An implementation for the ATLAS conditions data management based on relational DBMSs

Developing the trigger and the data acquisition (TDAQ) for A Toroidal LHC ApparatuS (ATLAS) detector at LHC poses new challenges including the requirement to monitor, calibrate and align the detector while the on line selection is running simultaneously over thousands of processors. These "conditions" include slowly evolving data such as detector alignment, calibration, and robustness, as well as data from the detector control system that cannot be stored with the event data, but must be directly accessible from each event. Motivated by the ATLAS data challenges and the recent evolution of Open Source database systems such as MySQL and Postgres, we have developed an implementation of the ATLAS Conditions database system based on the MySQL Relational DBMS. Together with Postgres, MySQL is well known for performance and simplicity and an increasing number of supported features. Several issues were investigated: how to develop a programmable API that could be used everywhere through the ATLAS online and offline computing systems; what are the optimal ways to map the conditions data model into a relational model; what is the data clustering model that is best suited to address the ATLAS data volume scalability problems. Some general issues of conditions databases are also studied, including: the schema; facilities for tagging and versioning; problems involved in managing the tiny detector control data as well as the complex; interrelated detector configuration objects. The extensive tests that were performed to monitor the development of the system are also described.