Speaker
Description
CHEP Track: 6 - Collaborative software and maintainability
The LHCb high-level trigger applications consists of components that run reconstruction algorithms and perform physics object selections, scaling from hundreds to tens of thousands depending on the selection stage. The configuration of the components, the data flow and the control flow are implemented in Python. The resulting application configuration is condensed in the basic form of a list of components with their properties and values.
It is often required to change configuration without deploying new binaries. Moreover, it is essential to be able to reproduce a given production configuration and to be able query it after it has been used. For these reasons, the basic form of the trigger configuration is captured and stored in a Git database.
This contribution is describing the infrastructure around generating and validating the configurations. The process is based on GitLab pipelines that are triggered on user defined specifications and run several steps ranging from basic checks to performance validation using dedicated runners. Upon merging, the configuration database is deployed on CVMFS. The process as implemented ensures consistency and reproducibility across all selection stages.
This project also aims to take advantage of the query-able nature of the configurations by creating an API that allows probing a single configuration in detail. This is further used to create human-readable summaries and to track changes across configurations to help analysts understand the selections used to collect their datasets.
