LCG Persistency Framework (CORAL, COOL, POOL): Status and Outlook in 2012

Trentadue, R.; Clemencic, M.; Dykstra, Dave; Frank, M.; Front, D; Kalkhof, A; Loth, A; Nowak, Marcin; Salnikov, A.; Valassi, A.; Wache, M.

doi:10.1088/1742-6596/396/5/052067

Cited by 9 publications

(5 citation statements)

References 19 publications

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“…Detector and trigger status, configuration, and other time-dependent information such as calibration constants, called detector conditions [16], are stored in the ATLAS conditions database [17]. This is an Oracle database hosting a COOL technology schema [18], which allows the storing of conditions data according to an interval of validity (IOV). The IOV has a start and end timestamp (in ns), or run and luminosity block identifier, between which the stored conditions are valid and -5 -applicable to the data.…”

Section: Jinst 15 P04003mentioning

confidence: 99%

ATLAS data quality operations and performance for 2015–2018 data-taking

et al. 2020

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Large Hadron Collider reads out particle collision data from over 100 million electronic channels at a rate of approximately 100 kHz, with a recording rate for physics events of approximately 1 kHz. Before being certified for physics analysis at computer centres worldwide, the data must be scrutinised to ensure they are clean from any hardware or software related issues that may compromise their integrity. Prompt identification of these issues permits fast action to investigate, correct and potentially prevent future such problems that could render the data unusable. This is achieved through the monitoring of detector-level quantities and reconstructed collision event characteristics at key stages of the data processing chain. This paper presents the monitoring and assessment procedures in place at ATLAS during 2015-2018 data-taking. Through the continuous improvement of operational procedures, ATLAS achieved a high data quality efficiency, with 95.6% of the recorded proton-proton collision data collected at √ s = 13 TeV certified for physics analysis.

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Section: Jinst 15 P04003mentioning

confidence: 99%

ATLAS data quality operations and performance for 2015–2018 data-taking

et al. 2020

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“…These data are stored in the AT-LAS Trigger, Geometry, and Conditions database schemas. These schemas are based on the LCG COOL and CORAL [3] database infrastructure which includes API access methods to query for the specific data needed via C++ or python programs. Most of the tasks requiring database information at grid sites are running Athena [4] (the ATLAS event processing software framework) to process event data.…”

Section: Atlas Databases and Replication Use Casesmentioning

confidence: 99%

Evaluation of the ATLAS model for remote access to database resident information for LHC Run 3

Dimitrov

2020

EPJ Web Conf.

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The ATLAS model for remote access to database resident information relies upon a limited set of dedicated and distributed Oracle database repositories complemented with the deployment of Frontier system infrastructure on the WLCG (Worldwide LHC Computing Grid). ATLAS clients with network access can get the database information they need dynamically by submitting requests to a Squid proxy cache server in the Frontier network which provides results from its cache or passes new requests along the network to launchpads co-located at one of the Oracle sites (the master Oracle database at CERN or one of the Tier 1 Oracle database replicas). Since the beginning of LHC Run 1, the system has evolved in terms of client, Squid, and launchpad optimizations but the distribution model has remained fundamentally unchanged. On the whole, the system has been broadly successful in providing data to clients with relatively few disruptions even while site databases were down due to overall redundancy. At the same time, its quantitative performance characteristics, such as the global throughput of the system, the load distribution between sites, and the constituent interactions that make up the whole, were largely unknown. But more recently, information has been collected from launchpad and Squid logs into an Elasticsearch repository which has enabled a wide variety of studies of various aspects of the system. This contribution*** will describe dedicated studies of the data collected in Elasticsearch over the previous year to evaluate the efficacy of the distribution model. Specifically, we will quantify any advantages that the redundancy of the system offers as well as related aspects such as the geographical dependence of wait times seen by clients in getting a response to its requests. These studies are essential so that during LS2 (the long shutdown between LHC Run 2 and Run 3), we can adapt the system in preparation for the expected increase in the system load in the ramp up to Run 3 operations.

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“…A dedicated service acts as interface between the GeoModel and the Oracle DB (see Figure 1b). All queries are performed via Coral [8] (a C++ access layer to the database) and executed via Frontier/SQUID [9], in a context of distributed computing.…”

Section: Access To Geometry Parameters From Athenamentioning

confidence: 99%

A new mechanism to use the Conditions Database REST API to serve the ATLAS detector description

Formica

Bianchi

Salvo³

2019

EPJ Web Conf.

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Efficient and fast access to the detector description of the ATLAS experiment is needed for many tasks, at different steps of the data chain: from detector development to reconstruction, from simulation to data visualization. Until now, the detector description was only accessible through dedicated services integrated into the experiment’s software framework, or by the use of external applications. In this work, we explore the possibility of using a web access-based conditions database to store and serve the detector description, aiming at a simplification of the software architecture of the experiment and a reduction in the number of software packages to be maintained.

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LCG Persistency Framework (CORAL, COOL, POOL): Status and Outlook in 2012

Cited by 9 publications

References 19 publications

ATLAS data quality operations and performance for 2015–2018 data-taking

ATLAS data quality operations and performance for 2015–2018 data-taking

Evaluation of the ATLAS model for remote access to database resident information for LHC Run 3

A new mechanism to use the Conditions Database REST API to serve the ATLAS detector description

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