Speakers
Description
The ATLAS Detector Control System manages over 68 TB of time-series data accumulated since 2007. This presentation describes the practical implementation and operational deployment of TimescaleDB—a PostgreSQL extension—to modernize DCS data access for the ATLAS experiment. We share our experience as PostgreSQL users and administrators implementing a production time-series database solution in a large-scale scientific environment. The implementation delivers significant value through improved user experience: scientists and detector experts now access 18 years of historical data through simple Python APIs, Grafana dashboards, and C++ interfaces, eliminating the need for complex SQL knowledge. TimescaleDB's native compression achieves 11× storage reduction (68 TB to 6.6 TB), while query performance improvements enable real-time correlation studies across multiple detector systems. The PostgreSQL ecosystem enabled rapid development—Grafana integration, LDAP authentication, and CERN's Database-on-Demand (DBOD) infrastructure worked seamlessly, accelerating deployment. This PostgreSQL-based approach has unlocked new operational capabilities: detector experts built custom monitoring applications, predictive maintenance workflows identify hardware failures before they impact data collection, and commissioning teams correlate real-time measurements across detector subsystems. The system now serves production users with continuous availability and sub-minute data latency. We discuss practical deployment challenges, infrastructure lessons learned while working with CERN DBOD team, performance tuning strategies (chunk sizing, WAL management, JIT optimization), and the organizational impact of choosing PostgreSQL's open-source ecosystem for long-term scientific data management.
