Speaker
Description
Within the EPICS framework, Archiver Appliance is commonly employed as the management and storage engine for Process Variable (PV) data. Its stability and ease of use have been validated over the long term at major international scientific facilities such as LCLS, NSLS-II, and LANSCE. However, as the scale of control systems continues to expand—the accelerator section of the China Initiative Accelerator Driven System (CiADS) currently has 232,070 PVs under archival, a number expected to exceed 300,000—PV registration, archival status tracking, and anomaly troubleshooting can no longer rely solely on native functionalities. Existing storage approaches are also becoming bottlenecks in terms of the validity, security, and access efficiency of massive historical data.
To address these challenges, we have carried out development work on two fronts based on Archiver Appliance. First, we established a unified PV information management and operational inspection platform, bringing hundreds of thousands of PVs and their archival statuses into a controllable, traceable management workflow. Second, we introduced a time-series database designed for long-term storage and high-performance retrieval, enabling rapid search of massive archival data and direct use in subsequent analyses. The former operates as a web service interfacing with the native Archiver Appliance APIs, enabling unified management of primary/backup redundant archival systems, batch submission and audit trails for PV archival requests, classification and retrieval of PVs by subsystem and segment, as well as monitoring views for various PV anomaly states with scheduled inspections and automatic recovery. The latter adds a new storage interface class within Archiver Appliance to write data in real time to the domestic time-series database DolphinDB. A stream data processor then categorizes PVs into low-frequency, medium-to-high-frequency, and waveform types, establishing separate databases for each. Storage and indexing are optimized according to the respective frequency and data-type characteristics, achieving cold/hot data separation and periodic cleanup of invalid data. This significantly reduces data retrieval time for massive datasets, compresses storage footprint, and provides users with standardized data processing capabilities—such as timestamp alignment—through built-in database functions.
Keywords: EPICS; Archiver Appliance; DolphinDB; PV information management; time-series database; stream data processing
