Design and Execution of make-like, distributed Analyses based on Spotify's Pipelining Package Luigi

TL;DR

This paper presents a scalable, make-like distributed analysis system based on Spotify's Luigi package, tailored for high-energy physics workflows, enabling automated, parallelized, and distributed data analysis.

Contribution

It introduces a generic analysis design pattern and extends Luigi with features for high-energy physics, including WLCG support, automated resubmission, and a Python interface for complex workflows.

Findings

Supports distributed execution of complex physics analyses

Integrates WLCG infrastructure for job submission and file access

Enables automated, parallelized data analysis workflows

Abstract

In high-energy particle physics, workflow management systems are primarily used as tailored solutions in dedicated areas such as Monte Carlo production. However, physicists performing data analyses are usually required to steer their individual workflows manually which is time-consuming and often leads to undocumented relations between particular workloads. We present a generic analysis design pattern that copes with the sophisticated demands of end-to-end HEP analyses and provides a make-like execution system. It is based on the open-source pipelining package Luigi which was developed at Spotify and enables the definition of arbitrary workloads, so-called Tasks, and the dependencies between them in a lightweight and scalable structure. Further features are multi-user support, automated dependency resolution and error handling, central scheduling, and status visualization in the web. In addition to already built-in features for remote jobs and file systems like Hadoop and HDFS, we added support for WLCG infrastructure such as LSF and CREAM job submission, as well as remote file access through the Grid File Access Library. Furthermore, we implemented automated resubmission functionality, software sandboxing, and a command line interface with auto-completion for a convenient working environment. For the implementation of a $t\bar{t}H$ cross section measurement, we created a generic Python interface that provides programmatic access to all external information such as datasets, physics processes, statistical models, and additional files and values. In summary, the setup enables the execution of the entire analysis in a parallelized and distributed fashion with a single command.