was presented earlier, with an extensive evaluation of this approach, from the single node to the distributed performance. Furthermore, we complement our study with a description of the challenges faced and the lessons learned while integrating DAQDB with the existing software framework of the ATLAS experiment.
Copyright/License	© 2020-2025 The Authors (License: CC-BY-4.0)

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