Speaker
Description
Until now, geometry information for the detector description of HEP experiments was only stored in online relational databases integrated in the experiments’ frameworks or described in files with text-based markup languages. In all cases, to build and store the detector description, a full software stack was needed.
In this paper we present a new and scalable mechanism to store the geometry data and to serve the detector description data through a REST web-based API. This new approach decouples the geometry information from the experiment’s framework. Moreover, it provides new functionalities to users, who can now search for specific volumes and get partial detector description, or filter geometry data based on custom criteria.
We present two approaches to build a REST API to serve geometry data, based on two different technologies used in other fields and communities: the search engine ElasticSearch and the graph database Neo4j. We describe their characteristics and we compare them using real-world usage tests to test their speed and scalability, targeted to a HEP usage.
