Integrating the PanDA Workload Management System with the Vera C. Rubin Observatory

TL;DR

This paper discusses the integration of the PanDA workload management system with the Vera C. Rubin Observatory to handle its massive data processing needs efficiently, supporting scalable workflows and rapid data processing.

Contribution

It presents the detailed process of integrating PanDA with the Rubin Observatory's data management system to enable scalable, flexible, and rapid data processing workflows.

Findings

Successful integration of PanDA with Rubin Observatory systems

Enhanced support for complex workflows and multi-site processing

Potential for prompt data processing within 60 seconds

Abstract

The Vera C. Rubin Observatory will produce an unprecedented astronomical data set for studies of the deep and dynamic universe. Its Legacy Survey of Space and Time (LSST) will image the entire southern sky every three to four days and produce tens of petabytes of raw image data and associated calibration data over the course of the experiment's run. More than 20 terabytes of data must be stored every night, and annual campaigns to reprocess the entire dataset since the beginning of the survey will be conducted over ten years. The Production and Distributed Analysis (PanDA) system was evaluated by the Rubin Observatory Data Management team and selected to serve the Observatory's needs due to its demonstrated scalability and flexibility over the years, for its Directed Acyclic Graph (DAG) support, its support for multi-site processing, and its highly scalable complex workflows via the intelligent Data Delivery Service (iDDS). PanDA is also being evaluated for prompt processing where data must be processed within 60 seconds after image capture. This paper will briefly describe the Rubin Data Management system and its Data Facilities (DFs). Finally, it will describe in depth the work performed in order to integrate the PanDA system with the Rubin Observatory to be able to run the Rubin Science Pipelines using PanDA.